System and methods for predicting rental vehicle use preferences

1. A computer-implemented method of determining a user preference, comprising:

training, by one or more processors, a machine learning model to identify relationships between a) telematics data and b) preference values associated with vehicle rental criteria;

receiving, by the one or more processors and in response to a first set of prompts provided by a vehicle-sharing application, a set of inputs from a vehicle owner;

determining, by the one or more processors and using the set of inputs, owner preference values;

receiving, by the one or more processors, owner telematics data corresponding to a vehicle of the vehicle owner, the owner telematics data indicating driving behavior of the vehicle owner while operating the vehicle;

determining, by the one or more processors executing the trained machine learning model using the owner preference values and the owner telematics data, one or more criteria required to share the vehicle of the vehicle owner, wherein the one or more criteria are unique to the vehicle owner;

mapping, by the one or more processors, one of the one or more criteria to a prompt for display via the vehicle-sharing application;

causing, by the one or more processors, the prompt to be displayed via the vehicle-sharing application, wherein user input received via the prompt causes a change to the one of the one or more criteria;

identifying, by the one or more processors, one or more potential vehicle renters from a plurality of potential vehicle renters registered with the vehicle-sharing application and who satisfy the one or more criteria; and

causing, by the one or more processors, an indication of the vehicle to be displayed via the vehicle-sharing application to the one or more potential vehicle renters.

2. The computer-implemented method of claim 1 , further comprising:

causing, by the one or more processors, the one or more criteria to be displayed via the vehicle-sharing application.

3. The computer-implemented method of claim 2 , further comprising:

generating, by the one or more processors, a first graphic user interface (GUI) including a second set of prompts, wherein the second set of prompts request the vehicle owner to confirm the one or more criteria; and

causing, by the one or more processors, the first GUI to be displayed via the vehicle-sharing application.

4. The computer-implemented method of claim 1 , further comprising:

modifying, by the one or more processors, the one of the one or more criteria based on receiving the user input via the prompt displayed via the vehicle-sharing application from the vehicle owner.

5. The computer-implemented method of claim 1 , wherein the first set of prompts is provided by the vehicle sharing application together with answer choices derived from historical data associated with the vehicle owner.

6. The computer-implemented method of claim 1 , wherein the first set of prompts is provided by the vehicle sharing application together with default answer choices not derived from historical data associated with the vehicle owner.

7. The computer-implemented method of claim 1 , further comprising:

receiving, by the one or more processors, historical data associated with the one or more potential vehicle renters; and

identifying the one or more potential vehicle renters based at least in part on the historical data.

8. The computer-implemented method of claim 7 , wherein the historical data comprises telematics data indicative of driving behavior of the one or more potential vehicle renters.

9. The computer-implemented method of claim 7 , wherein the historical data comprises evaluation data associated with the one or more potential vehicle renters and determined based on feedback received from other vehicle owners from whom the one or more potential vehicle renters rented vehicles.

10. A non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to:

train a machine learning model to identify relationships between a) telematics data and b) preference values associated with vehicle rental criteria;

receive, in response to a first set of prompts provided by a vehicle-sharing application, a set of inputs from a vehicle owner;

receive owner telematics data corresponding to a vehicle of the vehicle owner, the owner telematics data indicating driving behavior of the vehicle owner while operating the vehicle;

determine by executing the trained machine learning model using the set of inputs and the owner telematics data, one or more criteria required to share the vehicle of the vehicle owner, wherein the one or more criteria are unique to the vehicle owner;

map one of the one or more criteria to a prompt for display via the vehicle-sharing application;

causing the prompt to be displayed via the vehicle-sharing application, wherein user input received via the prompt causes a change to the one of the one or more criteria;

identify one or more potential vehicle renters from a plurality of potential vehicle renters registered with the vehicle-sharing application and who satisfy the one or more criteria; and

cause an indication of the vehicle to be displayed via the vehicle-sharing application to the one or more potential vehicle renters.

11. The non-transitory computer-readable medium of claim 10 , wherein the instructions when executed by the one or more processors further cause the one or more processors to:

generate a first graphic user interface (GUI) including the first set of prompts for display via the vehicle-sharing application; and

cause the first GUI to be displayed via the vehicle-sharing application, the first set of inputs being received via the first GUI.

12. The non-transitory computer-readable medium of claim 10 , wherein the instructions when executed by the one or more processors further cause the one or more processors to

modify the one of the one or more criteria based on receiving the user input via the prompt displayed via the vehicle-sharing application from the vehicle owner.

13. The non-transitory, computer-readable medium of claim 10 , wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:

receive historical data associated with the potential vehicle renters; and

identify the one or more potential vehicle renters based at least in part on the historical data.

14. The non-transitory, computer-readable medium of claim 13 , wherein the historical data comprises telematics data indicative of driving behavior of the one or more potential vehicle renters.

15. The non-transitory, computer-readable medium of claim 13 , wherein historical data comprises rental evaluation data associated with the one or more potential vehicle renters and determined based on feedback received from other vehicle owners from whom the one or more potential vehicle renters rented vehicles.

16. A system, comprising:

one or more processors; and

memory storing computer-executable instructions that, when executed by the one or more processors, cause the system to perform operations comprising:

receiving, in response to a first set of prompts provided by a vehicle-sharing application, a set of inputs from a vehicle owner;

receiving owner telematics data corresponding to a vehicle of the vehicle owner, the owner telematics data indicating driving behavior of the vehicle owner while operating the vehicle;

determining, by executing a machine learning model using the set of inputs and the owner telematics data, one or more criteria required to share the vehicle of the vehicle owner, wherein the one or more criteria are unique to the vehicle owner;

training the machine learning model to identify relationships between a) the owner telematics data and b) the one or more criteria;

mapping one of the one or more criteria to a prompt for display via the vehicle-sharing application;

causing the prompt to be displayed via the vehicle-sharing application, wherein user input received via the prompt causes a change to the one of the one or more criteria;

identifying one or more potential vehicle renters from a plurality of potential vehicle renters registered with the vehicle-sharing application and who satisfy the one or more criteria; and

causing an indication of the vehicle to be displayed via the vehicle-sharing application to the one or more potential vehicle renters.

17. The system of claim 16 , the operations further comprising:

recording a maximum speed driven by the vehicle owner from the telematics data; and

generating, based at least in part on the recorded maximum speed, a first graphic user interface (GUI), the first GUI providing a default user preference to the vehicle owner corresponding to a maximum speed allowed to be driven by the one or more potential vehicle renters.

18. The system of claim 16 , the operations further comprising

modifying the one of the one or more criteria based on receiving the user input via the prompt displayed via vehicle-sharing application from the vehicle owner.

19. The system of claim 16 , the operations further comprising:

generating a score for a renter of the one or more potential vehicle renters based at least in part on telematics data indicative of driving behavior of the renter and feedback from the vehicle owner.

20. The system of claim 16 , wherein the first set of prompts is accompanied with answer choices derived from historical data associated with the vehicle owner.