Vehicle recommendations based on driving habits

Patent metadata

Field	Value
Publication number	US-11461674-B2
Application number	US-201815967680-A
Country	US
Kind code	B2
Filing date	May 1, 2018
Priority date	May 1, 2018
Publication date	Oct 4, 2022
Grant date	Oct 4, 2022

Abstract

Disclosed embodiments provide techniques for providing vehicular recommendations based on driver habits. Embodiments utilize a variety of input data, including, but not limited to, static vehicular data, dynamic vehicular data, and/or environmental data. In embodiments, empirical rules are used to adjust recommended maintenance schedules based on the input conditions. Additionally, the adjusted recommendations along with unscheduled maintenance data are input to a machine learning system, such as a neural network. The machine learning system is used to further revise the maintenance schedule, estimate end of life of the vehicle, and issue recommendations for when to sell a vehicle and recommendations on attributes of a new vehicle for acquisition.

First claim

What is claimed is:

1. A computer-implemented method for generating a vehicle report for a vehicle, comprising:
obtaining static vehicular data for the vehicle;
obtaining dynamic vehicular data for the vehicle;
obtaining environmental data associated with the vehicle;
computing a maintenance score from a summation of a number of parameter based calculations, each of the parameter based calculations utilizing a function that is specific to a particular parameter of the number of parameters based on the static vehicular data, dynamic vehicular data, and environmental data;
obtaining a maintenance procedure for the vehicle, the maintenance procedure having a static recommendation point for performing the maintenance procedure, the static recommendation point including at least one of a fixed time interval or a fixed interval of elapsed vehicle miles;
adjusting an interval associated with the maintenance procedure based on the maintenance score; and
providing the adjusted interval in the vehicle report.

2. The method of claim 1 , wherein the obtaining of the environmental data includes obtaining ambient temperature data.

3. The method of claim 1 , wherein the obtaining of the environmental data includes obtaining ambient humidity data.

4. The method of claim 1 , wherein the obtaining of the environmental data includes obtaining airborne salinity data.

5. The method of claim 1 , wherein the obtaining of the environmental data includes obtaining terrain data, the terrain data including crowdsourced road data regarding road conditions.

6. The method of claim 1 , wherein the obtaining of the environmental data includes obtaining altitude data.

7. The method of claim 1 , wherein the obtaining of the environmental data includes obtaining storage data.

8. The method of claim 1 , wherein the obtaining of the dynamic vehicle data includes obtaining suspension deflection data from a device onboard the vehicle that collects data pertaining to how much a suspension is compressed during operation of the vehicle by a user.

9. The method of claim 8 , wherein the obtaining of the suspension deflection data includes obtaining average suspension deflection data.

10. The method of claim 8 , wherein the obtaining of the suspension deflection data includes obtaining significant deflection event data.

11. The method of claim 1 , wherein the obtaining of the dynamic vehicle data includes obtaining engine data.

12. The method of claim 11 , wherein the obtaining of the engine data includes obtaining average revolutions-per-minute data.

13. The method of claim 11 , wherein the obtaining of the engine data includes obtaining average idle time data.

14. The method of claim 1 , further comprising:
obtaining unscheduled maintenance data;
inputting unscheduled maintenance data into a machine learning system as training data;
inputting the static vehicular data, dynamic vehicular data, and environmental data for the vehicle into the machine learning system; and
generating a vehicular recommendation for the vehicle for by the machine learning system.

15. The method of claim 14 , wherein the vehicular recommendation includes a maintenance schedule.

16. The method of claim 14 , wherein the vehicular recommendation includes a vehicle sell recommendation.

17. The method of claim 14 , wherein the vehicular recommendation includes a vehicle purchase recommendation.

18. An electronic computing device comprising:
a processor;
a memory coupled to the processor, the memory containing instructions, that when executed by the processor, perform the steps of:
obtaining static vehicular data for a vehicle;
obtaining dynamic vehicular data for the vehicle;
obtaining environmental data associated with the vehicle;
computing a maintenance score from a summation of a number of parameter based calculations, each of the parameter based calculations utilizing a function that is specific to a particular parameter of the number of parameters based on the static vehicular data, dynamic vehicular data, and environmental data;
obtaining a maintenance procedure for the vehicle, the maintenance procedure having a static recommendation point for performing the maintenance procedure, the static recommendation point including at least one of a fixed time interval or a fixed interval of elapsed vehicle miles;
adjusting an interval associated with the maintenance procedure based on the maintenance score;
obtaining unscheduled maintenance data;
inputting the unscheduled maintenance data into a machine learning system as training data;
inputting the static vehicular data, dynamic vehicular data, and environmental data for the vehicle into the machine learning system; and
generating a vehicular recommendation for the vehicle from the machine learning system.

19. The electronic computing device of claim 18 , wherein the memory further comprises instructions, that when executed by the processor, perform the step of including a vehicle sell recommendation in the vehicular recommendation.

20. A computer program product for an electronic computing device comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the electronic computing device to:
obtain static vehicular data for a vehicle;
obtain dynamic vehicular data for the vehicle;
obtain environmental data associated with the vehicle;
compute a maintenance score from a summation of a number of parameter based calculations, each of the parameter based calculations utilizing a function that is specific to a particular parameter of the number of parameters based on the static vehicular data, dynamic vehicular data, and environmental data;
obtain a maintenance procedure for the vehicle, the maintenance procedure having a static recommendation point for performing the maintenance procedure, the static recommendation point including at least one of a fixed time interval or a fixed interval of elapsed vehicle miles;
adjust an interval associated with the maintenance procedure based on the maintenance score;
obtain unscheduled maintenance data;
input the unscheduled maintenance data into a machine learning system as training data;
input the static vehicular data, dynamic vehicular data, and environmental data for the vehicle into the machine learning system; and
generate a vehicular recommendation for the vehicle from the machine learning system.

Assignees

• Kyndryl Inc

Inventors

• Bender Michael
• Childress Rhonda L
• Eckert Kim A
• Pham Minh Q

Classifications

• G06N3/0499

  Feedforward networks · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N20/00

  Machine learning · CPC title

• G07C5/008 Primary

  communicating information to a remotely located station (transmission systems for measured values G08C) · CPC title

• G06N3/08

  Learning methods · CPC title