Vehicle recommendations based on driving habits

Patent metadata

Field	Value
Publication number	US-2019340519-A1
Application number	US-201815967680-A
Country	US
Kind code	A1
Filing date	May 1, 2018
Priority date	May 1, 2018
Publication date	Nov 7, 2019
Grant date	—

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 based on the static vehicular data, dynamic vehicular data, and environmental data; obtaining a maintenance procedure for the vehicle; 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 obtaining environmental data includes obtaining ambient temperature data.

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

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

5 . The method of claim 1 , wherein obtaining environmental data includes obtaining terrain data.

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

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

8 . The method of claim 1 , wherein obtaining dynamic vehicle data includes obtaining suspension deflection data.

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

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

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

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

13 . The method of claim 11 , wherein obtaining 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 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 based on the static vehicular data, dynamic vehicular data, and environmental data; obtaining a maintenance procedure for the vehicle; 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 based on the static vehicular data, dynamic vehicular data, and environmental data; obtain a maintenance procedure for the vehicle; 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

• IBM

Inventors

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

Classifications

• G06N3/08

  Learning methods · CPC title

• G07C5/085

  using electronic data carriers · CPC title

• G07C5/008 Primary

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

• G07C5/0808

  Diagnosing performance data (testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005) · CPC title

• G06N20/00

  Machine learning · CPC title