Crowd-sourced vehicle setting recommendations

Patent metadata

Field	Value
Publication number	US-2017305437-A1
Application number	US-201715494786-A
Country	US
Kind code	A1
Filing date	Apr 24, 2017
Priority date	Apr 26, 2016
Publication date	Oct 26, 2017
Grant date	—

Abstract

Driver vehicle setting values are crowd-sourced from multiple users in an anonymous manner. The crowd-sourced driver vehicle setting values are processed using machine learning to generate one or more vehicle setting recommendations to other users. The recommendations may be for vehicle settings that the user has not yet activated or vehicle setting values that are frequently used for a particular vehicle.

First claim

What is claimed is:

1 . A computer-implemented method comprising:
obtaining driver vehicle setting data from a plurality of vehicles; identifying a vehicle model associated with the driver vehicle setting data obtained from the plurality of vehicles; and generating a vehicle setting recommendation for the identified vehicle model based on the driver vehicle setting data obtained from the plurality of vehicles.

2 . The computer-implemented method of claim 1 , wherein the driver vehicle setting data obtained from the plurality of vehicles includes one or more of mirror positions, a driver seat position, steering wheel position, foot pedal positions, radio station presets, heating, ventilation, air conditioning (HVAC) settings, vehicle lighting preferences, wiper speed settings, navigation settings, camera bumper settings, safety alert settings, child lock settings, and window lock settings.

3 . The computer-implemented method of claim 1 , wherein the driver vehicle setting data obtained from the plurality of vehicles does not include identification information of a driver.

4 . The computer-implemented method of claim 1 , wherein generating the vehicle setting recommendation for the identified vehicle model based on the driver vehicle setting data obtained from the plurality of vehicles comprises:
generating the vehicle setting recommendation using machine learning.

5 . The computer-implemented method of claim 4 , wherein the machine learning comprises training one or more neural networks to determine the vehicle setting recommendation.

6 . The computer-implemented method of claim 1 , wherein:
obtaining the driver vehicle setting data from the plurality of vehicles comprises receiving crowd-sourced driver vehicle setting data from vehicles having the same model as the identified vehicle model; and generating the vehicle setting recommendation for the identified vehicle model based on the driver vehicle setting data obtained from the plurality of vehicles comprises:
determining, according to one or more criteria, a score for a vehicle setting;
determining that the score satisfies a particular threshold; and
generating the vehicle setting recommendation to recommend the vehicle setting that has the score that satisfies the particular threshold.

7 . The computer-implemented method of claim 6 , wherein the one or more criteria includes one or more of:
a frequency of use of the vehicle setting; a social network trending popularity of the vehicle setting; a safety rating of the vehicle setting; and a recommendation from a manufacturer of the identified vehicle model.

8 . The computer-implemented method of claim 7 , wherein determining, according to one or more criteria, the score for the vehicle setting included in the driver vehicle setting data comprises:
assigning each of the one or more criteria a weight; determining the score for the vehicle setting based, in part, on the assigned weights to the one or more criteria.

9 . The computer-implemented method of claim 1 , further comprising:
receiving an indication that a driver is within a threshold distance of a second vehicle; determining that a model of the second vehicle matches the identified vehicle model; and transmitting the vehicle setting recommendation to a device associated with the driver within the threshold distance of the second vehicle.

10 . The computer-implemented method of claim 9 , wherein the device associated with the driver includes a portable electronic device associated with the driver or a vehicle control module of the second vehicle.

11 . The computer-implemented method of claim 9 , further comprising:
generating a plurality of particular driver profiles; and determining that a likely profile of the driver within the threshold distance of the second vehicle matches one of the plurality of particular driver profiles.

12 . The computer-implemented method of claim 11 , wherein determining that the likely profile of the driver within the threshold distance of the second vehicle matches one of the plurality of particular driver profiles comprises:
determining that one or more of a likely height, a likely arm length, and a likely leg length of the driver within the threshold distance of the second vehicle respectively matches one or more of a likely height, a likely arm length, and a likely leg length in the one of the plurality of particular driver profiles, and wherein the vehicle setting recommendation is based at least in part on the likely profile of the driver.

13 . The computer-implemented method of claim 7 , further comprising:
receiving an indication that a driver is within a threshold distance of a second vehicle; determining that a model of the second vehicle matches the identified vehicle model; and transmitting the vehicle setting recommendation to a device associated with the driver within the threshold distance of the second vehicle, wherein the one or more criteria includes one or more of an interest of the driver and a vehicle setting preference of the driver, and wherein the vehicle setting recommendation includes a recommendation associated with the one or more of the interest of the driver and the vehicle setting preference of the driver.

14 . The computer-implemented method of claim 9 , further comprising:
receiving a selection by the driver indicating acceptance of the vehicle setting recommendation.

15 . The computer-implemented method of claim 14 , wherein the selection by the driver indicating acceptance of the vehicle setting recommendation is received from a portable electronic device associated with the driver or a vehicle control module of the second vehicle.

16 . The computer-implemented method of claim 9 , wherein the vehicle setting recommendation includes a recommendation associated with a vehicle setting that has not been activated in the second vehicle.

17 . A non-transitory computer-readable storage medium comprising instructions, which, when executed by one or more computers, cause the one or more computers to perform actions comprising:
obtaining driver vehicle setting data from a plurality of vehicles; identifying a vehicle model associated with the driver vehicle setting data obtained from the plurality of vehicles; and generating a vehicle setting recommendation for the identified vehicle model based on the driver vehicle setting data obtained from the plurality of vehicles.

18 . The non-transitory computer-readable storage medium of claim 17 , wherein the actions further comprise:
receiving an indication that a driver is within a threshold distance of a second vehicle; determining that a model of the second vehicle matches the identified vehicle model; and transmitting the vehicle setting recommendation to a device associated with the driver within the threshold distance of the second vehicle.

19 . A system comprising:
one or more computers and one or more storage devices storing instructions that are operable and when executed by one or more computers, cause the one or more computers to perform actions comprising:
obtaining driver vehicle setting data from a plurality of vehicles;
identifying a vehicle model associated with the driver vehicle setting data obtained from the plurality of vehicles; and
generating a vehicle setting recommendation for the identified vehicle model based on the driver vehicle setting data obtained from the plurality of vehicles.

20 . The system of claim 19 , wherein the actions further

Assignees

• Google Inc

Inventors

• Onorato Joseph M
• Brenner Andrew Scott
• Holle Daniel
• Brady Patrick

Classifications

• G06Q10/40

  Business processes related to social networking or social networking services · CPC title

• B60W50/0098 Primary

  Details of control systems ensuring comfort, safety or stability not otherwise provided for · CPC title

• B60R16/037 Primary

  for occupant comfort {, e.g. for automatic adjustment of appliances according to personal settings, e.g. seats, mirrors, steering wheel} · CPC title

• B60W2540/043

  Identity of occupants · CPC title

• H04L67/10

  in which an application is distributed across nodes in the network (software deployment G06F8/60; multiprogramming arrangements G06F9/46) · CPC title