Vehicles that can be customized and personalized via mobile user profiles

Patent metadata

Field	Value
Publication number	US-12043285-B2
Application number	US-202016844278-A
Country	US
Kind code	B2
Filing date	Apr 9, 2020
Priority date	Apr 9, 2020
Publication date	Jul 23, 2024
Grant date	Jul 23, 2024

Abstract

A vehicle that can be customized and personalized via a mobile user profile. The vehicle can include a body, a powertrain, vehicle electronics, and a computing system. The computing system of the vehicle can be configured to: receive data fields of a driver profile of a user from a mobile device; estimate, using machine learning, configurations of vehicle functions for the vehicle according to the data fields; and control settings of a set of components of the vehicle, via the vehicle electronics, according to the estimated configurations.

First claim

What is claimed is:

1. A system comprising:
at least one processing device configured on a first vehicle; and
memory containing instructions configured to instruct the at least one processing device to:
receive, from a mobile device of a user, signaling indicative of settings for components of the first vehicle; wherein at least a portion of the settings for the components of the first vehicle is from user entered data or sensor generated data from the first vehicle and at least a portion of the settings for the components of the first vehicle is an output from first vehicle machine-learning processing using input comprising at least one data field corresponding to a setting for a component of the first vehicle and at least one data field corresponding to a setting for a component of another vehicle; wherein the mobile device is configured to store, in data fields of a mobile user profile, the settings for the components of the first vehicle received from the first vehicle; wherein the signaling indicative of the settings for the components of the first vehicle comprising input to the first vehicle machine-learning processing comprises internet browsing history or social media preferences of the user indicating:
whether the user has online activities related to sports cars in the internet browsing history or the social media preferences of the user;
whether the user has online activities related to fuel economy in the internet browsing history or the social media preferences of the user; and
wherein the settings for the components of the first vehicle comprises a preferred driving mode, in a range between:
a sports or performance driving mode; and
a fuel economy mode; and wherein the preferred driving mode is determined based at least in part on online activities of the user related to sports cars and fuel economy in the internet browsing history or social media preferences;

determine, based on the received signaling, new settings using second vehicle machine-learning processing, wherein the settings for the components of the first vehicle comprise inputs to the second vehicle machine-learning processing, and the new settings correspond to an output from the second vehicle machine-learning processing; and
provide the new settings to the mobile device to cause the mobile device to store the new settings in the mobile user profile configured in the mobile device;

wherein in response to the mobile device being securely connected to a second vehicle, the mobile user profile stored in the mobile device is to configure, based on the new settings, components of the second vehicle to dynamically transform the second vehicle to behave according to the preferred driving mode.

2. The system of claim 1 , wherein the instructions are further configured to, when executed in the second vehicle, instruct at least one processing device to:
save additional data that indicates settings of the second vehicle; and
send, to the mobile device, signaling indicative of the additional data for updating the mobile user profile configured on the mobile device.

3. The system of claim 2 , wherein the second vehicle is configured to securely connect to the mobile device via exchanging a security key with the mobile device, prior to sending the signaling indicative of the additional data.

4. The system of claim 1 , wherein the data fields comprise settings for a dashboard or center console of the first vehicle, and the output from the second vehicle machine-learning processing comprises settings for a dashboard or center console of the second vehicle.

5. The system of claim 1 , wherein the data fields comprise historical data for brake settings of a braking system of the first vehicle, and the output from the second vehicle machine-learning processing comprises settings for a braking system of the second vehicle that are determined based on the historical data.

6. The system of claim 1 , wherein the data fields further correspond to settings for a third vehicle and wherein the settings for the components of the first vehicle comprise settings for the user when using the first vehicle as a driver; and the settings for the third vehicle comprise settings for the user when using the third vehicle as a passenger.

7. The system of claim 6 , wherein the third vehicle is an autonomous vehicle, and the settings for the third vehicle comprise an automated driving configuration requested by the user.

8. A method comprising:
configuring, in response to a mobile device of a user being securely connected to a first vehicle, components of the first vehicle to dynamically transform the vehicle to behave according to a preferred driving mode identified via a mobile user profile configured in the mobile device, via:
receiving, from the mobile device of the user and by a computing system of the first vehicle, signaling indicative of settings for components of a second vehicle; wherein the mobile device is configured to store, in data fields of the mobile user profile, the settings for the components of the second vehicle received from the second vehicle; wherein at least a portion of the settings for the components of the second vehicle is from user entered data or sensor generated data from the second vehicle and at least a portion of the settings of the components for the second vehicle is an output from machine-learning processing using input comprising at least one data field corresponding to a setting for a component of the second vehicle and at least one data field corresponding to a setting for a component of another vehicle; wherein the signaling indicative of the settings for the components of the second vehicle comprising input to the machine-learning processing comprises internet browsing history or social media preferences of the user indicating:
whether the user has online activities related to sports cars in the internet browsing history or the social media preferences of the user;
whether the user has online activities related to fuel economy in the internet browsing history or the social media preferences of the user; and
wherein the settings for the components of the second vehicle comprises the preferred driving mode, in a range between:
a sports or performance driving mode; and
a fuel economy mode; and wherein the preferred driving mode is determined based at least in part on online activities of the user related to sports cars and fuel economy in the internet browsing history or social media preferences;

estimating, based on the received signaling, a configuration of vehicle functions for the first vehicle, wherein the settings for components of the second vehicle comprise an input to first vehicle machine-learning processing, and the configuration corresponds to an output from the first vehicle machine-learning processing; and
controlling, by the computing system based on the configuration, the components of the first vehicle to dynamically transform the first vehicle to behave according to the preferred driving mode.

9. The method of claim 8 , comprising:
saving, by the computing system, the configuration of settings for the components of the first vehicle; and
sending, by the computing system to the mobile device, signaling indicative of data regarding the configuration of settings for the components of the first vehicle for updating a user profile on the mobile device.

10. The method of claim 8 , wherein the first vehicle machine-learning processing is achieved using an artificial neural network (ANN), and the estimating comprises:
processing data fields as input data according to the ANN to generate output data; and
deriving the configuration of settings of the components of the first vehicle according to the gener

Assignees

• Micron Technology Inc

Inventors

• Bielby Robert Richard Noel

Classifications

• G06N3/0499

  Feedforward networks · CPC title

• B60K35/85

  Arrangements for transferring vehicle- or driver-related data · CPC title

• B60K35/80

  Arrangements for controlling instruments · CPC title

• B60K37/00

  Dashboards (as road-vehicle superstructure sub-units B62D25/14) · CPC title

• B60K35/28

  characterised by the type of the output information, e.g. video entertainment or vehicle dynamics information; characterised by the purpose of the output information, e.g. for attracting the attention of the driver · CPC title