Situational recommendations and control

Patent metadata

Field	Value
Publication number	US-12459359-B2
Application number	US-202318460952-A
Country	US
Kind code	B2
Filing date	Sep 5, 2023
Priority date	Sep 5, 2023
Publication date	Nov 4, 2025
Grant date	Nov 4, 2025

Abstract

A system for providing situational recommendations within a vehicle includes a system controller in communication with a plurality of onboard sensors, the plurality of onboard sensors adapted to collect real-time data related to a location of the vehicle and operating conditions of the vehicle, a database in communication with the system controller adapted to store data related to past actions and data related to a location of the vehicle and operating conditions of the vehicle when such past actions occurred, the system controller including a driver specific machine learning model adapted to predict a desired action based on the real-time data related to the location and operating conditions of the vehicle and data from the database, the system controller further adapted to initiate the predicted desired action, receive input from an occupant within the vehicle, and update the driver specific machine learning model.

First claim

What is claimed is:

1 . A method of providing situational recommendations within a vehicle, comprising:
collecting, with a plurality of onboard sensors in communication with a system controller, real-time data related to a location of the vehicle and operating conditions of the vehicle; accessing, with the system controller, a database of stored data related to past actions and data related to a location of the vehicle and operating conditions of the vehicle when such past actions occurred; predicting, with a driver specific machine learning model within the system controller, a desired action based on the real-time data related to the location of the vehicle and the operating conditions of the vehicle and data from the database, including at least one of:
identifying a pattern of behavior indicating that the occupant takes a specific action each time the occupant arrives at a specific location; and
identifying a pattern of behavior indicating that the occupant takes a specific action each time a specific condition exists;
probabilistically predicting, with the machine learning model, the desired action based on the identified pattern of behavior; initiating the predicted desired action, including:
when a probability of the desired action exceeds fifty percent, prompting the occupant within the vehicle with a recommendation for the predicted desired action; and
when the probability of the desired action exceeds ninety percent, automatically initiating the predicted desired action;
receiving input from an occupant within the vehicle; updating the driver specific machine learning model; receiving, via communication between the system controller and onboard systems within the vehicle, data related to an action being taken by the occupant within the vehicle; comparing the action being taken by the occupant within the vehicle to the predicted desired action, and at least one of:
when the action being taken by the occupant does not match the predicted desired action, prompting the occupant within the vehicle to verify that the occupant within the vehicle wants to proceed with the action;
when the action being taken by the occupant is identified, by the machine learning model, as an anomaly, prompting the occupant within the vehicle to verify that the occupant within the vehicle wants to proceed with the action; and
when the action being taken by the occupant is identified by the system controller as an inherently unsafe action, prompt the occupant within the vehicle with a warning message.

2 . The method of claim 1 , further including:
training a generic machine learning model with data collected from a plurality of different vehicles located in a region and climate similar to the vehicle; uploading the generic machine learning model to the vehicle; and creating the driver specific machine learning model by updating the generic machine learning model.

3 . The method of claim 2 , wherein:
the prompting the occupant within the vehicle with a recommendation for the predicted desired action further includes at least one of: providing the recommendation for the predicted desired action audibly via a speaker connected to a human machine interface (HMI); and displaying the recommendation for the predicted desired action on a touch screen display of the HMI; and the receiving, with the system controller, input from the occupant within the vehicle includes at least one of: receiving verbal input from the occupant within the vehicle via a microphone connected to the HMI; and receiving input from the occupant within the vehicle via the touch screen display.

4 . The method of claim 3 , wherein the displaying the recommendation for the predicted desired action on a touch screen display of the HMI further includes:
identifying an icon for the predicted desired action within a plurality of pre-defined menus that are adapted to be displayed on the touch screen display of the HMI; and displaying, on the touch screen display, only the icon for the predicted desired action.

5 . The method of claim 1 , wherein the updating the driver specific machine learning model further includes:
receiving new data from other vehicles; selecting training data from the new data; and updating the driver specific machine learning model with the selected training data.

6 . The method of claim 1 , wherein the updating the driver specific machine learning model further includes:
comparing the input received from the occupant within the vehicle to the predicted desired action; and when the input from the occupant within the vehicle does not match the predicted desired action, updating the driver specific machine learning model.

7 . A system for providing situational recommendations within a vehicle, comprising:
a system controller in communication with a plurality of onboard sensors, the plurality of onboard sensors adapted to collect real-time data related to a location of the vehicle and operating conditions of the vehicle; a database in communication with the system controller adapted to store data related to past actions and data related to a location of the vehicle and operating conditions of the vehicle when such past actions occurred; and the system controller including a driver specific machine learning model adapted to predict a desired action based on the real-time data related to the location and operating conditions of the vehicle and data from the database, including at least one of:
identify a pattern of behavior indicating that the occupant takes a specific action each time the occupant arrives at a specific location; and
identify a pattern of behavior indicating that the occupant takes a specific action each time a specific condition exists;
the machine learning model further adapted to probabilistically predict the desired action based on the identified pattern of behavior; the system controller further adapted to:
initiate the predicted desired action, including:
when a probability of the desired action exceeds fifty percent, prompt the occupant within the vehicle with a recommendation for the predicted desired action; and
when the probability of the desired action exceeds ninety percent, automatically initiate the predicted desired action;

receive input from an occupant within the vehicle;
update the driver specific machine learning model;
receive, via communication between the system controller and onboard systems within the vehicle, data related to an action being taken by the occupant within the vehicle;
compare the action being taken by the occupant within the vehicle to the predicted desired action, and at least one of:
when the action being taken by the occupant does not match the predicted desired action, prompt the occupant within the vehicle to verify that the occupant within the vehicle wants to proceed with the action;
when the action being taken by the occupant is identified, by the machine learning model, as an anomaly, prompt the occupant within the vehicle to verify that the occupant within the vehicle wants to proceed with the action; and
when the action being taken by the occupant is identified by the system controller as an inherently unsafe action, prompt the occupant within the vehicle with a warning message.

8 . The system of claim 7 , wherein the driver specific machine learning model is created by:
training a generic machine learning model with data collected from a plurality of different vehicles located in a region and climate similar to the vehicle; uploading the generic machine learning model to the vehicle; and creating the driver specific machine learning model by updating the generic machine learning model.

9 . The system of claim 8 ,

Assignees

• Gm Global Tech Operations Llc
• Univ Ottawa

Inventors

• Sethu Ramesh
• Adiththan Arun
• Islam Md Mhafuzul
• Peranandam Prakash M
• Zolfagharian Amirhossein
• Aghababaeyan Zohreh

Classifications

• 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

• B60K2360/1468

  Touch gesture · CPC title

• B60K2360/1438

  Touch screens · CPC title

• B60K2360/119

  Icons · CPC title

• B60K35/10

  Input arrangements, i.e. from user to vehicle, associated with vehicle functions or specially adapted therefor · CPC title