Proactive autocomplete of a user's in-vehicle operations

Patent metadata

Field	Value
Publication number	US-9688281-B2
Application number	US-201514665685-A
Country	US
Kind code	B2
Filing date	Mar 23, 2015
Priority date	Mar 23, 2015
Publication date	Jun 27, 2017
Grant date	Jun 27, 2017

Abstract

The disclosure includes a method for autocompleting an in-vehicle operation that includes monitoring sensors for sensor data. The method includes determining an occurrence of an initial vehicle operation performed by a user based on the sensor data. The method includes determining a vehicle task from a digitally stored dataset based on the initial vehicle operation. The method includes determining a next vehicle operation to autocomplete based on the vehicle task. The method includes autocompleting the next vehicle operation. The method includes determining whether the vehicle task is complete.

First claim

What is claimed is:

1. A method for autocompleting an in-vehicle operation, the method comprising:
monitoring in-vehicle sensors for sensor data;
determining, by an onboard computer of a vehicle, an occurrence of an initial vehicle operation performed by a user based on the sensor data;
determining a confidence factor that indicates a likelihood that the initial vehicle operation performed by the user was accurately determined based on one or more images received from one or more of the in-vehicle sensors;
determining, by the onboard computer of the vehicle, a vehicle task from a digitally stored dataset based on the confidence factor exceeding a threshold value and the initial vehicle operation, the vehicle task including the initial vehicle operation and a next vehicle operation;
determining, by the onboard computer of the vehicle, the next vehicle operation to autocomplete based on the vehicle task;
autocompleting the next vehicle operation;
determining, by the onboard computer of the vehicle, whether the vehicle task is complete;
determining, by the onboard computer of the vehicle, the occurrence of a subsequent vehicle operation performed by the user in response to autocompleting the vehicle task; and
responsive to the user performing the subsequent vehicle operation a threshold number of times, updating the digitally stored dataset by adding the subsequent vehicle operation to the vehicle task as a first additional vehicle operation to be autocompleted, wherein the threshold number of times is a positive whole number.

2. The method of claim 1 , further comprising responsive to the vehicle task failing to be complete, determining a second additional vehicle operation to autocomplete based on the vehicle task, autocompleting the second additional vehicle operation, and repeating until the vehicle task is complete.

3. The method of claim 1 , wherein the next vehicle operation includes automatically calling a person listed in an address book associated with the user.

4. The method of claim 1 , wherein determining the next vehicle operation is also based on the confidence factor exceeding a threshold value.

5. The method of claim 1 , further comprising:
responsive to autocompleting the next vehicle operation, monitoring the in-vehicle sensors to identify one or more words spoken by the user; and
providing the user with an explanation of the next vehicle operation based on the one or more words spoken by the user.

6. The method of claim 1 , further comprising:
receiving traffic data from a network, the traffic data including information that would result in a delay if the user continued driving along a particular route; and
determining a next vehicle task based on the traffic data.

7. The method of claim 1 , wherein the in-vehicle sensors detect a weather condition and the vehicle task is based on the weather condition.

8. The method of claim 1 , further comprising:
determining driver habits of the user based on a list of vehicle operations performed by the user and a number of times vehicle operations in the list of vehicle operations were performed; and
responsive to the number of times exceeding a threshold value, generating the vehicle task; and
storing the vehicle task as the digitally stored dataset.

9. The method of claim 1 , wherein the digitally stored dataset is a digitally stored table that includes a list of the in-vehicle sensors to be monitored for determining the vehicle task.

10. The method of claim 1 , further comprising:
responsive to autocompleting the next vehicle operation, monitoring the in-vehicle sensors for sensor data that includes one or more images and one or more sounds of the user; and
responsive to at least one of the one or more images including the user frowning and the one or more sounds including a word that indicates confusion, generating an explanation of the next vehicle operation for the user.

11. The method of claim 10 , wherein the word that indicates confusion corresponds to a list, where the list includes words that each have a particular meaning.

12. The method of claim 1 , further comprising:
monitoring a sequence of vehicle operations performed by the user to complete the vehicle task; and
generating the digitally stored dataset by adding the vehicle task with the sequence of vehicle operations performed by the user.

13. The method of claim 1 , further comprising:
generating a user interface that includes graphical elements for selecting a level of autocomplete; and
responsive to the user selecting the level of autocomplete, updating the digitally stored dataset to include the level of autocomplete.

14. A non-transitory computer-readable medium having computer instructions stored thereon that are executable by an onboard computer of a vehicle to perform or control performance of steps comprising:
monitoring in-vehicle sensors for sensor data;
determining, by the onboard computer of the vehicle, an occurrence of an initial vehicle operation performed by a user based on the sensor data;
determining a confidence factor that indicates a likelihood that the initial vehicle operation performed by the user was accurately determined based on one or more images received from one or more of the in-vehicle sensors;
determining, by the onboard computer of the vehicle, a vehicle task from a digitally stored dataset based on the confidence factor exceeding a threshold value and the initial vehicle operation, the vehicle task including the initial vehicle operation and a next vehicle operation;
determining, by the onboard computer of the vehicle, the next vehicle operation to autocomplete based on the vehicle task;
autocompleting the next vehicle operation;
determining, by the onboard computer of the vehicle, whether the vehicle task is complete;
determining, by the onboard computer of the vehicle, the occurrence of a subsequent vehicle operation performed by the user in response to autocompleting the vehicle task; and
responsive to the user performing the subsequent vehicle operation a threshold number of times, updating the digitally stored dataset by adding the subsequent vehicle operation to the vehicle task as a first additional vehicle operation to be autocompleted.

15. The non-transitory computer-readable medium of claim 14 , the steps further comprising responsive to the vehicle task failing to be complete, determining a second additional vehicle operation to autocomplete based on the vehicle task, autocompleting the second additional vehicle operation, and repeating until the vehicle task is complete.

16. The non-transitory computer-readable medium of claim 14 , wherein determining the next vehicle operation is also based on the confidence factor exceeding a threshold value.

17. The non-transitory computer-readable medium of claim 14 , the steps further comprising:
responsive to autocompleting the next vehicle operation, monitoring the in-vehicle sensors to identify one or more words spoken by the user; and
providing the user with an explanation of the next vehicle operation based on the one or more words spoken by the user.

18. The non-transitory computer-readable medium of claim 14 , the steps further comprising:
receiving weather data from a network, the weather data including roadway weather conditions; and
determining a next vehicle task based on the weather data.

19. The non-transitory computer-readable medium of claim 14 , wherein the in-vehicle sensors detect a weather condition and the vehicle task is based on the weather condition.

Assignees

• Toyota Motor Co Ltd

Inventors

• Parundekar Rahul

Classifications

• B60W2555/20

  Ambient conditions, e.g. wind or rain · CPC title

• B60S1/0818

  including control systems responsive to external conditions, e.g. by detection of moisture, dirt or the like · CPC title

• B60W40/08 Primary

  related to drivers or passengers · CPC title

• B60W50/0098

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

• B60W2554/00

  Input parameters relating to objects · CPC title