Proactive autocomplete of a user's in-vehicle operations

• US 9,688,281 B2
• Filed: 03/23/2015
• Issued: 06/27/2017
• Est. Priority Date: 03/23/2015
• Status: Active Grant

• Current Assignee: Toyota Jidosha Kabushiki Kaisha (Toyota Motor Corporation)
• Original Assignee: Toyota Jidosha Kabushiki Kaisha (Toyota Motor Corporation)
• Inventors: Parundekar, Rahul
• Primary Examiner(s): Khatib, Rami
• Assistant Examiner(s): BOOMER, JEFFREY C
• Application Number:
  US14/665,685
• Publication Number:
  US 20160280220A1
• Time in Patent Office: 827 Days
• Field of Search: None
• CPC Class Codes: B60Q 1/143 combined with another condition, e.g. using vehicle recognition from camera images or activation of wipers
• CPC Class Codes: B60Q 2300/312 Adverse weather
• CPC Class Codes: B60S 1/0818 including control systems responsive to external conditions, e.g. by detection of moisture, dirt or the like
• CPC Class Codes: B60W 2554/00 Input parameters relating to objects
• CPC Class Codes: B60W 2555/20 Ambient conditions, e.g. wind or rain
• CPC Class Codes: B60W 40/08 related to drivers or passengers
• CPC Class Codes: B60W 50/0098 Details of control systems ensuring comfort, safety or stability not otherwise provided for
• CPC Class Codes: B60W 50/10 Interpretation of driver requests or demands

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.

Claims

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.

20. The non-transitory computer-readable medium of claim 14, the steps 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.

21. A method for autocompleting an in-vehicle operation, the method comprising:

monitoring sensors for first sensor data;

determining, by an onboard computer of a vehicle, an occurrence of an initial vehicle operation performed by a user based on the first sensor data;

determining a confidence factor that indicates a likelihood that the initial vehicle operation performed by the user was accurately determined based on 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 table based on the confidence factor exceeding a threshold value and the initial vehicle operation performed by the user, 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;

responsive to the vehicle task failing to be complete, determining, by the onboard computer of the vehicle, an additional vehicle operation to autocomplete based on the vehicle task, autocompleting the additional vehicle operation, and repeating until the vehicle task is complete;

monitoring the sensors for second sensor data that includes one or more images and one or more sounds of the user; and

responsive to determining that the one or more sounds of the user include a word that indicates confusion, generating an explanation of the next vehicle operation.