IP Library › Granted Patent US 10,571,294
Granted Patent B2
US 10,571,294 · App. 16/034,553 · Granted Feb 25, 2020

Systems and methods for trip planning

Inventors: Bijan Forutanpour (San Diego, CA); Jonathan Kies (Encinitas, CA)
Assignee: QUALCOMM Incorporated
G01C21/3679G01C21/20G01C21/343G01C21/3476G01C21/3602G01C21/3611G01C21/3617G01C21/3691G06Q10/047G06T2207/30196
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Quick Facts
Patent No.
US 10,571,294
App. No.
16/034,553
Granted
Feb 25, 2020
Kind
B2
Abstract

A method performed by an electronic device is described. The method includes obtaining one or more trip objectives. The method also includes obtaining one or more evaluation bases. The method further includes identifying an association between at least one site and the one or more trip objectives. The method additionally includes obtaining sensor data from the at least one site. The sensor data includes at least image data. The method also includes performing analysis on the image data to determine dynamic destination information corresponding to the at least one site. The method further includes performing trip planning based on the dynamic destination information, the one or more trip objectives, and the one or more evaluation bases. The method additionally includes providing one or more suggested routes based on the trip planning.

Claims (49)

1. A method performed by an electronic device, the method comprising:

obtaining one or more trip objectives;

providing a first suggested route based on the one or more trip objectives and based on a first trip planning analysis, the first trip planning analysis being based on first dynamic destination information that is based on first image data from a first site, second dynamic destination information that is based on second image data from a second site, and a weighting applied to the first dynamic destination information or the second dynamic destination information;

receiving feedback from a user indicating non-acceptance of the first suggested route; and

providing a second suggested route based on the one or more trip objectives and based on a second trip planning analysis, the second trip planning analysis being based on the first dynamic destination information, the second dynamic destination information, and an adjusted weighting applied to the first dynamic destination information or the second dynamic destination information.

2. The method of claim 1 , wherein the first dynamic destination information includes a first aspect and the second dynamic destination information includes a second aspect of a same type as the first aspect, and wherein the adjusted weighting is applied to the first aspect or to the second aspect.

3. The method of claim 1 , wherein the first dynamic destination information includes a first aspect and the second dynamic destination information includes a second aspect of a different type from the first aspect, and wherein the adjusted weighting is applied to the first aspect or to the second aspect.

4. The method of claim 1 , wherein the first dynamic destination information includes a first plurality of aspects and the second dynamic destination information includes a second plurality of aspects and wherein the adjusted weighting is applied to at least one of the first plurality of aspects or at least one of the second plurality of aspects.

5. The method of claim 1 , wherein machine learning is performed to determine the adjusted weighting based on the feedback from the user.

6. The method of claim 5 , wherein the machine learning is performed by the electronic device.

7. The method of claim 5 , wherein the machine learning is performed by a server.

8. The method of claim 1 , wherein trip planning training is performed based on whether the second suggested route is accepted.

9. The method of claim 1 , wherein the first trip planning analysis and the second trip planning analysis are performed by the electronic device.

10. The method of claim 1 , wherein the first trip planning analysis and the second trip planning analysis are performed by a server.

11. An electronic device, comprising:

a processor;

a memory in electronic communication with the processor;

instructions stored in the memory, the instructions being executable to:

obtain one or more trip objectives;

provide a first suggested route based on the one or more trip objectives and based on a first trip planning analysis, the first trip planning analysis being based on first dynamic destination information that is based on first image data from a first site, second dynamic destination information that is based on second image data from a second site, and a weighting applied to the first dynamic destination information or the second dynamic destination information;

receive feedback from a user indicating non-acceptance of the first suggested route; and

provide a second suggested route based on the one or more trip objectives and based on a second trip planning analysis, the second trip planning analysis being based on the first dynamic destination information, the second dynamic destination information, and an adjusted weighting applied to the first dynamic destination information or the second dynamic destination information.

12. The electronic device of claim 11 , wherein the first dynamic destination information includes a first aspect and the second dynamic destination information includes a second aspect of a same type as the first aspect, and wherein the adjusted weighting is applied to the first aspect or to the second aspect.

13. The electronic device of claim 11 , wherein the first dynamic destination information includes a first aspect and the second dynamic destination information includes a second aspect of a different type from the first aspect, and wherein the adjusted weighting is applied to the first aspect or to the second aspect.

14. The electronic device of claim 11 , wherein the first dynamic destination information includes a first plurality of aspects and the second dynamic destination information includes a second plurality of aspects and wherein the adjusted weighting is applied to at least one of the first plurality of aspects or at least one of the second plurality of aspects.

15. The electronic device of claim 11 , wherein machine learning is performed to determine the adjusted weighting based on the feedback from the user.

16. The electronic device of claim 15 , wherein the instructions are executable to perform the machine learning.

17. The electronic device of claim 15 , wherein the machine learning is performed by a server.

18. The electronic device of claim 11 , wherein trip planning training is performed based on whether the second suggested route is accepted.

19. The electronic device of claim 11 , wherein the instructions are executable to perform the first trip planning analysis and the second trip planning analysis.

20. The electronic device of claim 11 , wherein the first trip planning analysis and the second trip planning analysis are performed by a server.

21. A non-transitory tangible computer-readable medium storing computer-executable code, comprising:

code for causing an electronic device to obtain one or more trip objectives;

code for causing the electronic device to provide a first suggested route based on the one or more trip objectives and based on a first trip planning analysis, the first trip planning analysis being based on first dynamic destination information that is based on first image data from a first site, second dynamic destination information that is based on second image data from a second site, and a weighting applied to the first dynamic destination information or the second dynamic destination information;

code for causing the electronic device to receive feedback from a user indicating non-acceptance of the first suggested route; and

code for causing the electronic device to provide a second suggested route based on the one or more trip objectives and based on a second trip planning analysis, the second trip planning analysis being based on the first dynamic destination information, the second dynamic destination information, and an adjusted weighting applied to the first dynamic destination information or the second dynamic destination information.

22. The computer-readable medium of claim 21 , wherein the first dynamic destination information includes a first aspect and the second dynamic destination information includes a second aspect of a same type as the first aspect, and wherein the adjusted weighting is applied to the first aspect or to the second aspect.

23. The computer-readable medium of claim 21 , wherein the first dynamic destination information includes a first aspect and the second dynamic destination information includes a second aspect of a different type from the first aspect, and wherein the adjusted weighting is applied to the first aspect or to the second aspect.

24. The computer-readable medium of claim 21 , wherein the first dynamic destination information includes a first plurality of aspects and the second dynamic destination information includes a second plurality of aspects and wherein the adjusted weighting is applied to at least one of the first plurality of aspects or at least one of the second plurality of aspects.

25. The computer-readable medium of claim 21 , wherein machine learning is performed to determine the adjusted weighting based on the feedback from the user.

26. An apparatus, comprising:

means for obtaining one or more trip objectives;

means for providing a first suggested route based on the one or more trip objectives and based on a first trip planning analysis, the first trip planning analysis being based on first dynamic destination information that is based on first image data from a first site, second dynamic destination information that is based on second image data from a second site, and a weighting applied to the first dynamic destination information or the second dynamic destination information;

means for receiving feedback from a user indicating non-acceptance of the first suggested route; and

means for providing a second suggested route based on the one or more trip objectives and based on a second trip planning analysis, the second trip planning analysis being based on the first dynamic destination information, the second dynamic destination information, and an adjusted weighting applied to the first dynamic destination information or the second dynamic destination information.

27. The apparatus of claim 26 , wherein the first dynamic destination information includes a first aspect and the second dynamic destination information includes a second aspect of a same type as the first aspect, and wherein the adjusted weighting is applied to the first aspect or to the second aspect.

28. The apparatus of claim 26 , wherein the first dynamic destination information includes a first aspect and the second dynamic destination information includes a second aspect of a different type from the first aspect, and wherein the adjusted weighting is applied to the first aspect or to the second aspect.

29. The apparatus of claim 26 , wherein the first dynamic destination information includes a first plurality of aspects and the second dynamic destination information includes a second plurality of aspects and wherein the adjusted weighting is applied to at least one of the first plurality of aspects or at least one of the second plurality of aspects.

30. The apparatus of claim 26 , wherein machine learning is performed to determine the adjusted weighting based on the feedback from the user.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 14, 2018
From: FORUTANPOUR, BIJAN; KIES, JONATHAN
To: QUALCOMM INCORPORATED
Reel/Frame 046880/0501 →
Continuity (3)
Continuation 15457300 · Mar 13, 2017
Provisional Application 62421729 · Nov 14, 2016
Related Publication 20180328756A1 · Nov 15, 2018
Cited By (1)
US 12,190,270