System and methods for performing multivariate optimizations based on location data

• US 10,371,542 B2
• Filed: 02/17/2017
• Issued: 08/06/2019
• Est. Priority Date: 02/17/2017
• Status: Active Grant

• Current Assignee: Uber Technologies, Inc.
• Original Assignee: Uber Technologies, Inc.
• Inventors: O'Herlihy, Michael, De, Nirveek
• Primary Examiner(s): Black, Thomas G
• Assistant Examiner(s): Huynh, Luat T
• Application Number:
  US15/436,614
• Publication Number:
  US 20180238705A1
• Time in Patent Office: 900 Days
• Field of Search: 701424
• CPC Class Codes: G01C 21/3484 Personalized, e.g. from learned user behaviour or user-defined profiles
• CPC Class Codes: G01C 21/3617 using user history, behaviour, conditions or preferences, e.g. predicted or inferred from previous use or current movement
• CPC Class Codes: G06N 5/01 Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 50/40 Business processes related to the transportation industry shipping G06Q10/083

Abstract

An optimization and recommendation engine can receive user location data to programmatically generate customized recommendations regarding the user'"'"'s operation as a potential service provider for a network service. The optimization and recommendation engine can determine potential service routes based on the user location data indicating a frequent route of the user and on service data associated with the network service. The optimization and recommendation engine can also perform multivariate optimizations to select one or more optimal service routes from the potential service routes. The recommendations can include the one or more optimal service routes as well as parameters associated with such routes. The recommendations can further include a comparison of the parameters against characteristics of the user'"'"'s frequent route.

Claims

1. A system for performing multivariate optimizations based on user location data, comprising:

one or more processors; and

one or more memory resources storing instructions that, when executed by the one or more processors, cause the one or more processors to;

receive location data of a user, the location data recorded by a mobile computing device operated by the user;

analyze the location data to identify a first set of location data corresponding to a frequent route taken by the user;

retrieve service data associated with a network service;

analyze the first set of location data and the service data to;

(i) identify a plurality of potential service routes;

(ii) determine, for each potential service route, a respective set of parameters;

(iii) select, based on optimizing one or more of the respective sets of parameters, a first service route of the plurality of potential service routes, wherein optimizing one or more of the respective sets of parameters includes associating a respective weight for each parameter; and

generate content data for displaying a recommendation for the user based on the first service route.

2. The system of claim 1, wherein the executed instructions further cause the one or more processors to analyze the location data by identifying a second set of location data corresponding to a non-driving route taken by the user.

3. The system of claim 1, wherein the executed instructions further cause the one or more processors to identify the plurality of potential service routes by identifying a plurality of service-begin regions and a plurality of service-end regions.

4. The system of claim 3, wherein the plurality of service-begin regions and the plurality of service-end regions are determined based on the first set of location data and the service data.

5. The system of claim 1, wherein the respective set of parameters for each potential service route includes a travel time.

6. The system of claim 1, wherein the respective set of parameters for each potential service route includes a travel distance.

7. The system of claim 1, wherein the respective set of parameters for each potential service route includes an estimate of potential earnings.

8. The system of claim 1, wherein the respective set of parameters for each potential service route includes an estimated toll amount.

9. The system of claim 1, wherein the executed instructions further cause the one or more processors to select the first service route by receiving a user input corresponding to the respective weight for each parameter.

10. The system of claim 1, wherein the content data includes data for displaying the first service route.

11. The system of claim 1, wherein the content data includes data for displaying the frequent route of the user.

12. The system of claim 11, wherein the content data includes data for displaying a comparison of the respective set of parameters associated with the first service route with a set of characteristics determined for the frequent route of the user.

13. The system of claim 1, wherein the content data includes data for displaying a respective set of parameters associated with the first service route.

14. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to:

receive location data of a user, the location data recorded by a mobile computing device operated by the user;

analyze the location data to identify a first set of location data corresponding to a frequent route taken by the user;

retrieve service data associated with a network service;

analyze the first set of location data and the service data to;

(i) identify a plurality of potential service routes;

(ii) determine, for each potential service route, a respective set of parameters;

(iii) select, based on optimizing one or more of the respective sets of parameters, a first service route of the plurality of potential service routes, wherein optimizing one or more of the respective sets of parameters includes associating a respective weight for each parameter; and

generate content data for displaying a recommendation for the user based on the first service route.

15. The non-transitory computer-readable medium of claim 14, wherein the executed instructions further cause the one or more processors to identify the plurality of potential service routes by identifying a plurality of service-begin regions and a plurality of service-end regions based on the first set of location data and the service data.

16. The non-transitory computer-readable medium of claim 14, wherein the respective set of parameters for each potential service route includes a travel time, a travel distance, an estimate of potential earnings, and an estimated toll amount.

17. A computer-implemented method of multivariate optimizations based on location data, the method being performed by one or more processors and comprising:

receiving location data of a user, the location data recorded by a mobile computing device operated by the user;

analyzing the location data to identify a first set of location data corresponding to a frequent route taken by the user;

retrieving service data associated with a network service;

analyzing the first set of location data and the service data to;

(i) identify a plurality of potential service routes;

(ii) determine, for each potential service route, a respective set of parameters;

(iii) select, based on optimizing one or more of the respective sets of parameters, a first service route of the plurality of potential service routes wherein optimizing one or more of the respective sets of parameters includes associating a respective weight for each parameter; and

generating content data for displaying a recommendation for the user based on the first service route.

18. The method of claim 17, wherein identifying the plurality of potential service routes includes identifying a plurality of service-begin regions and a plurality of service-end regions based on the first set of location data and the service data.

19. The method of claim 17, wherein the respective set of parameters for each potential service route includes a travel time, a travel distance, an estimate of potential earnings, and an estimated toll amount.

20. The method of claim 17, wherein selecting the first service route includes receiving a user input corresponding to the respective weight for each parameter.