ENHANCED RIDE SHARING USER EXPERIENCE

• US 20180089605A1
• Filed: 09/23/2016
• Published: 03/29/2018
• Est. Priority Date: 09/23/2016
• Status: Active Application

• Current Assignee: Intel Corporation
• Original Assignee: Intel Corporation
• Inventors: Poornachandran, Rajesh, Wouhaybi, Rita H.
• Application Number:
  US15/273,988
• Publication Number:
  US 20180089605A1
• Time in Patent Office: Days
• CPC Class Codes: G06Q 10/06393 Score-carding, benchmarking or key performance indicator [KPI] analysis
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products

Abstract

Disclosed in some examples, are methods, systems, and machine readable mediums which provide for improved matching of drivers and passengers in ride sharing systems using automatically determined user contexts. A score may be generated for each particular nearby driver that describes a suitability of the passenger and the driver given their respective contexts. In some examples, the score may be generated through the use of machine learning techniques. A nearby driver may then be selected based upon (or at least based partially on) the score. The selected driver may then be routed to the passenger.

Claims

1. A device for matching a driver and a passenger, the device comprising:

a processor and a memory communicatively coupled to the processor and including instructions, which when performed by the processor cause the device to perform operations to;

receive a ride share request from the passenger requesting a ride;

determine, using a physical sensor on a computing device of the passenger, a context of the passenger;

determine a set of drivers within a predetermined distance of the passenger;

calculate a compatibility score measuring a compatibility of a respective driver of the set of drivers with the passenger based upon the context of the passenger and a context of the respective driver;

select one of the set of drivers as an assigned driver based upon the compatibility score; and

provide a respective Graphical User Interface (GUI) to the passenger and the assigned driver indicating a driver selection for the passenger.

2. The device of claim 1, wherein the operations to determine the context of the passenger comprises operations to determine an emotional state of the passenger.

3. The device of claim 1, wherein the operations further comprise operations to:

determine the context of the respective driver by determining an emotional state of the respective driver.

4. The device of claim 3, wherein the operations to determine the context of the respective driver comprises operations to determine an emotional state of the respective driver based upon information from a sensor of a computing device of the respective driver.

5. The device of claim 4, wherein the sensor is a video camera and the information from the sensor is a video.

6. The device of claim 4, wherein the sensor is a video camera and the information comprises a sequence of one or more images from the camera.

7. The device of claim 4, wherein the sensor is a video camera and the information comprises a three dimensional depth map.

8. At least one machine readable medium including instructions, which when performed by a machine, causes the machine to perform operations for matching a driver and a passenger of a network based service comprising:

receiving a ride share request from the passenger requesting a ride;

determining, using a physical sensor on a computing device of the passenger, a context of the passenger;

determining a set of drivers within a predetermined distance of the passenger;

calculating a compatibility score measuring a compatibility of a respective driver of the set of drivers with the passenger based upon the context of the passenger and a context of the respective driver;

selecting one of the set of drivers as an assigned driver based upon the compatibility score; and

providing a respective Graphical User Interface (GUI) to the passenger and the assigned driver indicating a driver selection for the passenger.

9. The at least one machine-readable medium of claim 8, wherein the operations comprise:

determining, using the physical sensor on the computing device of the passenger, an in-ride context of the passenger;

determining, using a physical sensor on a computing device of the assigned driver, an in-ride context of the assigned driver;

calculating an in-ride compatibility score measuring a compatibility of the assigned driver with the passenger based upon the in-ride context of the passenger and the in-ride context of the assigned driver;

providing to the passenger a Graphical User Interface (GUI) showing the in-ride context and which allows the passenger to input a review of the assigned driver; and

publishing the review along with the in-ride compatibility score.

10. The at least one machine-readable medium of claim 8, wherein the physical sensor is a camera, and wherein the operations of determining, using the physical sensor on the computing device of the passenger, the context of the passenger comprises operations of determining an emotional state of the passenger based upon a video recorded by the camera.

11. The at least one machine-readable medium of claim 8, wherein the operations of calculating the compatibility score measuring the compatibility of the respective driver with the passenger based upon the context of the passenger and the context of the respective driver comprises operations of:

using the context of the respective driver, the context of the passenger, and a model created by a machine learning algorithm to produce the compatibility score.

12. The at least one machine-readable medium of claim 11, wherein the machine learning algorithm is a logistic regression algorithm.

13. The at least one machine-readable medium of claim 11, wherein the operations comprise:

accessing a training data set, the training data set comprising sets of in-ride contexts of drivers and corresponding passengers labeled with their emotional reactions to the ride; and

training the model using the training data set as input to the supervised machine learning algorithm.

14. The at least one machine-readable medium of claim 8, wherein the operations of calculating the compatibility score measuring the compatibility of the respective driver with the passenger based upon the context of the passenger and the context of the respective driver comprises the operations of:

utilizing a weighted summation algorithm to produce the compatibility score.

15. A method for matching a driver and a passenger of a network based service, the method comprising:

receiving a ride share request from the passenger requesting a ride;

determining, using a physical sensor on a computing device of the passenger, a context of the passenger;

determining a set of drivers within a predetermined distance of the passenger;

calculating a compatibility score measuring a compatibility of a respective driver of the set of drivers with the passenger based upon the context of the passenger and a context of the respective driver;

selecting one of the set of drivers as an assigned driver based upon the compatibility score; and

providing a respective Graphical User Interface (GUI) to the passenger and the assigned driver indicating a driver selection for the passenger.

16. The method of claim 15, wherein determining the context of the passenger comprises determining an emotional state of the passenger.

17. The method of claim 15, further comprising:

determining the context of the respective driver by determining an emotional state of the respective driver.

18. The method of claim 17, wherein determining the context of the respective driver comprises determining an emotional state of the respective driver based upon information from a sensor of a computing device of the respective driver.

19. The method of claim 18, wherein the sensor is a video camera and the information from the sensor is a video.

20. The method of claim 18, wherein the sensor is a video camera and the information comprises a sequence of one or more images from the camera.