Trip anomaly detection system

• US 9,762,601 B2
• Filed: 06/17/2015
• Issued: 09/12/2017
• Est. Priority Date: 06/17/2015
• Status: Active Grant

• Current Assignee: Uber Technologies, Inc.
• Original Assignee: Uber Technologies, Inc.
• Inventors: Truong, Michael, Purdy, David, Mawas, Rami
• Primary Examiner(s): Shayanfar, Ali
• Application Number:
  US14/742,273
• Publication Number:
  US 20160373473A1
• Time in Patent Office: 818 Days
• CPC Class Codes: B60N 2/002 Seats provided with an occupancy detection means mounted therein or thereon detection means responsive to presence or absence of children B60N2/266
• CPC Class Codes: B60R 11/04 Mounting of cameras operative during drive; Arrangement of controls thereof relative to the vehicle
• CPC Class Codes: E05F 15/70 with automatic actuation
• CPC Class Codes: E05Y 2400/44 Sensors therefore E05Y2400/322, E05Y2400/33, E05Y2400/55, E05Y2400/56, E05Y2400/852 take precedence
• CPC Class Codes: E05Y 2400/45 Control modes
• CPC Class Codes: E05Y 2900/548 Trunk lids
• CPC Class Codes: G01C 21/005 with correlation of navigation data from several sources, e.g. map or contour matching G01C21/30 takes precedence
• CPC Class Codes: G01C 21/28 with correlation of data from several navigational instruments
• CPC Class Codes: G05D 1/0088 characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours using knowledge based models G06N5/00
• CPC Class Codes: G05D 1/0212 with means for defining a desired trajectory involving a plurality of land vehicles G05D1/0287
• CPC Class Codes: G05D 1/0276 using signals provided by a source external to the vehicle involving a plurality of vehicles G05D1/0287; automatically controlling vehicle speed responsive to externally generated signals B60K31/0058
• CPC Class Codes: G06Q 10/08 Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
• CPC Class Codes: G06Q 10/083 Shipping
• CPC Class Codes: G06Q 50/40 Business processes related to the transportation industry shipping G06Q10/083
• CPC Class Codes: G08G 1/0104 Measuring and analyzing of parameters relative to traffic conditions
• CPC Class Codes: H04L 63/1425 Traffic logging, e.g. anomaly detection
• CPC Class Codes: H04L 67/30 Profiles
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04W 4/02 Services making use of location information
• CPC Class Codes: H04W 4/023 using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
• CPC Class Codes: H04W 4/029 : Location-based management or tracking services
• CPC Class Codes: H04W 4/40 : for vehicles, e.g. vehicle-to-pedestrians [V2P]
• CPC Class Codes: H04W 4/90 : Services for handling of emergency or hazardous situations, e.g. earthquake and tsunami warning systems [ETWS]
• CPC Class Codes: H04W 76/50 : for emergency connections

Abstract

An anomaly detection system is provided in connection with a transport service. The anomaly detection system can construct routine route profiles for individual users of the transport service using historical route data. The anomaly detection system can monitor a current route traveled by a user. The anomaly detection system can further identify a matching routine route profile of the respective user. The anomaly detection system can utilize the matching routine route profile to identify a probable anomaly in the current route. In response to detecting the probable anomaly, the anomaly detection system can enable a safety protocol to perform a number of actions.

Claims

1. An anomaly detection system for a transport service comprising:

one or more processors; and

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

construct routine route profiles for users of the transport service by, for each of the users, (i) collecting historical route data, and (ii) identifying correlated route shapes in the historical route data;

monitor a current route traveled by a respective one of the users;

identify a matching routine route profile of the respective user;

based on the matching routine route profile, determine a probable anomaly in the current route by performing a real-time probabilistic calculation and determining that a number of probability factors collectively exceed an anomaly threshold; and

in response to determining the probable anomaly, perform one or more actions in accordance with a safety protocol, including transmitting a status query to a mobile device of the respective user or contacting an emergency authority to report the probable anomaly.

2. The anomaly detection system of claim 1, wherein the executed instructions further cause the anomaly detection system to, prior to monitoring the current route:

receive a pick-up request from the respective user; and

select a driver to service the pick-up request.

3. The anomaly detection system of claim 2, wherein the pick-up request includes a destination, and wherein the executed instructions further cause the anomaly detection system to:

identify the matching routine route profile prior to monitoring the current route, and based on an initial location of the respective user and the destination.

4. The anomaly detection system of claim 2, wherein the probability factors comprise (i) a divergence factor between the matching routine route profile and the current route, and (ii) a reputation factor of the selected driver.

5. The anomaly detection system of claim 4, wherein the reputation factor of the selected driver comprises (i) a complaint history of the selected driver, and (ii) reputation indicators of the selected driver received or retrieved from third-party resources.

6. The anomaly detection system of claim 2, wherein the executed instructions cause the anomaly detection system to select the driver based on (i) a proximity between the respective user and the selected driver, and (ii) data from a user profile of the respective user and data from a driver profile of the selected driver, the driver profile comprising reputation data.

7. The anomaly detection system of claim 6, wherein the executed instructions cause the anomaly detection system to select the driver based, at least in part, on (i) one or more factors for the respective user comprising one or more of an age, a gender, or user preferences in the user profile, and (ii) a reputation score for the selected driver based on the reputation data in the driver profile of the selected driver.

8. The anomaly detection system of claim 1, wherein the executed instructions cause the anomaly detection system to identify the matching routine route profile while the current route is being traveled by the user.

9. The anomaly detection system of claim 1, wherein the probability factors comprise the current route entering a flagged region.

10. The anomaly detection system of claim 9, wherein the executed instructions cause the anomaly detection system to determine the flagged region based on one or more of third-party crime data, or the flagged region being identified in a user profile of the respective user.

11. The anomaly detection system of claim 1, wherein the correlated route shapes comprise a plurality of distinct routes between a common start point or region and a common end point or region.

12. The anomaly detection system of claim 1, wherein an initial action of the safety protocol comprises transmitting a status query to a mobile device of the respective user, and wherein a subsequent action in the safety protocol comprises contacting an emergency authority to report the probable anomaly.

13. The anomaly detection system of claim 12, wherein the executed instructions further cause the anomaly detection system to:

in response to the status query, receive an indication of a negative condition from the mobile device of the respective user; and

in response to receiving the indication, transmit a driver directive to a mobile device of a driver providing transport service to the respective user indicating an emergency condition.

14. The anomaly detection system of claim 12, wherein the executed instructions cause the anomaly detection system to monitor the current route traveled by the respective user by receiving location data from global positioning system (GPS) resources of the mobile device of the respective user.

15. The anomaly detection system of claim 1, wherein the executed instructions further cause the anomaly detection system to:

compare the current route traveled by the respective user to a route inputted by the selected driver into a mapping resource of a mobile device of the selected driver.

16. The anomaly detection system of claim 1, wherein the one or more actions performed in accordance with the safety protocol include:

providing a selectable emergency feature on a graphical user interface for the respective user during the current route; and

receiving a user selection of the selectable emergency feature.

17. A non-transitory computer readable medium storing instructions that, when executed by one or more processors of an anomaly detection system, cause the anomaly detection system to:

construct routine route profiles for users of a transport service by, for each of the users, (i) collecting historical route data, and (ii) identifying correlated route shapes in the historical route data;

monitor a current route traveled by a respective one of the users;

identify a matching routine route profile of the respective user;

based on the matching routine route profile, determine a probable anomaly in the current route by performing a real-time probabilistic calculation and determining that a number of probability factors collectively exceed an anomaly threshold; and

in response to determining the probable anomaly, perform one or more actions in accordance with a safety protocol, including transmitting a status query to a mobile device of the respective user or contacting an emergency authority to report the probable anomaly.

18. A computer-implemented method for detecting route anomalies, the method performed by one or more processors of an anomaly detection system and comprising:

constructing routine route profiles for users of a transport service by, for each of the users, (i) collecting historical route data, and (ii) identifying correlated route shapes in the historical route data;

monitoring a current route traveled by a respective one of the users;

identifying a matching routine route profile of the respective user;

based on the matching routine route profile, determining a probable anomaly in the current route by performing a real-time probabilistic calculation and determining that a number of probability factors collectively exceed an anomaly threshold; and

in response to determining the probable anomaly, performing one or more actions in accordance with a safety protocol, including transmitting a status query to a mobile device of the respective user or contacting an emergency authority to report the probable anomaly.

19. The computer-implemented method of claim 18, further comprising:

prior to monitoring the current route;

receiving a pick-up request from the respective user; and

selecting a driver to service the pick-up request.

20. The computer-implemented method of claim 19, wherein the pick-up request includes a destination, and further comprising:

identifying the matching routine route profile prior to monitoring the current route, based on an initial location of the respective user and the destination.