Trip anomaly detection system

• US 10,123,199 B2
• Filed: 12/19/2017
• Issued: 11/06/2018
• 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:
  US15/846,858
• Publication Number:
  US 20180109935A1
• Time in Patent Office: 322 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 not directly associated with the wing movement
• 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 service, the anomaly detection system 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;

monitor, by receiving location data from a location-aware resource of a mobile device of a user or a driver, a current route traveled by the driver servicing a pick-up request for the user, the current route including an estimated time of arrival;

determine an anomaly threshold based, at least in part, on environmental data for a region that overlaps with at least a portion of the current route, the environmental data including at least one of traffic data, police report data, or crime data;

determine a probable anomaly in the current route based on the estimated time of arrival exceeding, by the anomaly threshold, a historical travel time for the current route, wherein determining the probable anomaly in the current route includes determining, using the location-aware resource of the mobile device of the user or the driver, that a geographic position of a vehicle of the driver coincides with the region; 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 the mobile device of the user or contacting an emergency authority to report the probable anomaly.

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

3. The anomaly detection system of claim 2, wherein the instructions further cause the anomaly detection system to:

receive an indication of a negative condition from the mobile device of the user; and

in response to receiving the indication, transmit a driver directive to a mobile device of the driver indicating the negative condition.

4. 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 of the mobile device of the user during the current route; and

receiving a user selection of the selectable emergency feature.

5. The anomaly detection system of claim 1, wherein the instructions cause the anomaly detection system to determine the probable anomaly by performing a real-time probabilistic calculation comprising a number of probability factors collectively exceeding the anomaly threshold.

6. The anomaly detection system of claim 5, wherein the probability factors include a divergence factor between the current route and historical routes for the pick-up request.

7. An anomaly detection system for a service, the anomaly detection system 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;

monitor, by receiving location data from a location-aware resource of a mobile device of a user or a driver, a current route traveled by the driver servicing a pick-up request for the user, the current route including an estimated time of arrival;

determine an anomaly threshold based, at least in part, on a score associated with the driver, wherein the score is based, at least in part, on historical data associated with the driver;

determine a probable anomaly in the current route based on the estimated time of arrival exceeding, by the anomaly threshold, a historical travel time for the current route, wherein determining the probable anomaly in the current route includes determining, using the location-aware resource of the mobile device of the user or the driver, that a geographic position of a vehicle of the driver coincides with a region identified in the historical data associated with the driver; 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 the mobile device of the user or contacting an emergency authority to report the probable anomaly.

8. The anomaly detection system of claim 7, wherein an initial action of the safety protocol comprises transmitting the status query to the mobile device of the user, and wherein a subsequent action in the safety protocol comprises contacting the emergency authority to report the probable anomaly.

9. The anomaly detection system of claim 8, wherein the instructions further cause the anomaly detection system to:

receive an indication of a negative condition from the mobile device of the user; and

in response to receiving the indication, transmit a driver directive to a mobile device of the driver indicating the negative condition.

10. The anomaly detection system of claim 7, wherein the one or more actions performed in accordance with the safety protocol include:

providing a selectable emergency feature on a graphical user interface of the mobile device of the user during the current route; and

receiving a user selection of the selectable emergency feature.

11. The anomaly detection system of claim 7, wherein the instructions cause the anomaly detection system to determine the probable anomaly by performing a real-time probabilistic calculation comprising a number of probability factors collectively exceeding the anomaly threshold.

12. The anomaly detection system of claim 11, wherein the probability factors include a divergence factor between the current route and historical routes for the pick-up request.

13. The anomaly detection system of claim 7, wherein the score is based, at least in part, on reputation indicators of the driver received or retrieved from third-party resources.

14. The anomaly detection system of claim 13, wherein the reputation indicators of the driver include a complaint history against the driver.

15. 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:

monitor, by receiving location data from a location-aware resource of a mobile device of a user or a driver, a current route traveled by the driver servicing a pick-up request for the user, the current route including an estimated time of arrival;

determine an anomaly threshold based, at least in part, on environmental data for a region that overlaps with at least a portion of the current route, the environmental data including at least one of traffic data, police report data, or crime data;

determine a probable anomaly in the current route based on the estimated time of arrival exceeding, by the anomaly threshold, a historical travel time for the current route, wherein determining the probable anomaly in the current route includes determining, using the location-aware resource of the mobile device of the user or the driver, that a geographic position of a vehicle of the driver coincides with the region; 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 the mobile device of the user or contacting an emergency authority to report the probable anomaly.

16. The non-transitory computer readable medium of claim 15, wherein an initial action of the safety protocol comprises transmitting the status query to the mobile device of the user, and wherein a subsequent action in the safety protocol comprises contacting the emergency authority to report the probable anomaly.

17. The non-transitory computer readable medium of claim 16, wherein the instructions further cause the anomaly detection system to:

receive an indication of a negative condition from the mobile device of the user; and

in response to receiving the indication, transmit a driver directive to a mobile device of the driver indicating the negative condition.

18. The non-transitory computer readable medium of claim 15, wherein the one or more actions performed in accordance with the safety protocol include:

providing a selectable emergency feature on a graphical user interface of the mobile device of the user during the current route; and

receiving a user selection of the selectable emergency feature.

19. The non-transitory computer readable medium of claim 15, wherein the instructions cause the anomaly detection system to determine the probable anomaly by performing a real-time probabilistic calculation comprising a number of probability factors collectively exceeding the anomaly threshold.

20. The non-transitory computer readable medium of claim 19, wherein the probability factors include a divergence factor between the current route and historical routes for the pick-up request.