Trip anomaly detection system

• US 9,883,371 B2
• Filed: 06/27/2017
• Issued: 01/30/2018
• 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:
  US15/634,234
• Publication Number:
  US 20170303110A1
• Time in Patent Office: 217 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 50/30 Transportation; Communications
• 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 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 location-aware resources 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 a probable anomaly in the current route based on the estimated time of arrival exceeding, by an 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 resources of the mobile device of the user or the driver, that a geographic position of a vehicle of the driver has remained static for a period of time exceeding a threshold period of time; 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 the executed instructions further cause the anomaly detection system to:

raise or lower the anomaly threshold and/or the threshold period of time based on a reputation factor of the driver, wherein the reputation factor includes (i) a complaint history of the driver, and (ii) reputation indicators of the driver received or retrieved from third-party resources.

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

raise or lower the anomaly threshold and/or the threshold period of time based on a comparison between a user profile of the user and a driver profile of the driver, the user profile comprising one or more of age, gender, and user preferences, and the driver profile comprising one or more of age, gender, and reputation data.

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

utilize environmental data for a region including the current route to raise or lower the anomaly threshold and/or the threshold period of time.

5. The anomaly detection system of claim 4, wherein the environmental data includes traffic data, police report data, local news, and crime data.

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

7. The anomaly detection system of claim 6, 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 user; and

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

8. 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.

9. 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 location-aware resources 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 a probable anomaly in the current route based on the estimated time of arrival exceeding, by an 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 resources of the mobile device of the user or the driver, that a geographic position of a vehicle of the driver has remained static for a period of time exceeding a threshold period of time; 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.

10. The non-transitory computer readable medium of claim 9, storing further instructions to cause the anomaly detection system to:

raise or lower the anomaly threshold and/or the threshold period of time based on a reputation factor of the driver, wherein the reputation factor includes (i) a complaint history of the driver, and (ii) reputation indicators of the driver received or retrieved from third-party resources.

11. The non-transitory computer readable medium of claim 9, storing further instructions to cause the anomaly detection system to:

raise or lower the anomaly threshold and/or the threshold period of time based on a comparison between a user profile of the user and a driver profile of the driver, the user profile comprising one or more of age, gender, and user preferences, and the driver profile comprising one or more of age, gender, and reputation data.

12. The non-transitory computer readable medium of claim 9, storing further instructions to cause the anomaly detection system to:

utilize environmental data for a region including the current route to raise or lower the anomaly threshold and/or the threshold period of time.

13. The non-transitory computer readable medium of claim 12, wherein the environmental data includes traffic data, police report data, local news, and crime data.

14. The non-transitory computer readable medium of claim 9, wherein an initial action of the safety protocol comprises transmitting a status query to the mobile device of the user, and wherein a subsequent action in the safety protocol comprises contacting an emergency authority to report the probable anomaly.

15. The non-transitory computer readable medium of claim 14, storing further instructions to 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 user; and

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

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

monitoring, by receiving location data from location-aware resources 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;

determining a probable anomaly in the current route based on the estimated time of arrival exceeding, by an 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 resources of the mobile device of the user or the driver, that a geographic position of a vehicle of the driver has remained static for a period of time exceeding a threshold period of time; 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 the mobile device of the user or contacting an emergency authority to report the probable anomaly.