Distributed data processing systems for processing remotely captured sensor data

• US 10,663,314 B2
• Filed: 07/06/2018
• Issued: 05/26/2020
• Est. Priority Date: 07/14/2017
• Status: Active Grant

• Current Assignee: Allstate Insurance Company (Allstate Corporation)
• Original Assignee: Allstate Insurance Company (Allstate Corporation)
• Inventors: Harish, Pratheek M., Yeomans, Benjamin Robertson, Herrmann, Alexander, Schmitt, Kyle Patrick, Karmarkar, Dipti
• Primary Examiner(s): Tiv, Backhean
• Application Number:
  US16/028,614
• Publication Number:
  US 20190019355A1
• Time in Patent Office: 690 Days
• Field of Search: None
• CPC Class Codes: G01C 21/20 Instruments for performing navigational calculations G01C21/24, G01C21/26 take precedence
• CPC Class Codes: G01C 21/3461 Preferred or disfavoured areas, e.g. dangerous zones, toll or emission zones, intersections, manoeuvre types, segments such as motorways, toll roads, ferries
• CPC Class Codes: G01C 21/3476 using point of interest [POI] information, e.g. a route passing visible POIs
• 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: G01C 21/362 received from an external device or application, e.g. PDA, mobile phone or calendar application
• CPC Class Codes: G01P 1/07 Indicating devices, e.g. for remote indication indicating working conditions of vehicles G07C5/00
• CPC Class Codes: G01P 13/00 Indicating or recording presence, absence, or direction, of movement electric switches H01H; counting moving objects G06M7/00
• CPC Class Codes: G05D 1/0011 associated with a remote control arrangement
• 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: G05D 1/223 Command input arrangements on the remote controller, e.g. joysticks or touch screens
• CPC Class Codes: G05D 1/247 using signals provided by artificial sources external to the vehicle, e.g. navigation beacons
• CPC Class Codes: G05D 1/646 Following a predefined trajectory, e.g. a line marked on the floor or a flight path
• CPC Class Codes: G06F 16/22 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 3/0484 for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
• CPC Class Codes: G06F 3/14 Digital output to display device ; Cooperation and interconnection of the display device with other functional units
• CPC Class Codes: G06F 3/1454 involving copying of the display data of a local workstation or window to a remote workstation or window so that an actual copy of the data is displayed simultaneously on two or more displays, e.g. teledisplay
• CPC Class Codes: G06F 3/147 using display panels
• CPC Class Codes: G06F 9/542 Event management; Broadcasting; Multicasting; Notifications
• CPC Class Codes: G06Q 40/08 Insurance
• CPC Class Codes: G07C 5/008 : communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G07C 5/02 : Registering or indicating driving, working, idle, or waiting time only apparatus forming part of taximeters G07B13/00
• CPC Class Codes: G07C 5/085 : using electronic data carriers
• CPC Class Codes: G08G 1/0112 : from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0129 : for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/096844 : where the complete route is dynamically recomputed based on new data
• CPC Class Codes: H04L 67/10 : in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46
• CPC Class Codes: H04L 67/12 : specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04L 67/52 : specially adapted for the location of the user terminal
• CPC Class Codes: H04W 4/021 : Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
• CPC Class Codes: H04W 4/026 : using orientation information, e.g. compass
• CPC Class Codes: H04W 4/029 : Location-based management or tracking services
• CPC Class Codes: H04W 4/38 : for collecting sensor information
• CPC Class Codes: H04W 4/40 : for vehicles, e.g. vehicle-to-pedestrians [V2P]

Abstract

Aspects of the disclosure relate to processing remotely captured sensor data. A computing platform having at least one processor, a communication interface, and memory may receive, via the communication interface, from a user computing device, sensor data captured by the user computing device using one or more sensors built into the user computing device. Subsequently, the computing platform may analyze the sensor data received from the user computing device by executing one or more data processing modules. Then, the computing platform may generate trip record data based on analyzing the sensor data received from the user computing device and may store the trip record data in a trip record database. In addition, the computing platform may generate user record data based on analyzing the sensor data received from the user computing device and may store the user record data in a user record database.

Claims

1. A computing platform, comprising:

at least one processor;

a communication interface; and

memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to;

receive, via the communication interface, from a first user computing device, sensor data captured by the first user computing device using one or more sensors built into the first user computing device during a trip in a vehicle;

analyze the sensor data received from the first user computing device to determine whether a user of the first user computing device was a driver of the vehicle or a passenger of the vehicle during the trip in the vehicle,

wherein analyzing the sensor data received from the first user computing device to determine whether the user of the first user computing device was a driver of the vehicle or a passenger of the vehicle during the trip in the vehicle comprises using a population-level model to analyze the sensor data received from the first user computing device, and

wherein using the population-level model to analyze the sensor data received from the first user computing device comprises using a quadrant-detection model to determine a quadrant of the vehicle in which the user of the first user computing device was located during the trip in the vehicle;

based on determining that the user of the first user computing device was a driver of the vehicle during the trip in the vehicle;

generate driver-trip data comprising at least a portion of the sensor data received from the first user computing device and information identifying a time of the trip and one or more locations associated with the trip; and

store, in at least one database maintained by the computing platform and accessible to one or more data analysis modules associated with the computing platform, the driver-trip data; and

based on determining that the user of the first user computing device was a passenger of the vehicle during the trip in the vehicle;

generate passenger-trip data comprising at least a portion of the sensor data received from the first user computing device and information identifying a time of the trip and one or more locations associated with the trip; and

store, in at least one database maintained by the computing platform and accessible to one or more data analysis modules associated with the computing platform, the passenger-trip data.

2. The computing platform of claim 1, wherein receiving the sensor data captured by the first user computing device using the one or more sensors built into the first user computing device comprises receiving data captured by one or more of an accelerometer, a gyroscope, a magnetometer, a barometer, a gravitometer, a proximity sensor, an ambient light sensor, an ambient temperature sensor, an orientation sensor, a pedometer, an altimeter, a satellite positioning sensor, or an activity recognition sensor built into the first user computing device.

3. The computing platform of claim 1, wherein analyzing the sensor data received from the first user computing device to determine whether the user of the first user computing device was a driver of the vehicle or a passenger of the vehicle during the trip in the vehicle comprises using one or more user-personalized driver signature models to analyze the sensor data received from the first user computing device.

4. The computing platform of claim 1, wherein analyzing the sensor data received from the first user computing device to determine whether the user of the first user computing device was a driver of the vehicle or a passenger of the vehicle during the trip in the vehicle comprises using a gyroscope module that processes the sensor data received from the first user computing device and determines whether the user of the first user computing device exited the vehicle on a left side of the vehicle or a right side of the vehicle.

5. The computing platform of claim 1, wherein analyzing the sensor data received from the first user computing device to determine whether the user of the first user computing device was a driver of the vehicle or a passenger of the vehicle during the trip in the vehicle comprises using a phone-handling module that processes the sensor data received from the first user computing device and determines an amount of phone handling that occurred during the trip in the vehicle.

6. The computing platform of claim 1, wherein analyzing the sensor data received from the first user computing device to determine whether the user of the first user computing device was a driver of the vehicle or a passenger of the vehicle during the trip in the vehicle comprises using a car tracker module that processes the sensor data received from the first user computing device and evaluates a distance between a starting point of a current trip and an ending point of a previous trip.

7. The computing platform of claim 1, wherein analyzing the sensor data received from the first user computing device to determine whether the user of the first user computing device was a driver of the vehicle or a passenger of the vehicle during the trip in the vehicle comprises evaluating a vertical acceleration profile experienced by the vehicle during the trip based on the sensor data received from the first user computing device.

8. The computing platform of claim 1, wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to:

based on analyzing the sensor data received from the first user computing device, generate a notification indicating whether the user of the first user computing device was determined to be a driver of the vehicle or a passenger of the vehicle during the trip in the vehicle; and

send, via the communication interface, to the first user computing device, the notification indicating whether the user of the first user computing device was determined to be a driver of the vehicle or a passenger of the vehicle during the trip in the vehicle, wherein sending the notification indicating whether the user of the first user computing device was determined to be a driver of the vehicle or a passenger of the vehicle during the trip in the vehicle to the first user computing device causes the first user computing device to prompt the first user associated with the first user computing device to confirm whether the user of the first user computing device was a driver of the vehicle or a passenger of the vehicle during the trip in the vehicle.

9. The computing platform of claim 1, wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to:

based on analyzing the sensor data received from the first user computing device, send driver detection data indicating whether the user of the first user computing device was determined to be a driver of the vehicle or a passenger of the vehicle during the trip in the vehicle to a data analyst console computing device, wherein sending the driver detection data indicating whether the user of the first user computing device was determined to be a driver of the vehicle or a passenger of the vehicle during the trip in the vehicle to the data analyst console computing device causes the data analyst console computing device to wake and display detection information corresponding to the driver detection data indicating whether the user of the first user computing device was determined to be a driver of the vehicle or a passenger of the vehicle during the trip in the vehicle.

10. A method, comprising:

at a computing platform comprising at least one processor, a communication interface, and memory;

receiving, by the at least one processor, via the communication interface, from a first user computing device, sensor data captured by the first user computing device using one or more sensors built into the first user computing device during a trip in a vehicle;

analyzing, by the at least one processor, the sensor data received from the first user computing device to determine whether a user of the first user computing device was a driver of the vehicle or a passenger of the vehicle during the trip in the vehicle,

wherein analyzing the sensor data received from the first user computing device to determine whether the user of the first user computing device was a driver of the vehicle or a passenger of the vehicle during the trip in the vehicle comprises using a population-level model to analyze the sensor data received from the first user computing device, and

wherein using the population-level model to analyze the sensor data received from the first user computing device comprises using a quadrant-detection model to determine a quadrant of the vehicle in which the user of the first user computing device was located during the trip in the vehicle;

based on determining that the user of the first user computing device was a driver of the vehicle during the trip in the vehicle;

generating, by the at least one processor, driver-trip data comprising at least a portion of the sensor data received from the first user computing device and information identifying a time of the trip and one or more locations associated with the trip; and

storing, by the at least one processor, in at least one database maintained by the computing platform and accessible to one or more data analysis modules associated with the computing platform, the driver-trip data; and

based on determining that the user of the first user computing device was a passenger of the vehicle during the trip in the vehicle;

generating, by the at least one processor, passenger-trip data comprising at least a portion of the sensor data received from the first user computing device and information identifying a time of the trip and one or more locations associated with the trip; and

storing, by the at least one processor, in at least one database maintained by the computing platform and accessible to one or more data analysis modules associated with the computing platform, the passenger-trip data.

11. The method of claim 10, wherein receiving the sensor data captured by the first user computing device using the one or more sensors built into the first user computing device comprises receiving data captured by one or more of an accelerometer, a gyroscope, a magnetometer, a barometer, a gravitometer, a proximity sensor, an ambient light sensor, an ambient temperature sensor, an orientation sensor, a pedometer, an altimeter, a satellite positioning sensor, or an activity recognition sensor built into the first user computing device.

12. The method of claim 10, wherein analyzing the sensor data received from the first user computing device to determine whether the user of the first user computing device was a driver of the vehicle or a passenger of the vehicle during the trip in the vehicle comprises using one or more user-personalized driver signature models to analyze the sensor data received from the first user computing device.

13. The method of claim 10, wherein analyzing the sensor data received from the first user computing device to determine whether the user of the first user computing device was a driver of the vehicle or a passenger of the vehicle during the trip in the vehicle comprises using a gyroscope module that processes the sensor data received from the first user computing device and determines whether the user of the first user computing device exited the vehicle on a left side of the vehicle or a right side of the vehicle.

14. The method of claim 10, wherein analyzing the sensor data received from the first user computing device to determine whether the user of the first user computing device was a driver of the vehicle or a passenger of the vehicle during the trip in the vehicle comprises using a phone-handling module that processes the sensor data received from the first user computing device and determines an amount of phone handling that occurred during the trip in the vehicle.

15. The method of claim 10, wherein analyzing the sensor data received from the first user computing device to determine whether the user of the first user computing device was a driver of the vehicle or a passenger of the vehicle during the trip in the vehicle comprises using a car tracker module that processes the sensor data received from the first user computing device and evaluates a distance between a starting point of a current trip and an ending point of a previous trip.

16. The method of claim 10, wherein analyzing the sensor data received from the first user computing device to determine whether the user of the first user computing device was a driver of the vehicle or a passenger of the vehicle during the trip in the vehicle comprises evaluating a vertical acceleration profile experienced by the vehicle during the trip based on the sensor data received from the first user computing device.

17. The method of claim 10, comprising:

based on analyzing the sensor data received from the first user computing device, generating, by the at least one processor, a notification indicating whether the user of the first user computing device was determined to be a driver of the vehicle or a passenger of the vehicle during the trip in the vehicle; and

sending, by the at least one processor, via the communication interface, to the first user computing device, the notification indicating whether the user of the first user computing device was determined to be a driver of the vehicle or a passenger of the vehicle during the trip in the vehicle, wherein sending the notification indicating whether the user of the first user computing device was determined to be a driver of the vehicle or a passenger of the vehicle during the trip in the vehicle to the first user computing device causes the first user computing device to prompt the first user associated with the first user computing device to confirm whether the user of the first user computing device was a driver of the vehicle or a passenger of the vehicle during the trip in the vehicle.

18. One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, a communication interface, and memory, cause the computing platform to:

receive, via the communication interface, from a first user computing device, sensor data captured by the first user computing device using one or more sensors built into the first user computing device during a trip in a vehicle;

analyze the sensor data received from the first user computing device to determine whether a user of the first user computing device was a driver of the vehicle or a passenger of the vehicle during the trip in the vehicle,

wherein analyzing the sensor data received from the first user computing device to determine whether the user of the first user computing device was a driver of the vehicle or a passenger of the vehicle during the trip in the vehicle comprises using a population-level model to analyze the sensor data received from the first user computing device, and

wherein using the population-level model to analyze the sensor data received from the first user computing device comprises using a quadrant-detection model to determine a quadrant of the vehicle in which the user of the first user computing device was located during the trip in the vehicle;

based on determining that the user of the first user computing device was a driver of the vehicle during the trip in the vehicle;

generate driver-trip data comprising at least a portion of the sensor data received from the first user computing device and information identifying a time of the trip and one or more locations associated with the trip; and

store, in at least one database maintained by the computing platform and accessible to one or more data analysis modules associated with the computing platform, the driver-trip data; and

based on determining that the user of the first user computing device was a passenger of the vehicle during the trip in the vehicle;

generate passenger-trip data comprising at least a portion of the sensor data received from the first user computing device and information identifying a time of the trip and one or more locations associated with the trip; and

store, in at least one database maintained by the computing platform and accessible to one or more data analysis modules associated with the computing platform, the passenger-trip data.

19. The one or more non-transitory computer-readable media of claim 18, wherein analyzing the sensor data received from the first user computing device to determine whether the user of the first user computing device was a driver of the vehicle or a passenger of the vehicle during the trip in the vehicle comprises using a car tracker module that processes the sensor data received from the first user computing device and evaluates a distance between a starting point of a current trip and an ending point of a previous trip.

20. The one or more non-transitory computer-readable media of claim 18, wherein analyzing the sensor data received from the first user computing device to determine whether the user of the first user computing device was a driver of the vehicle or a passenger of the vehicle during the trip in the vehicle comprises evaluating a vertical acceleration profile experienced by the vehicle during the trip based on the sensor data received from the first user computing device.