DISTRIBUTED DATA PROCESSING SYSTEMS FOR PROCESSING REMOTELY CAPTURED SENSOR DATA

• US 20190019351A1
• Filed: 07/06/2018
• Published: 01/17/2019
• 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 Mylanahalli, Yeomans, Benjamin Robertson, Herrmann, Alexander, Schmitt, Kyle Patrick
• Granted Patent: US 10,768,008 B2
• Time in Patent Office: Days
• 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;

load a sample dataset comprising first sensor data captured by a first user computing device using one or more sensors built into the first user computing device during a first trip in a first vehicle;

build a labeled dataset based on the sample dataset;

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

analyze the second sensor data captured by the second user computing device to identify one or more phone-handling events associated with the second user computing device during the second trip in the second vehicle;

based on analyzing the second sensor data captured by the second user computing device to identify the one or more phone-handling events associated with the second user computing device during the second trip in the second vehicle, generate event-detection data identifying the one or more phone-handling events associated with the second user computing device during the second trip in the second vehicle; 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 event-detection data identifying the one or more phone-handling events associated with the second user computing device during the second trip in the second vehicle.

2. The computing platform of claim 1, wherein receiving the second sensor data captured by the second user computing device using the one or more sensors built into the second 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 second user computing device.

3. The computing platform of claim 1, wherein the sample dataset comprises tracking data captured by an on-board diagnostics (OBD-II) device associated with the first vehicle during the first trip in the first vehicle.

4. The computing platform of claim 3, wherein building the labeled dataset based on the sample dataset comprises:

applying a cross-correlation function to the tracking data captured by the OBD-II device and the first sensor data captured by the first user computing device to correct for time shift;

removing first noise from an end portion of the first sensor data captured by the first user computing device;

removing second noise from accelerometer data included in the first sensor data captured by the first user computing device;

aligning at least one axis of a reference frame of the first user computing device with at least one axis of a reference frame of the vehicle; and

identifying one or more time frames in which an accelerometer reading in the accelerometer data included in the first sensor data captured by the first user computing device deviates from a corresponding reading in the tracking data captured by the OBD-II device.

5. The computing platform of claim 4, wherein building the labeled dataset based on the sample dataset comprises:

analyzing the first sensor data captured by the first user computing device during the one or more time frames to determine one or more features indicative of a phone-handling event; and

updating a phone-handling identification model based on the one or more features indicative of the phone-handling event.

6. The computing platform of claim 1, wherein analyzing the second sensor data captured by the second user computing device to identify the one or more phone-handling events associated with the second user computing device during the second trip in the second vehicle comprises:

dividing the second sensor data captured by the second user computing device into a plurality of one-second frames; and

analyzing the plurality of one-second frames based on the labeled dataset to determine whether each one-second frame of the plurality of one-second frames is indicative of phone handling activity or no phone handling activity.

7. 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 second sensor data captured by the second user computing device to identify the one or more phone-handling events associated with the second user computing device during the second trip in the second vehicle, generate a notification identifying the one or more phone-handling events associated with the second user computing device during the second trip in the second vehicle; and

send, via the communication interface, to the second user computing device, the notification identifying the one or more phone-handling events associated with the second user computing device during the second trip in the second vehicle, wherein sending the notification identifying the one or more phone-handling events associated with the second user computing device during the second trip in the second vehicle causes the second user computing device to prompt a user of the second user computing device to confirm whether the user of the second user computing device was handling the second user computing device at one or more times corresponding to the one or more phone-handling events associated with the second user computing device during the second trip in the second vehicle.

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:

provide, to a driver-detection module associated with the computing platform, the event-detection data identifying the one or more phone-handling events associated with the second user computing device during the second trip in the second vehicle, wherein the driver-detection module is configured to determine whether a user of the second user computing device was a driver during the second trip in the second vehicle or a passenger during the second trip in the second vehicle based on the event-detection data identifying the one or more phone-handling events associated with the second user computing device during the second trip in the second vehicle.

9. The computing platform of claim 8, wherein the driver-detection module associated with the computing platform maintains a population-level model for analyzing phone-handling patterns to determine whether a particular user of a particular user computing device was a driver or a passenger during a particular trip.

10. A method, comprising:

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

loading, by the at least one processor, a sample dataset comprising first sensor data captured by a first user computing device using one or more sensors built into the first user computing device during a first trip in a first vehicle;

building, by the at least one processor, a labeled dataset based on the sample dataset;

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

analyzing, by the at least one processor, the second sensor data captured by the second user computing device to identify one or more phone-handling events associated with the second user computing device during the second trip in the second vehicle;

based on analyzing the second sensor data captured by the second user computing device to identify the one or more phone-handling events associated with the second user computing device during the second trip in the second vehicle, generating, by the at least one processor, event-detection data identifying the one or more phone-handling events associated with the second user computing device during the second trip in the second vehicle; 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 event-detection data identifying the one or more phone-handling events associated with the second user computing device during the second trip in the second vehicle.

11. The method of claim 10, wherein receiving the second sensor data captured by the second user computing device using the one or more sensors built into the second 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 second user computing device.

12. The method of claim 10, wherein the sample dataset comprises tracking data captured by an on-board diagnostics (OBD-II) device associated with the first vehicle during the first trip in the first vehicle.

13. The method of claim 12, wherein building the labeled dataset based on the sample dataset comprises:

applying a cross-correlation function to the tracking data captured by the OBD-II device and the first sensor data captured by the first user computing device to correct for time shift;

removing first noise from an end portion of the first sensor data captured by the first user computing device;

removing second noise from accelerometer data included in the first sensor data captured by the first user computing device;

aligning at least one axis of a reference frame of the first user computing device with at least one axis of a reference frame of the vehicle; and

identifying one or more time frames in which an accelerometer reading in the accelerometer data included in the first sensor data captured by the first user computing device deviates from a corresponding reading in the tracking data captured by the OBD-II device.

14. The method of claim 13, wherein building the labeled dataset based on the sample dataset comprises:

analyzing the first sensor data captured by the first user computing device during the one or more time frames to determine one or more features indicative of a phone-handling event; and

updating a phone-handling identification model based on the one or more features indicative of the phone-handling event.

15. The method of claim 10, wherein analyzing the second sensor data captured by the second user computing device to identify the one or more phone-handling events associated with the second user computing device during the second trip in the second vehicle comprises:

dividing the second sensor data captured by the second user computing device into a plurality of one-second frames; and

analyzing the plurality of one-second frames based on the labeled dataset to determine whether each one-second frame of the plurality of one-second frames is indicative of phone handling activity or no phone handling activity.

16. The method of claim 10, comprising:

based on analyzing the second sensor data captured by the second user computing device to identify the one or more phone-handling events associated with the second user computing device during the second trip in the second vehicle, generating, by the at least one processor, a notification identifying the one or more phone-handling events associated with the second user computing device during the second trip in the second vehicle; and

sending, by the at least one processor, via the communication interface, to the second user computing device, the notification identifying the one or more phone-handling events associated with the second user computing device during the second trip in the second vehicle, wherein sending the notification identifying the one or more phone-handling events associated with the second user computing device during the second trip in the second vehicle causes the second user computing device to prompt a user of the second user computing device to confirm whether the user of the second user computing device was handling the second user computing device at one or more times corresponding to the one or more phone-handling events associated with the second user computing device during the second trip in the second vehicle.

17. The method of claim 10, comprising:

providing, by the at least one processor, to a driver-detection module associated with the computing platform, the event-detection data identifying the one or more phone-handling events associated with the second user computing device during the second trip in the second vehicle, wherein the driver-detection module is configured to determine whether a user of the second user computing device was a driver during the second trip in the second vehicle or a passenger during the second trip in the second vehicle based on the event-detection data identifying the one or more phone-handling events associated with the second user computing device during the second trip in the second vehicle.

18. The method of claim 17, wherein the driver-detection module associated with the computing platform maintains a population-level model for analyzing phone-handling patterns to determine whether a particular user of a particular user computing device was a driver or a passenger during a particular trip.

19. 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:

load a sample dataset comprising first sensor data captured by a first user computing device using one or more sensors built into the first user computing device during a first trip in a first vehicle;

build a labeled dataset based on the sample dataset;

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

analyze the second sensor data captured by the second user computing device to identify one or more phone-handling events associated with the second user computing device during the second trip in the second vehicle;

based on analyzing the second sensor data captured by the second user computing device to identify the one or more phone-handling events associated with the second user computing device during the second trip in the second vehicle, generate event-detection data identifying the one or more phone-handling events associated with the second user computing device during the second trip in the second vehicle; 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 event-detection data identifying the one or more phone-handling events associated with the second user computing device during the second trip in the second vehicle.

20. The one or more non-transitory computer-readable media of claim 19, wherein receiving the second sensor data captured by the second user computing device using the one or more sensors built into the second 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 second user computing device.