# US 10,768,008 B2 · Distributed data processing systems for processing remotely captured sensor data > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,768,008 B2 - Document ID: US10768008B2 - Status: Granted - Kind: B2 - Application number: 16/028,785 - Filed: 2018-07-06 - Published: 2019-01-17 - Granted: 2020-09-08 - Earliest claimed: 2017-07-14 - Assignees: Allstate Insurance Company - Applicants: Allstate Insurance Company - Inventors: Pratheek M. Harish; Benjamin Robertson Yeomans; Alexander Herrmann; Kyle Patrick Schmitt; Venu Madhav Tammali; Samantha Mait - Web page: https://patentgrep.com/patent/US10768008B2 ## CPC - G01C 21/3617: {using user history, behaviour, conditions or preferences, e.g. predicted or inferred from previous use or current movement} - G01C 21/20: Instruments for performing navigational calculations (G01C21/24, G01C21/26 take precedence) - G01C 21/3461: {Preferred or disfavoured areas, e.g. dangerous zones, toll or emission zones, intersections, manoeuvre types or segments such as motorways, toll roads or ferries} - G01C 21/3476: {using point of interest [POI] information, e.g. a route passing visible POIs} - G01C 21/362: {received from an external device or application, e.g. PDA, mobile phone or calendar application} - G01P 1/07: Indicating devices, e.g. for remote indication (indicating working conditions of vehicles G07C5/00) - G01P 13/00: Indicating or recording presence, absence, or direction, of movement (electric switches H01H; counting moving objects G06M7/00) - G05D 1/0011: {associated with a remote control arrangement} - G05D 1/0212: {with means for defining a desired trajectory (involving a plurality of land vehicles G05D1/0287)} - 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)} - G05D 1/223: Command input arrangements on the remote controller, e.g. joysticks or touch screens - G05D 1/247: using signals provided by artificial sources external to the vehicle, e.g. navigation beacons - G05D 1/646: Following a predefined trajectory, e.g. a line marked on the floor or a flight path - G06F 3/14: Digital output to display device {; Cooperation and interconnection of the display device with other functional units} - 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} - G06F 3/147: using display panels - G06F 9/542: {Event management; Broadcasting; Multicasting; Notifications} - G06Q 40/08: Insurance - G07C 5/008: {communicating information to a remotely located station (transmission systems for measured values G08C)} - G07C 5/02: Registering or indicating driving, working, idle, or waiting time only (apparatus forming part of taximeters G07B13/00) - G07C 5/085: {using electronic data carriers} - G08G 1/0112: {from the vehicle, e.g. floating car data [FCD]} - G08G 1/0129: {for creating historical data or processing based on historical data} - G08G 1/096844: {where the complete route is dynamically recomputed based on new data} - H04L 67/10: in which an application is distributed across nodes in the network (software deployment G06F8/60; multiprogramming arrangements G06F9/46) - 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 - H04L 67/52: specially adapted for the location of the user terminal - H04W 4/021: Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences - H04W 4/026: {using orientation information, e.g. compass} - H04W 4/029: Location-based management or tracking services - H04W 4/38: for collecting sensor information - 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 - G06F 16/22: Indexing; Data structures therefor; Storage structures - H04W 4/40: for vehicles, e.g. vehicle-to-pedestrians [V2P] - G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S) - G01P: MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT (measuring or recording blood flow A61B5/02, A61B8/06; monitoring speed or deceleration of electrically-propelled vehicles B60L3/00; vehicle lighting systems adapted to indicate speed B60Q1/54; determining position or course in navigation, measuring ground distance in geodesy or surveying G01C; combined measuring devices for measuring two or more variables of movement G01C23/00; measuring velocity of sound G01H; measuring velocity of light G01J7/00; measuring direction or velocity of solid objects by reception or emission of radiowaves or other waves and based on propagation effects, e.g. Doppler effect, propagation time, direction of propagation, G01S; measuring speed of nuclear radiation G01T; measuring acceleration of gravity G01V; {measuring or recording the speed of trains B61L23/00; speed indicators incorporated in motor vehicles B60K35/00; measuring frequency or phase G01R; traffic control G08G}) - G05D: SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - G07C: TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE - G08G: TRAFFIC CONTROL SYSTEMS (guiding railway traffic, ensuring the safety of railway traffic B61L; arrangement of road signs or traffic signals E01F9/00; radar or analogous systems, sonar systems, lidar systems specially adapted for traffic control G01S13/91, G01S15/88, G01S17/88; {radar or analogous systems, sonar systems, lidar systems specially adapted for anti-collision purposes G01S13/93, G01S15/93, G01S17/93}) - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) ## 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 ### Claim 1 A computing platform, comprising: at least one processor; a communication interface; and non-transitory 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; 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; and 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, 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. ### Claim 2 (depends on 1) 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. ### Claim 3 (depends on 1) 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. ### Claim 4 (depends on 3) 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. ### Claim 5 (depends on 4) 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. ### Claim 6 (depends on 1) 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. ### Claim 7 (depends on 1) 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. ### Claim 8 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; 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; and 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, 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. ### Claim 9 (depends on 8) The method of claim 8, 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. ### Claim 10 (depends on 8) The method of claim 8, 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. ### Claim 11 (depends on 10) The method of claim 10, 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. ### Claim 12 (depends on 11) The method of claim 11, 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. ### Claim 13 (depends on 8) The method of claim 8, 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. ### Claim 14 (depends on 8) The method of claim 8, 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. ### Claim 15 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; 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; and 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, 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. ### Claim 16 (depends on 15) The one or more non-transitory computer-readable media of claim 15, 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. ### Claim 17 A computing platform, comprising: at least one processor; a communication interface; and non-transitory 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, 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, and 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. ### Claim 18 (depends on 17) The computing platform of claim 17, 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. ### Claim 19 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, 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, and 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. ### Claim 20 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, 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, and 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.