# US 12,008,842 B2 · Systems and methods for detecting full-stops to reduce vehicle accidents > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,008,842 B2 - Document ID: US12008842B2 - Status: Granted - Kind: B2 - Application number: 17/680,667 - Filed: 2022-02-25 - Published: 2022-07-28 - Granted: 2024-06-11 - Earliest claimed: 2020-02-12 - Assignees: Blueowl, LLC - Applicants: BlueOwl, LLC - Inventors: Vinay Kumar - Web page: https://patentgrep.com/patent/US12008842B2 ## CPC - G07C 5/008: {communicating information to a remotely located station (transmission systems for measured values G08C)} - B60Q 9/00: Arrangement or adaptation of signal devices not provided for in one of main groups B60Q1/00 - B60Q7/00 {, e.g. haptic signalling} - G01C 21/32: Structuring or formatting of map data - 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 21/3617: {using user history, behaviour, conditions or preferences, e.g. predicted or inferred from previous use or current movement} - G01C 21/3815: {Road data} - G07C 5/0825: {using optical means} - G07C 5/085: {using electronic data carriers} - G08G 1/0129: {for creating historical data or processing based on historical data} - G06Q 40/08: Insurance - 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 - B60Q: ARRANGEMENT OF SIGNALLING OR LIGHTING DEVICES, THE MOUNTING OR SUPPORTING THEREOF OR CIRCUITS THEREFOR, FOR VEHICLES IN GENERAL - 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) - 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}) - 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 ## Abstract A telematics analysis (TA) computing device including a processor in communication with a memory device for monitoring driving behavior of a driver of a vehicle may be provided. The processor may be configured to: (i) aggregate historical location data and historical telematics data from a plurality of users, (ii) generate mapping data based at least in part upon the historical location data and the historical telematics data, (iii) identify one or more stop locations based at least in part upon the mapping data, (iv) store the one or more stop locations, (v) receive current location data and current telematics data after each trip taken by the driver, (vi) compare the current location data and the current telematics data to the one or more stop locations, (vii) generate stop data associated with the driver for each trip taken by the driver, wherein the stop data includes whether the driver stopped at the one or more stop locations during each trip, and (viii) determine one or more driving behaviors of the driver based at least in part upon the stop data. ## Claims ### Claim 1 A telematics analysis (TA) computing device for monitoring driving behavior of a driver of a vehicle, the TA computing device comprising one or more processors in communication with a memory device, the one or more processors configured to: receive historical location data and historical telematics data, wherein the historical location data and the historical telematics data are gathered by one or more devices associated with a plurality of users; identify one or more stop locations based at least in part upon the historical location data and the historical telematics data; receive, from a driver computing device associated with the driver of the vehicle, a set of location data and a set of telematics data after each trip of a plurality of trips taken by the driver; analyze the set of location data and the set of telematics data with the one or more stop locations; generate stop data associated with the driver for the plurality of trips taken by the driver, wherein the stop data includes whether the driver stopped at the one or more stop locations during the plurality of trips; and determine one or more driving behaviors of the driver at the one or more stop locations based at least in part upon the stop data. ### Claim 2 (depends on 1) The TA computing device of claim 1, wherein to determine one or more driving behaviors of the driver at the one or more stop locations, the one or more processors are further configured to: determine whether the driver performs a full stop at one stop location of the one or more stop locations based at least in part upon the stop data. ### Claim 3 (depends on 1) The TA computing device of claim 1, wherein the one or more processors are further configured to: determine, based at least in part upon the stop data, one or more flagged locations where the driver does not stop during at least one trip of the plurality of trips; and display, on the driver computing device, the one or more flagged locations, as determined. ### Claim 4 (depends on 1) The TA computing device of claim 1, wherein to identify the one or more stop locations based at least in part upon the historical location data and the historical telematics data, the one or more processors are further configured to: determine, using one or more machine learning and artificial intelligence techniques, the one or more stop locations, wherein the one or more stop locations includes a stop sign location or a stoplight location. ### Claim 5 (depends on 1) The TA computing device of claim 1, wherein the one or more processors are further configured to: receive, from a plurality of user computing devices associated with a plurality of drivers, a plurality of sets of location data and a plurality of sets of telematics data after each driving trip of a plurality of driving trips taken by the plurality of drivers; generate stop data for the plurality of driving trips taken by the plurality of drivers, wherein the stop data includes whether the plurality of drivers stopped at the one or more stop locations during the plurality of driving trips; and determine, based at least in part upon the stop data for the plurality of stopped locations, one or more flagged locations where at least one of the plurality of drivers does not stop during the plurality of driving trips. ### Claim 6 (depends on 5) The TA computing device of claim 5, wherein the one or more processors are further configured to: determine a safety score for each stop location of the one or more stop locations, wherein the safety score is low for each flagged location of the one or more flagged locations; and transmit one or more safety suggestions to the plurality of user computing devices, wherein the one or more safety suggestions include at least one selected from a group consisting of i) identifying one or more stop locations each with a low safety score, ii) suggesting avoiding the one or more stop locations each with a low safety score, and iii) providing one or more alternate routes to avoid the one or more stop locations each with a low safety score. ### Claim 7 (depends on 6) The TA computing device of claim 6, wherein the one or more processors are further configured to: receive the plurality of sets of telematics data and the plurality of sets of location data in real-time; analyze the plurality of sets of telematics data and the plurality of sets of location data in real-time; and transmit one or more corrective action alerts to the plurality of user computing devices in real-time, wherein the one or more corrective action alerts include at least one selected from a group consisting of i) warning the plurality of drivers that a stop location with a low safety score is upcoming, ii) providing the plurality of drivers with an alternative route to avoid the stop location with the low safety score that is upcoming with the low safety score, and iii) warning the plurality of drivers of erratic driving behaviors of other drivers, as determined by the plurality of sets of telematics data and the plurality of sets of location data of the other drivers. ### Claim 8 (depends on 1) The TA computing device of claim 1, wherein the TA computing device is associated with an insurance provider device, and wherein the insurance provider device is associated with an insurance provider of the driver, wherein the one or more processors are further configured to: transmit the stop data associated with the driver to the insurance provider device. ### Claim 9 A computer-implemented method for monitoring driving behavior of a driver of a vehicle, the method implemented by a telematics analysis (TA) computing device including one or more processors in communication with a memory device, the computer-implemented method comprising: receiving historical location data and historical telematics data, wherein the historical location data and the historical telematics data are gathered by one or more devices associated with a plurality of users; identifying one or more stop locations based at least in part upon the historical location data and the historical telematics data; receiving, from a driver computing device associated with the driver of the vehicle, a set of location data and a set of telematics data after each trip of a plurality of trips taken by the driver; analyzing the set of location data and the set of telematics data with the one or more stop locations; generating stop data associated with the driver, wherein the stop data includes whether the driver performed a stop at the one or more stop locations during the plurality of trips; and determining driving behaviors of the driver at the one or more stop locations based at least in part upon the stop data. ### Claim 10 (depends on 9) The computer-implemented method of claim 9, wherein the determining one or more driving behaviors of the driver at the one or more stop locations includes determining whether the driver performs a full stop at one stop location of the one or more stop locations based at least in part upon the stop data. ### Claim 11 (depends on 9) The computer-implemented method of claim 9 further comprising: determining, based at least in part upon the stop data, one or more flagged locations where the driver does not stop during at least one trip of the plurality of trips; and displaying, on the driver computing device, the one or more flagged locations, as determined. ### Claim 12 (depends on 9) The computer-implemented method of claim 9 further comprising, wherein identifying the one or more stop locations based at least in part upon the historical location data and the historical telematics data includes: determining, using one or more machine learning and artificial intelligence techniques, one or more stop locations, wherein the one or more stop locations includes one of a stop sign location or a stoplight location. ### Claim 13 (depends on 9) The computer-implemented method of claim 9 further comprising: receiving, from a plurality of user computing devices associated with a plurality of drivers, a plurality of sets of location data and a plurality of sets of telematics data after each driving trip of a plurality of driving trips taken by the plurality of drivers; generating stop data for the plurality of driving trips taken by the plurality of drivers, wherein the stop data includes whether the plurality of drivers stopped at the one or more stop locations during the plurality of driving trips; and determining, based at least in part upon the stop data, one or more flagged locations where at least one driver of the plurality of drivers does not stop during the plurality of driving trips. ### Claim 14 (depends on 13) The computer-implemented method of claim 13 further comprising: determining a safety score for each stop location of the one or more stop locations, wherein the safety score is low for each flagged location of the one or more flagged locations; and transmitting one or more safety suggestions to the plurality of user computing devices, wherein the one or more safety suggestions include at least one selected from a group consisting of i) identifying one or more stop locations each with a low safety score, ii) suggesting avoiding the one or more stop locations each with a low safety score, and iii) providing one or more alternate routes avoiding the one or more stop locations each with a low safety score. ### Claim 15 (depends on 14) The computer-implemented method of claim 14 further comprising: receiving the plurality of sets of telematics data and the plurality of sets of location data in real-time; analyzing the plurality of sets of telematics data and the plurality of sets of location data in real-time; and transmitting one or more corrective action alerts to the plurality of user computing device in real-time, wherein the one or more corrective action alerts include at least one selected from a group consisting of i) warning that a stop location with a low safety score is upcoming, ii) providing an alternative route to avoid the stop location with the low safety score that is upcoming with the low safety score, and iii) warning at least one driver of the plurality of drivers of erratic driving behaviors of other drivers, as determined by the plurality of sets of telematics data and the plurality of sets of location data. ### Claim 16 One or more non-transitory computer-readable media having computer-executable instructions embodied thereon, wherein when executed by a telematics analysis (TA) computing device including one or more processors in communication with a memory device, the computer-executable instructions cause the one or more processors to: receive historical location data and historical telematics data, wherein the historical location data and historical telematics data are gathered by one or more devices associated with a plurality of users; identify one or more stop locations based at least in part upon the historical location data and the historical telematics data; receive, from a driver computing device associated with a driver of a vehicle, a set of location data and a set of telematics data after each trip of a plurality of trips taken by the driver; analyze the set of location data and the set of telematics data with the one or more stop locations; generate stop data associated with the driver for the plurality of trips taken by the driver, wherein the stop data includes whether the driver stopped at the one or more stop locations during the plurality of trips; and determine one or more driving behaviors of the driver at the one or more stop locations based at least in part upon the stop data. ### Claim 17 (depends on 16) The one or more non-transitory computer-readable media of claim 16, wherein to determine one or more driving behaviors of the driver at the one or more stop locations, the computer-executable instructions further cause the one or more processors to: determine whether the driver performs a full stop at one stop location of the one or more stop locations based at least in part upon the stop data. ### Claim 18 (depends on 16) The one or more non-transitory computer-readable media of claim 16, wherein the computer-executable instructions further cause the one or more processors to: determine, based at least in part upon the stop data, one or more flagged locations where the driver does not stop during at least one trip of the plurality of trips; and display, on the driver computing device, the one or more flagged locations, as determined. ### Claim 19 (depends on 16) The one or more non-transitory computer-readable media of claim 16, wherein to identify the one or more stop locations based at least in part upon the historical location data and the historical telematics data, the computer-executable instructions further cause the one or more processors to: determine, using one or more machine learning and artificial intelligence techniques, the one or more stop locations, wherein the one or more stop locations includes a stop sign location or a stoplight location. ### Claim 20 (depends on 16) The one or more non-transitory computer-readable media of claim 16, wherein the computer-executable instructions further cause the one or more processors to: receive, from a plurality of user computing devices associated with a plurality of drivers, a plurality of sets of location data and a plurality of sets of telematics data after each driving trip of a plurality of driving trips taken by the plurality of drivers; generate stop data for the plurality of driving trips taken by the plurality of drivers, wherein the stop data includes whether the plurality of drivers stopped at the one or more stop locations during the plurality of driving trips; and determine, based at least in part upon the stop data for the plurality of stopped locations, one or more flagged locations where at least one of the plurality of drivers does not stop during the plurality of driving trips.