Patent Issued for Context-based grading (USPTO 11260873): Allstate Insurance Company

2022 MAR 22 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- From Alexandria, Virginia, NewsRx journalists report that a patent by the inventors Ferguson, Dana (Chicago, IL, US), filed on April 14, 2020, was published online on March 1, 2022.

The patent’s assignee for patent number 11260873 is Allstate Insurance Company (Northbrook, Illinois, United States).

News editors obtained the following quote from the background information supplied by the inventors: “The collection and analysis of driving data, such as the identification of driving behaviors and traffic accidents, has many applications. For example, insurance companies and financial institutions may offer rate discounts or other financial incentives to customers based on safe driving behaviors and accident-free driving records. Law enforcement or government personnel may collect and analyze driving data and traffic accident statistics to identify dangerous driving roads or times, and to detect moving violations and other unsafe driving behaviors. In other cases, driving data may be used for navigation applications, vehicle tracking and monitoring applications, and on-board vehicle maintenance applications, among others.

“Vehicle-based computer systems, such as on-board diagnostics (OBD) systems and telematics devices, may be used in automobiles and other vehicles, and may be capable of collecting various driving data and vehicle sensor data. For example, OBD systems may receive information from the vehicle’s on-board computers and sensors in order to monitor a wide variety of information relating to the vehicle systems, such as engine RPM, emissions control, vehicle speed, throttle position, acceleration and braking rates, use of driver controls, etc. Vehicles may also include Global Positioning System (GPS) receivers and devices installed within or operating at the vehicle configured to collect vehicle location and time data. Such vehicle-based systems may be capable of collecting driving data which may be used to perform various driving data analyses such as statistical driving evaluations, driver score calculations, etc. Vehicle-based systems also may be configured to detect the occurrence of traffic accidents, for instance, using vehicle body impact sensors and airbag deployment sensors. However, not all vehicles are equipped with systems capable of collecting, analyzing, and communicating driving data. Moreover, a single vehicle may be used by multiple different drivers, and conversely, a single driver may drive multiple different vehicles. Thus, vehicle driving data and/or accident records collected by vehicle-based systems might not include the vehicle occupants that correspond to the collected driving and accident data.

“In contrast to vehicle-based systems, mobile devices such as smartphones, personal digital assistants, tablet computers, and the like, are often carried and/or operated by a single user. Some mobile devices may include movement sensors, such as an accelerometer, gyroscope, speedometer, and/or GPS receivers, capable of detecting movement.”

As a supplement to the background information on this patent, NewsRx correspondents also obtained the inventors’ summary information for this patent: “The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosure. The summary is not an extensive overview of the disclosure. It is neither intended to identify key or critical elements of the disclosure nor to delineate the scope of the disclosure. The following summary merely presents some concepts of the disclosure in a simplified form as a prelude to the description below.

“Aspects of the disclosure relate to methods, computer-readable media, and apparatuses for receiving telematics data (e.g., vehicle operation data, driver data, environmental data, etc.) associated with one or more vehicles from one or more mobile devices respectively disposed within the one or more vehicles. Telematics data may include, for example, a geographic location of the vehicle, a route being traversed by the vehicle, driving events or maneuvers (e.g., braking, turning, accelerating) performed by the vehicle, corresponding timestamps or timeframes, etc. In some instances, the telematics data may include sensor data from various sensors operationally coupled to the vehicle and configured to sense the immediate surroundings of the vehicle. A computing device may group the vehicles based on their telematics data. For instance, vehicles may be grouped based on route traversed, geographic proximity with one another, etc. The computing device may also use telematics data to determine driving patterns representative of driving behaviors of each vehicle as well as group driving patterns representative of the normalized driving behavior for a group of vehicles. One or more outlier vehicles for each group may be identified based on the outlier vehicles’ driving pattern being different from its group’s group driving pattern beyond a dissimilarity metric threshold (or a similarity metric threshold). A driver score of the outlier vehicle may be positively or negatively adjusted based on whether the outlier was safer or less safe than its group. A measure of safety for a vehicle may be a number of unsafe events (e.g., hard-braking, swerving, excessive speeding, etc.) determined from a vehicle’s telematics data for a particular time period. A measure of safety for a group of vehicles may be an average number of unsafe events determined from the telematics data of the vehicles in the group for the particular time period.

“In accordance with further aspects of the present disclosure, contextual data (e.g., extenuating circumstances) may be considered in determining a driver score. For instance, a high-risk or unsafe driving event performed by a vehicle and identified from the vehicle’s telematics data may generally result in a negative adjustment to the driver’s score. However, if the driver performed the high-risk or unsafe driving event to avoid an accident with another vehicle that is behaving badly (e.g., performing a high-risk or unsafe driving event), the driver’s score might not be negatively adjusted. Rather, the driver score may be maintained or positively adjusted. As an example, a driver who performs a hard-braking event as a result of going too fast on a curve may result in a negative adjustment to the driver’s score. However, a driver who performs a hard-braking event to avoid a collision with another vehicle that is swerving and cutting off the driver’s vehicle might not result in a negative adjustment to the driver’s score. In some instances, such a driver may receive a positive adjustment to his or her driving score.

“In accordance with further aspects of the present disclosure, a computing device may determine whether a vehicle that was not involved in a collision or accident determination but nonetheless caused the collision or accident.

“Other features and advantages of the disclosure will be apparent from the additional description provided herein.”

The claims supplied by the inventors are:

“1. A method comprising: receiving telematics data from two or more vehicles; determining a time of an accident based on the telematics data received from the two or more vehicles; determining, from the two or more vehicles, a first vehicle that was involved in the accident; determining, from the two or more vehicles, a second vehicle that was not involved in the accident and that was within a preset distance of the first vehicle; determining whether the second vehicle performed a high-risk driving event based on the telematics data compared with at least a threshold during a timeframe prior to the time of the accident; responsive to determining that the second vehicle performed the high-risk driving event during the timeframe prior to the accident, determining whether the high-risk driving event caused the accident; adjusting, based on the determination the high-risk driving event caused the accident, a driver score of the second vehicle, wherein the driver score is an indication of a driving pattern of the second vehicle compared to a normalized group driving pattern representative of driving behavior of the two or more vehicles; and outputting the driver score for display to a graphical user interface to a driver of the second vehicle.

“2. The method of claim 1, further comprising: determining that the accident occurred based on a shift in linear or rotational acceleration, or linear of two vehicles of the two or more vehicles.

“3. The method of claim 1, further comprising: determining that the accident occurred based on a shift in linear or angular velocity of two vehicles of the two or more vehicles.

“4. The method of claim 1, wherein an end time of the timeframe is the time of the accident.

“5. The method of claim 1, wherein the determining the second vehicle that was within the preset distance of the first vehicle is performed based on the telematics data received from the two or more vehicles.

“6. The method of claim 1, wherein the determining the second vehicle that was not involved in the accident comprises determining that the second vehicle did not collide with any object or vehicle.

“7. The method of claim 1, wherein the second vehicle is identified from image/video data received from the first vehicle.

“8. A non-transitory computer readable media storing instructions that, executed by a computing device, cause the computing device to: receive telematics data from two or more vehicles; determine a time of an accident based on the telematics data received from the two or more vehicles; determine, from the two or more vehicles, a first vehicle that was involved in the accident; determine, from the two or more vehicles, a second vehicle that was not involved in the accident and that was within a preset distance of the first vehicle; determine whether the second vehicle performed a high-risk driving event based on the telematics data compared with at least a threshold during a timeframe prior to the time of the accident; responsive to determining that the second vehicle performed the high-risk driving event during the timeframe prior to the accident, determining whether the high-risk driving event caused the accident; adjust, based on the determination the high-risk driving event caused the accident, a driver score of the second vehicle, wherein the driver score is an indication of a driving pattern of the second vehicle compared to a normalized group driving pattern representative of driving behavior of the two or more vehicles; and output the driver score for display to a graphical user interface to a driver of the second vehicle.

“9. The non-transitory computer readable medium of claim 8, storing instructions that, executed by a computing device, cause the computing device to: determine that the accident occurred based on a shift in linear or rotational acceleration, or linear of two vehicles of the two or more vehicles.

“10. The non-transitory computer readable media of claim 8, storing instructions that, executed by a computing device, cause the computing device to: determine that the accident occurred based on a shift in linear or angular velocity of two vehicles of the two or more vehicles.

“11. The non-transitory computer readable media of claim 8, wherein an end time of the timeframe is the time of the accident.

“12. The non-transitory computer readable media of claim 8, wherein the determining that the second vehicle that was within the preset distance of the first vehicle is performed based on the telematics data received from the two or more vehicles.

“13. The non-transitory computer readable media of claim 8, wherein the determining the second vehicle that was not involved in the accident comprises determining that the second vehicle did not collide with any object or vehicle.

“14. The non-transitory computer readable media of claim 8, wherein the second vehicle is identified from image/video data received from the first vehicle.

“15. An apparatus comprising: one or more processors; and memory storing instructions that, executed by the one or more processors, cause the apparatus to: receive telematics data from two or more vehicles; determine a time of an accident based on the telematics data received from the two or more vehicles; determine, from the two or more vehicles, a first vehicle that was involved in the accident; determine, from the two or more vehicles, a second vehicle that was not involved in the accident and that was within a preset distance of the first vehicle; determine whether the second vehicle performed a high-risk driving event based on the telematics data compared with at least a threshold during a timeframe prior to the time of the accident; responsive to determining that the second vehicle performed the high-risk driving event during the timeframe prior to the accident, determining whether the high-risk driving event caused the accident; adjust, based on the determination the high-risk driving event caused the accident, a driver score of the second vehicle, wherein the driver score is an indication of a driving pattern of the second vehicle compared to a normalized group driving pattern representative of driving behavior of the two or more vehicles; and output the driver score for display to a graphical user interface to a driver of the second vehicle.

“16. The apparatus of claim 15, the memory storing instructions that, executed by the one or more processors, cause the apparatus to: determine that the accident occurred based on a shift in linear or rotational acceleration, or linear of two vehicles of the two or more vehicles.

“17. The apparatus of claim 15, the memory storing instructions that, executed by the one or more processors, cause the apparatus to: determine that the accident occurred based on a shift in linear or angular velocity of two vehicles of the two or more vehicles.

“18. The apparatus of claim 15, wherein an end time of the timeframe is the time of the accident.

“19. The apparatus of claim 15, wherein the determining the second vehicle that was within the preset distance of the first vehicle is performed based on the telematics data received from the two or more vehicles.

“20. The apparatus of claim 15, wherein the determining the second vehicle that was not involved in the accident comprises determining that the second vehicle did not collide with any object or vehicle.”

For additional information on this patent, see: Ferguson, Dana. Context-based grading. U.S. Patent Number 11260873, filed April 14, 2020, and published online on March 1, 2022. Patent URL: http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=11260873.PN.&OS=PN/11260873RS=PN/11260873

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