Patent Issued for Authenticating Drivers (USPTO 10,938,825)

2021 MAR 16 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- According to news reporting originating from Alexandria, Virginia, by NewsRx journalists, a patent by the inventors Herrmann, Alexander (Chicago, IL); Walsh, Connor (Lake Forest, IL); Harish, Pratheek M. (Ontario, CA), filed on January 23, 2019, was published online on March 15, 2021.

The assignee for this patent, patent number 10,938,825, is Allstate Insurance Company (Northbrook, Illinois, United States).

Reporters obtained the following quote from the background information supplied by the inventors: “The ability to collect and analyze data to determine who is driving a vehicle has many valuable applications, for example, relating to vehicle and driver insurance, vehicle financing, product safety and marketing, government and law enforcement, and various other applications in other industries. The goal of driver detection, or driver fingerprinting, is to determine whether a user recording a car trip with a computing device is a driver or a passenger of the vehicle. If driver profiles are known or have been determined for all potential drivers of a vehicle, then the solution becomes one of driver identification. If all potential drivers are known the solution becomes one of a forced task choice that determines which driver profile is the closest match in the database.

“In contrast, solving the problem of driver authentication involves determining the driver from a pool of drivers that may be largely unknown. Solving such a problem is needed and would have many valuable applications. Further complications that need to be overcome in the context of driver authentication include making such determinations based on unsupervised, i.e. unlabeled data. Additionally, there is a need to determine driver authentication based on a method which is agnostic to road, traffic, and weather conditions. Finally, a need exists for a method and system to determine driver authentication based on collected real-time data. Such real-time data may be collected in non-uniform/varying road, traffic, and weather conditions.”

In addition to obtaining background information on this patent, NewsRx editors 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 systems, apparatuses, computer-implemented methods, and computer-readable media for receiving and analyzing GPS and movement data to identify driving patterns and drivers based on the data. In some cases, the movement data may correspond to acceleration data, speed data, or other movement data collected by various movement sensors in one or more mobile devices, such as smartphones, tablet computers, and on-board vehicle systems.

“According to some aspects of the disclosure, data such as GPS data and movement data may be received and used to determine whether a user is a driver or passenger of a vehicle. A small amount of labeled trips may be used to generate a user driver profile, e.g. by sampling routine trips that have a high likelihood of the user driving or by asking the user to label a small amount of trips. Once this small subset of labeled trips is obtained, a driving pattern may be determined for the driver. The determined driving pattern may be used to generate a driving profile for the driver. In an embodiment, the generated driving profile from a new test trip may be compared to previously generated driver profiles and a stored background driver profile in order to authenticate the driver identity based on the comparison.

“According to some aspects of the disclosure, driving patterns may be determined based on statistical analyses of the GPS and movement data. Trip attributes such as number of stopping points during a trip, number of turns, acceleration rate, deceleration rate, time of day etc. may be used to determine driving patterns. Other features and advantages of the disclosure will be apparent from the additional description provided herein.”

The claims supplied by the inventors are:

“What is claimed is:

“1. A method, comprising: receiving, by a computing device, global positioning system (GPS) data collected by a user device during a driving trip; analyzing the GPS data to determine a plurality of time-series data, wherein each time-series data comprises data corresponding to a different physical parameter reflected in the GPS data; dividing each of the plurality of time-series data into a plurality of overlapping window frames of a predetermined length; for each of the plurality of overlapping window frames, analyzing corresponding data for the window frame to determine a first probability score reflecting a probability that the window frame comprises driver trip data and a second probability score reflecting a probability that the window frame comprises non-driver trip data; calculating, using the first probability scores for each of the plurality of overlapping window frames, a first total probability score reflecting a probability that the driving trip comprises driver-trip data; calculating, using the second probability scores for each of the plurality of overlapping window frames, a second total probability score reflecting a probability that the driving trip comprises non-driver trip data; and identifying a user of the user device as a driver of the driving trip, based on a comparison of the first total probability score and the second total probability score.

“2. The method of claim 1, wherein the data corresponding to the different physical parameter reflected in the GPS data is reflective of at least one of: a steering wheel angle, a gas pedal position, and a brake pedal position, and wherein the physical parameter comprises at least one of: speed, course, acceleration, jerk, angular speed, angular acceleration, angular jerk, and power per mass ratio.

“3. The method of claim 1, wherein the GPS data comprises speed data and course data, and wherein the method further comprises calculating, using the speed data and the course data, acceleration, jerk, angular speed, angular acceleration, angular jerk, and power per mass ratio.

“4. The method of claim 1, wherein the predetermined length of the plurality of overlapping window frames is between approximately 30 and 50 seconds, and wherein an overlapping portion of consecutive window frames, of the plurality of overlapping window frames, comprises approximately one-third of the predetermined length.

“5. The method of claim 1, wherein analyzing the corresponding data for the window frame to determine the first probability score and the second probability score comprises: extracting, from the corresponding data for the window frame, statistical, dynamic, and spectral features of the corresponding data; generating, using the extracted features of the corresponding data, a feature vector for the window frame; and determining a trip state associated with the feature vector for the window frame, wherein the trip state comprises at least one of: a road type, a traffic condition, and a driving maneuver.

“6. The method of claim 5, further comprising: retrieving a driver probability profile associated with the user of the user device; comparing the feature vector for the window frame to a portion of the driver probability profile determined to correspond to the determined trip state associated with the feature vector for the window frame; and generating, based on the comparison, the first probability score.

“7. The method of claim 5, further comprising: retrieving an average driver probability profile associated with an average driver, wherein the average driver probability profile represents an average of all known driving profiles; comparing the feature vector for the window frame to a portion of the average driver probability profile determined to correspond to the determined trip state associated with the feature vector for the window frame; and generating, based on the comparison, the second probability score.

“8. The method of claim 1, wherein identifying the user of the user device as the driver comprises: identifying the user of the user device as the driver of the driving trip based on determining that the first total probability score is greater than the second total probability score.

“9. A computing device, comprising: a processor; and memory storing computer-executable instructions that, when executed by the processor, cause the computing device to: receive global positioning system (GPS) data collected by a user device during a driving trip; analyze the GPS data to determine a plurality of time-series data, wherein each time-series data comprises data corresponding to a different physical parameter reflected in the GPS data; divide each of the plurality of time-series data into a plurality of overlapping window frames of a predetermined length; for each of the plurality of overlapping window frames, analyze corresponding data for the window frame to determine a first probability score reflecting a probability that the window frame comprises driver trip data and a second probability score reflecting a probability that the window frame comprises non-driver trip data; calculate, using the first probability scores for each of the plurality of overlapping window frames, a first total probability score reflecting a probability that the driving trip comprises driver trip data; calculate, using the second probability scores for each of the plurality of overlapping window frames, a second total probability score reflecting a probability that the driving trip comprises non driver trip data; and identify a user of the user device as a driver of the driving trip, based on a comparison of the first total probability score and the second total probability score.

“10. The computing device of claim 9, wherein the data corresponding to the different physical parameter reflected in the GPS data is reflective of at least one of: a steering wheel angle, a gas pedal position, and a brake pedal position, and wherein the physical parameter comprises at least one of: speed, course, acceleration, jerk, angular speed, angular acceleration, angular jerk, and power per mass ratio.

“11. The computing device of claim 9, wherein the GPS data comprises speed data and course data, and wherein the computer-executable instructions, when executed by the processor, further cause the computing device to calculate, using the speed data and the course data, acceleration, jerk, angular speed, angular acceleration, angular jerk, and power per mass ratio.

“12. The computing device of claim 9, wherein the computer-executable instructions, when executed by the processor, further cause the computing device to analyze the corresponding data for the window frame to determine the first probability score and the second probability score by: extracting, from the corresponding data for the window frame, statistical, dynamic, and spectral features of the corresponding data; generating, using the extracted features of the corresponding data, a feature vector for the window frame; and determining a trip state associated with the feature vector for the window frame, wherein the trip state comprises at least one of: a road type, a traffic condition, and a driving maneuver.

“13. The computing device of claim 12, wherein the computer-executable instructions, when executed by the processor, further cause the computing device to: retrieve a driver probability profile associated with the user of the user device; compare the feature vector for the window frame to a portion of the driver probability profile determined to correspond to the determined trip state associated with the feature vector for the window frame; and generate, based on the comparison, the first probability score.

“14. The computing device of claim 12, wherein the computer-executable instructions, when executed by the processor, further cause the computing device to: retrieve an average driver probability profile associated with an average driver, wherein the average driver probability profile represents an average of all known driving profiles; compare the feature vector for the window frame to a portion of the average driver probability profile determined to correspond to the determined trip state associated with the feature vector for the window frame; and generate, based on the comparison, the second probability score.

“15. The computing device of claim 9, wherein the computer-executable instructions, when executed by the processor, further cause the computing device to identify the user of the user device as the driver of the driving trip based on determining that the first total probability score is greater than the second total probability score.

“16. A non-transitory, computer-readable storage medium storing instructions that, when executed by a processor of a computing device, cause the computing device to: receive global positioning system (GPS) data collected by a user device during a driving trip; analyze the GPS data to determine a plurality of time-series data, wherein each time-series data comprises data corresponding to a different physical parameter reflected in the GPS data; divide each of the plurality of time-series data into a plurality of overlapping window frames of a predetermined length; for each of the plurality of overlapping window frames, analyze corresponding data for the window frame to determine a first probability score reflecting a probability that the window frame comprises driver trip data and a second probability score reflecting a probability that the window frame comprises non-driver trip data; calculate, using the first probability scores for each of the plurality of overlapping window frames, a first total probability score reflecting a probability that the driving trip comprises driving driver trip data; calculate, using the second probability scores for each of the plurality of overlapping window frames, a second total probability score reflecting a probability that the driving trip comprises non-driving driver trip data; and identify a user of the user device as a driver of the driving trip, based on a comparison of the first total probability score and the second total probability score.

“17. The non-transitory, computer-readable storage medium of claim 16, wherein the data corresponding to the different physical parameter reflected in the GPS data is reflective of at least one of: a steering wheel angle, a gas pedal position, and a brake pedal position, wherein the physical parameter comprises at least one of: speed, course, acceleration, jerk, angular speed, angular acceleration, angular jerk, and power per mass ratio, and wherein the instructions, when executed by the processor of the computing device, further cause the computing device to calculate, using speed data and course data from the GPS data, the acceleration, the jerk, the angular speed, the angular acceleration, the angular jerk, and the power per mass ratio.

“18. The non-transitory, computer-readable storage medium of claim 16, the instructions, when executed by the processor of the computing device, further cause the computing device to analyze the corresponding data for the window frame to determine the first probability score and the second probability score by: extracting, from the corresponding data for the window frame, statistical, dynamic, and spectral features of the corresponding data; generating, using the extracted features of the corresponding data, a feature vector for the window frame; and determining a trip state associated with the feature vector for the window frame, wherein the trip state comprises at least one of: a road type, a traffic condition, and a driving maneuver.

“19. The non-transitory, computer-readable storage medium of claim 18, wherein the instructions, when executed by the processor of the computing device, further cause the computing device to: retrieve a driver probability profile associated with the user of the user device; compare the feature vector for the window frame to a portion of the driver probability profile determined to correspond to the determined trip state associated with the feature vector for the window frame; generate, based on the comparison, the first probability score; retrieve an average driver probability profile associated with an average driver, wherein the average driver probability profile represents an average of all known driving profiles; compare the feature vector for the window frame to a portion of the average driver probability profile determined to correspond to the determined trip state associated with the feature vector for the window frame; and generate, based on the comparison, the second probability score.

“20. The method of claim 1, wherein analyzing the GPS data to determine the plurality of time-series data comprises analyzing the GPS data after the driving trip has ended.”

For more information, see this patent: Herrmann, Alexander; Walsh, Connor; Harish, Pratheek M. Authenticating Drivers. U.S. Patent Number 10,938,825, filed January 23, 2019, and published online on March 15, 2021. 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=10,938,825.PN.&OS=PN/10,938,825RS=PN/10,938,825

(Our reports deliver fact-based news of research and discoveries from around the world.)