Granted Patent B2

US 12,738,173 · App. 19/025,405 · Granted Sep 15, 2026

Inventors: Eric Christopher Dahl (Newman Lake, WA); Scott Murray Anderson (Stanwell Park, AU); James Patrick Ryan (Roswell, GA)

Assignee: QUANATA, LLC

G09B5/02G09B7/06

Abstract

Claims (94)

1 . A computer-implemented method comprising:

capturing telematics data, using one or more sensors of an electronic device of a user, wherein the one or more sensors comprise one or more of a Global Positioning System (GPS), a camera, or an accelerometer;

assessing driving behavior of the user based on the telematics data;

generating a personalized digital training program for the user based at least on the driving behavior of the user;

generating one or more driving scores and a driving credential for the user based on performance of the user on the personalized digital training program, wherein the driving credential is based on the one or more driving scores and comprises an authentic verification that the user possesses a threshold level of driving skills;

storing data of the one or more driving scores and the driving credential in a secure database having access controls operated by the user; and

transmitting one or more of the one or more driving scores or the driving credential to a third party to verify that the user possesses the threshold level of driving skills,

wherein:

the personalized digital training program is generated using one or more machine learning models that analyze the telematics data to identify driving skill weaknesses of the user;

the one or more machine learning models comprise one or more of decision trees, KNN, or neural networks;

the one or more machine learning models are trained using supervised, semi-supervised, or unsupervised learning; and

training data for the one or more machine learning models is collected from historical input and output data and updated periodically to re-train the one or more machine learning models.

2 . The computer-implemented method of claim 1 further comprising:

generating digital summaries based on the telematics data over a predetermined time period; and

identifying one or more types of skills based on the digital summaries corresponding to the driving behavior of the user.

3 . The computer-implemented method of claim 2 , wherein assessing the driving behavior of the user further comprises:

rating each of the one or more types of skills using a respective skill level assessment to generate a skill rating for each of the one or more types of skills;

determining an overall level of skill based on the skill ratings for the one or more types of skills; and

storing the overall level of skill on a driver profile of the user.

4 . The computer-implemented method of claim 3 further comprising:

visualizing the skill ratings for the one or more types of skills as concentric circles displayed on a graphical user interface (GUI) of a mobile device of the user, wherein a center of the concentric circles indicates a baseline level associated with a good driving standard, wherein the baseline level comprises a range of respective skill levels that meet or exceed the good driving standard, and wherein each outer circle indicates another level associated with another driving standard.

5 . The computer-implemented method of claim 3 further comprising:

updating the driver profile of the user when a new respective skill level assessment is completed.

6 . The computer-implemented method of claim 1 further comprising:

initiating one or more digital training programs based on a driver profile of the user for display on a graphical user interface of the electronic device of the user.

7 . The computer-implemented method of claim 6 further comprising:

displaying one or more interactive interfaces on the electronic device of the user, wherein the one or more interactive interfaces comprise one or more of a driver training mode, a drive mode, a driving credentials mode, or a rewards mode.

8 . The computer-implemented method of claim 1 , wherein generating the personalized digital training program for the user comprises:

identifying one or more types of digital training courses corresponding to one or more skill ratings that fall below a predetermined threshold, wherein the one or more types of digital training courses comprise at least one of: monitoring vehicle trips, interacting with driving simulations, or responding to online quizzes.

9 . The computer-implemented method of claim 1 , wherein generating the one or more driving scores for the user comprises:

rating training performance results for the user using the personalized digital training program; and

displaying the training performance results on a graphical user interface (GUI) of a mobile device of the user.

10 . The computer-implemented method of claim 1 , further comprising:

prior to accessing the secure database, verifying an identity of the user using a verified digital credential.

11 . A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations comprising:

capturing telematics data, using one or more sensors of an electronic device of a user, wherein the one or more sensors comprise one or more of a Global Positioning System (GPS), a camera, or an accelerometer;

assessing driving behavior of the user based on the telematics data;

generating a personalized digital training program for the user based at least on the driving behavior of the user;

generating one or more driving scores and a driving credential for the user based on performance of the user on the personalized digital training program, wherein the driving credential is based on the one or more driving scores and comprises an authentic verification that the user possesses a threshold level of driving skills;

storing data of the one or more driving scores and the driving credential in a secure database having access controls operated by the user; and

transmitting one or more of the one or more driving scores or the driving credential to a third party to verify that the user possesses the threshold level of driving skills,

wherein:

the personalized digital training program is generated using one or more machine learning models that analyze the telematics data to identify driving skill weaknesses of the user;

the one or more machine learning models comprise one or more of decision trees, KNN, or neural networks;

the one or more machine learning models are trained using supervised, semi-supervised, or unsupervised learning; and

training data for the one or more machine learning models is collected from historical input and output data and updated periodically to re-train the one or more machine learning models.

12 . The system of claim 11 , wherein the operations further comprise:

generating digital summaries based on the telematics data over a predetermined time period; and

identifying one or more types of skills based on the digital summaries corresponding to the driving behavior of the user.

13 . The system of claim 12 , wherein assessing the driving behavior of the user further comprises:

rating each of the one or more types of skills using a respective skill level assessment to generate a skill rating for each of the one or more types of skills;

determining an overall level of skill based on the skill ratings for the one or more types of skills; and

storing the overall level of skill on a driver profile of the user.

14 . The system of claim 11 , wherein the operations further comprise:

initiating one or more digital training programs based on a driver profile of the user for display on a graphical user interface of the electronic device of the user; and

displaying one or more interactive interfaces on the electronic device of the user, wherein the one or more interactive interfaces comprise one or more of a driver training mode, a drive mode, a driving credentials mode, or a rewards mode.

15 . The system of claim 11 , wherein the operations further comprise:

prior to accessing the secure database, verifying an identity of the user using a verified digital credential.

16 . One or more non-transitory computer-readable media storing computing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

capturing telematics data, using one or more sensors of an electronic device of a user, wherein the one or more sensors comprise one or more of a Global Positioning System (GPS), a camera, or an accelerometer;

assessing driving behavior of the user based on the telematics data;

generating a personalized digital training program for the user based at least on the driving behavior of the user;

generating one or more driving scores and a driving credential for the user based on performance of the user on the personalized digital training program, wherein the driving credential is based on the one or more driving scores and comprises an authentic verification that the user possesses a threshold level of driving skills;

storing data of the one or more driving scores and the driving credential in a secure database having access controls operated by the user; and

transmitting one or more of the one or more driving scores or the driving credential to a third party to verify that the user possesses the threshold level of driving skills,

wherein:

the personalized digital training program is generated using one or more machine learning models that analyze the telematics data to identify driving skill weaknesses of the user;

the one or more machine learning models comprise one or more of decision trees, KNN, or neural networks;

the one or more machine learning models are trained using supervised, semi-supervised, or unsupervised learning; and

training data for the one or more machine learning models is collected from historical input and output data and updated periodically to re-train the one or more machine learning models.

17 . The one or more non-transitory computer-readable media of claim 16 , wherein the operations further comprise:

generating digital summaries based on the telematics data over a predetermined time period; and

identifying one or more types of skills based on the digital summaries corresponding to the driving behavior of the user.

18 . The one or more non-transitory computer-readable media of claim 17 , wherein assessing the driving behavior of the user further comprises:

rating each of the one or more types of skills using a respective skill level assessment to generate a skill rating for each of the one or more types of skills;

determining an overall level of skill based on the skill ratings for the one or more types of skills; and

storing the overall level of skill on a driver profile of the user.

19 . The one or more non-transitory computer-readable media of claim 16 , wherein the operations further comprise:

initiating one or more digital training programs based on a driver profile of the user for display on a graphical user interface of the electronic device of the user; and

displaying one or more interactive interfaces on the electronic device of the user, wherein the one or more interactive interfaces comprise one or more of a driver training mode, a drive mode, a driving credentials mode, or a rewards mode.

20 . The one or more non-transitory computer-readable media of claim 16 , wherein the operations further comprise:

prior to accessing the secure database, verifying an identity of the user using a verified digital credential.

21 . A system comprising:

first means for capturing telematics data, using one or more sensors of an electronic device of a user, wherein the one or more sensors comprise one or more of a Global Positioning System (GPS), a camera, or an accelerometer;

second means for assessing driving behavior of the user based on the telematics data;

third means for generating a personalized digital training program for the user based at least on the driving behavior of the user;

fourth means for generating one or more driving scores and a driving credential for the user based on performance of the user on the personalized digital training program, wherein the driving credential is based on the one or more driving scores and comprises an authentic verification that the user possesses a threshold level of driving skills;

fifth means for storing data of the one or more driving scores and the driving credential in a secure database having access controls operated by the user; and

sixth means for transmitting one or more of the one or more driving scores or the driving credential to a third party to verify that the user possesses the threshold level of driving skills,

wherein:

the personalized digital training program is generated using one or more machine learning models that analyze the telematics data to identify driving skill weaknesses of the user;

the one or more machine learning models comprise one or more of decision trees, KNN, or neural networks;

the one or more machine learning models are trained using supervised, semi-supervised, or unsupervised learning; and

training data for the one or more machine learning models is collected from historical input and output data and updated periodically to re-train the one or more machine learning models.

Assignments (1)

Continuity (1)

References Cited (26)