Telematics Risk Modeling Using Geographic Driving Behavior Baselines

Solution Overview

Problem

Current systems face limitations in utilizing telematics data from connected vehicles and mobile devices due to the need for human reconciliation, leading to timeliness issues, inaccuracies, and inconvenience in generating refined risk models for insurance and risk assessment.

Innovation Solution

A data analytics computing device employing machine-learning and artificial-intelligence techniques processes geolocation and telematics data to define standard driving behavior within geographic areas, generating location-dependent and precise risk models that account for vehicle characteristics and environmental factors.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If human reconciliation is used to process telematics data, then accuracy may be improved, but timeliness and productivity deteriorate

Engineering Contradiction:

Improvedata accuracyVSAvoidtimeliness

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system enables self-service automated processing where the computing device automatically reconciles telematics data without human intervention. The processor independently performs data validation, risk model generation, and profile creation, eliminating the need for manual human reconciliation while maintaining processing accuracy and significantly improving timeliness.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical human reconciliation process with an automated computational system. The processor executes algorithms to automatically validate telematics data, generate risk models, and create driver profiles, substituting human manual work with machine-based automated processing that achieves both accuracy and speed.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If human reconciliation is used to process telematics data, then accuracy may be improved, but convenience and productivity deteriorate

Engineering Contradiction:

Improvedata accuracyVSAvoidprocessing efficiency

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The system enables self-service automated processing where the computing device automatically reconciles telematics data without human intervention. The processor independently performs data validation, risk model generation, and profile creation, eliminating the need for manual human reconciliation while maintaining processing accuracy and significantly improving timeliness.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical human reconciliation process with an automated computational system. The processor executes algorithms to automatically validate telematics data, generate risk models, and create driver profiles, substituting human manual work with machine-based automated processing that achieves both accuracy and speed.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Loss of time

If automated processing is implemented, then timeliness and productivity are improved, but measurement precision may deteriorate

Engineering Contradiction:

ImprovetimelinessVSAvoiddata accuracy

Core Design Contradiction:

Loss of timeVSMeasurement precision

Solution Approach 1:

The system incorporates feedback mechanisms where the processor continuously validates telematics data against established criteria and adjusts processing parameters accordingly. The automated system uses feedback loops to verify data quality, ensure accuracy thresholds are met, and refine risk model generation, maintaining high measurement precision while achieving rapid automated processing.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12187289B1Systems and methods for generating refined risk models responsive to vehicle-based telematics data

Publication Date: 2025.01.07 STATE FARM MUTAL AUTOMOBILE INSURANCE COMPANY

AI summary

A data analytics computing device for generating refined risk models responsive to vehicle-based telematics data is configured to receive geolocation data and telematics data associated with a plurality of vehicles. The geolocation data is captured at a location sensor of a vehicle computing device and representative of a location of the vehicle, and the telematics data is captured at a plurality of sensors of at least one of the vehicle or the vehicle computing device and representative of movement of the vehicle. The plurality of vehicles is associated with a geographic area. The data analytics computing device is also configured to apply machine-learning techniques to the geolocation data and the telematics data to define standard driving behavior within the geographic area, and generate driving behavior risk models for the geographic area based upon the standard driving behavior.