Driver Telematic Signatures via AI Normalization

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Solution Overview

Problem

Existing solutions for creating driver telematic signatures face challenges in being device and vehicle independent, and struggle with collecting and updating driver habit data effectively, leading to inconsistencies and difficulties in determining accurate signatures.

Innovation Solution

A method and system utilizing artificial intelligence (AI) analyzed Big Data sets, calibrated on a cloud Software as a Service (SaaS) network, which communicates with vehicles in real-time to create device-independent and vehicle-independent driver safety scoring systems, providing current performance and habit data, and determining insurance costs based on real-time driver maneuvers.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Quantity of substance

If driver behavior data is collected from multiple devices and vehicles, then the quantity of driver habit data increases, but device and vehicle independence becomes difficult to achieve

Engineering Contradiction:

Improvedriver habit dataVSAvoiddevice and vehicle independence

Core Design Contradiction:

Quantity of substanceVSAdaptability or versatility

Solution Approach 1:

The system creates a universal driver telematic signature that functions across multiple devices and vehicles. By normalizing driver behavior data through AI analysis and creating a standardized signature format, the system achieves device and vehicle independence while maintaining the ability to collect data from diverse sources including OBD devices, smartphone applications, and event data recorders

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system transforms raw driver behavior data into normalized parameters through AI analysis. By changing the state of the data from device-specific raw metrics to standardized behavioral parameters, the system enables cross-device compatibility while preserving the quantity and variety of collected data

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If real-time driver monitoring is implemented, then driver behavior assessment accuracy improves, but system complexity increases

Engineering Contradiction:

Improvedriver behavior assessmentVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system introduces an intermediary AI analysis layer that processes raw driver behavior data from multiple sources. This intermediary component normalizes and synthesizes data from OBD devices, smartphone applications, and event data recorders, achieving accurate real-time assessment without requiring direct complex integration of all monitoring components

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system replaces complex mechanical and procedural integration methods with AI-based data processing. Instead of using complex hardware integration to achieve real-time monitoring, the system uses artificial intelligence to analyze and synthesize data from various sources, reducing system complexity while maintaining assessment accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If driver telematic signatures are updated continuously, then the accuracy of insurance pricing increases, but data collection and processing time increases

Engineering Contradiction:

Improveinsurance pricing accuracyVSAvoiddata collection and processing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system implements periodic updates of driver telematic signatures rather than continuous real-time updates. By analyzing accumulated driver behavior data at scheduled intervals and updating signatures periodically, the system achieves accurate insurance pricing while avoiding the time loss associated with continuous data collection and processing

Inventive Principle:

Principle #19Periodic action

Solution Approach 2:

The system performs preliminary AI analysis and normalization of driver behavior data as data is collected, preparing it in advance for signature updates. This preliminary processing reduces the time required for actual signature updates and insurance pricing calculations, achieving accuracy without excessive time loss

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11037378B2Method and system for creating driver telematic signatures

Publication Date: 2021.06.15 IGEN NETWORKS CORP

AI summary

A method and system for creating driver telematic signatures. The driver telematic signatures include device-independent and vehicle-independent, artificial intelligence (AI) analyzed and dynamic Big Data set (e.g., 100,000−1 Million+ data values) calibrated, driver safety scoring system. The driver telematics signatures are created and used in real-time from a cloud Software as a Service (SaaS) on a cloud server network device and a cloud communications network that communicates with a driver's vehicle when it is on and moving. The driver telematics signatures provide current driver performance data, driver habit data and allow determination in real-time of drivers performing risky driver maneuvers. The driver telematics signature are also used to determine a cost of insurance for vehicles as the result of reducing rating errors by establishing a baseline for the driver's behavior while driving a vehicle.