Machine Learning Accident Assessment Server
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Solution Overview
Problem
Conventional accident assessment systems rely on human inspectors, leading to delays and potential safety risks as vehicle owners may continue to drive damaged vehicles, as these systems require physical inspections and are dependent on inspector availability.
Innovation Solution
A machine learning-based system using an accident assessment server that processes vehicle operational data from sensors and telematics devices to determine if an accident resulted in a total loss, by comparing received data with known data, requesting further information, and calculating a vehicle's baseline and final value, enabling remote assessment and payment determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human inspectors are used to assess vehicle damage, then measurement precision can be maintained through physical inspection, but loss of time increases due to inspector availability constraints
Solution Approach 1:
The patent replaces the mechanical inspection process (human inspectors physically examining vehicles) with an automated electronic system using machine learning algorithms that process telematics data, sensor data, and image data to assess vehicle damage. This substitution eliminates the time constraint of inspector availability while maintaining assessment accuracy through multiple data sources and algorithms.
Solution Approach 2:
The system enables self-service assessment by automatically collecting and processing data from the vehicle's own sensors, telematics devices, and cameras without requiring external inspectors. The machine learning model autonomously evaluates damage based on data already captured during normal vehicle operation and at the time of accident.
2Reliability
If physical inspections are required to determine vehicle damage, then reliability of assessment can be ensured through direct observation, but productivity decreases due to sequential processing requirements
Solution Approach 1:
The patent replaces sequential physical inspection with parallel electronic data processing. Multiple data streams (telematics, sensors, images) are processed simultaneously by machine learning algorithms, enabling multiple vehicles to be assessed concurrently without the sequential constraints of physical inspector availability.
Solution Approach 2:
The system performs multiple assessment functions simultaneously using the same infrastructure: damage detection, severity classification, repair cost estimation, and safety evaluation. This multi-functionality increases productivity without compromising reliability, as all assessments use the same validated machine learning model.
3Measurement precision
If conventional inspection systems are used, then measurement precision can be maintained, but device complexity increases due to coordination of inspectors and scheduling
Solution Approach 1:
The patent extracts the inspection function from the human inspector and embeds it directly in the vehicle's electronic systems. The telematics device, sensors, and cameras that already exist in modern vehicles are utilized to capture data, eliminating the need for separate inspection coordination while maintaining detection accuracy through automated analysis.
Solution Approach 2:
The machine learning model serves as an intermediary that automatically processes raw data from multiple sources (sensors, telematics, images) and converts it into reliable damage assessments. This intermediary simplifies the system by replacing complex human coordination with automated data processing and algorithmic decision-making.
Data Source
AI summary
Aspects of the disclosure relate to using machine learning algorithms to analyze vehicle operational data associated with a vehicle accident. In some instances, an accident assessment server may receive data indicating that a vehicle was involved in an accident. The accident assessment server may compare the data with other known data, based on machine learning algorithms, to identify whether the accident resulted in a total loss. Responsive to determining that the accident resulted in the total loss, the accident assessment server may request further information regarding the vehicle and may identify a baseline value range for the vehicle. The accident assessment server may request updated information from the owner of the vehicle, identify, based on the updated information, a final value of the vehicle, and may pay the owner of the vehicle an amount corresponding to the final value if the final value is within the baseline value range.