Pre-FNOL System for Damage Assessment and Repair Cost Estimation
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Solution Overview
Problem
Insurance policy holders face challenges in quantifying damage compensation and assessing the impact on their insurance policy after an accident, lacking efficient methods to determine repair costs and associated policy changes before submitting a claim.
Innovation Solution
A pre-FNOL (First Notification of Loss) system that assesses damage by receiving indications of accidents, prompting owners for questions and photos, analyzing responses and images to determine repair costs and insurance policy changes, and facilitating repair agreements or direct payments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional claims processing procedures are used, then insurance policy holders can determine compensation for losses, but they cannot assess damage compensation and policy impacts before submitting a claim
Solution Approach 1:
The system performs preliminary damage assessment and provides repair cost estimates before the policy holder submits a formal claim. The pre-FNOL system captures images, analyzes damage using computer vision, and provides cost estimates in advance, allowing policy holders to make informed decisions about whether to file a claim.
2Measurement precision
If detailed damage analysis is performed to provide accurate repair cost estimates, then the quality of information improves, but the complexity of the assessment system increases
Solution Approach 1:
The system enables policy holders to perform self-assessment of their own damage by capturing images with their mobile devices and receiving automated analysis. The computer vision system automatically processes images, identifies damage types and severity, and generates repair cost estimates without requiring manual inspection, thereby maintaining high accuracy while minimizing system complexity.
Solution Approach 2:
The patent replaces manual mechanical inspection processes with automated computer vision technology. Instead of requiring physical examination by adjusters, the system uses image processing algorithms to detect, classify, and quantify damage, substituting mechanical human assessment with automated optical and computational systems.
3Measurement precision
If multiple images and detailed information are collected to improve damage assessment accuracy, then the quality of analysis improves, but the ease of operation for policy holders deteriorates
Solution Approach 1:
The system allows policy holders to capture images of only the most obvious or severe damage areas rather than requiring comprehensive documentation of every damaged surface. The computer vision system processes these partial images to generate reasonable repair cost estimates, accepting that complete coverage is unnecessary for obtaining useful preliminary assessment information.
Data Source
AI summary
Apparatuses, systems, and methods are provided for the usage of enhanced pictures (e.g., photos) of tangible objects (e.g., property, cars, etc.) damaged in an accident and answers to questions about the accident to better assess the effect of the damage (e.g., repair expenses and accompanying changes to an insurance policy). A pre-FNOL system may receive responses to one or more questions regarding an accident and one or more enhanced pictures of the tangible property damaged in the accident. The pre-FNOL system may use the responses to the one or more questions and the one or more enhanced pictures to determine repair costs associated with the damaged property and accompanying changes to the insurance policy if an insurance claim were to be filed to cover the determined repairs costs.