AI Image Analysis for Hail Damage Labeling
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Solution Overview
Problem
The insurance industry faces inefficiencies in processing large volumes of claims for storm damage, particularly hail damage to structures, due to the reliance on manual analysis by human domain experts, which is time-consuming and prone to inconsistent outputs.
Innovation Solution
An image analysis system utilizing artificial intelligence and machine learning algorithms to identify and label hail damage on rooftops, enabling automated damage prediction and overlaying virtual objects on images to highlight damage areas, thereby streamlining the claims processing and reducing the need for manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis by human domain experts is used to process storm damage claims, then accuracy in damage assessment can be maintained through expert judgment, but processing time increases significantly and productivity decreases when handling large volumes of claims
Solution Approach 1:
The patent introduces an image analysis system with automated damage detection algorithms as an intermediary between the raw image data and final claims decisions. This intermediary system pre-processes and flags potential damage areas, allowing human experts to focus their judgment on contested or complex cases rather than reviewing every claim from scratch, thus maintaining accuracy while improving throughput
Solution Approach 2:
The patent segments the claims processing workflow into distinct stages: automated image preprocessing and damage detection, human expert review of flagged cases, and final claims adjudication. This segmentation allows different tasks to be performed by optimally suited methods (automated processing for routine analysis, human judgment for complex decisions), resolving the contradiction between speed and accuracy
2Measurement precision
If manual data labeling is performed to identify damaged regions in images, then labeling accuracy can be ensured through human expertise, but the time and effort required increases linearly with data volume
Solution Approach 1:
The patent implements self-service through automated image analysis algorithms that perform preliminary damage detection and generate candidate damage regions without human intervention. The system automatically processes images, identifies potential damage areas, and creates initial labels that can be quickly reviewed and corrected by humans, dramatically reducing the time and effort required for data labeling while maintaining high accuracy through automated correction of algorithm errors
3Productivity
If advanced analytics techniques are deployed to automate damage detection, then processing speed and productivity improve, but output reliability decreases resulting in more work for human domain experts
Solution Approach 1:
The patent incorporates feedback mechanisms where the results of automated damage detection are fed back to human experts for verification and correction. The system learns from these corrections and continuously improves its algorithms. This feedback loop ensures that while automated processing maintains high speed, the reliability of outputs improves over time, reducing rather than increasing the workload on human experts
Solution Approach 2:
The patent employs dynamic adjustment of processing thresholds and parameters based on the specific characteristics of each image and claim type. The system adapts its sensitivity and detection criteria dynamically, allowing it to maintain high productivity across diverse claim types while ensuring reliable outputs by adjusting parameters to match the specific context of each case
Data Source
AI summary
An image analysis (“IA”) computer system for analyzing images of hail damage includes at least one processor in communication with at least one memory device. The at least one processor is programmed to: (i) store a damage prediction model associated with a rooftop, wherein the damage prediction model utilizes an artificial intelligence algorithm; (ii) display, to a user, an image of a rooftop; (iii) receive, from the user, a request to analyze damage to the rooftop; (iv) apply, by the at least one processor, the damage prediction model to the image, the damage prediction model outputting a plurality of damage prediction locations of the rooftop in relation to the image; and/or (v) display, by the at least one processor, an overlay box at each of the plurality of damage prediction locations, the overlay box being a virtual object overlaid onto the image for labeling the damage prediction locations.