Computer Vision Model for Aerial Property Damage Prediction

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Solution Overview

Problem

Enterprise organizations face inefficiencies and inaccuracies in predicting property damage due to time-consuming and error-prone manual input of customer information, leading to delayed processing and potential inaccuracies in damage assessment.

Innovation Solution

A computing platform that receives historical images and loss data to train a computer vision model, enabling the direct analysis of new images to output likelihood of damage scores, thereby streamlining the prediction of property damage and reducing manual input requirements.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual input of customer information is used to predict property damage, then data can be collected, but processing time increases and errors occur

Engineering Contradiction:

Improveaccuracy of damage predictionVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces the mechanical manual data input system with an automated computer vision system that processes aerial images. The system uses machine learning models to automatically extract property characteristics and assess damage risks from images, eliminating the need for manual customer input while improving both speed and accuracy of damage prediction

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system creates digital copies of property data through aerial imaging and processing. Instead of manually collecting information, the system captures visual copies of properties via aerial images and automatically extracts relevant data, significantly reducing processing time while maintaining data accuracy

Inventive Principle:

Principle #26Copying

2Ease of operation

If manual input of customer information is required, then data can be obtained, but customer experience deteriorates and processing is delayed

Engineering Contradiction:

Improvecustomer experienceVSAvoidprocessing time

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The system enables self-service by allowing the automated computer vision system to independently collect and process property data without requiring customer intervention. The aerial images and machine learning models automatically extract necessary information, freeing customers from manual data entry while accelerating processing

Inventive Principle:

Principle #25Self-service

3Reliability

If manual data input is used, then information can be collected, but data accuracy decreases due to human error

Engineering Contradiction:

Improvereliability of damage assessmentVSAvoidaccuracy of input data

Core Design Contradiction:

ReliabilityVSMeasurement precision

Solution Approach 1:

The patent replaces manual data collection with automated computer vision technology. Machine learning models process aerial images to extract property characteristics, eliminating human error in data entry while improving the reliability and accuracy of damage assessment data

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12051114B2Computer vision methods for loss prediction and asset evaluation based on aerial images

Publication Date: 2024.07.30 ALLSTATE INSURANCE COMPANY

AI summary

Aspects of the disclosure relate to using computer vision methods to forecast damage. A computing platform may receive historical images comprising aerial images of residential properties and historical loss data corresponding to the residential properties. Using the historical images and the historical loss data, the computing platform may train a computer vision model, which may configure the computer vision model to output loss prediction information directly from an image. The computing platform may receive a new image corresponding to a particular residential property, and may analyze the new image, using the computer vision model, which may directly result in a likelihood of damage score. Based on the likelihood of damage score, the computing platform may send likelihood of damage information and one or more commands directing a user device to display the likelihood of damage information, which may cause the user device to display the likelihood of damage information.