Local 3D Printed Component Replacement for Faster Damage Claims
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Solution Overview
Problem
Insurance claim processing for 3D printed structures is inefficient due to manual adjuster visits, expertise gaps, and time-consuming subrogation processes, which increase costs and delays.
Innovation Solution
A system and method that utilizes machine learning to analyze aerial images for 3D printed structures, determines damage, and routes repair requests to local service partners, enabling automated subrogation and direct component replacement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual adjuster visits are used to assess damage, then expertise verification is possible, but processing time increases and costs rise
Solution Approach 1:
The patent replaces the mechanical system of manual adjuster visits with an automated image analysis system using machine learning. The system processes aerial images to detect and assess damage to 3D printed structures, eliminating the need for physical adjuster travel while maintaining assessment accuracy through automated detection algorithms.
Solution Approach 2:
The system enables self-service by allowing the damage assessment process to occur automatically without human intervention. The machine learning model independently analyzes images, identifies damage, and generates assessments, freeing adjusters from routine assessment tasks and reducing processing time.
2Reliability
If adjusters travel to insured properties, then on-site assessment is possible, but overhead costs increase
Solution Approach 1:
The patent substitutes the mechanical process of adjuster travel with automated remote sensing technology. Aerial images are captured and processed through machine learning algorithms that perform damage verification without requiring physical presence, thereby eliminating vehicle costs, time, and other travel-related overhead expenses.
Solution Approach 2:
The system creates a digital copy of the insured property through aerial imaging. This visual replica allows adjusters to perform assessments remotely by analyzing the captured images, eliminating the need for physical travel while maintaining the ability to verify damage conditions.
3Reliability
If specialized specialists are contacted for specific damage types, then assessment expertise is ensured, but follow-up visits and processing time increase
Solution Approach 1:
The patent implements a universal machine learning system capable of assessing multiple types of damage across different 3D printed structures. The single system performs functions that previously required multiple specialized specialists, including detecting print failures, material defects, and environmental damage, thereby eliminating the need for sequential specialist consultations and follow-up visits.
Data Source
AI summary
A system and method for verifying and managing claims involving three-dimensional (3D) print structures and components thereof is disclosed. The system employs a verification model that is trained to detect whether a given structure includes a 3D printed portion, as well as the type of 3D print mixture used and/or its manufacturer. This information can be used to reduce the likelihood of fraud by insured persons following damage to their insured structures. In some embodiments, the information can also be used to determine whether a subrogation clause should be triggered, and the 3D print replacement part be requested for printing from the manufacturer. In another example, the damaged 3D printed part can be printed by a local service partner in or near the region where the damaged structure is located, and installation performed by a trusted contractor.