Service Provider Ranking Using Incident-Based Vehicle Damage Assessment
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Solution Overview
Problem
Existing SaaS providers face inefficiencies in claim processing, particularly in insurance claims, due to time-consuming manual procedures and suboptimal use of computational resources, leading to frustration for policy holders and inefficiencies for providers.
Innovation Solution
A computing system that utilizes artificial intelligence and machine learning to optimize claim processes, including guided content capture, dynamic scripting, and intelligent search, to streamline information gathering and automate negotiations, reducing processing time and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual procedures are used for claim processing, then claim processing can be performed with simple systems, but processing time and resource usage increase significantly
Solution Approach 1:
The patent replaces manual mechanical procedures with automated machine learning models and AI systems. Specifically, machine learning models automatically perform claim processing tasks such as document review, damage assessment, and settlement calculation, eliminating the need for manual intervention and significantly reducing processing time while maintaining accuracy.
Solution Approach 2:
The system enables self-service claim processing where the machine learning models autonomously handle claim evaluations without requiring manual oversight. The AI system independently processes claims, generates assessments, and recommends settlements, allowing the system to serve itself and reducing dependency on human operators for routine tasks.
2Measurement precision
If more computational resources are allocated to claim processing, then processing accuracy can be improved, but resource consumption and costs increase
Solution Approach 1:
The patent applies partial action by using machine learning models that process only the most relevant features and data points necessary for accurate claim assessment. Rather than analyzing every piece of available data, the system identifies and processes key information, achieving high accuracy while minimizing computational resource consumption and avoiding unnecessary processing overhead.
3Ease of operation
If traditional service provider selection methods are used, then simplicity is maintained, but service quality and user satisfaction decrease
Solution Approach 1:
The system implements feedback mechanisms where machine learning models continuously learn from claim outcomes, service provider performance data, and user satisfaction metrics. This feedback loop enables the system to automatically refine its service provider recommendations, improving matching quality over time while maintaining ease of operation through automated, data-driven selection processes.
Data Source
AI summary
A computing system can receive incident information corresponding to a vehicle incident involving a vehicle of a user, and execute a trained machine learning model on the incident data to determine damage to the vehicle from the vehicle incident and generate a list of service providers to facilitate in handling the vehicle.