Guided Vehicle Image Capture for Faster Claim Processing
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Solution Overview
Problem
Existing Software as a Service (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 optimizes claim processing by using artificial intelligence and machine learning to streamline information gathering, automate negotiations, and reduce computing time through large language models (LLMs), integrating with call center representatives and users for efficient data collection and analysis.
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 claim processing time is excessive and productivity is low
Solution Approach 1:
The patent replaces manual mechanical claim processing procedures with an automated computing system that uses computer vision to analyze images, natural language processing to extract information from documents, and machine learning models to determine claim outcomes. This substitution of mechanical human operations with automated computational processes directly resolves the contradiction by dramatically reducing processing time while maintaining or improving processing quality.
Solution Approach 2:
The system enables self-service claim processing where the computing system automatically performs image analysis, document processing, and claim determination without requiring manual intervention at each step. The system serves itself by autonomously navigating the claim processing workflow, extracting necessary information from various sources, and making decisions based on analyzed data, thereby eliminating time-consuming manual procedures.
2Measurement precision
If computational resources are heavily utilized for advanced processing, then claim processing accuracy improves, but computational resource consumption increases
Solution Approach 1:
The patent applies partial action by selectively using different levels of computational processing for different aspects of claim processing. The system uses computer vision for image analysis, natural language processing for document extraction, and machine learning for decision-making, but only to the extent necessary for each specific claim type and complexity level. This avoids excessive computational resource consumption while maintaining sufficient accuracy for reliable claim processing.
Solution Approach 2:
The claim processing system is segmented into multiple specialized modules: image analysis module, document processing module, claim determination module, and communication module. Each module performs specific functions with appropriate computational intensity, allowing the system to distribute computational resources efficiently across different processing tasks rather than applying heavy computation uniformly to all aspects of claim processing.
Data Source
AI summary
A computing system can process incident information corresponding to a vehicle incident involving a vehicle of a user. The system can initiate a guided content capture process with a user to capture images of a vehicle of the user. The system generates a sequential set of vehicle outlines of the vehicle on an image interface presented on a computing device of the user to implement the guided content capture process. The system performs computer vision on image data captured by a camera of the computing device to determine when the vehicle is aligned with a particular vehicle outline of the sequential set of vehicle outlines.