Collision Reconstruction Engine for Faster Claim 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 delays for providers.
Innovation Solution
A computing system that optimizes claim processes using artificial intelligence and machine learning to streamline information gathering, automate negotiations, and reduce processing time by leveraging large language models (LLMs) and real-time communications with users and call center representatives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual procedures are used for claim processing, then processing accuracy can be maintained through human judgment, but processing time increases and productivity decreases
Solution Approach 1:
The patent replaces manual mechanical claim processing with an automated computing system that uses machine learning models, large language models, and computer vision algorithms to process claims, extract information from images and documents, and generate assessments automatically, thereby eliminating the trade-off between human judgment accuracy and processing speed
Solution Approach 2:
The patent introduces AI intermediaries including machine learning models, large language models, and computer vision systems that act as mediators between claim data and processing decisions, enabling automated accurate assessment without requiring direct human intervention for each claim while maintaining high processing standards
2Device complexity
If traditional claim processing methods are used, then system complexity remains manageable, but resource utilization is suboptimal and processing delays occur
Solution Approach 1:
The patent segments the claim processing system into specialized modular components including document processing modules, image analysis modules, machine learning inference modules, and communication modules, each handling specific tasks independently, which manages overall system complexity while enabling parallel processing and high productivity
Solution Approach 2:
The patent creates a universal computing system that can handle multiple types of claims, various document formats, different image types, and diverse communication channels through a single integrated platform using AI models that adapt to different processing needs, thereby improving resource utilization and productivity without proportionally increasing complexity
3Reliability
If comprehensive information gathering is performed manually, then data accuracy improves, but time consumption increases significantly
Solution Approach 1:
The patent replaces manual information gathering with automated AI systems including large language models that extract and verify data from documents, computer vision systems that analyze images, and machine learning models that assess claims, achieving comprehensive accurate data collection instantaneously without manual time investment
Solution Approach 2:
The patent implements continuous automated information gathering where AI systems continuously process documents, analyze images, verify data, and update claim assessments in real-time throughout the claims lifecycle, eliminating gaps and delays associated with manual periodic reviews while maintaining data accuracy
Data Source
AI summary
Embodiments include a computing system, computing device, computer-implemented method, and non-transitory computer readable for a collision reconstruction engine. Embodiments provide for obtaining an information corpus corresponding to a vehicle incident involving a vehicle over one or more sessions with a user. Based on the information corpus, embodiments generate a vehicle incident simulation of the vehicle incident.