Injury Assistance AI for Faster, Accurate 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 delays for providers.
Innovation Solution
A computing system that optimizes claim processing through artificial intelligence (AI) and machine learning (ML) techniques, including large language models (LLMs), dynamic content generation, and real-time communication with users and call center representatives to streamline information gathering, damage assessment, and settlement negotiation, reducing computational and communication overhead.
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 significantly and productivity decreases
Solution Approach 1:
The patent replaces manual mechanical review processes with an automated machine learning model that analyzes injury claims. The model uses natural language processing to evaluate claim descriptions, determine injury types, and assign compensation amounts automatically, eliminating the need for manual mechanical review while maintaining consistency and accuracy.
Solution Approach 2:
The system enables self-service claim processing where the machine learning model autonomously evaluates claims without requiring human intervention for routine cases. The model independently processes claim descriptions, determines injury classifications, and generates compensation recommendations, allowing the system to serve itself for standard claim scenarios.
2Reliability
If comprehensive manual review of each claim is performed, then claim accuracy improves, but resource consumption and operational complexity increase
Solution Approach 1:
The patent replaces complex manual review systems with a streamlined machine learning model that automatically processes claims. The model uses natural language processing to extract relevant information from claim descriptions and applies predefined criteria to determine injury types and compensation, reducing system complexity while maintaining reliability.
Solution Approach 2:
The system uses template-based claim description formats where users select from predefined injury types and scenarios. This copying approach standardizes input data structure, making it easier for the machine learning model to process claims consistently without requiring complex manual analysis of varied free-text descriptions.
3Measurement precision
If detailed manual assessment of each claim is conducted, then measurement precision improves, but time consumption and loss of time increase
Solution Approach 1:
The patent replaces time-consuming manual assessment with automated machine learning evaluation. The model quickly analyzes claim descriptions using natural language processing, matches injuries to predefined categories, and determines compensation amounts in seconds, maintaining accuracy while dramatically reducing processing time compared to manual review.
Solution Approach 2:
The system performs preliminary classification of claims by injury type before detailed compensation calculation. The machine learning model first identifies the injury category based on the claim description, then applies appropriate compensation criteria for that category, streamlining the assessment process while maintaining precision through structured evaluation steps.
Data Source
AI summary
A computing system can train an injury assistance model to provide individualized injury assistance to users involved in injury events. The system can receive incident data identifying an injury to a user. The system may then initiate the injury assistance model on the incident data to provide individualized injury assistance to the user while the user recovers from the injury.