AI Prompting for Faster Insurance Incident Summarization

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Solution Overview

Problem

Existing Software as a Service (SaaS) providers face inefficiencies in computational resource usage and time-consuming manual processes in claim handling, particularly in insurance claim processing, leading to frustration for policy holders and delays for providers.

Innovation Solution

A computing system that optimizes claim processes through artificial intelligence prompts, machine learning, and deep-learning techniques to streamline information gathering, automate negotiations, and reduce computing time by leveraging large language models (LLMs) for efficient communication and data processing.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual processes are used for claim handling, then accuracy and human judgment can be applied, but processing time is excessive and productivity is low

Engineering Contradiction:

Improveclaim processing speedVSAvoidmanual processing time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical processes with automated AI systems. Large language models and machine learning algorithms substitute human claim handlers for tasks including initial claim assessment, document review, fraud detection, and settlement negotiation, dramatically reducing processing time while maintaining accuracy through automated decision-support systems

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service capabilities where AI models automatically perform claim triage, prioritize claims based on complexity and risk, and conduct preliminary negotiations without human intervention. The automated system serves itself by continuously learning from outcomes and improving its own performance through feedback loops

Inventive Principle:

Principle #25Self-service

2Use of energy by moving object

If traditional computing resources are used, then system simplicity is maintained, but computational efficiency is insufficient for AI workloads

Engineering Contradiction:

Improvecomputational resource efficiencyVSAvoidcomputing system architecture

Core Design Contradiction:

Use of energy by moving objectVSDevice complexity

Solution Approach 1:

The computing system is segmented into specialized components: prompt engineering modules that optimize AI interactions, separate machine learning inference engines, dedicated large language model processing units, and distributed cloud computing resources. This segmentation allows each component to be optimized independently for its specific function, improving overall computational efficiency

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent implements universal AI processing platforms that can handle multiple claim types, languages, and complexity levels through a single system architecture. The large language models are fine-tuned to perform diverse functions including document classification, entity extraction, sentiment analysis, and negotiation strategy generation, eliminating the need for separate systems for each task

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20260050775A1Automated artificial intelligence prompting and incident summarization

Publication Date: 2026.02.19 ASSURED INSURANCE TECH INC

AI summary

Embodiments include a computing system, computing device, non-transitory computer readable medium, and computer-implemented method for automating artificial intelligence (AI) prompting and incident summarization. Embodiments provide for automatically generating AI prompts based at least in part on incident information, transmitting the AI prompts to a remote computing system on which a large language model (LLM) is implemented, and providing an AI summary of an incident.