ML Claim Processing Platform for Shared Economy Insights

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Solution Overview

Problem

Existing claim processing systems for commercial and shared economy services are inefficient, leading to excessive resource expenditure due to suboptimal processing of claims, which are not adequately addressed by current technologies.

Innovation Solution

A computing platform utilizing machine learning to process claims, including training a model with historical data, enabling automated claim processing, decision-making, and providing insights, while integrating with user devices and third-party services for seamless claim management.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional manual claim processing methods are used, then human judgment and flexibility are maintained, but processing time and resource expenditure increase significantly

Engineering Contradiction:

Improveclaim processing speedVSAvoidtime for manual review

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The machine learning model enables the claim processing system to autonomously evaluate claims, determine coverage, and calculate settlements without requiring manual intervention. The system self-services by automatically analyzing claim data against historical patterns and policy rules, thereby eliminating the time-consuming manual review process while maintaining processing accuracy.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical manual processing system with an automated machine learning-based system. The ML model substitutes human adjusters in evaluating claim severity, determining coverage eligibility, and calculating settlement amounts, thereby dramatically reducing processing time and resource expenditure while maintaining consistent and objective decision-making.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If machine learning automation is implemented, then processing efficiency improves, but system complexity increases

Engineering Contradiction:

Improveclaim processing efficiencyVSAvoidsystem architecture complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The machine learning model serves as an intermediary layer between the claim intake system and the settlement execution system. It processes raw claim data, applies learned patterns from historical data, and outputs processed claims ready for execution. This intermediary function simplifies the overall system architecture by encapsulating complex decision logic within the ML model rather than requiring complex manual processing workflows.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system transforms claim processing from a manual qualitative assessment to an automated quantitative analysis. By changing the parameters of processing from human judgment to machine-based numerical evaluation, the system achieves efficiency while managing complexity through standardized, reproducible computational operations rather than complex procedural workflows.

Inventive Principle:

Principle #35Parameter changes

3Loss of energy

If automated processing is used for all claims, then resource expenditure decreases, but accuracy and nuance in complex cases may be compromised

Engineering Contradiction:

Improveresource expenditureVSAvoidclaim processing accuracy

Core Design Contradiction:

Loss of energyVSReliability

Solution Approach 1:

The machine learning model continuously learns from historical claim data and processing outcomes, providing feedback that refines its accuracy over time. This feedback mechanism allows the system to improve its decision-making reliability by adapting to new patterns and correcting errors, thereby maintaining high accuracy while processing claims automatically and reducing resource expenditure.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs preliminary automated processing for the majority of claims that fit established patterns, reserving manual review only for complex or atypical cases. This preliminary action approach maximizes automated processing efficiency and resource savings while ensuring that complex claims receive human attention only when necessary, thereby maintaining overall processing accuracy.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20260094215A1Commercial claim processing platform using machine learning to generate shared economy insights

Publication Date: 2026.04.02 ALLSTATE INSURANCE COMPANY

AI summary

Aspects of the disclosure relate to using machine learning methods to produce commercial and shared economy insights. A computing platform may receive historical claim processing information. The computing platform may train a machine learning model using the historical claim processing information, which may configure the machine learning model to output new claim processing information based on claim information. The computer platform may receive a new claim, including claim information, and may process the new claim using the machine learning model, which may result in the new claim processing information. The computing platform may send, to a user computing device, the new claim processing information and one or more commands directing the user computing device to display the new claim processing information, which may cause the user computing device to display the new claim processing information.