Parametric Analytics Engine for ML Triggered Coverage Updates

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Solution Overview

Problem

Traditional methods for protecting against parametric events, such as weather-related damages, are vulnerable to uncertainty and change factors, rendering coverage insufficient or unnecessary, and lack accuracy and security.

Innovation Solution

A parametric engine utilizing a distributed ledger and machine learning algorithms to analyze weather data, determine trigger events, and execute smart contracts for secure and efficient coverage updates.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional methods are used for protecting against parametric events, then coverage can be provided, but accuracy and security are insufficient due to uncertainty and change factors

Engineering Contradiction:

Improvecoverage accuracyVSAvoiddata accuracy

Core Design Contradiction:

ReliabilityVSLoss of information

Solution Approach 1:

The patent replaces traditional mechanical/manual insurance assessment systems with an automated parametric engine that uses machine learning algorithms and distributed ledgers to objectively determine coverage triggers and losses, eliminating human judgment variability and improving accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces a distributed ledger as an intermediary between weather data sources and insurance coverage determination, creating a trusted, immutable record that both parties can verify, thereby resolving information asymmetry and improving security

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If traditional coverage methods are used, then protection is provided, but the system is vulnerable to fraud and uncertainty

Engineering Contradiction:

Improvecoverage securityVSAvoidfraud vulnerability

Core Design Contradiction:

ReliabilityVSObject-affected harmful factors

Solution Approach 1:

The patent replaces traditional insurance claim assessment with automated machine learning models that objectively evaluate parametric triggers, removing human discretion that could be exploited for fraud while maintaining fair coverage determination

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The distributed ledger acts as a trusted intermediary that records and verifies weather data and coverage triggers in an immutable manner, preventing fraud by ensuring that neither the insurer nor insured can manipulate the data or outcome

Inventive Principle:

Principle #24Intermediary (Mediator)

3Productivity

If manual assessment methods are used, then coverage can be determined, but efficiency and speed are reduced

Engineering Contradiction:

Improvecoverage determination speedVSAvoidassessment time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent implements a self-service system where the parametric engine automatically determines coverage triggers and losses using pre-programmed algorithms and machine learning models, eliminating the need for manual claims assessment and significantly accelerating the process

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent performs preliminary actions by pre-configuring coverage parameters, thresholds, and machine learning models before events occur, enabling automatic real-time assessment when triggers happen without requiring post-event manual analysis

Inventive Principle:

Principle #10Preliminary action

4Adaptability or versatility

If simple coverage models are used, then ease of operation is maintained, but adaptability to changing conditions is reduced

Engineering Contradiction:

Improvecoverage adaptabilityVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent uses parameter changes by allowing the machine learning models and coverage thresholds to be dynamically adjusted based on new data and changing conditions, enabling the system to adapt to different scenarios while maintaining a unified operational interface

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20260030684A1Parametric engine to implement methods using parametric analytics

Publication Date: 2026.01.29 STATE FARM MUTAL AUTOMOBILE INSURANCE COMPANY

AI summary

Systems and methods are described for performing analysis of parametric events. The method may include: (1) measuring, by one or more processors, an initial composition for an area via one or more sensors associated with the area; (2) using a trained machine learning algorithm, a likelihood of a trigger activation for a parametric event for a user, wherein the calculating includes: (a) predicting a total composition fluctuation for the area, (b) calculating a predicted composition change from the initial composition for the area based upon the total composition fluctuation, and (c) calculating the likelihood of the trigger activation, wherein the trigger activation occurs when the predicted composition change from the initial composition for the area reaches a predetermined threshold value; and (3) an estimated loss for the user based at least upon the likelihood of the trigger activation.