Home Score Modeling Using Insurance Claim Data

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Solution Overview

Problem

Existing systems for determining and displaying home scores and subscores in insurance contexts are inefficient and inaccurate, lacking effective integration of insurance claim data to enhance score accuracy.

Innovation Solution

A computer-implemented method and system that generates and modifies home scores and subscores using insurance claim data, incorporating machine learning techniques to improve accuracy, and displays the scores through various devices and platforms.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional home scoring systems are used without insurance claim data integration, then the system complexity remains low, but the measurement precision of home scores deteriorates

Engineering Contradiction:

Improvehome score accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent combines traditional home scoring systems with insurance claim data processing capabilities into a unified system. The scoring system integrates multiple data sources including property characteristics, historical claims data, and risk factors to generate comprehensive home scores, thereby improving measurement precision through data fusion.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The patent introduces machine learning models as intermediary components that process and analyze insurance claim data to generate risk assessments. These models act as mediators between raw claims data and final home score calculations, transforming unstructured data into meaningful scoring inputs without requiring direct complex integration of all data sources.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If insurance claim data is integrated into home scoring, then the reliability of home scores improves, but the loss of information processing increases

Engineering Contradiction:

Improvehome score reliabilityVSAvoiddata processing load

Core Design Contradiction:

ReliabilityVSLoss of information

Solution Approach 1:

The patent extracts only the most relevant features and patterns from extensive insurance claim data using machine learning models. Instead of processing all raw claims data, the system identifies and extracts key risk indicators, claim frequencies, and severity metrics that directly impact home scoring, thereby maintaining reliability while reducing information processing burden.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent transforms raw insurance claim data into standardized parameters and metrics suitable for home scoring. By changing the parameter representation from detailed claims narratives to aggregated statistical measures (e.g., claim frequency, average severity, risk scores), the system maintains reliability through accurate risk assessment while reducing the volume of information that needs to be processed.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20260017723A1Home Scores Determined from Claims Data

Publication Date: 2026.01.15 STATE FARM MUTAL AUTOMOBILE INSURANCE COMPANY

AI summary

The following relates generally to generating and/or modifying home scores based upon insurance claim data. In some embodiments, one or more processors: generate an overall home score for a subject property; receive insurance claim data for an insurance claim corresponding to the subject property, wherein the insurance claim data includes (i) a monetary amount of the insurance claim, and/or (ii) a category of the insurance claim; modify the overall home score based upon the insurance claim data; and/or display the overall home score.