Home Score Generation Using Machine Learning Analysis
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Solution Overview
Problem
Current methods for providing information to homeowners, especially when moving to new homes or performing maintenance, are inefficient, lack security and privacy, and fail to provide essential details necessary for informed decisions.
Innovation Solution
A computer-implemented method using machine learning to evaluate home telematics data, generate a home health indicator, and calculate a home score, which includes receiving and analyzing data on property components, user-reported information, and verifying data through third-party databases to provide secure and private recommendations for modifications and maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional methods are used to provide information to homeowners, then information delivery is simple and direct, but the methods are inefficient and lack security and privacy
Solution Approach 1:
The patent introduces a machine learning-based home scoring system as an intermediary between raw home data and homeowners. This intermediary processes telematics data, maintenance records, and property information through ML models to generate comprehensive home scores and recommendations, thereby improving security and privacy while maintaining efficiency
Solution Approach 2:
The patent replaces conventional manual information provision methods with an automated machine learning system. The ML model automatically analyzes home data, generates scores, and provides recommendations without human intervention, thereby eliminating security and privacy concerns associated with manual processes while significantly improving efficiency
2Loss of information
If comprehensive home data analysis is performed to provide detailed recommendations, then decision-making information is improved, but the complexity of the system increases
Solution Approach 1:
The patent segments the comprehensive home evaluation into distinct components: telematics data collection, maintenance record analysis, property information processing, and ML-based scoring. Each component handles a specific aspect of home assessment, reducing overall system complexity while maintaining comprehensive information coverage
Solution Approach 2:
The patent creates a multi-functional home scoring system that simultaneously performs data collection, analysis, scoring, and recommendation generation. The machine learning model serves multiple purposes by processing various data types and producing comprehensive home assessments, thereby managing complexity through functional integration
3Measurement precision
If machine learning models are used to analyze home telematics data, then the precision of home score generation is improved, but the computational resources and time required increase
Solution Approach 1:
The patent implements preliminary data processing steps that prepare and organize telematics data before ML model analysis. By pre-processing and structuring data in advance, the system reduces the computational burden on the ML model, thereby maintaining high scoring accuracy while decreasing processing time
Solution Approach 2:
The patent applies partial ML analysis by focusing the machine learning model on the most critical data features and aspects of home assessment. Rather than analyzing all data equally, the system identifies and processes key indicators, maintaining precision for the most important scoring factors while reducing overall computational time
Data Source
AI summary
Systems and methods are described for evaluating and analyzing home data to generate a home score. The method may include: (1) receiving home telematics data associated with a property for a user; (2) analyzing, according to a machine learning model, the home telematics data to determine age data for one or more components of the property, wherein the one or more components of the property are associated with a structural integrity of the property; (3) determining, based upon at least the determined age data, a home health indicator for the property, wherein the home health indicator for the property is associated with a determined age for the property; and (4) generating, based upon at least the determined home health indicator, a home score for the property.