Home Score Generation Using Machine Learning Evaluation Models
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Solution Overview
Problem
Current methods for providing information to homeowners, especially when moving between different locations or performing maintenance, are inefficient, lack security and privacy, and fail to provide essential details for informed decisions.
Innovation Solution
A computer-implemented method using machine learning models to evaluate and generate a home score, which includes retrieving and analyzing home data to determine relevant factors, and providing recommendations for modifications, while ensuring security and privacy through anonymization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
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 intermediary system that processes and analyzes home data before presenting information to homeowners. This intermediary layer (the evaluation system) efficiently processes multiple data sources and provides personalized recommendations while maintaining security through anonymized data handling and controlled information disclosure.
Solution Approach 2:
The patent replaces conventional manual information delivery methods with an automated machine learning system. The ML models automatically evaluate home data, generate scores, and provide recommendations without human intervention, significantly improving efficiency while maintaining security through algorithmic data processing and privacy-preserving techniques.
2Loss of information
If detailed home data is collected and analyzed, then important features and hazards are identified, but privacy and security risks increase
Solution Approach 1:
The patent extracts only the essential features and hazards needed for home evaluation from the complete home data set. The ML models identify and extract critical information (such as maintenance needs, safety hazards, and property features) while discarding unnecessary personal data, thus maintaining evaluation completeness while reducing privacy risks.
Solution Approach 2:
The patent applies different processing qualities to different data elements. Sensitive personal information is anonymized or aggregated, while critical safety and maintenance data is analyzed in detail. This local differentiation allows comprehensive hazard identification while protecting privacy through selective data handling approaches.
3Measurement precision
If machine learning models are used to evaluate home data, then evaluation accuracy and personalization improve, but system complexity increases
Solution Approach 1:
The patent divides the complex evaluation system into multiple specialized ML models, each handling specific aspects of home evaluation (e.g., maintenance needs, safety hazards, energy efficiency). This segmentation allows each model to be optimized for its specific function, improving overall accuracy while managing complexity through modular architecture.
Solution Approach 2:
The patent develops universal ML models that can handle multiple types of home evaluation tasks simultaneously. The same core evaluation framework processes diverse data types (sensor data, maintenance records, environmental information) to generate comprehensive home scores, reducing system complexity through multi-functional processing capabilities.
Data Source
AI summary
Systems and methods are described for evaluating and gamifying maintenance for a property by a user. The method may include: (1) retrieving home data for a first property; (2) determining, using a first trained machine learning evaluation model, one or more home score factors based upon at least the home data; (3) generating, based upon the one or more home score factors, a home score for the first property; (4) determining, using a second trained machine learning evaluation model, that one or more additional properties are similar to the first property; (5) retrieving past hazard data associated with a second property of the one or more additional properties; and (6) generating, based upon at least the past hazard data and at least one of the one or more home score factors, a learning module for the first property.