Home Score Generation Using ML Anonymization
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Solution Overview
Problem
Conventional methods for presenting property information to potential homeowners and builders are inefficient, lack security and privacy, and fail to provide essential details necessary for informed decisions, especially in online or virtual contexts.
Innovation Solution
A computer-implemented method using machine learning models to evaluate and generate a home score by analyzing property data, including characteristics and hazard data, to provide a comprehensive and secure metric for properties, which can be displayed to users through mobile devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional methods are used to present property information, then information can be provided to potential homeowners, but the methods are inefficient and lack security and privacy
Solution Approach 1:
The patent introduces a machine learning model as an intermediary that processes property data and generates home scores. This intermediary layer enables efficient automated evaluation while maintaining security by anonymizing underlying data, thus resolving the contradiction between efficiency and security/privacy
Solution Approach 2:
The patent replaces conventional manual or semi-manual property evaluation methods with an automated machine learning-based system. This substitution dramatically improves efficiency through automated processing while enhancing security through algorithmic anonymization of sensitive data
2Loss of information
If conventional methods are used to present property information, then basic information can be conveyed, but important details necessary for informed decisions are lacking
Solution Approach 1:
The patent segments property evaluation into multiple distinct home score factors (fire hazard, safety, environmental, etc.), each evaluated by the machine learning model separately. This segmentation provides comprehensive detailed information while maintaining system manageability and clarity
Solution Approach 2:
The patent transforms complex property data into standardized home score factors with specific parameters (fire hazard score, safety score, environmental score). This parameter transformation enables comprehensive information delivery in a structured, easily comparable format that enhances decision-making
3Productivity
If a machine learning model is used to generate home scores, then efficient and secure evaluation is achieved, but the system requires processing and analysis capabilities
Solution Approach 1:
The patent uses the machine learning model to create a simplified representation (home score) that copies only the essential characteristics of complex property data. This copying approach enables efficient evaluation by working with compressed representations rather than full data sets, reducing processing complexity while maintaining evaluation accuracy
Data Source
AI summary
Systems and methods are described for analyzing home data to generate a home score. The method may include: retrieving home data for a first property; determining that the first property shares home characteristics with a second property; retrieving past hazard data associated with the second property; determining, based upon at least the home data and the past hazard data, one or more first home score factors, wherein the determining includes: analyzing, using a trained machine learning data evaluation model, the home data to determine home characteristic data for the first property, determining second home score factors for the second property based at least upon the past hazard data, and determining, based upon the home characteristic data and the second home score factors, the one or more first home score factors; and generating, based upon the one or more first home score factors, a home score for the first property.