Home Score Generation Using Machine Learning for Property Evaluation
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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 fail to provide essential details necessary for informed decisions, often missing important features and risks associated with properties.
Innovation Solution
A computer-implemented method using machine learning models to evaluate and generate a home score based on property and user data, incorporating factors like weather risks, construction codes, and environmental data, while ensuring security and privacy through anonymization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional methods are used to provide information to homeowners, then information delivery is simple and direct, but the information is inefficient, lacks security, and misses important details
Solution Approach 1:
The patent segments property information into multiple categories including property characteristics, environmental factors, maintenance requirements, and risk assessments. This segmentation allows comprehensive information delivery without overwhelming the user, addressing the contradiction by organizing detailed information into manageable segments.
Solution Approach 2:
The patent introduces an intermediary system that processes and filters property data before presenting it to homeowners. This intermediary layer ensures security and privacy while delivering comprehensive information, resolving the contradiction between information completeness and system complexity.
2Measurement precision
If comprehensive property data is collected and analyzed, then accurate home scores and recommendations are generated, but security and privacy risks increase
Solution Approach 1:
The patent applies different processing and protection levels to different types of data. Sensitive personal information receives enhanced protection while still allowing accurate home scoring. This local quality approach maintains precision while mitigating privacy risks through differentiated data handling.
Solution Approach 2:
The patent implements preliminary security measures and privacy protections before data is fully processed. By establishing security frameworks in advance, the system can collect comprehensive data for accurate scoring while pre-emptively addressing privacy and security concerns.
3Adaptability or versatility
If detailed maintenance information and risk factors are provided to users, then users can make informed decisions, but the information delivery becomes inefficient and complex
Solution Approach 1:
The patent dynamically adapts information delivery based on user needs, property type, and context. The system adjusts the level of detail and presentation format to maintain ease of operation while providing comprehensive decision-making support when required.
Solution Approach 2:
The patent incorporates feedback mechanisms that allow users to indicate their information needs and preferences. Based on this feedback, the system adjusts information delivery efficiency, providing detailed maintenance information and risk factors only when relevant to the user's specific situation.
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 training data captured by one or more sensors associated with one or more properties or one or more users; (2) retrieving at least one of home data for a property or user data for a user; (3) determining, using a machine learning model, home score factors based upon at least one of the home data or the user data, wherein the machine learning model is trained with the training data; (4) generating, based upon the home score factors, a home score for the property; (5) determining, based upon at least one of the home data or the user data, difference factors between the property and a previous property associated with the user; and (6) causing a user device to display the home score and difference factors.