Property Selection Decision Support With Vector-Based Matching
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Solution Overview
Problem
The process of matching home buyers with suitable homes is complicated by financial constraints, location considerations, and the availability of desirable features, exacerbated by limited inventory and outdated information in real estate search tools, leading to a need for improved decision-making support.
Innovation Solution
A decision support tool utilizing a computer system with a controller assembly, memory assembly, and user interface, incorporating a content database and large language models to provide personalized property recommendations through customizable filters, interactive maps, and real-time video imagery, along with machine learning algorithms to analyze user preferences and property data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional real estate search tools are used, then basic property listing is provided, but information is outdated and matching accuracy is poor
Solution Approach 1:
The system pre-calculates and stores comprehensive property vectors including spatial, temporal, material, risk, and financial variables before user queries are submitted. This preliminary preparation ensures that when users search for properties, the matching algorithm can immediately compare user preferences against pre-processed property data, improving both matching accuracy and information currency without real-time computation delays.
Solution Approach 2:
The patent transforms property characteristics into a multi-dimensional vector space representation, converting qualitative property features into quantifiable parameters across five dimensions (spatial, temporal, material, risk, financial). This parameter transformation enables precise mathematical comparison between user preferences and property attributes, significantly improving matching accuracy while the vectors are continuously updated to maintain information currency.
2Adaptability or versatility
If multiple property criteria are considered, then comprehensive evaluation is achieved, but decision complexity increases
Solution Approach 1:
The patent resolves decision complexity by projecting multiple property criteria into a unified vector space where each property and user preference is represented as a vector with components across five dimensions (spatial, temporal, material, risk, financial). This dimensional transformation allows the system to comprehensively evaluate multiple criteria simultaneously through vector operations rather than requiring users to manually weigh and compare each individual criterion, thus maintaining evaluation comprehensiveness while simplifying the user decision process.
3Ease of operation
If manual property search and comparison is performed, then user control is maintained, but time consumption increases
Solution Approach 1:
The system implements self-service by automatically performing property vector construction, user preference analysis, and matching optimization without requiring manual user intervention. The machine learning algorithms autonomously compare user preferences against the property database, rank properties by compatibility, and present personalized recommendations. This automation maintains user control through configurable parameters and adjustable preferences while dramatically reducing the time users would otherwise spend on manual property search and comparison.
4Loss of information
If real-time data processing is implemented, then information currency is improved, but computational load increases
Solution Approach 1:
The system applies preliminary action by pre-computing and storing property vectors with all relevant spatial, temporal, material, risk, and financial variables before user queries arrive. This pre-processing allows the system to maintain information currency through periodic updates of the property database without requiring intensive real-time computation during user interactions. The machine learning models perform lightweight vector comparisons rather than full re-analysis, significantly reducing computational load while keeping information current.
Data Source
AI summary
Decision support tool for real estate selection having at least one computer system and user interface. A content database of real estate properties includes spatial, temporal, material, risk, and financial variables for a set of at least one real estate property from the real estate properties, wherein the at least one real estate property can include a set of at least one real estate property with at least one second real estate property. At least one software program is disposed on at least one computer system designed to calculate user vectors wherein at least one machine learning program is designed to build the set of at least one real estate property from the real estate properties. The user interface is designed to receive inputs and present outputs wherein users may create user sets of at least one selected real estate property from the real estate properties.