Neural Network Home Identification via Visual Feature Analysis
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Solution Overview
Problem
The process of identifying homes matching a user's preferences is time-consuming and inefficient, as real estate agents often rely on manual searches with imperfect knowledge, and online databases can be complex, lack transparency, and contain outdated information, making it difficult for users to find suitable homes within their desired geographic area and price range.
Innovation Solution
A method and system that uses neural networks trained with landmark data to analyze images of homes, generating confidence scores to identify and classify homes based on key features, allowing users to quickly find visually similar homes within their area of interest and price range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual search processes are used by real estate agents, then they can access real estate databases, but the search process becomes time-consuming and inefficient
Solution Approach 1:
The patent replaces manual mechanical search processes with an automated image recognition system using neural networks. The system automatically analyzes property images, extracts visual features, and identifies matching homes without requiring manual intervention from real estate agents, thereby dramatically improving productivity and eliminating time loss associated with manual searching.
Solution Approach 2:
The system enables self-service by allowing users to upload property images and automatically receive matching home recommendations without requiring real estate agent intervention. The neural network system autonomously performs image analysis, feature extraction, and home matching, empowering users to conduct their own property searches efficiently.
2Loss of information
If real estate databases are used, then home information can be accessed, but the databases lack transparency and contain outdated information
Solution Approach 1:
The system performs preliminary action by conducting real-time image analysis and feature extraction when users upload property images. Instead of relying on pre-stored database information that may be outdated, the system processes current images to identify and match properties, ensuring that the information provided is up-to-date and accurate reflecting the current state of properties.
Solution Approach 2:
The patent introduces an intermediary image recognition system between the user and the real estate database. This intermediary layer analyzes property images directly and identifies matching homes based on visual features rather than relying solely on database records, thereby providing more accurate and transparent information that reflects actual property characteristics.
3Measurement precision
If users visit multiple home showings to find suitable homes, then they can evaluate properties firsthand, but the process is time-consuming given limited showings per day
Solution Approach 1:
The system creates visual copies of property characteristics by analyzing and extracting key visual features from uploaded home images. These extracted features serve as digital representations of property attributes, allowing the system to identify and recommend similar homes without requiring users to physically visit each property, thereby enabling evaluation of many more homes per day while maintaining matching accuracy.
Solution Approach 2:
The patent transitions the home evaluation process from the physical dimension (visiting homes in person) to the digital dimension (comparing image features). By converting physical property characteristics into digital image data and using neural networks to analyze visual similarities, the system enables users to evaluate homes remotely and efficiently, dramatically increasing the number of properties that can be assessed daily.
4Adaptability or versatility
If traditional real estate search methods are used, then agents can provide home descriptions, but they cannot efficiently find visually similar homes within specific geographic and price constraints
Solution Approach 1:
The system fundamentally changes the search parameter from text-based descriptions to image-based visual features. By using neural networks to extract and compare visual characteristics such as architectural style, exterior appearance, and property features from images, the system enables users to find visually similar homes that match their preferences, while the automated process maintains simplicity and ease of operation.
Data Source
AI summary
A method of identifying a home to facilitate a real estate transaction includes generating a set of neural network objects wherein each object includes a neural network model, an address, and an image, training the neural network model in each object to emit a confidence score by a neural network analyzing the image to identify key features, receiving an example address and example image, selecting a set of trained neural network objects wherein each object in the set of trained neural network objects includes a trained neural network model, landmark address, and landmark image, generating a set of confidence scores by applying the example image to the trained neural network model in each object, generating based on the set of confidence scores a result set, transmitting the result set to a user, and displaying the result in the user device.