Image-Based Housing Search System with Collaborative Preference Matching
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Solution Overview
Problem
Existing real estate search systems are inefficient as they focus on objective criteria like room numbers and square footage, ignoring subjective preferences such as internal style, layout, and finish-out. Additionally, these systems do not facilitate collaborative searches among multiple users, leading to slow and ineffective house searches.
Innovation Solution
A system that allows users to review and rate images of home features, facilitating collaborative searches based on user preferences. The system uses machine learning to analyze user preferences and behaviors, matching them to suitable housing options and incorporating external trends and data for more accurate recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing search systems use objective criteria filtering (room numbers, square footage), then search results are easily obtainable, but search relevance and user preference matching deteriorate
Solution Approach 1:
The system changes the search parameters from objective physical criteria (room numbers, square footage) to subjective preference-based parameters (image ratings, style preferences, layout preferences). Users rate images of home features on a scale, and the system uses these subjective parameter evaluations to match users with properties, thereby improving search relevance while maintaining ease of use through automated processing.
Solution Approach 2:
The patent replaces the mechanical filtering system (manual criteria-based filtering) with an automated machine learning system that processes image data and user preferences. The system uses computer vision and machine learning algorithms to analyze images of home features and automatically match them with user preferences, eliminating the need for manual criteria-based filtering while improving search accuracy.
2Adaptability or versatility
If existing systems focus on single-user selections, then individual user preferences are captured, but collaborative search capability deteriorates
Solution Approach 1:
The system merges multiple user preferences and ratings into a unified search results ranking. When multiple users collaborate on evaluating home images, their individual ratings and preferences are combined and aggregated to produce a comprehensive ranking of properties that reflects the group's collective preferences, thereby enabling effective collaborative searching while maintaining individual customization.
Solution Approach 2:
The system implements feedback mechanisms where users can rate and provide feedback on home images and properties. This feedback loop allows the system to learn from user preferences, adjust search results in real-time, and improve collaboration by incorporating multiple users' inputs into the final recommendations, thereby enhancing both customization and search efficiency.
3Device complexity
If existing search systems use traditional filtering methods, then search processing is simple, but search speed and efficiency deteriorate
Solution Approach 1:
The patent replaces traditional mechanical filtering methods with machine learning-based automated processing. The system uses computer vision algorithms to automatically analyze images of home features, extract relevant characteristics, and match them with user preferences. This substitution of manual filtering with automated AI processing significantly increases search speed and efficiency while reducing the complexity of manual processing steps.
Solution Approach 2:
The system performs preliminary processing of home images and features before the actual search occurs. By pre-processing images to extract key characteristics, styles, and features using machine learning, the system prepares data in advance that can be quickly matched against user preferences during the search process, thereby reducing search time and improving overall search efficiency without increasing processing complexity.
Data Source
AI summary
Disclosed are systems, methods, and media having at least one processor; and a non-transitory computer-readable medium storing instruction which, when executed by the at least one processor, cause the at least one processor to perform operations comprising receiving from one or more sources images related to a plurality of features of housing offerings, said images being associated with other images related to features of housing offerings; presenting to a first user an initial prompt and based on response to that initial prompt, presenting a plurality of selectable images related to a first feature of housing offerings; receiving from the first user input regarding the desirability of the images related to the first feature of housing offerings; and iteratively deriving, based on inputs regarding the desirability of the images related to one or more of the plurality of features of housing offerings, a hypothesis regarding a different feature of housing offerings; based at least in part on the hypothesis, presenting to the first user images related to the different feature of housing offerings; and receiving from the first user input regarding the desirability of the images related to the different feature of housing offerings.