Non-binary Profile Matching via Synthetic Representation
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Solution Overview
Problem
Current dating and relationship applications rely on binary acceptance/rejection mechanisms, which are inadequate for providing a clear match signal, as they are limited by visual criteria and influenced by other factors like age, location, and religion, resulting in a 'needle-in-a-haystack' scenario.
Innovation Solution
A system and method for non-binary profile mapping using machine learning networks to process user profile data, including images and characteristic information, to create synthetic profile representations, allowing for more nuanced matching based on user preferences and characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If binary acceptance/rejection visual component is used for matching, then the system is simple to operate, but the match signal becomes unclear and unreliable
Solution Approach 1:
The patent segments the matching process into multiple independent components: visual component analysis, attribute-based filtering, and scoring mechanisms. This allows the system to move beyond simple binary decisions by breaking down the complex matching task into manageable segments that can be evaluated separately and combined to produce a reliable match signal.
Solution Approach 2:
The patent introduces additional dimensions beyond binary acceptance/rejection by implementing multi-criteria evaluation including visual attributes, demographic attributes, and behavioral signals. This dimensional expansion transforms the matching system from a single-axis binary decision to a multi-dimensional evaluation space, improving match signal reliability while maintaining operational simplicity through structured presentation.
2Measurement precision
If multiple criteria beyond appearance are considered, then matching accuracy improves, but the signal becomes muddied and harder to interpret
Solution Approach 1:
The patent introduces an intermediary scoring mechanism that translates multiple complex criteria into a unified, interpretable match score. This intermediary layer processes visual attributes, demographic information, and behavioral signals through weighted evaluation, producing a consolidated match probability that maintains signal clarity while incorporating diverse matching criteria for improved accuracy.
Solution Approach 2:
The patent dynamically adjusts parameter weights based on user preferences and context. By changing the relative importance of different criteria (visual vs. demographic vs. behavioral) according to user settings and interaction patterns, the system maintains measurement precision across multiple criteria while preserving signal interpretability through adaptive parameter optimization.
3Productivity
If users must mutually accept each other in binary fashion, then the system is easy to operate, but it creates a needle-in-a-haystack scenario reducing productivity
Solution Approach 1:
The patent performs preliminary filtering and pre-matching actions by automatically evaluating user profiles against multiple criteria before presentation. The system pre-computes compatibility scores, filters incompatible matches, and prioritizes promising connections, reducing the haystack size before user interaction. This preliminary action improves matching efficiency while maintaining ease of operation by presenting only relevant options to users.
Solution Approach 2:
The patent implements feedback mechanisms where user interactions (swipes, messages, profile views) are continuously analyzed to refine matching algorithms. This feedback loop allows the system to learn from actual user behavior and improve productivity over time by adjusting criteria weights and recommendation strategies, while maintaining simple operation through automated adaptation rather than requiring users to manually configure complex parameters.
Data Source
AI summary
A system and method for providing non-binary profile mapping of individuals according to the present invention is disclosed. The system includes a memory having instructions stored thereon and a processor configured to execute the instructions on the memory to cause the electronic apparatus to perform the method. The method receives user profile data from an external user, the user profile data comprises captured media having a user image and user characteristic information data, pre-processes a user image into training, testing, and updating learning networks, stores processed user profile data into a database, receive user synthetic profile data to create a synthetic profile representation, adjust the synthetic profile representation in response to user input, generates a first set of matching results from processing the synthetic profile representation against user profile data from the database using the learning networks, and provides the first set of matching results for selection.