Social Media Matrix Matching Engine for Customized Interpersonal Connections
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Solution Overview
Problem
Conventional interpersonal matching applications fail to accurately address the true needs and desires of users, lacking the ability to target specific mismatches in profile categories, resulting in ineffective matching.
Innovation Solution
A networked interpersonal matching system and method that utilizes a social media matrix database, allowing users to input a match matrix indicating desired matches, mismatches, or degrees of separation in various categories, enabling the engine to link users based on customized preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional matching algorithms use simple scoring based on like-answers in profile categories, then the matching process is computationally efficient and easy to implement, but the matching accuracy and user satisfaction deteriorate because the true needs and desires of users are not captured
Solution Approach 1:
The patent segments the matching process into multiple independent modules: profile data collection, match matrix definition, database searching, and result generation. Each module handles a specific aspect of the matching process, allowing complex functionality to be achieved through coordinated simple components. The match matrix itself is segmented into multiple categories (geographic location, occupation, interests, etc.) that can be independently configured and evaluated.
Solution Approach 2:
The patent introduces a new dimension to traditional matching by adding the match matrix concept with multiple categories and weighting factors. Instead of simple binary matching, the system evaluates profiles across multiple dimensions (geographic separation, occupational mismatch, interest alignment) with customizable weights, transforming a one-dimensional scoring problem into a multi-dimensional evaluation space.
2Adaptability or versatility
If conventional apps provide standardized matching without customization, then the app is easy to operate and requires minimal user input, but the ability to target specific mismatches in profile categories deteriorates, preventing users from finding their true needs
Solution Approach 1:
The system performs preliminary action by pre-defining the match matrix structure with multiple categories and weighting options before the actual matching process. Users can configure their preferences in advance, specifying which categories are important (geographic location, occupation, interests) and assigning weights to each. This preliminary configuration enables highly customized matching without complicating the actual matching operation.
Solution Approach 2:
The system enables users to self-configure their own matching criteria through the match matrix. Users independently define which profile categories matter to them and assign appropriate weights, allowing each user to customize the matching process to their specific needs without requiring complex system configuration or expert intervention.
3Reliability
If conventional matching systems assume higher matching scores indicate better matches, then the system is simple to implement, but it fails to account for cases where users desire mismatches in specific categories, reducing matching effectiveness
Solution Approach 1:
The patent changes the fundamental parameter of matching from seeking similarity (high scores) to seeking specific patterns of match and mismatch across different categories. The system allows users to specify that high scores are desired in some categories (interests, values) while low scores or specific separations are desired in others (geographic location, occupation). This transforms the matching algorithm from a simple maximization problem into a constrained optimization problem with multiple objective functions.
Data Source
AI summary
A method for providing a social media matrix is disclosed. Leveraging the social media matrix between two or more users, matches are realized using one or more different categories based on profile information provided by the users.