Server Video Call Matching Using Facial and Personal Data
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Solution Overview
Problem
Existing video call mediation systems do not effectively match users for optimal satisfaction during video calls, lacking a method to predict and improve user satisfaction based on personal and facial characteristics.
Innovation Solution
A server system that mediates video calls by calculating correlations between user satisfaction indices, personal information, and facial characteristics to predict and choose the most suitable match for new video call sessions, using deep learning algorithms to enhance match satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If video call mediation is provided between terminals without relationship, then user connection capability is improved, but match satisfaction deteriorates
Solution Approach 1:
The system performs preliminary actions by collecting personal information and facial characteristic data before video calls occur. Satisfaction information is gathered from past call sessions in advance, allowing the server to pre-calculate correlations and prepare matching strategies before new calls are requested, thereby improving match satisfaction while maintaining connection capability.
Solution Approach 2:
The system implements feedback mechanisms by receiving satisfaction information from users after video call sessions. This feedback is used to recalculate and update correlations between personal information, facial characteristics, and satisfaction levels. The updated correlations are then used to improve future match selections, creating a continuous improvement loop that enhances match satisfaction over time.
2Device complexity
If random video call matching is used, then system complexity is reduced, but match satisfaction deteriorates
Solution Approach 1:
The system performs preliminary data collection and correlation calculation actions. Personal information and facial characteristic data are gathered in advance, and correlations are pre-calculated based on past satisfaction information. This preliminary processing enables more sophisticated matching without adding real-time complexity during actual call sessions.
Solution Approach 2:
The system replaces simple random matching mechanisms with a data-driven correlation-based selection mechanism. Instead of using straightforward random algorithms, the system substitutes a more complex but effective approach that uses calculated correlations between personal information, facial characteristics, and satisfaction data to determine match selections, thereby improving satisfaction while managing system complexity through structured data processing.
3Measurement precision
If personal information and facial characteristics are collected, then match precision is improved, but information processing complexity increases
Solution Approach 1:
The system segments the information processing into distinct modules: personal information collection, facial characteristic extraction, satisfaction information gathering, and correlation calculation. By dividing the complex information processing into separate, manageable segments, the system can handle each type of data independently and combine the results through structured correlation analysis, improving match precision while making the overall processing architecture more manageable.
Solution Approach 2:
The server acts as an intermediary that processes and correlates personal information, facial characteristics, and satisfaction data. Rather than requiring terminals to perform complex analysis locally, the server serves as a central intermediary that receives data from terminals, performs correlation calculations, and returns match selections. This intermediary approach distributes complexity appropriately across the system architecture.
Data Source
AI summary
An operating method of a server, comprising, mediating a video call session between a first terminal and a second terminal; receiving satisfaction information on the video call session of a user of the first terminal from the first terminal; preparing combination of a specific type of personal information about a user of the first terminal and facial characteristic information about a user of the second terminal; calculating a correlation between the combination and the satisfaction information corresponding to the combination; receiving a video call mediating request from a third terminal and from a plurality of candidate terminals, and predicting satisfaction information of a user of the third terminal with respect to each of the candidate terminals; and choosing a fourth terminal among the candidate terminals.