Virtual Environment User Matching via Social Interaction Monitoring
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Solution Overview
Problem
Existing virtual environments face challenges in matching users for group activities, particularly for younger audiences, as current methods are either too complex or do not effectively utilize social interactions, leading to suboptimal user experience and satisfaction.
Innovation Solution
A system that monitors social interactions in virtual environments to programmatically match users based on their recent interactions and eligibility criteria, allowing for more personalized and effective user matching for group activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user matching is based on complex criteria and algorithms, then matching precision may improve, but device complexity and ease of operation deteriorate
Solution Approach 1:
The patent extracts and monitors specific social interaction metrics (chat frequency, voice calls, gestures, symbols) from the complex virtual environment, isolating the relevant factors for matching while ignoring unrelated system complexities. This extraction approach maintains high matching precision by focusing only on essential social interaction data.
Solution Approach 2:
The system automatically monitors social interactions and generates matches without requiring manual user configuration or complex user-side processing. The automated monitoring and matching algorithms operate independently, reducing the operational burden on users while maintaining sophisticated matching capabilities.
2Measurement precision
If user matching considers multiple social interaction factors, then matching precision improves, but device complexity increases
Solution Approach 1:
The monitoring system is designed to track multiple types of social interactions (chatting, voice calls, gestures, symbols) using a unified multi-functional approach. This universal monitoring framework captures diverse interaction types through consistent methodologies, improving matching precision without proportionally increasing system complexity.
Solution Approach 2:
The system monitors and weights different social interaction parameters (frequency of chatting, voice calls, gestures, symbols) dynamically. By adjusting the significance of each parameter based on observed patterns, the system achieves high matching precision while managing complexity through adaptive parameter management rather than fixed complex rules.
3Productivity
If automated matching algorithms are used, then productivity of user matching improves, but ease of operation deteriorates
Solution Approach 1:
The automated matching system operates autonomously by monitoring social interactions and generating matches without requiring user intervention. This self-service approach dramatically improves matching productivity and speed while maintaining simplicity for users, as the system handles all complex operations automatically without requiring user-side configuration or control.
Solution Approach 2:
The system continuously monitors social interactions and uses this feedback to dynamically adjust and improve matching decisions. This real-time feedback mechanism enables high-speed automated matching that adapts to changing user behaviors, maintaining both productivity and operational simplicity through data-driven automatic adjustments.
Data Source
AI summary
Techniques are disclosed to facilitate user matching in a virtual environment. Social interactions of a first user in the virtual environment are monitored. A request to participate in a desired activity is received from the first user. A set of users currently eligible to participate in the first desired activity is retrieved. A match is programmatically generated between the first user and at least a second user of the retrieved set of users, based on the monitored social interactions. The first user and the second user participate in the desired activity in the virtual environment.