Server-Based Social Group Continuity in Modular Virtual Environments
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Solution Overview
Problem
Maintaining continuity of social groupings in modular online virtual environments is challenging, especially before groupings are fully established, as users switch between different online environments.
Innovation Solution
A server-based system that analyzes user behavior to classify users into social groups and selects appropriate instances of modules within the virtual environment to ensure that members of the same social group remain together, even when transitioning between zones, by identifying users who exhibit similar actions or interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users are allowed to switch between different online environments freely, then user accessibility and flexibility are improved, but social group continuity deteriorates
Solution Approach 1:
The system performs preliminary classification of users into social groups based on their behavior patterns before they switch environments. By analyzing user actions, interactions, and temporal patterns in advance, the system pre-establishes social groupings that can be maintained across environment transitions, thus preserving social continuity while allowing user flexibility.
Solution Approach 2:
The system continuously monitors user behavior and provides feedback by adjusting instance selections to maintain social group integrity. When users interact within an environment, the system analyzes these interactions and uses the feedback to determine appropriate instance assignments that keep social groups together, thereby maintaining continuity despite environment switching.
2Stability of the object's composition
If behavior analysis is performed to classify users into social groups, then social group continuity is improved, but system complexity increases
Solution Approach 1:
The system enables users to self-classify into social groups through their natural behavior patterns rather than requiring explicit user input or complex manual configuration. Users simply interact within the environment, and the system automatically analyzes these interactions to classify them into social groups, reducing the perceived complexity for users while maintaining social continuity.
Solution Approach 2:
The system replaces complex manual social group management mechanisms with automated behavior analysis. Instead of requiring users to explicitly declare friendships or manually organize groups, the system uses computational analysis of user actions, interactions, and temporal patterns to automatically classify users into social groups, thereby reducing system complexity from the user perspective.
3Quantity of substance
If multiple parallel instances of modules are provided, then user capacity accommodation is improved, but difficulty in maintaining social groups worsens
Solution Approach 1:
The system introduces an intermediary instance selection mechanism that acts as a mediator between multiple parallel instances and social groups. This intermediary analyzes social group compositions and selectively assigns instances to maintain group integrity, thereby managing the complexity of multiple instances while preserving social continuity and accommodating user capacity.
4Measurement precision
If explicit friendship declaration is required, then social group definition accuracy is improved, but ease of operation worsens
Solution Approach 1:
The system enables social groups to form automatically through user behavior patterns without requiring explicit friendship declarations. Users simply interact within the environment, and the system self-service classifies them into social groups based on analyzed behaviors, thereby maintaining ease of operation while achieving sufficient social group definition accuracy.
Solution Approach 2:
The system replaces the mechanical process of explicit friendship declaration with automated behavior analysis. Instead of requiring users to manually declare friendships, the system uses computational analysis of user interactions, actions, and temporal patterns to automatically define social groups, thereby improving ease of operation while maintaining reasonable accuracy in social group definition.
Data Source
AI summary
In a modular on-line virtual environment, in which each module of the on-line virtual environment has a plurality of parallel instances each able to host a limited number of users, a server arranged to administer the modular on-line virtual environment comprises a network communications arrangement operable to receive data representative of the actions of users within an instance of a first module of the modular on-line virtual environment, and to transmit to each user within that instance of that module data representative of the actions of each other user within that instance of that module, behavior analysis means operable to analyze user behavior within that instance of the first module, in which if the behavior of two or more users satisfies a predetermined criterion then the two or more users are classified as belonging to a social group comprising the two or more users, module instance selection means operable to select an instance of a second module for a user when that user moves within the modular on-line virtual environment from the first module to a second module, in which the module instance selection means is operable to select an instance of the second module that has the capacity to accommodate the greatest number of the common social group once one member of that social group moves within the modular on-line virtual environment from the first module to the second module and the server is operable to place a subsequent respective member of the social group in that same selected instance of the second module if that member moves within the modular on-line virtual environment from the first module to the second module.