Virtual Presence Prediction for MMOG Social Coordination
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Solution Overview
Problem
Establishing and maintaining online social relationships in massively multiplayer online games (MMOGs) is challenging due to differences in players' real-world schedules and the lack of mechanisms to predict when avatars are virtually present, making it difficult for players to coordinate interactions.
Innovation Solution
The technology accumulates virtual presence information to create temporal profiles for entities within a persistent virtual environment, using general least-squares-fit analysis to generate coefficient vectors that predict future presence and compatibility, allowing for the recommendation of compatible entities and scheduling of interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If players coordinate interactions manually through in-game chat or email, then online social relationships can be established, but the process is time-consuming and inefficient due to lack of predictive information
Solution Approach 1:
The system performs preliminary analysis of player presence patterns and schedules before coordination is needed. By pre-processing presence data and predicting future availability, the system eliminates the need for time-consuming manual back-and-forth coordination, directly resolving the contradiction between relationship establishment efficiency and time consumption
Solution Approach 2:
The invention introduces an intermediary coordination system that acts as a mediator between players. This system collects presence information, predicts availability, and suggests optimal interaction times, replacing direct manual coordination and significantly reducing the time required to establish social relationships
2Measurement precision
If the system tracks and analyzes player presence patterns continuously, then prediction accuracy improves, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the complex prediction problem into manageable components: data collection module, pattern analysis module, and prediction module. Each component handles a specific aspect of the analysis, making the overall system more tractable and maintainable while achieving high prediction accuracy through specialized processing at each stage
Solution Approach 2:
The system automatically collects presence data and performs analysis without requiring manual input or configuration. The automated data collection and self-adjusting algorithms reduce operational complexity while maintaining high prediction accuracy through continuous learning from observed patterns
Data Source
AI summary
Apparatus, methods, and computer program products are disclosed that accesses coefficient vectors each of which represent an entity within a persistent virtual environment. Each accessed coefficient vector includes coefficients having coefficient values related to the represented entity. The coefficients represent a temporal profile of the entity in the persistent virtual environment. This aspect assigns a coefficient weight to at least one of the coefficients. and partitions the coefficient vectors responsive to the coefficient weight and at least one of the coefficients of each of the coefficient vectors into clusters. Finally, the technology presents a recommendation responsive to the clusters. Furthermore, a compatibility metric can be determined by comparing weighted coefficient vectors of two entities, and the compatibility metric can also be presented.