Graph-Based Avatar Spawn Positioning for Immediate Engagement
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Solution Overview
Problem
Conventional initial player positioning in virtual experiences often results in lack of immediate engagement, lack of adjacent activity of interest, and absence of proximate players the user is acquainted with, leading to unsatisfactory user experience.
Innovation Solution
A method involving graph-based avatar embeddings is used to determine optimal spawn positions by analyzing social and activity engagement data, calculating weighted predictions, and placing avatars in the highest ranked positions based on these metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If predetermined or random initial positions are used, then positioning simplicity is maintained, but user engagement and interaction quality deteriorate
Solution Approach 1:
The system performs preliminary analysis of social graphs and activity data before player spawning to pre-determine optimal positions. This preliminary action enables the system to automatically select positions that maximize engagement potential without requiring complex real-time calculations during gameplay, thus maintaining operational simplicity while improving engagement reliability.
Solution Approach 2:
The positioning system serves itself by automatically analyzing social graphs, identifying relevant players and activities, and selecting optimal spawn positions without manual intervention. This self-service mechanism maintains the simplicity of the positioning process while achieving high engagement outcomes through automated intelligent selection.
2Reliability
If graph-based avatar embeddings and social analysis are implemented, then user engagement and immediate interaction are improved, but system complexity increases
Solution Approach 1:
The patent introduces graph-based avatar embeddings as an intermediary representation that translates complex social relationships and activity patterns into a standardized format. This intermediary enables the system to process and analyze player data more efficiently, reducing the computational complexity burden while maintaining high engagement reliability.
Solution Approach 2:
The system transforms complex social graph data into simplified numerical parameters through embedding techniques. By converting multi-dimensional social relationships into compressed vector representations, the system reduces processing complexity while preserving the essential information needed for optimal positioning and engagement prediction.
3Manufacturing precision
If analysis of social and activity engagement data is performed, then spawn position optimization is achieved, but processing time and computational resources increase
Solution Approach 1:
The system performs data analysis and embedding generation in advance during system initialization or idle periods, rather than in real-time during player spawning. This preliminary action pre-computes the necessary position recommendations, significantly reducing processing time during actual spawning operations while maintaining high optimization precision.
Solution Approach 2:
The system analyzes only the most relevant portions of social graph data and activity information necessary for position optimization, rather than processing all available data exhaustively. This selective analysis approach achieves sufficient optimization precision while dramatically reducing processing time and computational resource consumption.
Data Source
AI summary
Implementations described herein relate to methods, systems, and computer-readable media to automatically position players within a virtual experience. A method can include generating graph-based avatar embeddings for a plurality of avatars engaged with a virtual experience hosted on a virtual experience platform, identifying avatars of the plurality of avatars that a new avatar that is not currently in a virtual environment is likely to engage with, determining possible spawn positions for the new avatar in the virtual environment, the possible spawn positions based upon respective distances between the identified avatars, calculating a plurality of spawn positions, ranking the plurality of spawn positions based on a weighted prediction of engagement, and placing the new avatar in the virtual environment at the highest ranked spawn position.