Dynamic Player Selection Using Social Graph Data
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Solution Overview
Problem
Conventional multiplayer games often match players with unfamiliar players, hindering camaraderie and teamwork development due to constant changes in player selection based on skill level, which can lead to a lack of community building and social interaction.
Innovation Solution
Implementing a dynamic player selection system that utilizes social graph data to match players based on shared gaming experiences, skill level, and relationship metrics, allowing for recurring matches with familiar players and fostering a sense of community.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If players are matched based on skill level from a pool of available players, then players can play with others of similar skill level even as skill changes, but players will frequently be matched with unfamiliar players making it difficult to connect and develop camaraderie
Solution Approach 1:
The patent applies dynamics by transitioning from static skill-based matching to dynamic social graph-based matching. The system continuously updates player relationships and social connections, allowing the matching criteria to adapt over time. This enables players to be matched with familiar teammates who have developed camaraderie, while still maintaining skill level appropriateness through the evolving social graph data.
Solution Approach 2:
The patent introduces social graph data as an intermediary between skill level matching and player selection. This intermediary layer captures relationship metrics, interaction history, and social connections, allowing the system to balance both skill compatibility and player familiarity. The social graph acts as a mediator that enables camaraderie development while preserving skill-based matching integrity.
2Reliability
If players are constantly matched with different players based on skill level, then skill-appropriate matching is maintained, but players are prevented from learning to work as a team and balance strengths and weaknesses
Solution Approach 1:
The patent applies preliminary action by using social graph data to pre-establish player relationships and team compositions before matching occurs. The system proactively identifies players who have previously collaborated successfully and are likely to continue working well together, allowing teams to form around established synergies. This enables players to learn teamwork and balance strengths over multiple sessions with the same teammates.
Solution Approach 2:
The patent ensures continuity of useful action by maintaining persistent team compositions across multiple game sessions. Rather than constantly shuffling players, the system continues to match players who have demonstrated effective teamwork, allowing them to refine their collaborative skills. The social graph preserves relationship continuity, enabling players to build upon existing team dynamics rather than starting fresh each session.
3Ease of operation
If social graph data is used to match players with familiar players, then camaraderie and community building are enhanced, but matching players with similar skill levels may become more difficult
Solution Approach 1:
The patent applies parameter changes by adjusting the weighting and importance of different matching criteria based on social graph data. When familiar players are available, the system increases the weight of relationship metrics while maintaining skill level constraints. When familiar players are unavailable, the system shifts weight back to skill-based matching. This dynamic parameter adjustment allows the system to optimize for camaraderie when possible while preserving skill matching reliability when necessary.
Data Source
AI summary
The dynamic selection of players for a gaming session, or users for an application session, can be based at least in part upon player relationship data. Social graph data can be maintained that indicates game experience that particular users have shared in prior game sessions. This relationship data can be used with other selection criteria, such as similar skill level or complementary play characteristics, to select players for a gaming session that are familiar to a specific player, enabling the player to have recurring experience with similar players in order to build relationships with those players and help to develop a sense of community within the game, which can improve the user experience and thus utilization of the game. Other factors can be considered as well, such as whether players have connected or blocked each other through a social network or gaming environment.