Server Ranking Prediction for Casual Player Engagement
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Solution Overview
Problem
Existing multi-player games lack a ranking system that motivates casual players to participate, as most rely on single ranking criteria, making the ranking function unfamiliar and unengaging for them.
Innovation Solution
A server that communicates with multiple terminals, manages various ranking criteria, predicts player rankings, determines recommended criteria based on predicted ranks, and presents them to players, allowing for multiple participation options and automatic generation of new criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single ranking criterion is used, then the ranking system is simple to implement, but casual players are not motivated to participate
Solution Approach 1:
The patent segments the single ranking system into multiple independent ranking criteria (e.g., attack power, defense power, speed, skill variety). Each criterion operates independently with its own ranking table, allowing players to choose criteria that match their play style. This segmentation enables casual players to find rankings they can compete in without overwhelming complexity.
Solution Approach 2:
The patent adds multiple dimensions to the ranking system by introducing various ranking criteria beyond a single metric. Instead of one-dimensional ranking based on overall score, the system creates multi-dimensional rankings where players can be evaluated on different aspects of gameplay, providing more pathways for casual players to achieve recognition.
2Adaptability or versatility
If multiple ranking criteria are provided, then player motivation increases, but the complexity of the system increases
Solution Approach 1:
The patent implements a universal ranking management system that handles multiple ranking criteria through a common framework. The server apparatus provides multi-functional capabilities to create, manage, and display various ranking tables using the same underlying architecture, reducing the actual complexity despite supporting multiple criteria. The system universally applies the same processing logic across different ranking types.
Solution Approach 2:
The ranking system is designed to be dynamic, allowing ranking criteria to be added, removed, or modified without restructuring the entire system. The server can dynamically generate new ranking criteria based on player feedback and game updates, adapting the complexity level to match player needs while maintaining system manageability through flexible configuration.
3Manufacturing precision
If ranking criteria are manually created, then the quality of criteria is high, but the time and resources required are significant
Solution Approach 1:
The patent implements a self-service mechanism where the system automatically generates new ranking criteria based on analysis of player behavior data and game statistics. The server monitors gameplay patterns and autonomously creates relevant ranking criteria without requiring manual intervention from developers, maintaining high quality through data-driven insights while significantly reducing the time and resources needed for creation.
Solution Approach 2:
The system performs preliminary analysis of gameplay data to identify potential ranking criteria before they are formally implemented. By pre-processing game data and anticipating useful ranking dimensions, the system prepares candidate criteria in advance, reducing the actual creation time when new rankings are needed while ensuring they are based on meaningful gameplay patterns.
Data Source
AI summary
A ranking rank identification unit identifies a rank of a participant for each of a plurality of kinds of ranking criteria. A predicted rank calculation unit of a ranking criterion recommendation unit calculates predicted ranks for at least some of the plurality of kinds of ranking criteria for the case where a subject player participates in the individual ranking criteria. A recommended ranking criterion determination unit determines, as recommended ranking criteria, ranking criteria to be recommended to the subject player on the basis of the predicted ranks. A recommendation result presentation unit presents the recommended ranking criteria to the player terminal of the subject player.