Neural Network User Pairing Evaluation for Multiplayer Games
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Solution Overview
Problem
Current methods for pairing users in online multi-user applications, such as multiplayer games, are inadequate for games that require complementary playing styles or complex player interactions, as they rely on crude criteria like connection speed and in-game ranking, failing to ensure optimal user satisfaction.
Innovation Solution
A user-pairing evaluation method utilizing an artificial neural network that takes various player parameters, including language, ranking, reputation, and gameplay metrics, to calculate a pairing fitness value, determining the likelihood of user satisfaction and selecting suitable pairings based on these values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If crude pairing criteria such as connection speed and in-game ranking are used, then the pairing process is simple and fast, but the user satisfaction and compatibility between players are insufficient
Solution Approach 1:
The patent transforms the pairing evaluation from using simple crude criteria to using a comprehensive set of parameters including communication-based metrics (tone of voice, language, humor detection) and gameplay metrics. This parameter transformation enables the system to achieve both operational simplicity through automated evaluation and high measurement precision in user satisfaction prediction.
Solution Approach 2:
The patent introduces an intermediary evaluation system that analyzes communication patterns and gameplay data to predict compatibility. This intermediary layer processes raw data from players' interactions and translates it into compatibility scores, bridging the gap between simple pairing operations and accurate satisfaction prediction without requiring direct complex player-to-player matching logic.
2Measurement precision
If comprehensive player parameters including communication patterns and gameplay metrics are analyzed, then user satisfaction prediction accuracy improves, but the evaluation complexity and processing requirements increase
Solution Approach 1:
The patent replaces complex manual or rule-based evaluation mechanics with an artificial intelligence-based system that automatically analyzes communication patterns and gameplay metrics. The AI system processes multiple parameters including tone of voice, language compatibility, humor detection, and gameplay statistics, transforming complex data analysis into automated machine learning predictions that improve accuracy while managing system complexity through algorithmic processing.
Solution Approach 2:
The evaluation system performs self-service by automatically collecting, processing, and analyzing player data without requiring manual intervention. The system autonomously evaluates communication patterns, gameplay metrics, and compatibility factors, generating pairing recommendations through self-contained AI processing that reduces the need for external complexity management.
3Manufacturing precision
If multiple parameters such as language, reputation, and gameplay metrics are considered, then the quality of player matching improves, but the data processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and storing player parameters including communication patterns, language preferences, reputation scores, and gameplay metrics before actual pairing occurs. The system prepares evaluation data in advance and uses pre-trained AI models to quickly generate compatibility scores when pairing is needed, reducing real-time processing requirements while maintaining high matching quality through comprehensive parameter analysis.
Data Source
AI summary
A method of evaluating a pairing between a first player and a second player of a multiplayer application comprises the steps of obtaining one or more parameter values for each of the first player and the second player, inputting the parameter values for each of the first player and the second player into an artificial neural network, obtaining at least a first pairing fitness value output from the artificial neural network, and selecting to pair the first player and the second player in dependence upon a criterion responsive to the each pairing fitness value.