Generative Gameplay Content Models for Cultural Localization
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Solution Overview
Problem
Existing video games often suffer from repetitive gameplay and require extensive development time to personalize content for different cultures or countries, leading to player disengagement and inefficiencies.
Innovation Solution
A computing system utilizing machine-learning models to generate personalized and localized gameplay content based on player data, evolving gameplay through training, and localizing content for individual players, ensuring cultural appropriateness and compliance with norms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If extensive development time is spent hard-coding variations in dialogue or imagery for different cultures, then cultural accessibility and localization quality are improved, but development efficiency and time-to-market deteriorate
Solution Approach 1:
The patent replaces the mechanical system of hard-coding cultural variations with an AI-based generative system. Instead of manually programming different dialogue and imagery for various cultures, the system uses machine learning models to automatically generate culturally appropriate content based on player data and cultural parameters, thereby improving both adaptability and development efficiency
Solution Approach 2:
The system enables self-service localization where the AI model automatically adapts game content to different cultures without requiring extensive manual intervention from developers. The model learns from training data and autonomously generates culturally appropriate dialogue, imagery, and gameplay elements, reducing the development burden while maintaining high quality
2Quantity of substance
If game content is made repetitive to cover all possible aspects of storyline or game world, then completeness of gameplay is improved, but player engagement deteriorates due to repetition
Solution Approach 1:
The patent introduces dynamics to static game content by using AI-generated variations that adapt to each player's preferences and playstyle. Instead of fixed repetitive content, the system dynamically generates unique dialogue, quests, and imagery based on real-time player data, ensuring both completeness and engagement by tailoring content to individual players
Solution Approach 2:
The system applies local quality by customizing specific aspects of game content for each player based on their unique characteristics and preferences. Rather than uniform repetitive content, the AI generates localized variations in dialogue, imagery, and gameplay elements that are specifically tailored to each player's cultural background, preferences, and play history, thereby maintaining engagement while providing comprehensive gameplay
3Adaptability or versatility
If AI models are trained on extensive player data and global game data, then content personalization and cultural relevance are improved, but system complexity and computational resources required deteriorate
Solution Approach 1:
The patent segments the training process into manageable components, training AI models on specific subsets of data (player data, global game data, cultural parameters) separately and then combining the results. This segmentation allows the system to handle complex personalization tasks by breaking them down into smaller, more manageable training modules, reducing overall system complexity while maintaining high personalization quality
Data Source
AI summary
The present disclosure provides a system for generating gameplay content by a generative modeling system. The system can generate gameplay content via one or more machine-learning models trained using game and player data. The system can add content generated by the one or more machine-learning models to the game and player data and retrain the models using the generated content. The system can also localize generated content based on player locations and language preferences.