AI Model Training for Predictable Game Input Data
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Solution Overview
Problem
In video games with millions of players, the increase in network traffic due to unpredictable human input data leads to higher network latency and inefficient data transfer, making it challenging to manage network traffic effectively during gameplay.
Innovation Solution
The system trains an artificial intelligence (AI) model by analyzing user interaction patterns within the game, allowing the AI to learn from user actions and generate more predictable input data, reducing the amount of data transferred between client devices and servers, and enabling custom matches with AI models for improved gaming experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If human players control avatars in a multiplayer game, then the gaming experience is enhanced through unpredictable human behavior, but network traffic increases and network latency increases due to unpredictable human input data
Solution Approach 1:
The patent creates AI models that copy and learn from human player behaviors through analysis of interaction patterns. These AI models then generate predictable input data that replicates human-like gameplay without requiring actual human input, thereby maintaining gaming experience while reducing network latency
Solution Approach 2:
The patent introduces AI models as an intermediary between human players and the game system. Instead of directly processing unpredictable human inputs in real-time, the system uses pre-trained AI models to generate predictable gameplay data, acting as a mediator that eliminates the need for continuous real-time human input transmission
2Adaptability or versatility
If human players control avatars in a multiplayer game, then diverse gameplay patterns are achieved, but data transfer efficiency decreases due to unpredictable human input data
Solution Approach 1:
The patent performs preliminary training of AI models by analyzing human interaction patterns before actual gameplay. This pre-processing of human behavior data allows the AI models to generate predictable gameplay patterns without requiring real-time human input, thereby improving data transfer efficiency while maintaining gameplay diversity
Solution Approach 2:
The patent creates AI models that copy human player behaviors through pattern analysis. These models reproduce diverse gameplay patterns that were originally generated by human players, but in a predictable and efficient manner that improves data transfer efficiency
3Loss of time
If AI models are trained using user interaction patterns, then network latency is reduced by minimizing input data transfer, but the complexity of the training system increases
Solution Approach 1:
The patent extracts interaction patterns from human player data and uses them to train AI models. By separating the training phase (where complexity is concentrated) from the gameplay phase (where simplicity is achieved), the system reduces network latency during actual gameplay while managing training complexity through efficient data extraction and model training processes
Data Source
AI summary
A method for training a character for a game is described. The method includes facilitating a display of one or more scenes of the game. The one or more scenes include the character and virtual objects. The method further includes receiving input data for controlling the character by a user to interact with the virtual objects and analyzing the input data to identify interaction patterns for the character in the one or more scenes. The interaction patterns define inputs to train an artificial intelligence (AI) model associated with a user account of the user. The method includes enabling the character to interact with a new scene based on the AI model. The method includes tracking the interaction with the new scene by the character to perform additional training of the AI model.