AI-Assisted In-Game Communication for Relevant Real-Time Messaging
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Solution Overview
Problem
Conventional video game systems struggle with timely and relevant communication processing, particularly for gesture-based messages, leading to latency and potential confusion or adverse effects on player experience due to delayed or irrelevant communications.
Innovation Solution
An AI-assisted communication system that includes an input processor, message identification engine, message interpretation engine, message relevancy assessment engine, and communication conveyance engine, utilizing AI model components to identify, interpret, assess relevance, and compose messages in real-time based on the game state and context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional message processing systems are used in video games, then system complexity is reduced, but message processing speed and relevance accuracy deteriorate
Solution Approach 1:
The message processing system is divided into distinct functional modules: message identification engine, message interpretation engine, message relevancy assessment engine, and communication conveyance engine. Each module performs a specific task in the processing pipeline, enabling parallel processing and reducing overall latency while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
AI model components are introduced as intermediary elements between the input processor and output processor. These AI models act as mediators that automatically perform message identification, interpretation, and relevancy assessment, bridging the gap between raw input data and meaningful game communications without requiring complex manual processing logic.
2Measurement precision
If comprehensive message analysis is performed, then message relevancy accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary message identification and interpretation before relevancy assessment. By pre-processing messages to extract key information and meaning upfront, the system prepares data structures that enable faster relevancy checking against game state, reducing the time required for comprehensive analysis while maintaining accuracy.
Solution Approach 2:
The message processing pipeline operates continuously with multiple AI model components working in sequence without idle periods. The system maintains continuous analysis of incoming messages through the entire pipeline (identification → interpretation → relevancy assessment → conveyance), ensuring no processing delays while sustaining high relevancy accuracy through uninterrupted analytical operations.
3Productivity
If AI model components are integrated for real-time processing, then player engagement is enhanced, but computational resource consumption increases
Solution Approach 1:
The system applies partial AI processing by focusing AI model components only on identifying and processing messages that require intelligent interpretation and relevancy assessment. Not all game communications undergo the full AI processing pipeline - only those where AI-added value is necessary - thereby reducing overall computational resource consumption while maintaining high player engagement for critical communications.
Solution Approach 2:
Different levels of processing intensity are applied to different message types based on their importance and context. High-priority messages receiving full AI analysis while lower-priority communications use simplified processing paths. This local differentiation of processing quality optimizes computational resource allocation, concentrating resources where they most directly enhance player engagement.
Data Source
AI summary
Input data is received that includes a message for communication to a target player of a video game. A portion of the input data defining the message is automatically identified using a first artificial intelligence model component. A meaning of the message defined by the portion of the input data is automatically determined using a second artificial intelligence model component. A third artificial intelligence model component is used to automatically determine, based on the meaning of the message, whether or not the message is relevant to a current game state and game context of the target player. A communication to the target player that conveys the meaning of the message is automatically composed through use of a fourth artificial intelligence model component, when the message is determined to be relevant to the current game state and game context of the target player. The communication is delivered to the target player.