AI Gameplay Query Response Using Video Context Recognition
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Solution Overview
Problem
Existing video game technologies struggle to provide real-time answers to player queries during gameplay due to processing constraints and player reluctance to engage in detailed searches or self-research, especially when in the midst of gameplay.
Innovation Solution
Implementing an AI model that captures video frames to determine the context of gameplay and generates responses to active or passive queries using artificial intelligence, allowing for context-aware query identification and response generation without requiring extensive user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search applications or walkthroughs are used to answer player queries, then complete information can be provided, but real-time response is not achieved due to processing constraints and player time limitations
Solution Approach 1:
The system performs preliminary actions by capturing video frames and determining gameplay context in advance, before the player actually submits a query. The AI model pre-processes visual information and maintains readiness to generate responses, eliminating the need for time-consuming processing after query submission. This allows the system to provide complete information rapidly when players need it during gameplay.
Solution Approach 2:
The patent introduces an AI model as an intermediary between the player's query and the game information system. This intermediary automatically analyzes video frames, determines context, and generates appropriate responses without requiring players to manually search through walkthroughs or wait for complex processing. The AI mediator bridges the gap between player needs and information delivery, achieving both completeness and real-time responsiveness.
2Loss of information
If players engage in detailed searches or self-research to answer queries, then comprehensive information can be obtained, but player time and attention are consumed during gameplay
Solution Approach 1:
The system implements self-service by automatically capturing video frames, analyzing gameplay context, and generating query responses without requiring player intervention. The AI model autonomously processes visual information and provides comprehensive answers directly to players, eliminating the need for them to engage in time-consuming searches or research during gameplay while ensuring complete information delivery.
3Loss of time
If context-aware AI processing is implemented to identify queries and generate responses, then real-time answers can be provided, but system complexity increases
Solution Approach 1:
The system extracts only the essential visual features from video frames that are relevant to gameplay context, rather than processing entire frames or all possible data. By selectively extracting key elements needed for query understanding and response generation, the system achieves real-time processing capability while managing complexity through focused, targeted analysis of only necessary visual information.
Data Source
AI summary
A method includes capturing one or more video frames of a game play of the video game controlled by a user. The method including executing an artificial intelligence (AI) model to determine a context of a current point in the game play based on the one or more video frames that are captured. The method including determining a query based on the context of the game play using the AI model. The method including generating a response to the query using the AI model based on the context of the game play. The method including presenting the response to the query via a device of the player.