Gameplay Video Analysis for Personalized Player Recommendations
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Solution Overview
Problem
Existing methods for improving a player's gaming skills, such as reading tutorials or hiring coaches, are limited and do not effectively utilize gameplay data to provide personalized content recommendations.
Innovation Solution
A system that analyzes gameplay video using neural networks to identify scenes, objects, and actions, generating keywords that are used to update a player's profile and recommend relevant games, tips, and improvements based on actual gameplay interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks analyze gameplay video to generate personalized recommendations, then recommendation accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing gameplay video data and pre-generating keywords during off-peak times. Gameplay videos are segmented and analyzed in advance, with keywords extracted and stored for later recommendation generation, reducing real-time processing requirements.
Solution Approach 2:
The neural network analysis is divided into multiple segments: video segmentation into clips, clip segmentation into scenes, and scene segmentation into objects/actions. This hierarchical segmentation allows parallel processing of different video portions, reducing overall processing time while maintaining comprehensive analysis.
2Measurement precision
If neural networks analyze gameplay video to generate personalized recommendations, then recommendation accuracy improves, but computational resource consumption increases
Solution Approach 1:
The system extracts only the essential and relevant features from gameplay videos using keyword extraction. Instead of analyzing entire video content, the neural network identifies and extracts key objects, actions, and scenes, converting them into compact keyword representations that consume fewer resources during recommendation generation.
Solution Approach 2:
The system uses lightweight keyword representations instead of storing or processing heavy video data. The extracted keywords serve as disposable, low-cost proxies for the original video content, enabling efficient recommendation processing without requiring continuous access to resource-intensive video files.
3Loss of information
If the system processes complete gameplay videos for analysis, then analysis completeness improves, but processing time increases
Solution Approach 1:
The system extracts representative samples from complete gameplay videos rather than processing every frame. Key scenes, objects, and actions are identified and extracted as discrete elements, capturing the essential gameplay content while reducing processing requirements.
Solution Approach 2:
The system performs partial analysis on selected video segments rather than complete frame-by-frame analysis. By focusing on representative clips and key moments, the system achieves sufficient analysis completeness for recommendation purposes without the computational burden of exhaustive processing.
Data Source
AI summary
Apparatuses, systems, and techniques are presented to determine feedback for a user. In at least one embodiment, feedback is provided to one or more players of a game to help those players improve their performance playing the game.