Gameplay Clip Classification with Unsupervised Feature Vectors
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Solution Overview
Problem
Existing systems for identifying interesting video clips in gaming sessions are error-prone due to reliance on object-detection models that require high-quality visual data and large labeled training sets, which are difficult to obtain and become outdated with game updates, leading to inaccuracies and resource inefficiencies.
Innovation Solution
An unsupervised approach using game-agnostic input-device metrics to identify durations of predicted interest in gameplay sessions, followed by clustering and classification of video clips without relying on specific game objects, utilizing a trained feature extractor to generate feature vectors and cluster frames for classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If object-detection models are used to identify interesting video clips, then video clip identification can be performed, but the system becomes error-prone and requires large labeled training sets that are difficult to obtain and maintain
Solution Approach 1:
The patent replaces the mechanical system of supervised learning with object-detection models with an unsupervised learning approach using autoencoders. The autoencoder learns compressed representations of gameplay data without requiring labeled training sets, thereby eliminating the complexity of obtaining and maintaining large labeled datasets while improving reliability through game-agnostic feature extraction
Solution Approach 2:
The patent changes the parameter of training data requirements from large labeled datasets to unsupervised learning with no labels needed. By using autoencoders to learn intrinsic patterns in gameplay data, the system transforms the problem from requiring extensive manual annotation to automatically discovering meaningful features, thereby reducing the complexity burden
2Reliability
If object-detection models are retrained with new game content, then accuracy can be maintained, but time and computational resources are consumed
Solution Approach 1:
The patent creates a universal autoencoder model that learns game-agnostic features from gameplay data without being specific to any particular game. This universal model can process multiple games and their updates without requiring retraining, as it captures fundamental gameplay patterns that transcend specific game content, thereby maintaining accuracy while eliminating retraining time
Solution Approach 2:
The patent performs preliminary unsupervised learning to extract meaningful features from raw gameplay data before any classification or analysis is performed. The autoencoder pre-processes the data by learning compressed representations that capture essential patterns, so when new game content is introduced, the system can adapt using these pre-learned features without time-consuming retraining
3Measurement precision
If high-quality visual data is used for object detection, then detection accuracy improves, but storage and processing resources increase
Solution Approach 1:
The patent extracts only the essential features from gameplay data using an autoencoder that learns compressed representations. Instead of using high-quality visual data directly for object detection, the system extracts meaningful patterns and features that capture the essence of gameplay events, thereby achieving detection accuracy with significantly reduced data quantity
Solution Approach 2:
The patent changes the parameter of data quality from high-resolution visual data to compressed feature representations. The autoencoder transforms raw visual data into lower-dimensional feature vectors that retain the essential information needed for accurate gameplay event identification, thereby reducing storage and processing requirements while maintaining measurement precision
Data Source
AI summary
In various examples, potentially highlight-worthy video clips are identified from a gameplay session that a gamer might then selectively share or store for later viewing. The video clips may be identified in an unsupervised manner based on analyzing game data for durations of predicted interest. A classification model may be trained in an unsupervised manner to classify those video clips without requiring manual labeling of game-specific image or audio data. The gamer can select the video clips as highlights (e.g., to share on social media, store in a highlight reel, etc.). The classification model may be updated and improved based on new video clips, such as by creating new video-clip classes.