Game Spectating Highlight Service Using ML Analysis
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Solution Overview
Problem
Current online gaming spectating systems lack an efficient method to determine and present highlights from live and recorded game streams, limiting the engagement and enjoyment of spectators by not effectively curating notable events.
Innovation Solution
A game spectating system with a highlight service that processes game data, participant interactions, and video content using machine learning and statistical analysis to identify and present highlight segments and reels based on various criteria, allowing spectators to selectively view interesting moments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automated highlight detection is implemented using machine learning and statistical analysis, then spectator engagement and viewing experience are improved, but system complexity and processing requirements increase
Solution Approach 1:
The system segments highlight detection into multiple independent analysis components: machine learning-based event detection, statistical analysis of game data, participant interaction monitoring, and video content analysis. Each component processes specific aspects independently and their results are aggregated to form comprehensive highlight segments, reducing overall system complexity while maintaining high engagement quality
Solution Approach 2:
The patent introduces an intermediary highlight service layer between the game broadcasting system and spectator interfaces. This service automatically processes raw game data, participant interactions, and video content through multiple analysis methods, then presents curated highlight reels to spectators. The intermediary handles the computational complexity internally while providing simplified, engaging content to users
2Measurement precision
If multiple analysis methods (machine learning, statistical analysis, participant interactions) are used to determine highlights, then the accuracy and relevance of highlighted events improve, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing by pre-analyzing participant interactions, game data patterns, and video metadata before actual highlight detection occurs. Statistical baselines and machine learning models are pre-trained on historical game data, enabling faster real-time detection during live broadcasts. Participant interaction data is continuously monitored and pre-processed to identify potential highlight candidates ahead of time
Solution Approach 2:
The patent employs multiple analysis methods with varying levels of depth depending on the situation. For routine gameplay, lighter statistical analysis suffices. For exceptional events, the system activates full machine learning and video analysis pipelines. Participant interactions trigger targeted analysis only when relevant, avoiding unnecessary computational overhead while maintaining high accuracy for significant moments
Data Source
AI summary
Methods and apparatus for determining highlights from broadcasts in spectating environments. A highlight service obtains highlight data for broadcasts including game- and participant-specified events, audio input, and text chat, analyzes the highlight data to determine notable events (highlights) in the broadcasts, and extracts highlight segments from the broadcasts according to the determined events. Highlight reels may be created from the highlight segments according to one or more highlight selection criteria and/or spectator preferences. The highlight service may provide access to the highlights and highlight reels via a highlight user interface (UI). The spectators may selectively view the highlights or highlight reels via the highlight UI.