Dynamic Summary Queue for Cloud Gaming Recaps
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Solution Overview
Problem
Current video game and media content delivery systems lack an efficient method to summarize and recap interactive online events for late-joining participants, failing to provide a comprehensive and relevant overview of the content they missed.
Innovation Solution
A cloud gaming system generates a dynamic summary queue by processing participant-generated content, identifying key portions based on user interactions and media item metadata, and transmitting this summary to clients for real-time streaming, allowing users to catch up on events with customized recaps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the complete media item is transmitted to all participants, then everyone can see the full content, but late-joining participants waste time watching content they already know and the system consumes excessive bandwidth
Solution Approach 1:
The system extracts only the essential key portions of the media item that are relevant to late-joining participants, rather than transmitting the complete media item. This extraction is based on analyzing participant-generated content and metadata to identify what information is actually needed, thereby reducing both time loss and bandwidth consumption while maintaining information completeness.
Solution Approach 2:
The media item is segmented into key portions and non-key portions based on participant-generated content analysis. The system transmits only the segmented key portions to late-joining participants, allowing them to quickly catch up on essential information without watching the entire media item from the beginning.
2Productivity
If a static summary is provided to late-joining participants, then they can quickly catch up, but the summary cannot adapt to different participants' interests and may include irrelevant content
Solution Approach 1:
The summary is made dynamic by continuously analyzing participant-generated content and metadata in real-time. The system adjusts the summary content based on current participant interests, activity levels, and preferences, allowing late-joining participants to receive customized recaps that adapt to their specific needs rather than a fixed static summary.
Solution Approach 2:
The system uses participant-generated content as feedback to continuously refine and customize summaries for different participants. By monitoring what participants are doing, saying, and reacting to, the system adapts the summary content to match each participant's interests and engagement level, improving both speed and relevance.
3Adaptability or versatility
If participant-generated content is processed in real-time to create customized summaries, then each participant receives relevant content, but the server computing resources and processing time increase significantly
Solution Approach 1:
The system applies partial processing by focusing computational resources only on analyzing participant-generated content that is most relevant to current media item portions. Rather than processing all participant data uniformly, the system selectively processes only the necessary subset of information needed to generate customized summaries, reducing overall computational complexity while maintaining personalization.
4Loss of information
If the system transmits the complete media item stream to late-joining participants, then they receive all content, but network bandwidth is wasted on content they already know
Solution Approach 1:
The system extracts and transmits only the essential key portions of the media item that late-joining participants need to understand, rather than transmitting the complete media item. This extraction is intelligently performed by analyzing participant-generated content and metadata to identify what information is actually necessary, thereby reducing network bandwidth consumption while maintaining information completeness.
Data Source
AI summary
A stream of a media item is transmitted from a server computing system to one or more client computing systems. The media item has a timeline for playback of the media item. Participant-generated content is received at the server computing system from one or more participants at any of the one or more client computing systems. The participant-generated content is time-indexed to the timeline of the media item. The participant-generated content is processed to identify key portions of the media item based on indications present within the participant-generated content. A dynamic summary queue is generated that includes the key portions of the media item. The dynamic summary queue is assembled for streaming. A request to view the dynamic summary queue is received from a requestor. In response to the request, a stream of the dynamic summary queue is transmitted to a computing device of the requestor.