Gameplay Video Assistance via Spatial Metadata Filtering
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Solution Overview
Problem
Current gaming technologies lack efficient methods to provide players with relevant help videos for specific areas of a game, as existing systems require manual processing and do not automatically catalog gameplay videos with precise location metadata, limiting the availability of assistance to users struggling with particular game sections.
Innovation Solution
The system collects and correlates metadata from gameplay videos, including spatial and temporal location information, to intelligently catalog and surface relevant videos for players, allowing for dynamic selection and presentation of videos based on the player's current location and progress within the game.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processing methods are used to catalog gameplay videos, then the system complexity is reduced, but the productivity and coverage of help content is limited
Solution Approach 1:
The system extracts location metadata from gameplay videos in advance during the video upload process, rather than waiting for manual cataloging. This preliminary action enables automatic matching with player locations later, significantly improving help video provision efficiency without requiring complex real-time processing
Solution Approach 2:
The system uses location metadata (spatial coordinates, level identifiers, scene tags) as a simplified copy or representation of the actual gameplay content. This metadata copy enables automatic cataloging and retrieval without needing to analyze the entire video content manually, balancing productivity improvement with acceptable system complexity
2Adaptability or versatility
If no location metadata is correlated with videos, then the ease of operation is improved, but the adaptability of help content to player needs deteriorates
Solution Approach 1:
The system segments video metadata into distinct location-related components (spatial coordinates, level identifiers, scene tags) that can be independently extracted and processed. This segmentation enables targeted metadata collection without requiring complex full-video analysis, improving help video relevance while managing processing complexity
Solution Approach 2:
The location metadata structure is designed to be universal across different video sources and game types, using standardized fields (spatial coordinates, level identifiers, scene tags) that work for various gameplay scenarios. This universality enables the system to adapt to different player needs without requiring complex custom processing for each case
3Quantity of substance
If help videos are only manually documented by developers, then the device complexity is reduced, but the quantity and coverage of help content is limited
Solution Approach 1:
The system enables gameplay videos to self-catalog by automatically extracting location metadata from the video content and associated game state data. This self-service approach eliminates the need for manual developer documentation, significantly increasing help video coverage while maintaining reasonable automation levels through automated metadata extraction processes
Data Source
AI summary
A method, including: receiving videos generated from sessions of a video game respectively associated to first client devices; for each video, determining location metadata that identifies spatial locations in a virtual environment of the video game at which gameplay depicted by the video occurs, the spatial locations defining a path of the gameplay; during a session of the video game associated to a second client device, determining a current spatial location in the virtual environment of gameplay occurring in the session; filtering the videos based on the current spatial location to identify one or more videos having paths that intersect a region proximate to the current spatial location; presenting, over the network, the identified one or more of the videos at the second client device; continuously tracking updates to the current spatial location and responsively updating the filtering of the videos based on said updates to the current spatial location.