Gameplay Moment Asset Generation Using AI Event Extraction
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Solution Overview
Problem
Generating memorable gaming session achievements via video clips or screenshots is time-consuming and inefficient, consuming significant computing resources and requiring specialized software.
Innovation Solution
A method utilizing a machine learning process to extract features from gameplay state data, combined with user profile data, to generate moment assets such as images, videos, or 3-D structures, using an image generation AI process to create selective representations of gameplay activities, which can be certified with a non-fungible token (NFT) for authenticity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If players use specialized software to generate video clips or screenshots of memorable achievements, then the quality and memorability of the captured moments is improved, but the time consumption and operational complexity increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically detecting and capturing memorable moments during gameplay without requiring post-game processing. The moment capture module continuously monitors gameplay data and automatically identifies significant events, saving the moment immediately when detected, thus eliminating the time-consuming search and manual capture process.
Solution Approach 2:
The system enables self-service by automatically generating and saving moment clips without player intervention. The moment capture module autonomously detects memorable events, extracts relevant video segments, and saves them with metadata, allowing the gameplay system to serve itself in creating shareable content without requiring specialized software or player effort.
2Manufacturing precision
If players use specialized software to capture and process gameplay moments, then the quality of the moment asset is improved, but the device complexity and ease of operation deteriorate
Solution Approach 1:
The system merges the moment capture functionality directly into the gameplay system itself. The moment capture module is integrated with the game execution environment, combining video recording, event detection, and moment saving capabilities into a unified system that operates transparently during gameplay, eliminating the need for separate specialized software.
Solution Approach 2:
The gameplay system is designed with multi-functionality, serving both as the game execution environment and as the moment capture system. The same system that runs the game also monitors gameplay data, detects memorable events, and generates moment assets, making the system universal and eliminating the need for additional specialized tools.
3Ease of manufacture
If the system generates moment assets using traditional processing methods, then the creation process is straightforward, but the computing resources and network bandwidth consumed increase significantly
Solution Approach 1:
The system extracts only the essential and memorable portions of gameplay data for moment creation. The moment capture module identifies specific significant events and extracts only those relevant video segments and metadata, rather than processing entire gameplay recordings, thus reducing computing resources and network bandwidth requirements while maintaining moment asset quality.
4Reliability
If the system captures all gameplay moments, then the completeness of the moment library is improved, but the quantity of data and processing burden increase
Solution Approach 1:
The system uses feedback mechanisms to efficiently identify memorable moments. The moment capture module continuously monitors gameplay data against predefined criteria for significant events, using real-time feedback from game state changes to determine when to capture moments, thus maintaining completeness while improving generation efficiency through intelligent filtering.
Data Source
AI summary
A method for generating a moment asset to represent interactive activity that occurred during a session of gameplay includes executing an instance of a game for a user, with the executing generating state data descriptive of interactive activity occurring during the session, and examining the state data by a machine learning process, which extracts features for classification and input to a moment model. The method also includes accessing user profile data, which includes labeled user profile feature data that identifies characteristics of playing the game by the user and is input to the moment model. The method further includes outputting, by the moment model, two or more images and at least one text descriptor of interactive activities that occurred during the session, and inputting the images and text descriptor to an image generation artificial intelligence process with priority data to influence a layout of image content for the moment asset.