Video Stream ML Detection for Interactive Content Acquisition
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Solution Overview
Problem
Live streaming of video games presents challenges in identifying and providing information about specific in-game content items in real-time, as metadata cannot be predefined due to the dynamic nature of gameplay, making it difficult for viewers to track or acquire items mentioned by streamers.
Innovation Solution
Utilizing trained machine learning models, such as twin neural networks, to detect and identify specific content items in gameplay video streams, and presenting associated information to viewers through graphical elements or links for acquisition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional methods (verbal mentions or comments section links) are used to provide content item information, then the system complexity remains low, but viewers have difficulty tracking down items and the information delivery is inefficient
Solution Approach 1:
The patent introduces an intermediary system comprising video analysis models and metadata generation components that automatically process live video streams. This intermediary layer extracts content items from video frames, generates corresponding metadata, and creates interactive graphical elements without requiring manual configuration, thereby improving information delivery efficiency while managing system complexity through automated processing pipelines
Solution Approach 2:
The patent replaces the mechanical/manual process of configuring metadata and tracking content items with automated machine learning-based video analysis. The system uses trained models to automatically detect, identify, and track content items in real-time video streams, substituting manual operations with intelligent automated systems that improve productivity
2Adaptability or versatility
If pre-defined metadata is used for content identification, then the system is simpler to implement, but it cannot adapt to the dynamic nature of live streamed video where content appearance is unpredictable
Solution Approach 1:
The patent implements a dynamic metadata generation system that processes video frames in real-time during live streaming. The system continuously analyzes incoming video data, detects content items as they appear, and generates metadata dynamically without pre-definition. This dynamic approach allows the system to adapt to unpredictable content appearance while maintaining operational simplicity through automated real-time processing
Solution Approach 2:
The patent employs pre-trained video analysis models and object detection algorithms that are prepared in advance. These pre-trained models enable the system to quickly adapt to new content by leveraging previously learned features, reducing the complexity of real-time adaptation while maintaining high versatility for detecting various content items in dynamic live streams
3Loss of time
If manual tracking of mentioned items is required, then the system remains simple, but viewers experience difficulty and time loss in finding content items
Solution Approach 1:
The patent implements a self-service system where the video stream itself provides the information needed. The system automatically extracts content item information from the video frames, generates metadata, and creates interactive graphical elements that directly link to content items. This self-service approach eliminates the need for manual tracking by streamers or manual searching by viewers, reducing time loss while managing complexity through automated processing
Solution Approach 2:
The patent creates a feedback loop where the system continuously monitors video frames, detects content items, and immediately generates corresponding interactive elements. This real-time feedback mechanism ensures that content item information is delivered promptly as it appears in the stream, minimizing viewer search time while using automated systems to manage the complexity of continuous monitoring and generation
Data Source
AI summary
In various examples, one or more Machine Learning Models (MLMs) are used to identify content items in a video stream and present information associated with the content items to viewers of the video stream. Video streamed to a user(s) may be applied to an MLM(s) trained to detect an object(s) therein. The MLM may directly detect particular content items or detect object types, where a detection may be narrowed to a particular content item using a twin neural network, and/or an algorithm. Metadata of an identified content item may be used to display a graphical element selectable to acquire the content item in the game or otherwise. In some examples, object detection coordinates from an object detector used to identify the content item may be used to determine properties of an interactive element overlaid on the video and presented on or in association with a frame of the video.