Transmedia Content Graph Linking for Efficient Visualization
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Solution Overview
Problem
Existing systems for managing transmedia content are inefficient in linking and visualizing large datasets, particularly on lower-power devices, and lack tools to predict trends or effectively group time-ordered content for specific audiences, making it difficult to process and share multimedia information in a non-linear manner.
Innovation Solution
An apparatus and method for linking transmedia content subsets using a memory to store data items and time-ordered links, represented as nodes and edges in a graph structure, allowing for efficient storage and visualization of transmedia content, with a harvesting engine to extract common nodes and edges and a metadata generation engine to create metadata for non-linearly connected content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If transmedia content data items are stored with time-ordered links in a graph structure, then content linking and visualization efficiency is improved, but device complexity increases
Solution Approach 1:
The patent segments transmedia content into discrete content data items that can be independently stored and linked through time-ordered relationships. Each content item becomes a separate node in the graph structure, allowing efficient individual processing while maintaining overall system organization through the graph's linking mechanism.
Solution Approach 2:
The patent introduces a temporal dimension to content linking by implementing time-ordered links between content items. This transforms traditional flat content structures into multi-dimensional graph structures where content items are connected not only by thematic relationships but also by temporal sequence, enabling sophisticated content navigation and analysis.
2Ease of operation
If large amounts of transmedia content data are visualized in real-time, then user interaction and feedback are improved, but processing performance deteriorates on lower-power devices
Solution Approach 1:
The patent extracts and stores metadata separately from the full transmedia content data. This metadata contains essential linking information and temporal relationships that can be processed efficiently on lower-power devices, while the full content can be accessed when needed without burdening the processing resources of mobile devices.
Solution Approach 2:
The system performs preliminary processing of transmedia content to pre-compute and store time-ordered links and graph structure relationships. This upfront processing reduces the computational burden during real-time visualization, allowing mobile devices to efficiently render content navigations without performing complex calculations on-the-fly.
3Adaptability or versatility
If content from different users and types is grouped in a non-linear time-ordered manner, then content sharing and distribution capabilities are improved, but data organization complexity increases
Solution Approach 1:
The patent implements a universal graph structure that can accommodate multiple types of content data items from different users within a single unified framework. This graph structure handles various content types (text, images, video, interactive elements) and user-generated content uniformly through standardized nodes and edges, enabling flexible non-linear content groupings without requiring separate organizational systems for different content types.
Data Source
AI summary
Disclosed is a system for linking transmedia content subsets. A memory stores a plurality of transmedia content data items and associated linking data, which define time-ordered content links between the transmedia content data items. The transmedia content data items are arranged into linked transmedia content subsets comprising different groups of the transmedia content data items and different content links therebetween; a transmedia content model that represents the transmedia content data items as nodes and the content links between the transmedia content data items as edges in one or more time-varying graphs. A processor is configured to associate the transmedia content data items with the time-ordered content links and store the linking data in the memory. It assigns the transmedia content data items to nodes of a graph structure, assign the time-ordered content links to edges of the graph structure, and store them in the transmedia content model.