Transmedia Content Graph Visualization for Mobile Devices
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Solution Overview
Problem
Existing systems for managing and navigating non-linearly connected transmedia content are inefficient, particularly in visualizing and processing large datasets on lower-power devices like smartphones, and lack tools to predict trends or effectively group time-ordered content for specific audiences.
Innovation Solution
A computer-implemented method that retrieves and structures non-linearly connected transmedia content data objects into a two-dimensional graph, allowing users to navigate through linked content items by generating thumbnail representations and modifying visual display characteristics based on user interaction, enabling efficient processing and visualization of complex content networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing systems are used to manage and navigate non-linearly connected transmedia content, then content sharing and distribution can increase, but the systems become hard to control and lack adequate tools for predicting trends or effectively grouping time-ordered content
Solution Approach 1:
The patent segments the complex non-linear content network into manageable linear groups of time-ordered content items. Each linear group represents a sequential pathway through content, making the overall non-linear structure easier to navigate and control while maintaining the benefits of diverse content sharing and distribution channels.
2Difficulty of detecting and measuring
If large amounts of multimedia information are visualized to enable user exploration, then content discovery improves, but performance deteriorates especially on lower-power devices like smartphones or tablets
Solution Approach 1:
The visualization is segmented into linear groups that can be processed and displayed sequentially rather than rendering the entire non-linear content network at once. This reduces the computational burden on lower-power devices while still enabling comprehensive content discovery through structured navigation.
Solution Approach 2:
The patent transforms the non-linear content relationships into a two-dimensional graph structure with nodes representing content items and edges representing relationships. This dimensional transformation enables efficient visualization and navigation on standard display screens while maintaining the complexity of non-linear connections.
3Adaptability or versatility
If content from different users and of different types is grouped in a time-ordered manner to enable non-linear linking, then content connectivity improves, but real-time visualization performance deteriorates
Solution Approach 1:
The system pre-processes and structures content into linear groups with defined time-ordering before visualization is needed. This preliminary organization of content reduces the computational complexity during real-time visualization, enabling fast rendering while maintaining the adaptability of non-linear content connectivity across different users and content types.
Data Source
AI summary
The invention relates to systems and methods for navigating, outputting and displaying non-linearly connected groups of transmedia content. Specifically, the invention involves retrieving, from a database, an ordered group of transmedia content data objects comprising a plurality of transmedia content data objects and linking data, whereby each element of the linking data defines a directional link from one of the transmedia content data objects to another of the transmedia content data objects; generating a two-dimensional graph structure representing the ordered group of transmedia content data objects whereby each graph node corresponds to a transmedia content data object and each graph edge corresponds to an element of the linking data; and outputting the graph structure on a display screen.