Dynamic Video Metadata Tracking via Temporal Context Analysis
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Solution Overview
Problem
Existing video content management systems fail to effectively track and regenerate metadata dynamically, especially after publication, limiting the ability to utilize temporal variations in video content context for repurposing and new application development.
Innovation Solution
An apparatus and method that combines static metadata generated during video content creation with external data collected post-publication, using a first metadata generator/tagger, external data collector, and dynamic metadata-based tracker/analyzer to regenerate and tag metadata over time, providing trend analysis reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static metadata is generated during initial video content publication, then metadata generation is efficient and simple, but the metadata cannot track temporal variations in video content context
Solution Approach 1:
The system transitions from static metadata generation to dynamic metadata regeneration by continuously collecting external data and updating metadata tags over time. The metadata evolves from a one-time generation process to an ongoing dynamic process that adapts to temporal variations in video content context, resolving the contradiction between simplicity and adaptability.
Solution Approach 2:
The system implements continuous metadata regeneration by repeatedly collecting external data and updating metadata tags after video publication. This continuous action ensures metadata remains current and reflective of temporal variations, while building upon the initial static metadata generation process.
2Adaptability or versatility
If external data is collected and used to regenerate metadata after video publication, then temporal variation tracking is improved, but system complexity increases
Solution Approach 1:
The system divides metadata generation into two distinct components: initial static metadata generation and subsequent dynamic metadata regeneration. This segmentation allows each component to be optimized independently, managing complexity by separating the simple initial generation process from the more complex but necessary continuous update process.
Solution Approach 2:
The system performs preliminary static metadata generation during video publication before external data collection begins. This preliminary action establishes a foundation that simplifies subsequent dynamic regeneration, as the system only needs to update and refine existing metadata rather than generate everything from scratch.
3Measurement precision
If metadata is dynamically regenerated continuously, then video content context tracking is accurate and current, but processing time and computational resources increase
Solution Approach 1:
The system implements periodic metadata regeneration rather than truly continuous regeneration, collecting external data and updating metadata at regular intervals. This periodic approach maintains adequate context tracking accuracy while reducing computational burden and processing time compared to continuous regeneration.
Data Source
AI summary
There are provided an apparatus and method for tracking temporal variation of a video content context using dynamically generated metadata, wherein the method includes generating static metadata on the basis of internal data held during an initial publication of video content and tagging the generated static metadata to the video content, collecting external data related to the video content generated after the video content is published, generating dynamic metadata related to the video content on the basis of the collected external data and tagging the generated dynamic metadata to the video content, repeating regeneration and tagging of the dynamic metadata with an elapse of time, tracking a change in content of the dynamic metadata, and generating and providing a trend analysis report corresponding to a result of tracking the change in the content.