Video Segment Curation via Metadata Tagging and Rubric-Based Exchange
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Solution Overview
Problem
Conventional video content production methods are inefficient in catering to diverse viewer preferences, requiring significant human effort to create variations of content with specific edits, such as removing sensitive content or altering emotional impact, and struggle with content piracy and advertising disruptions.
Innovation Solution
A method involving metadata tagging of video segments, using artificial intelligence to select and modify segments based on curating rubrics, allowing for automated creation of curated video sequences that match viewer preferences, including product placement and content modification, while reducing piracy risks through unique watermarks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional production methods are used to create customized video versions, then viewer preferences can be satisfied, but production time and effort increase significantly
Solution Approach 1:
The video content is divided into multiple segments that are independently tagged with metadata. This segmentation allows the system to selectively assemble and modify segments based on viewer preferences without reprocessing the entire video, significantly reducing production time for customized versions.
Solution Approach 2:
Video segments are pre-tagged with comprehensive metadata during initial processing. This preliminary action enables rapid retrieval and assembly of segments according to different curating rubrics later, eliminating the need for time-consuming manual editing when creating customized versions.
2Adaptability or versatility
If multiple customized video versions are produced manually, then diverse viewer interests can be met, but production effort doubles or triples
Solution Approach 1:
A single set of tagged video segments can be used to generate multiple customized versions by applying different curating rubrics. The system universally processes segments according to various rules (e.g., different content ratings, emotional tones, product placements) without requiring separate manual editing for each version.
Solution Approach 2:
The system changes parameters such as content filtering thresholds, emotional tone weights, and product placement preferences to generate different video versions from the same segmented content. By adjusting these parameters rather than recreating content, the system maintains high productivity while delivering diverse customized versions.
3Loss of energy
If paid subscriptions are used to provide video content, then content providers can earn revenue, but viewers experience advertising interruptions
Solution Approach 1:
The system creates customized copies of video content with embedded product placements that replace traditional advertising. These copied versions provide continuous viewing experience without interruptions, while still generating revenue through targeted product integration in the video segments.
4Ease of operation
If video content is easily copied and transmitted, then viewers can access free content, but copyright violations occur
Solution Approach 1:
Unique watermarks are applied to specific localized regions of video segments rather than uniformly across the entire video. This local quality approach maintains visual integrity while providing traceable protection against unauthorized copying and distribution, allowing the system to track and protect content without compromising accessibility.
Data Source
AI summary
A method of providing curated video may include tagging each of a plurality of video segments with at least one metadata tag, defining a curating rubric to be applied to a baseline sequence of video segments that is at least a subset of the plurality of video segments, the curating rubric comprising a target descriptor and a replacement descriptor associated with the target descriptor, selecting a target segment having at least one metadata tag matching the target descriptor of the curating rubric, the target segment being selected from among the baseline sequence, identifying a potential replacement segment by referring to one of the at least one metadata tags of the target segment, exchanging the replacement segment with the target segment within the baseline sequence to form a curated sequence of video segments, and encoding the curated sequence in preparation for streaming to a viewer.