LLM Video Tag Generation for Accurate Content-Based Metadata
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Solution Overview
Problem
Video libraries often lack consistent and accurate metadata tagging, with existing tools relying on title, description, or keywords rather than the actual content, leading to incomplete and inaccurate linking of similar videos.
Innovation Solution
An AI-powered platform utilizing large language models (LLMs) to analyze video content, generate tags based on existing tags and descriptions, and apply them to individual videos, enabling consistent and accurate tagging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional tag generation tools are used that operate on title, descriptions, or keywords, then the tagging process is simple and fast, but the tagging accuracy and completeness deteriorate
Solution Approach 1:
The patent introduces an intermediary AI model that acts as a bridge between the video content and the tagging system. This model processes actual video content (visual frames, audio, transcripts) rather than relying solely on metadata, thereby improving tagging accuracy while maintaining automated efficiency
Solution Approach 2:
The patent replaces traditional keyword-based mechanical tagging systems with an AI-driven system that analyzes actual video content. This substitution enables more accurate understanding of video semantics, leading to improved tagging precision without sacrificing automation
2Measurement precision
If manual tagging is performed to ensure accurate and consistent metadata, then the tagging quality improves, but the time and labor required increase
Solution Approach 1:
The patent implements a self-service tagging system where the AI model automatically generates consistent tags by analyzing video content itself. The system learns from existing tags and descriptions to maintain consistency without human intervention, achieving both high quality and efficiency
Solution Approach 2:
The patent incorporates feedback mechanisms where the AI model learns from existing tags and descriptions to improve its tagging consistency. The system continuously refines its tagging approach based on patterns observed in already-tagged videos, maintaining high consistency without manual oversight
3Productivity
If tags are generated based on existing tags and descriptions only, then the process is efficient, but the relevance to actual video content deteriorates
Solution Approach 1:
The patent segments the video content into multiple analyzable components including visual frames, audio segments, and transcript text. The AI model processes these segmented elements separately and synthesizes them to generate tags that are both efficient to produce and highly relevant to the actual video content
4Measurement precision
If comprehensive video content analysis is performed to generate accurate tags, then the tagging accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent divides the complex task of video analysis into manageable segments: frame extraction, audio processing, transcript analysis, and tag synthesis. This segmentation reduces computational complexity at each stage while maintaining overall tagging accuracy through the integration of multiple analysis dimensions
Data Source
AI summary
Aspects of the subject disclosure may include, for example, obtaining a first set of tags and a first set of descriptions associated with a set of content items, causing an LLM to generate a plurality of tags based on the first set of tags and the first set of descriptions, resulting in a second set of tags, wherein the second set of tags includes the first set of tags and the plurality of tags, and causing the LLM to apply one or more tags from the second set of tags to one or more content items based on information regarding the one or more content items. Other embodiments are disclosed.