Automated Video Tagging via Transcript Ontology Ranking
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Solution Overview
Problem
Current video content tagging and searching methods are manual, inefficient, and often produce inaccurate results, limiting the ability to effectively categorize and retrieve archived video content, which hampers revenue generation for content owners.
Innovation Solution
An automated method for generating video tags using a relevancy engine that analyzes transcripts, ranks words based on multiple scoring factors, and creates heat maps to identify top concepts, enabling accurate tagging and improved search functionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tagging process is used, then tagging accuracy can be maintained through human judgment, but productivity is too low to handle archived video content
Solution Approach 1:
The system enables automated self-tagging of video content by extracting transcripts, generating ontologies, and automatically ranking words to create tags without human intervention, thereby resolving the contradiction between maintaining accuracy and increasing productivity
Solution Approach 2:
The patent replaces the manual mechanical tagging process with an automated computational system that uses transcript analysis, ontology generation, and algorithmic word ranking to generate tags, achieving both high productivity and maintained accuracy
2Ease of operation
If keyword searching from closed captioning transcripts is used, then search functionality is provided, but search completeness and accuracy are insufficient
Solution Approach 1:
The system performs preliminary generation of comprehensive tags and ontologies from video transcripts before searching occurs, creating a enriched index that improves search completeness and accuracy while maintaining ease of operation
Solution Approach 2:
The patent introduces an intermediary ontology layer that connects search queries to video content more effectively, using ranked words and conceptual relationships to bridge the gap between simple keyword searching and comprehensive content retrieval
3Adaptability or versatility
If metadata-based related video suggestions are used, then video recommendations are provided, but recommendation quality is flawed due to metadata limitations
Solution Approach 1:
The system performs preliminary generation of comprehensive tags and ontologies from video transcripts before recommendations are made, creating an enriched index that improves recommendation quality while maintaining adaptability
Solution Approach 2:
The patent introduces an intermediary ontology layer that connects video content more effectively, using ranked words and conceptual relationships to improve the accuracy of related video suggestions
Data Source
AI summary
Techniques for generating automated tags for a video file are described. The method includes receiving one or more manually generated tags associated with a video file, based at least in part on the one or more manually entered tags, determining a preliminary category for the video file, and based on the preliminary category, generating a targeted transcript of the video file, wherein the targeted transcript includes a plurality of words. The method further includes generating an ontology of the plurality of words based on the targeted transcript, ranking the plurality of words in the ontology based on a plurality of scoring factors, and based on the ranking of the plurality of words, generating one or more automated tags associated with the video file.