Programmatic Media Preview Generation via ML Ranking
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Solution Overview
Problem
The exponential growth of video content has made manual processes for generating engaging media previews inefficient and scalable solutions are needed to improve content discovery by providing users with relevant and concise previews.
Innovation Solution
A system and method for programmatic media preview generation, which involves ingesting media items, performing multiple analyses of audio and video components, generating candidate previews, and utilizing a ranking process to select a final set of previews that can be deployed or reviewed by administrators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used to generate media previews, then quality and engagement can be maintained, but scalability and efficiency deteriorate as media libraries expand
Solution Approach 1:
The system enables automated preview generation that operates independently without human intervention. The machine learning model automatically analyzes media content, identifies key segments, generates synopses, and creates preview videos, allowing the system to serve itself rather than relying on manual curators for each preview creation task
Solution Approach 2:
The patent replaces the mechanical manual process of editors watching and selecting video segments with an automated machine learning system. The ML model substitutes human editors by performing content analysis, scene detection, and preview generation through computational algorithms rather than human effort
2Ease of manufacture
If manual curation is used for preview generation, then content quality can be controlled, but the process becomes inefficient and difficult to scale
Solution Approach 1:
The system performs preliminary analysis of media content during the ingestion phase, extracting metadata, transcribing audio, and identifying key segments before preview generation is requested. This pre-processing work is done in advance so that when a preview is needed, the system can quickly assemble it from pre-analyzed components rather than starting from scratch
Solution Approach 2:
The system uses text metadata and synopses as intermediate representations that can be rapidly copied and transformed into different preview formats. The structured text data serves as a reusable template that can be converted into multiple preview versions without re-analyzing the original video content
3Reliability
If multiple analyses are performed on media content, then preview relevance and engagement improve, but processing complexity and time increase
Solution Approach 1:
The system divides the preview generation process into distinct modular components: audio analysis module for transcription and speaker identification, video analysis module for scene detection and visual content analysis, metadata extraction module for text processing, and synthesis module for combining results. Each module handles a specific aspect independently, making the complex process manageable and maintainable while ensuring comprehensive analysis
Data Source
AI summary
A system and method for programmatic media preview generation, including: a preview generation system executing on a computer processor and configured to receive a request to generate a preview video of a source video file; select a source video for analysis; obtain a set of text metadata comprising groupings of subtitles of the source video; invoke a machine learning model using the set of text metadata to infer a set of candidate previews for the source video file; and provide a final set of candidate previews in response to the request; and a ranking module comprising functionality to rank the set of candidate previews to generate the final set of candidate previews.