ML Media Segment Tagging for Short-Form Content Extraction
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Solution Overview
Problem
Generating short-form media content from large libraries of media files is prohibitively time-consuming and prone to human bias and inconsistency due to the need for analyzing numerous features across thousands of hours of content.
Innovation Solution
Utilizing machine learning models to automate the identification and generation of short-form content by analyzing media files for specific features that meet classification criteria, employing techniques like deep learning and user interaction models to enhance accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual analysis of media files is used to create short-form content, then human judgment and flexibility are maintained, but the process becomes prohibitively time-consuming and produces inconsistent results
Solution Approach 1:
The patent replaces manual human analysis with an automated machine learning system that uses deep learning models to detect features and generate short-form content. The system automatically analyzes media files, identifies relevant segments, and creates short-form content without human intervention, thereby dramatically increasing productivity while maintaining consistent, high-quality output through algorithmic processing
2Measurement precision
If comprehensive feature analysis is performed on large media libraries, then content quality and relevance improve, but the time required for analysis becomes prohibitively large
Solution Approach 1:
The patent employs deep learning models that automatically learn to identify relevant features and patterns in media files. These models can analyze comprehensive features across large media libraries with high accuracy while requiring minimal manual time, as the system learns from training data and applies sophisticated algorithms to quickly identify and extract meaningful content segments
Solution Approach 2:
The system performs preliminary training on a comprehensive dataset to learn feature identification patterns before analyzing new media files. This preliminary action enables the system to quickly and accurately identify relevant features in subsequent analysis without requiring time-consuming manual review, as the learning model is already optimized for the task
3Reliability
If human analysts review and verify short-form content segments, then quality control improves, but the overall process becomes slower and less scalable
Solution Approach 1:
The patent replaces human quality control with an automated machine learning system that consistently applies the same criteria and algorithms to evaluate and select content segments. This ensures uniform quality standards are maintained across all generated content while enabling high-volume production, as the system can process and quality-check large numbers of media files simultaneously without the limitations of human capacity
Data Source
AI summary
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product aspects, and/or combinations and sub-combinations thereof, for generating short-form content. An example aspect operates by analyzing a media file in a library using a machine learning model. To analyze the media file, the embodiment determines, using the machine learning model, a first portion of the media file that has a feature that satisfies a classification that the machine learning model is configured to identify. The embodiment tags the first portion using one or more position tags indicative of a beginning of the first portion of the media file or an end of the first portion of the media file. The embodiment then generates a segment from the media file based on the one or more position tags. The segment comprises the portion of the media file and excludes one or more second portions of the media file.