Automated Media Clip Generation via ML Subtitle Matching
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Solution Overview
Problem
The manual generation of media content segments is resource-intensive, time-consuming, and costly due to the vast amount of media content available, requiring significant human effort and subjective determinations by media content management teams, which limits the efficiency and scalability of media clip generation.
Innovation Solution
The use of a trained machine learning model to identify and generate media clips based on relationships between media content and supplemental information, such as third-party data, manually determined audio and video information, reducing the need for manual effort by automating the determination and editing of media segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual generation of media content segments is used, then media clips can be created with human judgment and selection, but resources, time, and costs increase significantly
Solution Approach 1:
The patent replaces the manual mechanical process of watching, analyzing, cutting, and splicing media content with an automated system using machine learning models. The ML model automatically identifies media clips based on relationships between media content and supplemental information, eliminating the need for human operators to manually review and select content portions.
Solution Approach 2:
The system enables self-service by allowing the media content management system to automatically generate media clips without human intervention. The ML model autonomously processes media content, identifies relevant segments, and creates media clips based on learned patterns and relationships, making the system self-sufficient for clip generation tasks.
2Reliability
If manual generation of media content segments is used, then human expertise can be applied to selection and editing, but time consumption increases
Solution Approach 1:
The system performs preliminary action by pre-processing media content and supplemental information to create structured data that the ML model can efficiently process. The ML model is trained in advance on large datasets to learn patterns and relationships, enabling it to quickly generate high-quality media clips without requiring time-consuming manual review during operation.
3Manufacturing precision
If manually generated media content segments are created, then content quality can be controlled, but costs and computing resources increase
Solution Approach 1:
The system changes parameters by adjusting the complexity and configuration of the ML model based on requirements. The ML model can be configured with different parameters such as clip duration, quality thresholds, and processing priorities, allowing the system to optimize the balance between output quality and computing resource consumption for different use cases.
Data Source
AI summary
A system can be utilized to determine media clips for media content based on a comparison between subtitles of the media content and information associated with a third-party system. The comparisons can be utilized to identify subtitles based on the comparison and determine the media clips based on portions of the media content associated with the identified subtitles. A smoothing algorithm can be applied to the media clips to modify start times and end times of the media clips. The system can cause the media clips or the corresponding media content to be output by an external device.