Media Marker Alignment and Transfer for Timed Content Sync
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Solution Overview
Problem
Existing media supply chains face challenges in efficiently aligning timed components such as video, audio, captions, and advertisements, leading to misalignments that detract from consumer experience, and manual checks are inefficient and ineffective in managing the growing volume of content.
Innovation Solution
An automated system using machine learning techniques and regression models to detect and correct temporal misalignments in media components, aligning chapter markers and closed captions, and transfer metadata markers across different versions while preserving narrative integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual checks are used to align timed components, then alignment accuracy can be maintained, but productivity and efficiency deteriorate due to the growing volume of content
Solution Approach 1:
The patent replaces manual mechanical alignment processes with an automated machine learning system that uses regression models to detect and correct temporal misalignments. The system processes video content, audio, captions, and advertisements automatically, eliminating the need for manual review while maintaining alignment accuracy through algorithmic detection of timing offsets.
Solution Approach 2:
The patent introduces an automated alignment system as an intermediary between content production and delivery. This system acts as a mediator that automatically detects and corrects temporal misalignments in timed components, bridging the gap between manual processing limitations and the need for high-volume content processing without sacrificing alignment quality.
2Productivity
If automated alignment systems are implemented, then productivity and efficiency improve, but device complexity and implementation difficulty increase
Solution Approach 1:
The patent creates a universal alignment system that handles multiple types of timed components (video, audio, captions, advertisements) through a single regression model framework. This multi-functional approach consolidates what would otherwise require separate alignment processes for each component type, reducing overall system complexity while maintaining high productivity across diverse content formats.
Solution Approach 2:
The patent transforms the alignment problem from a complex multi-parameter optimization challenge into a simpler regression problem by changing the approach to detecting temporal offsets. The system uses parameter changes in the regression model to automatically adapt to different content types and alignment scenarios, simplifying implementation while preserving alignment accuracy.
3Reliability
If multiple timed components are packaged together, then content completeness improves, but alignment difficulty and error risk increase
Solution Approach 1:
The patent segments the alignment detection process by analyzing each timed component (video, audio, captions, advertisements) separately through individual regression models, then integrating the results. This segmentation approach makes it easier to detect and measure temporal misalignments in each component independently while maintaining the completeness of the multi-component content package.
Data Source
AI summary
Techniques for alignment of temporal markers and media content. These techniques include identifying a first plurality of temporal markers for alignment with a first item of media content, and determining, using a computer processor, alignment for the markers and media content. This includes: (i) identifying a plurality of differences between the first plurality of temporal markers and a second plurality of temporal markers, where the second plurality of markers relate to the first item of media content and are alternatively labeled compared with the first plurality of temporal markers, or (ii) determining a cumulative frame-by-frame difference (CD) for each temporal marker of the plurality of temporal markers, the first item of media content, and a second item of media content. The techniques further include performing an alignment action for the first plurality of temporal markers and the first item of media content.