Machine-Learning Time Marking for Faster Media Segment Access
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Solution Overview
Problem
Media item creators face significant time and resource challenges in determining and marking informative content segments for efficient user access, leading to increased latency and inefficiency.
Innovation Solution
A machine-learning model is trained using historical media item data to automatically identify and mark distinct content segments, allowing users to access specific portions without consuming the entire media item.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual segment identification is used by creators, then accuracy of content segment marking is improved, but time consumption and resource expenditure increase significantly
Solution Approach 1:
The system enables self-service by allowing the machine-learning model to automatically identify and mark content segments without requiring manual intervention from creators. The model processes media items independently, extracting informative segments based on trained patterns, thereby eliminating the time-consuming manual marking process while maintaining segmentation accuracy.
Solution Approach 2:
The patent replaces the mechanical manual marking process with an automated machine-learning system. The model uses computational algorithms to analyze media items, identify informative content segments, and generate time marks automatically, substituting human labor with an intelligent automated system that achieves both speed and accuracy.
2Manufacturing precision
If manual segment identification is used by creators, then quality of content segmentation is improved, but computing resources and efficiency deteriorate
Solution Approach 1:
The machine-learning model performs self-service by autonomously analyzing media items and generating content segmentations without requiring creator intervention. The system processes media items independently, applying trained algorithms to identify informative segments and generate time marks, thereby achieving high segmentation quality while significantly improving computing efficiency and productivity.
3Measurement precision
If the machine-learning model processes all media items sequentially, then processing depth is improved, but processing speed and latency increase
Solution Approach 1:
The system applies segmentation by dividing the media item processing into discrete content segments based on informative content detection. The machine-learning model identifies and processes distinct content segments independently, allowing for optimized processing depth for each segment while maintaining overall processing speed through parallelization and efficient sequential handling of segmented data.
Data Source
AI summary
Methods and systems for time marking of media items at a platform using machine-learning are provided herein. An indication of a identified media item is provided as input to a machine-learning model and one or more outputs of the machine-learning model is obtained. The one or more obtained outputs comprise time marks identifying each of the plurality of content segments of the media item. Each of the plurality of content segments is associated with a segment start indicator for a timeline of the media item. A resulting duration is determined of a combination of the plurality of content segments for which the time marks were obtained from the one or more of outputs of the machine-learning model. Responsive to determining that the resulting duration is less than the duration of the media item, one or more further inputs is provided to the machine learning model.