Video Clip Learning Model for Predictive Clip Ranking
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Solution Overview
Problem
Human users face challenges in selecting video clips from media items that effectively entice viewers due to the overwhelming number and variety of clips available, making it difficult to identify those that will most appeal to different audiences.
Innovation Solution
A machine learning model is trained to analyze video clips for patterns that correlate with user views, predicting a 'take fraction value' indicating the clip's effectiveness in attracting viewers, and iteratively improves based on feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human users manually select video clips from a wide range of available clips, then they can choose clips to promote media items, but the overwhelming number and variety of clips make it difficult to identify suitable clips efficiently
Solution Approach 1:
The patent replaces the manual mechanical selection process with an automated machine learning system. The ML model analyzes video clips and predicts their performance metrics automatically, eliminating the need for human users to manually evaluate each clip. This substitution of automated intelligence for manual effort directly resolves the contradiction by maintaining high productivity while managing complexity through algorithmic processing.
Solution Approach 2:
The patent introduces an intermediary ML model that acts as a mediator between the large set of available clips and the selection decision. The model processes the complex variety of clips and outputs a simplified prediction, serving as an intermediary layer that manages the complexity while enabling efficient selection. This intermediary system handles the burden of clip variety, allowing users to make informed decisions without being overwhelmed.
2Adaptability or versatility
If multiple video clips are used to promote a media item, then more options are available for different audiences, but it becomes harder to determine which clips will most effectively entice viewers
Solution Approach 1:
The patent implements a feedback mechanism where the ML model's predictions are continuously refined based on actual performance data. The system tracks how well selected clips perform in enticing viewers and uses this feedback to improve future predictions. This feedback loop enhances measurement precision by learning from real-world outcomes, while maintaining adaptability through iterative model improvement.
Solution Approach 2:
The patent performs preliminary analysis and prediction before final clip selection. The ML model evaluates clips in advance and provides performance predictions, allowing users to make informed decisions about which clips to use for different audiences. This preliminary action enables versatile clip selection while improving prediction accuracy by analyzing clips before deployment.
3Reliability
If video clips are selected based on human judgment, then subjective expertise can be applied, but the process is time-consuming and less scalable
Solution Approach 1:
The patent replaces time-consuming manual evaluation with automated ML-based assessment. The system maintains reliability by using sophisticated models trained on performance data, while dramatically reducing the time required for clip selection. This substitution preserves selection quality through algorithmic expertise while eliminating the time loss associated with manual processes.
Solution Approach 2:
The patent enables the system to perform self-service evaluation of video clips through automated ML analysis. The model independently assesses clips without requiring extensive human time investment, maintaining reliable predictions while minimizing time loss. This self-service capability scales the selection process without sacrificing quality.
Data Source
AI summary
A computer-implemented method may include receiving, at a pre-processing module of a video clip learning model (VCLM), an input media item, segmenting, via the pre-processing module, the media item into a plurality of video clips, filtering, via the pre-processing module, the plurality of video clips to remove sensitive content, and providing the filtered plurality of video clips to at least one downstream module of the VCLM for at least one of selection, ranking, or presentation as a video clip used to promote the input media item. Various other methods, systems, and computer-readable media are also disclosed.