Automated Trailer Clip Selection Using Engagement Feedback
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Solution Overview
Problem
Conventional trailer generation for multimedia content is inefficient, costly, and subjective, failing to scale properly and optimize for user engagement.
Innovation Solution
An automated system using machine learning and AI models to generate trailers by selecting optimal starting points based on user engagement data, employing Multi-Armed Bandit models to optimize clip selection and filtering for content appropriateness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual trailer generation is used, then trailer quality can be controlled by human judgment, but it does not scale properly and is costly and time consuming
Solution Approach 1:
The patent replaces the manual mechanical process of trailer creation with an automated computer-based system. The system uses algorithms to automatically select clips, determine their order, and generate trailers without human operators, thereby increasing productivity while managing complexity through software automation.
Solution Approach 2:
The system enables trailers to generate themselves automatically using computational algorithms. The computer-based system performs self-service by autonomously selecting and ordering clips based on predefined criteria and user engagement data, eliminating the need for human operators to manually create each trailer.
2Manufacturing precision
If random clips are chosen for trailers, then the process is simple and fast, but it relies on guesses and assumptions rather than optimization for user engagement
Solution Approach 1:
The system implements feedback loops where user engagement data from trailer performances is collected and analyzed. This feedback is then used to refine and improve future clip selections, allowing the system to learn from past performance and continuously optimize trailer creation accuracy over time.
Solution Approach 2:
The system performs preliminary actions by collecting conversion data and analyzing user engagement patterns before finalizing trailer clip selections. This advance preparation and data gathering enables more accurate and optimized clip selection rather than relying on random choices.
3Extent of automation
If human operators manually create trailers, then subjectivity in clip selection can be applied, but the process is only capable of being produced by human operators and does not scale
Solution Approach 1:
The system changes the parameters of trailer generation from human-based subjective judgment to computer-based objective algorithmic selection. By transforming the generation process into automated computational operations with consistent rules and criteria, the system achieves both high automation and reliable consistent quality across all trailers.
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 trailers (previews) for multimedia content. An example aspect operates by generating an initial set of candidate points to generate a trailer for a media content; determining conversion data for each of the initial set of candidate points; determining an updated set of candidate points based on the conversion data; determining an estimated mean and upper bound for each of the updated set of candidate points; computing a value for each of the updated set of candidate points; generating a ranked list based on the value computed for each of the updated set of candidate points; and repeating the process until an optimal candidate point is converged upon.