Automated Video Ad Break Location Identification System
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Solution Overview
Problem
Existing methods for inserting ad breaks in video content are time-consuming, costly, and prone to human error, as they require manual identification of suitable locations, which is not feasible for large amounts of uncurated video content.
Innovation Solution
An automated system that analyzes video content to identify candidate locations for ad breaks by detecting specific characteristics such as black frames, silence, scene changes, songs, stunt scenes, mood changes, and theme music, using a combination of modules to determine suitable insertion points while avoiding undesirable locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification of ad break locations is used, then accuracy of location selection is improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent replaces the manual mechanical process of watching and analyzing video content with an automated computer-based system that uses audio and video processing algorithms to identify ad break locations, thereby eliminating time-consuming human intervention while maintaining accurate detection through systematic analysis of silence, scene changes, and other video characteristics
Solution Approach 2:
The system enables the video content itself to identify suitable ad break locations by automatically analyzing its own characteristics (audio silence, scene transitions, black frames) without requiring external human curators, allowing the content to serve its own monetization needs through self-analysis
2Productivity
If automated identification system is implemented, then productivity is improved, but system complexity increases
Solution Approach 1:
The automated system is divided into distinct functional modules: audio analysis module for detecting silence, video analysis module for detecting scene changes and black frames, and integration module for combining results. This segmentation allows each module to handle specific tasks independently, improving processing speed while managing complexity through modular design
Solution Approach 2:
The system employs multiple detection modules that serve different functions (audio silence detection, scene change detection, black frame detection) but all contribute to the same goal of identifying ad break locations. This multi-functionality approach allows the system to process various video characteristics simultaneously, enhancing productivity without proportionally increasing overall system complexity
3Quantity of substance
If ad breaks are inserted frequently, then revenue from advertisements is improved, but viewer experience deteriorates
Solution Approach 1:
The system identifies specific local characteristics within the video content (such as silent periods, scene transitions, or black frames) and places ad breaks only at these locally suitable positions rather than uniformly distributing them. This ensures that ad breaks occur at naturally appropriate moments in the content, maximizing the number of ad breaks while minimizing disruption to the viewing experience
4Object-affected harmful factors
If ad breaks are placed at suitable locations, then viewer experience is improved, but number of ad breaks decreases
Solution Approach 1:
The system dynamically adjusts the placement strategy by analyzing multiple video characteristics simultaneously (audio silence duration, scene change frequency, black frame presence) and adaptively selecting locations that satisfy both viewer experience requirements and revenue optimization goals, rather than using fixed placement rules
Data Source
AI summary
An automated method is provided for identifying candidate locations in video content for inserting advertisement (ad) breaks. Each candidate location is a different offset time from the beginning of the video content. Different distinct characteristics of the video content are identified at offset times. Certain characteristics are desirable and certain other characteristics are not desirable. Candidate locations are identified which have the most desirable characteristics at particular offset times, but which do not have any of the undesirable characteristics at any of the offset times.