Program Promotion Detection for Adaptive Video Ad Replacement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video streaming systems struggle to effectively increase revenue by optimizing the delivery of commercial content, particularly program promotions, which often have limited value and decrease in value after the advertised program has aired.
Innovation Solution
An automated process in a video streaming system detects program promotions using criteria such as time adjacency, timespan, audio, video, and textual factors, and replaces them with more valuable commercial content based on user demographics and streaming context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If program promotions are delivered to users in video streaming systems, then user engagement with future programs is improved, but revenue generation is limited and decreases after the advertised program has aired
Solution Approach 1:
The system performs preliminary detection and identification of program promotions in the video stream before they are delivered to users. By pre-identifying promotion content blocks using multiple criteria (time adjacency, timespan, audio, video, textual factors), the system can prepare replacement commercial content in advance, ensuring that high-value ads are ready to immediately replace expired promotions, thus maximizing revenue opportunities.
Solution Approach 2:
The system dynamically changes the state of commercial content by replacing program promotions with different commercial content based on multiple parameters including user demographics, streaming context, and promotion expiration status. This parameter-based substitution transforms the content delivery from static to adaptive, optimizing revenue by serving different commercial content to different user segments and contexts.
2Measurement precision
If automated detection of program promotions is implemented using multiple criteria, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The detection system is segmented into multiple independent analysis modules, each responsible for evaluating a specific criterion (time adjacency analysis, timespan verification, audio feature detection, video content analysis, textual banner recognition). This segmentation allows each module to specialize in one type of analysis, improving overall detection accuracy while making the complex system more manageable and maintainable through modular architecture.
Solution Approach 2:
The automated detection process serves multiple functions simultaneously: it identifies program promotions, analyzes their characteristics against multiple criteria, determines their expiration status, and triggers replacement actions. This multi-functionality consolidates what could be separate systems into a single integrated process, reducing overall system complexity while maintaining high detection accuracy.
3Loss of energy
If program promotions are replaced with paid commercial content, then revenue is increased, but user experience may be affected
Solution Approach 1:
The system applies different qualities of commercial content to different local contexts and user segments. Instead of uniformly replacing all program promotions with the same commercial content, it tailors the replacement content to specific user demographics, viewing contexts, and promotion characteristics. This local customization maintains user experience by showing relevant, non-intrusive ads while maximizing revenue through targeted advertising.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor user responses to replaced commercial content and adjustment of replacement strategies. By analyzing user engagement metrics, viewing patterns, and demographic data, the system continuously optimizes which program promotions to replace and with what commercial content, balancing revenue generation with user experience preservation through data-driven decision making.
Data Source
AI summary
Systems, devices and processes are provided to facilitate video streaming. The various embodiments facilitate the detection of program promotions in a video stream, where program promotions include commercial content that advertises future programs. In general, the embodiments facilitate the detection of program promotions in a video stream through the use of automated process monitors the video stream and determines that a content block is a program promotion when that content block meets a defined set of criteria. This set of criteria can include criteria such as time adjacency, timespan, audio, video and textual factors, and when the content was previously first detected. In such embodiments the ability to detect program promotions using the set of criteria facilitates the replacement of these program promotions with other, potentially more valuable, content.