AI Ad-Break Prediction Using Black Frames and Audio Continuity

The patent replaces the mechanical system of manual human editor evaluation with an automated machine learning system that analyzes video and audio features. The ML model processes content objectively without human intervention, substituting the manual mechanical process with an automated computational system that maintains accuracy while dramatically reducing processing time.

The system enables content to evaluate itself for suitable ad-break points through automated analysis of its own video and audio features. The ML model processes the content's intrinsic characteristics (black frames, silent frames, narrative transitions) to identify optimal ad-break locations without requiring external human evaluation, making the process self-service and highly scalable.

The patent replaces the expensive mechanical system of human editor labor with an automated machine learning system. This substitution eliminates the need to pay human editors for manual evaluation, significantly reducing production costs while maintaining ad-break placement accuracy through objective algorithmic analysis of content features.

The content evaluation process becomes self-service, where the ML model automatically analyzes video and audio features to identify ad-break points without requiring human labor. This eliminates the cost of human editorial services while maintaining high accuracy through automated processing of content characteristics.

The patent implements an automated machine learning system that processes content at high speed while maintaining accuracy. The ML model analyzes video frames for black frames, audio for silent frames, and detects narrative transitions automatically, achieving both fast processing and accurate ad-break identification without requiring manual human evaluation.

The system changes the parameters of analysis by examining multiple content features simultaneously (video black frames, audio silent frames, narrative transitions) rather than relying on a single parameter. This multi-parameter approach enables the automated system to achieve high accuracy comparable to human editors while maintaining fast processing speed through efficient algorithmic evaluation.

The patent replaces manual human editor evaluation with an automated ML system that can process unlimited content at high speed. The system analyzes video and audio features to identify content-aware ad-breaks automatically, enabling scalability to handle large content libraries without the bottleneck of human editorial capacity while maintaining accurate content awareness.

Each content title is evaluated autonomously by the ML model without requiring human editor involvement. The system processes content independently, analyzing its own video and audio characteristics to identify suitable ad-break points, enabling unlimited parallel processing and scaling to large content libraries without additional human resources.