Machine Learning Video Insertion Zone Detection

The patent introduces machine learning models as intermediary systems between the video content and human operators. These models automatically analyze video frames, identify insertion zones, and suggest suitable objects, thereby mediating the analysis process. This allows operators to review pre-processed content rather than analyzing everything from scratch, significantly increasing the volume of video material that can be efficiently reviewed while maintaining contextual relevance through human-in-the-loop validation.

The patent applies preliminary action by using machine learning models to perform automated pre-analysis of video content before human operators intervene. The system pre-identifies insertion zones, pre-determines suitable objects, and pre-ranks opportunities based on contextual relevance. This preliminary processing reduces the time operators need to spend on manual analysis while maintaining comprehensive coverage through the multi-stage approach that combines automated screening with human validation.

The patent segments the complex object insertion analysis system into distinct functional modules: video processing module, insertion zone detection module, object suggestion module, and scoring module. Each module handles a specific aspect of the analysis independently, which simplifies the overall system architecture while maintaining high processing speed. The segmentation allows each component to be optimized separately and facilitates easier debugging and maintenance despite the automated nature of the system.