Edge Inference System for Real-Time Video Augmentation

The system segments the video processing workflow into distinct functional modules: object detection service, natural language processing service, video augmentation service, and workflow automation service. Each service operates independently but coordinates through standardized interfaces, allowing the complex AI/ML processing to be broken down into manageable components that can be developed, deployed, and scaled separately while maintaining high detection accuracy.

The patent introduces an intermediary orchestration layer that coordinates between the endpoint system, edge inference system, and backend services. This intermediary manages the complex interactions between multiple AI/ML models and services, handling service registration, discovery, and coordination without requiring the endpoint system to directly manage the complexity of multiple AI/ML components.

The system performs preliminary actions by pre-processing video frames and preparing multiple AI/ML models for parallel execution. The endpoint system pre-parses incoming video streams and identifies regions of interest before passing them to the edge inference system, where multiple models are already prepared and can process different aspects of the same frame simultaneously, reducing overall processing time while maintaining comprehensive detection capability.

The patent implements continuous processing pipelines where video frames are processed through multiple AI/ML models in an overlapping manner. While one model processes a frame, another model is already preparing to process the next frame, ensuring continuous useful action without idle time. The system maintains multiple inference pipelines running concurrently to eliminate processing gaps and reduce overall latency.

The system applies local quality by deploying different processing capabilities at different locations in the network hierarchy. The endpoint system performs local pre-processing and basic analysis, the edge inference system handles real-time object detection and augmentation with moderate computational requirements, and the backend cloud system provides heavy computational power for model training and complex analysis. This distribution ensures that time-sensitive operations occur locally with minimal network latency while still utilizing cloud computational power where appropriate.

The patent introduces a spatial dimension to the processing architecture by implementing a multi-tiered system (endpoint-edge-cloud) that processes video data at different network locations. This dimensional approach allows the system to balance computational power and latency by placing appropriate processing capabilities at each tier, rather than concentrating all processing in a single cloud location, thus reducing network latency for time-sensitive operations while maintaining access to cloud computational resources.