Object Recognition Device Selective Processing

The patent segments the object recognition task into multiple specialized machine learning models, each dedicated to a specific object class (e.g., face recognition model, product recognition model). Instead of using one general model for all objects, the system divides the recognition task and assigns specialized models to different object types, thereby achieving both versatility and precision.

The patent creates a universal object recognition system that can handle multiple object classes through a multi-functional architecture. The system includes multiple machine learning models that can be selected and applied based on the detected object class, making the system capable of performing various recognition tasks while maintaining high accuracy for each specific class.

The patent applies local quality by providing detailed recognition information selectively rather than uniformly for all objects. The system determines which objects require detailed analysis based on user input matching the detection region, and only then applies specialized machine learning models to generate detailed information. This localized approach to detailed processing significantly reduces computing resource consumption while still providing comprehensive information when needed.

The patent implements partial action by performing detailed object recognition only on selected objects that match user interest, rather than processing all detected objects equally. The system first performs general object detection, then selectively applies resource-intensive detailed recognition only when user input indicates interest in specific regions, avoiding unnecessary computation on objects that will not be queried.