Lens Apparatus Machine Learning Control for Seamless Focus and Zoom

The patent changes the control parameter from discrete conditional branches to continuous learning model outputs. The machine learning model generates control signals that continuously adapt to different conditions without abrupt transitions, resolving the contradiction between adaptability and continuity by using parameter-based adaptive control instead of branching logic.

The patent replaces the mechanical control system based on conditional branching with an intelligent control system using machine learning. The learning model substitutes the traditional control algorithm, enabling seamless transitions between control states through continuous function evaluation rather than discrete decision branches.

The patent merges multiple control factors and decision-making processes into a single machine learning model. The model simultaneously processes multiple input parameters (focus position, zoom level, lighting conditions, etc.) and generates coordinated control signals, reducing control complexity by integrating分散ed control logic into a unified intelligent system.

The machine learning model serves as a universal control mechanism that handles multiple control functions (focus adjustment, zoom control, aperture management) through a single system. This multi-functional approach improves comprehensiveness while managing complexity by using one adaptive controller instead of multiple specialized control algorithms.