AI Configuration Control for Fast, Memory-Efficient Parameter Tuning

The patent applies preliminary action by pre-training AI models offline to generate operational parameters, but only storing the trained model weights rather than pre-computing all possible operational modes. The first AI model quickly generates initial parameter estimates, and the second AI model refines them, achieving both speed and precision without requiring extensive memory for pre-computed data tables.

The patent replaces traditional mechanical lookup tables and pre-computed parameter sets with AI-based computational models. Instead of storing exhaustive parameter databases in memory, the system uses neural network models that can generate optimized parameters on-demand, substituting physical memory storage with intelligent algorithms that compute parameters as needed.

The patent segments the parameter optimization process into two distinct stages performed by separate AI models: a first model that rapidly generates initial parameter estimates, and a second model that refines these estimates to achieve optimal precision. This segmentation allows the system to deliver quick initial results while continuing to improve precision through the second stage, rather than requiring a single lengthy optimization process.

The system implements periodic action by using the first AI model to provide frequent, quick parameter updates during the measurement setup phase, then periodically refining these parameters with the second AI model. This allows the measurement process to begin with good initial parameters while continuing to improve precision through subsequent refinement cycles.