Dual-Mode Insulin Delivery Settings From Closed-Loop Data

The patent implements dynamic adjustment of infusion device settings by analyzing closed-loop operation data and automatically updating parameters such as basal rate and insulin sensitivity factor. The system transitions from static fixed settings to dynamic adaptive settings that evolve based on patient response patterns, thereby improving glycemic control precision while maintaining ease of use through automated optimization.

The patent employs feedback mechanisms where glucose sensor data and insulin delivery data from closed-loop operation are collected, analyzed, and used to generate recommendations for setting adjustments. This closed feedback loop allows the system to continuously learn from patient responses and refine manual mode settings, resolving the contradiction between operational simplicity and control precision.

The patent segments the optimization process into distinct functional modules: data collection from closed-loop operation, data analysis to identify patterns, generation of setting adjustment recommendations, and implementation of updated parameters. This modular segmentation allows complex optimization functionality to be managed through organized, independent components, reducing overall system complexity while maintaining high precision.

The system performs self-optimization by automatically analyzing its own closed-loop operation data and generating recommendations for improving its manual mode performance. This self-service capability eliminates the need for external manual tuning, reducing the operational complexity burden on users while achieving precise glycemic control through automated learning and adaptation.