Automated Insulin Delivery Adaptation Using Medicament-On-Board

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Solution Overview

Problem

Existing automated medicament delivery systems struggle to provide optimal glucose control due to the need to avoid modifying algorithms for temporary insulin needs, leading to sub-optimal outcomes that persist for extended durations.

Innovation Solution

A computer-implemented method that incorporates real-time updates to medicament delivery based on user's carbohydrate ingestion and current medicament-on-board, using a model predictive control algorithm to adjust insulin doses, thereby personalizing the system's behavior and reducing the risk of hypo- or hyper-glycemic events.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If the automated medicament delivery system uses previous medicament delivery and analyte histories to assess algorithm modifications, then the system maintains stability and avoids compensating for temporary disturbances, but sub-optimal glucose control outcomes persist for extended durations

Engineering Contradiction:
Improvestability of medicament deliveryVSAvoidresponse speed to medicament needs changes
Core Design Contradiction:
ReliabilityVSProductivity

Solution Approach 1:

The system dynamically adjusts the assessment period for algorithm modifications based on detected changes in medicament needs. When a significant change is detected, the system shortens the assessment period from the default extended duration to a reduced duration, allowing faster adaptation to new medicament requirements while maintaining stability during normal conditions

Inventive Principle:
Principle #15Dynamics

Solution Approach 2:

The system changes the time parameter of algorithm assessment based on detected conditions. The assessment period is adjusted between extended duration (for stability) and reduced duration (for rapid adaptation), allowing the system to optimize between reliability and responsiveness by modifying this critical parameter

Inventive Principle:
Principle #35Parameter changes

2Stability of the object's composition

If the system personalizes medicament delivery algorithms over longer periods, then it avoids temporary disturbances, but glucose control outcomes remain sub-optimal for extended durations

Engineering Contradiction:
Improvealgorithm stabilityVSAvoidglucose control accuracy
Core Design Contradiction:
Stability of the object's compositionVSMeasurement precision

Solution Approach 1:

The system dynamically switches between extended and reduced assessment periods based on whether a change in medicament needs is detected. This dynamic adjustment allows the system to maintain algorithm stability during normal operation while rapidly improving glucose control accuracy when needs change

Inventive Principle:
Principle #15Dynamics

Solution Approach 2:

The system uses feedback from analyte measurements and medicament delivery history to detect changes in medicament needs. When changes are detected, the feedback triggers a switch to reduced assessment period, allowing the system to adapt and improve glucose control accuracy while maintaining overall stability

Inventive Principle:
Principle #23Feedback

Data Source

PatentEP4679441A1Methods to rapidly assess changes in medicament needs for accelerated short-term adaptivity of automated medicament delivery systems
Publication Date: 2026.01.14 INSULET CORP
  • EP4679441A1 patent drawingFigure 1
  • EP4679441A1 patent drawingFigure 2A~2B
  • EP4679441A1 patent drawingFigure 3A~3C

AI summary

Exemplary embodiments relate to automated medicament delivery (AMD) devices. Exemplary methods and apparatuses allow the AMD to account for the eventual impact of remaining medicament-on-board (MOB) on reduction in glucose concentrations, and do not incorporate the impact of meals on increases in glucose concentrations. This allows the AMD system to estimate the user's final glucose concentration if the impact of both carbohydrate ingestion and existing MOB are fully realized. Consequently, an AMD system can personalize its behaviors to a particular user more rapidly than if the system were to rely simply on previous medicament delivery and glucose histories. This is especially useful when a user begins using an AMD system with an inaccurate or poorly estimated initial value for total daily medicament (TDM) delivery.