State Machine Framework for Implantable Medical Device Therapy Control
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Solution Overview
Problem
Current medical devices require frequent firmware updates for new control policy algorithms, which is time-consuming, power-intensive, and burdensome, limiting the ability to test and implement different algorithms during the device's lifespan due to storage constraints and potential download issues.
Innovation Solution
A state machine framework that allows for the programming and download of new closed-loop control policy algorithms without requiring new firmware code, using programmable state parameters to define the structure of a state machine that can be executed on an implantable medical device, enabling flexible control of therapy delivery based on sensed patient states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If firmware updates are performed to implement new control policy algorithms, then the device can adopt new therapy strategies, but the process is time-consuming and power-intensive
Solution Approach 1:
The patent segments the control policy algorithm into two parts: fixed firmware infrastructure and configurable state parameters. The state parameters (state definitions, transitions, actions, conditions) can be independently modified and downloaded without updating the entire firmware, enabling partial updates that reduce time and power consumption while maintaining adaptability.
Solution Approach 2:
The patent uses parameter changes by allowing the state machine configuration to be modified through downloadable state parameters rather than firmware updates. The runtime environment interprets these parameters dynamically, enabling algorithm changes through simple parameter downloads that are much faster and less power-intensive than traditional firmware updates.
2Adaptability or versatility
If firmware updates are performed to implement new control policy algorithms, then new therapy strategies can be deployed, but power consumption increases
Solution Approach 1:
The patent segments the control policy algorithm into two parts: fixed firmware infrastructure and configurable state parameters. The state parameters (state definitions, transitions, actions, conditions) can be independently modified and downloaded without updating the entire firmware, enabling partial updates that reduce time and power consumption while maintaining adaptability.
Solution Approach 2:
The patent uses parameter changes by allowing the state machine configuration to be modified through downloadable state parameters rather than firmware updates. The runtime environment interprets these parameters dynamically, enabling algorithm changes through simple parameter downloads that are much faster and less power-intensive than traditional firmware updates.
3Adaptability or versatility
If firmware updates are required for new algorithms, then control policy can be changed, but the process is burdensome and complex
Solution Approach 1:
The patent introduces a state machine runtime environment as an intermediary layer between the fixed firmware and the configurable state parameters. This runtime environment handles the interpretation and execution of state parameters, shielding users from complex firmware update processes and providing a simplified interface for algorithm configuration and deployment.
4Adaptability or versatility
If new firmware code is downloaded for new algorithms, then control policy can be updated, but storage constraints and download issues limit flexibility
Solution Approach 1:
The patent segments the control policy algorithm into two parts: fixed firmware infrastructure and configurable state parameters. The state parameters (state definitions, transitions, actions, conditions) can be independently modified and downloaded without updating the entire firmware, enabling partial updates that reduce time and power consumption while maintaining adaptability.
Data Source
AI summary
This disclosure describes a state machine framework for programming closed-loop algorithms that control the delivery of therapy to a patient by an implantable medical device (IMD). The state machine framework may use one or more programmable state parameters to define at least part of a structure of a state machine that generates one or more therapy decisions based on one or more sensed states of the patient. The state machine framework may include a state machine runtime environment that executes on an IMD and that is configurable to implement a variety of different state machines depending on programmable state parameters that are received from an external device. The techniques of this disclosure may, in some cases, allow IMD developers and/or users to program, change, and/or download new closed-loop control policy algorithms during the lifespan of the IMD without requiring new firmware code to be downloaded onto the IMD.