Next-Generation Electrical Stimulation Therapy Optimization

A system for optimizing electrical stimulation therapy using machine learning, neural networks, and real-time patient data to improve therapy efficacy and patient outcomes.

The original patent, 'Managing storage of sensed information,' addressed the limitations of traditional medical devices in delivering electrical stimulation therapy. However, the original patent's approach relies on static ECAP information and lacks real-time adaptability. The new inventive concept addresses this limitation by integrating machine learning and neural networks to optimize stimulation parameters in real-time, ensuring more effective and personalized therapy.

The new inventive concept comprises a system for optimizing electrical stimulation therapy, featuring a neural network trained to predict optimal stimulation parameters based on real-time ECAP information and patient-specific data. The system includes processing circuitry configured to adjust stimulation parameters in response to predictions from the neural network. Additionally, the system can incorporate wearable sensors or cameras to detect changes in patient posture, and machine learning algorithms to adjust stimulation parameters in real-time. The system can also include a database of patient-specific ECAP information and therapy outcomes, enabling personalized stimulation parameters generation. Furthermore, the system can predict and prevent adverse effects of electrical stimulation therapy by analyzing real-time ECAP information and patient-specific data using machine learning algorithms.

The new claims introduce the novel application of machine learning and neural networks in optimizing electrical stimulation therapy, which is a significant departure from the original patent's static approach. The inventive step lies in the integration of real-time patient data, machine learning algorithms, and neural networks to adapt stimulation parameters and ensure more effective and personalized therapy.

Alternative embodiments of the inventive concept could include the use of other machine learning algorithms, such as deep learning or reinforcement learning, to optimize stimulation parameters. Additionally, the system could be integrated with other medical devices, such as implantable sensors or wearable devices, to expand its capabilities and applications.

The new inventive concept has significant commercial potential in the medical device industry, particularly in the areas of electrical stimulation therapy, pain management, and neurological disorders. The system's ability to optimize therapy efficacy and patient outcomes could lead to increased adoption and market share, as well as new business opportunities in the development of personalized therapy solutions.