Medtronic had five patents in big data during Q4 2023. Medtronic Plc filed patents in Q4 2023 for a method of retrospective calibration of a glucose sensor using machine learning models, a system for stimulating an anatomical element for glycemic control using personalized therapy settings determined by a machine learning algorithm, and a sensing device to monitor and track food intake events and behaviors, providing feedback to influence eating habits and patterns. GlobalData’s report on Medtronic gives a 360-degreee view of the company including its patenting strategy. Buy the report here.
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Medtronic grant share with big data as a theme is 40% in Q4 2023. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Retrospective sensor systems, devices, and methods (Patent ID: US20230360799A1)
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The patent by Medtronic Plc describes a method for retrospectively calibrating a glucose sensor by using stored values of measured working electrode current to calculate a final sensor glucose value. The process involves preprocessing the Isig values, applying discrete wavelet decomposition, and utilizing machine learning models like Genetic Programming and Regression Decision Tree to calculate SG values based on Isig values and wavelet decomposition. Additionally, other inputs such as counter electrode voltage and Electrochemical Impedance Spectroscopy data can be used. Multiple machine learning models can be employed to generate respective SG values, which are then fused to create a final SG value. The fused SG values can be filtered for data smoothing and blanked if necessary.
The method outlined in the patent involves measuring Isig values for the working electrode, storing them, preprocessing them, and decomposing them using wavelet decomposition. Machine learning models like Genetic Programming and Regression Decision Tree are used to calculate final SG values based on Isig values and wavelet decomposition. The patent also discusses the fusion of multiple SG values, the use of Electrochemical Impedance Spectroscopy data, and the smoothing of EIS-based parameters. The process can be repeated periodically or continuously to generate a stream of fused SG values over time, which can be further smoothed and blanked based on noise levels or specific parameters. Additionally, the method includes smoothing the preprocessed Isig values using a polynomial model and calculating signal noise for the smoothed Isig values.
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