公开(公告)号：US20200245910A1

公开(公告)日：2020-08-06

申请号：US16773422

申请日：2020-01-27

Applicant: MEDTRONIC MINIMED, INC

Inventor： Georgios Mallas ,  Andrea Varsavsky ,  Peter Ajemba ,  Jeffrey Nishida ,  Keith Nogueira ,  Elaine Gee ,  Leonardo Nava-Guerra ,  Jing Liu ,  Sadaf S. Seleh ,  Taly G, Engel ,  Benyamin Grosman ,  Steven Lai ,  Luis A. Torres ,  Chi A. Tran ,  David M. Sniecinski

IPC: A61B5/145 ,  A61B5/1468

Abstract: A continuous glucose monitoring system may utilize electrode current (Isig) signals, Electrochemical Impedance Spectroscopy (EIS), and Vcntr values to optimize sensor glucose (SG) calculation in such a way as to enable reduction of the need for blood glucose (BG) calibration requests from users.

公开(公告)号：US12114972B2

公开(公告)日：2024-10-15

申请号：US16773422

申请日：2020-01-27

Applicant: MEDTRONIC MINIMED, INC.

Inventor： Georgios Mallas ,  Andrea Varsavsky ,  Peter Ajemba ,  Jeffrey Nishida ,  Keith Nogueira ,  Elaine Gee ,  Leonardo Nava-Guerra ,  Jing Liu ,  Sadaf S. Seleh ,  Taly G. Engel ,  Benyamin Grosman ,  Steven Lai ,  Luis A. Torres ,  Chi A. Tran ,  David M. Sniecinski

IPC: A61B5/145 ,  A61B5/1468

CPC classification number: A61B5/14532 ,  A61B5/1468 ,  A61B2560/0223 ,  A61B2562/16

Abstract: A continuous glucose monitoring system may utilize electrode current (Isig) signals, Electrochemical Impedance Spectroscopy (EIS), and Vcntr values to optimize sensor glucose (SG) calculation in such a way as to enable reduction of the need for blood glucose (BG) calibration requests from users.

公开(公告)号：US20250134416A1

公开(公告)日：2025-05-01

申请号：US18908892

申请日：2024-10-08

Applicant: Medtronic MiniMed, Inc.

Inventor： Leonardo Nava-Guerra ,  Molly E. Emig ,  Juan E. Arguelles Morales ,  Anup V. Kanale ,  Kelly J. Qiu ,  Mona M. Sharifi Sarabi ,  Chi A. Tran ,  Anthony Haas ,  Francesca Piccinini ,  Bahram Notghi ,  Georgios Mallas ,  Yi Zhang

IPC: A61B5/145

Abstract: Techniques disclosed herein relate to glucose level measurement and/or management. In some embodiments, the techniques involve obtaining in vivo characteristics of a glucose sensor predicted using fabrication process measurement data associated with the glucose sensor, the in vivo characteristics including an in vivo sensitivity, an in vivo intercept, or a combination thereof; receiving sensor measurement data measured by the glucose sensor, the sensor measurement data including sensor current (Isig), counter voltage (Vcntr), electrochemical impedance spectroscopy (EIS) data, an age of the glucose sensor, or a combination thereof; and estimating a sensor glucose (SG) value using an SG model, wherein input parameters of the SG model include the in vivo characteristics of the glucose sensor and the sensor measurement data, and the SG value is an output of the SG model.

公开(公告)号：US20250160703A1

公开(公告)日：2025-05-22

申请号：US18934534

申请日：2024-11-01

Applicant: Medtronic MiniMed, Inc.

Inventor： Juan Enrique Arguelles Morales ,  Sadaf S. Seleh ,  Leonardo Nava-Guerra ,  Bahram B. Notghi ,  Georgios Mallas ,  Francesca Piccinini ,  Sarkis D. Aroyan ,  Ellis Garai ,  Zachary A. Shah

IPC: A61B5/1495 ,  A61B5/00 ,  A61B5/145

Abstract: A system for reducing sensor variability includes a sensor configured to generate real-time data relating to glucose sensitivity. The system causes performance of accessing the real-time data from the sensor relating to glucose sensitivity and inputting the real-time data into a machine learning model. The system also causes performance of estimating by the machine learning model an expected glucose sensitivity based on the real-time data and correcting the glucose sensitivity based on the expected glucose sensitivity.

公开(公告)号：US20250134417A1

公开(公告)日：2025-05-01

申请号：US18908980

申请日：2024-10-08

Applicant: Medtronic MiniMed, Inc.

Inventor： Jake G. Basilico ,  Leonardo Nava-Guerra ,  Bahram B. Notghi ,  Molly E. Emig ,  Jing Liu ,  Adam A. Willats ,  Georgios Mallas ,  Yi Zhang

IPC: A61B5/145 ,  A61B5/00

Abstract: A processor-implemented method includes receiving sensor measurement data from a glucose sensor; selecting, based on the sensor measurement data, a first regional sensor glucose (SG) model from a first plurality of regional SG models for respective regions of a first plurality of regions of an input parameter space associated with the sensor measurement data, and a second regional SG model from a second plurality of regional SG models for respective regions of a second plurality of regions of the input parameter space; estimating a first SG value and a second SG value using the first regional SG model and the second regional SG model, respectively; and determining a predicted SG value based on a combination of the first SG value and the second SG value. The input parameter space is partitioned into the first plurality of regions and the second plurality of regions using different partition schemes.