公开(公告)号：US12161464B2

公开(公告)日：2024-12-10

申请号：US17163149

申请日：2021-01-29

Applicant: MEDTRONIC MINIMED, INC.

Inventor： Peter Ajemba ,  Keith Nogueira

IPC: A61B5/145 ,  A61B5/00 ,  G06N20/00 ,  G06N20/20 ,  G16H40/63 ,  G16H50/20 ,  G16H50/30

Abstract: Methods, systems, and devices for improving continuous glucose monitoring (“CGM”) are described herein. More particularly, the methods, systems, and devices describe applying micro machine learning models to generate predicted sensor glucose values. The system may use the predicted sensor glucose values to display a sensor glucose value to a user. The layered models may generate more reliable sensor glucose predictions across many scenarios, leading to a reduction of sensor glucose signal blanking. The methods, systems, and devices described herein further comprise applying a plurality of micro model to estimate sensor glucose values under outlier conditions. The system may prioritize the models that are trained for certain outlier conditions when the system detects those outlier condition based on the sensor data.

公开(公告)号：US12119119B2

公开(公告)日：2024-10-15

申请号：US16848695

申请日：2020-04-14

Applicant: MEDTRONIC MINIMED, INC.

Inventor： Elaine Gee ,  Peter Ajemba ,  Bahman Engheta ,  Jeffrey Nishida ,  Andrea Varsavsky ,  Keith Nogueira

IPC: G16H50/00 ,  A61B5/00 ,  A61B5/145 ,  G06F18/214 ,  G06N3/045 ,  G06N3/0475 ,  G06N3/088 ,  G16H10/60 ,  G16H20/17 ,  G16H40/40 ,  G16H40/67 ,  G16H50/20 ,  G16H50/30 ,  G16H50/50 ,  G16H50/70 ,  G16H70/60 ,  A61B5/1495 ,  A61M5/172

CPC classification number: G16H50/50 ,  A61B5/0022 ,  A61B5/1451 ,  A61B5/14532 ,  A61B5/7253 ,  A61B5/7267 ,  A61B5/7275 ,  G06F18/214 ,  G06N3/045 ,  G06N3/0475 ,  G06N3/088 ,  G16H10/60 ,  G16H20/17 ,  G16H40/40 ,  G16H40/67 ,  G16H50/20 ,  G16H50/30 ,  G16H50/70 ,  G16H70/60 ,  A61B5/1495 ,  A61B2560/0223 ,  A61B2560/0228 ,  A61M5/1723 ,  A61M2205/3303 ,  A61M2205/50 ,  A61M2205/52 ,  A61M2205/70 ,  A61M2230/201

Abstract: Medical devices and related systems and methods are provided. A method of estimating a physiological condition using a first sensing arrangement involves obtaining a sensor translation model associated with a relationship between the first sensing arrangement and a second sensing arrangement, wherein the second sensing arrangement is different from the first sensing arrangement, obtaining one or more measurements from a sensing element coupled to the processing system of the first sensing arrangement, determining simulated measurement data for the second sensing arrangement by applying the sensor translation model to the one or more measurements from the sensing element of the first sensing arrangement, and determining an estimated value for the physiological condition by applying an estimation model associated with the second sensing arrangement to the simulated measurement data.

公开(公告)号：US11963768B2

公开(公告)日：2024-04-23

申请号：US17737236

申请日：2022-05-05

Applicant: Medtronic MiniMed, Inc.

Inventor： Keith Nogueira ,  Peter Ajemba ,  Michael E. Miller ,  Steven C. Jacks ,  Jeffrey Nishida ,  Andy Y. Tsai ,  Andrea Varsavsky

IPC: A61B5/1495 ,  A61B5/00 ,  A61B5/0205 ,  A61B5/021 ,  A61B5/024 ,  A61B5/11 ,  A61B5/145 ,  A61B5/1455 ,  A61B5/1468 ,  A61B5/1486 ,  G01N27/02 ,  G06N5/022 ,  G16H20/17 ,  G16H40/40 ,  G16H50/30 ,  G16H50/70

CPC classification number: A61B5/1495 ,  A61B5/14532 ,  A61B5/1468 ,  A61B5/14865 ,  A61B5/6849 ,  A61B5/686 ,  G01N27/026 ,  G06N5/022 ,  G16H20/17 ,  G16H40/40 ,  G16H50/30 ,  G16H50/70 ,  A61B5/0075 ,  A61B5/02055 ,  A61B5/021 ,  A61B5/024 ,  A61B5/1118 ,  A61B5/14546 ,  A61B5/1455 ,  A61B5/7203 ,  A61B5/7221 ,  A61B5/7267 ,  A61B5/742 ,  A61B2505/07 ,  A61B2560/0223 ,  A61B2560/0252 ,  A61B2560/0257 ,  A61B2562/028 ,  A61B2562/029 ,  A61B2562/164

Abstract: A continuous glucose monitoring system may utilize externally sourced information regarding the physiological state and ambient environment of its user for externally calibrating sensor glucose measurements. Externally sourced factory calibration information may be utilized, where the information is generated by comparing metrics obtained from the data used to generate the sensor's glucose sensing algorithm to similar data obtained from each batch of sensors to be used with the algorithm in the future. The output sensor glucose value of a glucose sensor may also be estimated by analytically optimizing input sensor signals to accurately correct for changes in sensitivity, run-in time, glucose current dips, and other variable sensor wear effects. Correction actors, fusion algorithms, EIS, and advanced ASICs may be used to implement the foregoing, thereby achieving the goal of improved accuracy and reliability without the need for blood-glucose calibration, and providing a calibration-free, or near calibration-free, sensor.

公开(公告)号：US11957464B2

公开(公告)日：2024-04-16

申请号：US17574700

申请日：2022-01-13

Applicant: Medtronic MiniMed, Inc.

Inventor： Peter Ajemba ,  Keith Nogueira ,  Brian T. Kannard

IPC: A61B5/1495 ,  A61B5/00 ,  A61B5/0205 ,  A61B5/021 ,  A61B5/024 ,  A61B5/11 ,  A61B5/145 ,  A61B5/1455 ,  A61B5/1468 ,  A61B5/1486 ,  G01N27/02 ,  G06N5/022 ,  G16H20/17 ,  G16H40/40 ,  G16H50/30 ,  G16H50/70

CPC classification number: A61B5/1495 ,  A61B5/14532 ,  A61B5/1468 ,  A61B5/14865 ,  A61B5/6849 ,  A61B5/686 ,  G01N27/026 ,  G06N5/022 ,  G16H20/17 ,  G16H40/40 ,  G16H50/30 ,  G16H50/70 ,  A61B5/0075 ,  A61B5/02055 ,  A61B5/021 ,  A61B5/024 ,  A61B5/1118 ,  A61B5/14546 ,  A61B5/1455 ,  A61B5/7203 ,  A61B5/7221 ,  A61B5/7267 ,  A61B5/742 ,  A61B2505/07 ,  A61B2560/0223 ,  A61B2560/0252 ,  A61B2560/0257 ,  A61B2562/028 ,  A61B2562/029 ,  A61B2562/164

Abstract: A continuous glucose monitoring system may utilize externally sourced information regarding the physiological state and ambient environment of its user for externally calibrating sensor glucose measurements. Externally sourced factory calibration information may be utilized, where the information is generated by comparing metrics obtained from the data used to generate the sensor's glucose sensing algorithm to similar data obtained from each batch of sensors to be used with the algorithm in the future. The output sensor glucose value of a glucose sensor may also be estimated by analytically optimizing input sensor signals to accurately correct for changes in sensitivity, run-in time, glucose current dips, and other variable sensor wear effects. Correction actors, fusion algorithms, EIS, and advanced ASICs may be used to implement the foregoing, thereby achieving the goal of improved accuracy and reliability without the need for blood-glucose calibration, and providing a calibration-free, or near calibration-free, sensor.

公开(公告)号：US20230000402A1

公开(公告)日：2023-01-05

申请号：US17939067

申请日：2022-09-07

Applicant: Medtronic MiniMed, Inc.

Inventor： Peter Ajemba ,  Keith Nogueira ,  Jeffrey Nishida ,  Andy Y. Tsai

IPC: A61B5/1495 ,  A61B5/145 ,  A61B5/1486 ,  A61B5/00 ,  G06N5/02 ,  G16H50/30 ,  G01N27/02 ,  G16H20/17 ,  G16H50/70 ,  A61B5/1468 ,  G16H40/40

Abstract: A continuous glucose monitoring system may utilize externally sourced information regarding the physiological state and ambient environment of its user for externally calibrating sensor glucose measurements. Externally sourced factory calibration information may be utilized, where the information is generated by comparing metrics obtained from the data used to generate the sensor's glucose sensing algorithm to similar data obtained from each batch of sensors to be used with the algorithm in the future. The output sensor glucose value of a glucose sensor may also be estimated by analytically optimizing input sensor signals to accurately correct for changes in sensitivity, run-in time, glucose current dips, and other variable sensor wear effects. Correction actors, fusion algorithms, EIS, and advanced ASICs may be used to implement the foregoing, thereby achieving the goal of improved accuracy and reliability without the need for blood-glucose calibration, and providing a calibration-free, or near calibration-free, sensor.

公开(公告)号：US20210174949A1

公开(公告)日：2021-06-10

申请号：US16848687

申请日：2020-04-14

Applicant: MEDTRONIC MINIMED, INC.

Inventor： Elaine Gee ,  Peter Ajemba ,  Bahman Engheta ,  Jeffrey Nishida ,  Andrea Varsavsky ,  Keith Nogueira

IPC: G16H40/40 ,  G16H50/30 ,  G16H50/50 ,  G16H50/20 ,  G16H40/67 ,  G16H20/17 ,  G16H10/60 ,  A61B5/00 ,  A61B5/145

Abstract: Medical devices and related systems and methods are provided. A method of estimating a physiological condition involves determining a translation model based at least in part on relationships between first measurement data corresponding to instances of a first sensing arrangement and second measurement data corresponding to instances of a second sensing arrangement, obtaining third measurement data associated with the second sensing arrangement, determining simulated measurement data for the first sensing arrangement by applying the translation model to the third measurement data, and determining an estimation model for a physiological condition using the simulated measurement data, wherein the estimation model is applied to subsequent measurement output provided by an instance of the first sensing arrangement to obtain an estimated value for the physiological condition.

公开(公告)号：US20210077717A1

公开(公告)日：2021-03-18

申请号：US16569401

申请日：2019-09-12

Applicant: MEDTRONIC MINIMED, INC.

Inventor： Akhil Srinivasan ,  Peter Ajemba ,  Steven C. Jacks ,  Robert C. Mucic ,  Tyler R. Wong ,  Melissa Tsang ,  Chi-En Lin ,  Mohsen Askarinya ,  David Probst

IPC: A61M5/172 ,  A61B5/145 ,  A61M5/142 ,  A61B5/1495

Abstract: Medical devices and related systems and methods are provided. A method of calibrating an instance of a sensing element involves obtaining fabrication process measurement data from a substrate having the instance of the sensing element fabricated thereon, obtaining a calibration model associated with the sensing element, determining calibration data associated with the instance of the sensing element for converting the electrical signals into a calibrated measurement parameter based on the fabrication process measurement data using the calibration model, and storing the calibration data in a data storage element associated with the instance of the sensing element.

公开(公告)号：US20190076067A1

公开(公告)日：2019-03-14

申请号：US16117617

申请日：2018-08-30

Applicant: MEDTRONIC MINIMED, INC.

Inventor： Peter Ajemba ,  Keith Nogueira ,  Brian T. Kannard

IPC: A61B5/1495 ,  A61B5/145 ,  A61B5/1486 ,  A61B5/00 ,  G06N5/02 ,  G16H50/30 ,  G01N27/02

Abstract: A continuous glucose monitoring system may utilize externally sourced information regarding the physiological state and ambient environment of its user for externally calibrating sensor glucose measurements. Externally sourced factory calibration information may be utilized, where the information is generated by comparing metrics obtained from the data used to generate the sensor's glucose sensing algorithm to similar data obtained from each batch of sensors to be used with the algorithm in the future. The output sensor glucose value of a glucose sensor may also be estimated by analytically optimizing input sensor signals to accurately correct for changes in sensitivity, run-in time, glucose current dips, and other variable sensor wear effects. Correction actors, fusion algorithms, EIS, and advanced ASICs may be used to implement the foregoing, thereby achieving the goal of improved accuracy and reliability without the need for blood-glucose calibration, and providing a calibration-free, or near calibration-free, sensor.

公开(公告)号：US20250049352A1

公开(公告)日：2025-02-13

申请号：US18926013

申请日：2024-10-24

Applicant: Medtronic MiniMed, Inc.

Inventor： Peter Ajemba ,  Keith G. Nogueira

IPC: A61B5/145 ,  A61B5/00 ,  G06N20/00 ,  G06N20/20 ,  G16H40/63 ,  G16H50/20 ,  G16H50/30

Abstract: Methods, systems, and devices for improving continuous glucose monitoring (“CGM”) are described herein. More particularly, the methods, systems, and devices describe applying micro machine learning models to generate predicted sensor glucose values. The system may use the predicted sensor glucose values to display a sensor glucose value to a user. The layered models may generate more reliable sensor glucose predictions across many scenarios, leading to a reduction of sensor glucose signal blanking. The methods, systems, and devices described herein further comprise applying a plurality of micro model to estimate sensor glucose values under outlier conditions. The system may prioritize the models that are trained for certain outlier conditions when the system detects those outlier condition based on the sensor data.

公开(公告)号：US12138047B2

公开(公告)日：2024-11-12

申请号：US17156490

申请日：2021-01-22

Applicant: MEDTRONIC MINIMED, INC.

Inventor： Peter Ajemba ,  Keith Nogueira

IPC: A61B5/145 ,  A61B5/00 ,  G06N20/00 ,  G06N20/20 ,  G16H40/63 ,  G16H50/20 ,  G16H50/30

Abstract: Methods, systems, and devices for improving continuous glucose monitoring (“CGM”) are described herein. More particularly, the methods, systems, and devices describe applying layered machine learning models to generate predicted sensor glucose values. The system may use the predicted sensor glucose values to display a sensor glucose value to a user. The layered models may generate more reliable sensor glucose predictions across many scenarios, leading to a reduction of sensor glucose signal blanking. The methods, systems, and devices described herein further comprise applying a plurality of micro model to estimate sensor glucose values under outlier conditions. The system may prioritize the models that are trained for certain outlier conditions when the system detects those outlier condition based on the sensor data.