IP Library Granted Patent US 12,311,146
Granted Patent B2
US 12,311,146 · App. 18/639,434 · Granted May 27, 2025

Personalized closed loop optimization systems and methods

Inventors: Di Wu (Palo Alto, CA); Benyamin Grosman (Winnetka, CA); Louis J. Lintereur (Boise, ID); Anirban Roy (Agoura Hills, CA); Neha J. Parikh (Pleasanton, CA); Patrick E. Weydt (Moorpark, CA); Ali Dianaty (Porter Ranch, CA)
Assignee: MEDTRONIC MINIMED, INC.
A61M5/1723G16H10/40G16H20/10G16H20/17A61M2205/3569A61M2205/3584A61M2205/502A61M2205/52A61M2230/201
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,311,146
App. No.
18/639,434
Granted
May 27, 2025
Kind
B2
Abstract

Techniques disclosed herein involve automatically adjusting a control parameter for an operating mode of a medical device. In some embodiments, the techniques involve obtaining data pertaining to a physiological condition of a patient during operation of the medical device. In some embodiments, the techniques further involve determining an optimized value for the control parameter using the data pertaining to the physiological condition of the patient and a cost function, wherein the cost function disproportionately penalizes for an amount of time that a physiological parameter is below a lower bound of a target range and includes a first weighting factor that is dependent on an amount of time that the physiological parameter is outside of the target range, and a second weighting factor, different from the first weighting factor, dependent on the amount of time that the physiological parameter is below the lower bound of the target range.

Claims (36)

1. A method of automatically adjusting a control parameter for an operating mode of a medical device, the method comprising:

obtaining, by one or more processors, data pertaining to a physiological condition of a patient during operation of the medical device;

determining an optimized value for the control parameter using the data pertaining to the physiological condition of the patient and a cost function, wherein the cost function disproportionately penalizes for an amount of time that a physiological parameter is below a lower bound of a target range and includes a first weighting factor that is dependent on an amount of time that the physiological parameter is outside of the target range, and a second weighting factor, different from the first weighting factor, dependent on the amount of time that the physiological parameter is below the lower bound of the target range; and

controlling, by the one or more processors, fluid delivery of the medical device to deliver fluid to the patient according to the control parameter at the optimized value.

2. The method of claim 1 , wherein the cost function penalizes for the amount of time the physiological parameter is below the lower bound of the target range more than for an amount of time the physiological parameter is above a higher bound of the target range.

3. The method of claim 1 , further comprising normalizing the amount of time that the physiological parameter is below the lower bound of the target range, wherein the cost function utilizes the normalized amount of time that the physiological parameter is below the lower bound of the target range.

4. The method of claim 3 , wherein normalizing the amount of time that the physiological parameter is below the lower bound of the target range comprises applying a plurality of normalization factors selected to balance a safety risk associated with time that the physiological parameter is below the lower bound of the target range and a safety risk associated with time that the physiological parameter is outside of the target range.

5. The method of claim 1 , wherein determining the optimized value for the control parameter comprises:

determining a plurality of candidate values for the control parameter;

determining a plurality of costs corresponding to the plurality of candidate values using the cost function; and

selecting the optimized value based on the plurality of costs.

6. The method of claim 5 , wherein determining a respective cost associated with a candidate value of the plurality of candidate values comprises determining a simulated profile of the physiological condition.

7. The method of claim 6 , further comprising determining the amount of time the physiological parameter is below the lower bound of the target range and the amount of time the physiological parameter is outside of the target range based on the simulated profile of the physiological condition.

8. The method of claim 6 , wherein the simulated profile of the physiological condition is determined using a patient-specific physiological simulation model.

9. The method of claim 1 , wherein the optimized value for the control parameter is determined by a device other than the medical device.

10. The method of claim 1 , wherein the target range is a target glucose range.

11. A system for automatically adjusting a control parameter for an operating mode of a medical device, the system comprising:

one or more processors; and

one or more processor-readable storage media storing instructions which, when executed by the one or more processors, cause performance of:

obtaining data pertaining to a physiological condition of a patient during operation of the medical device;

determining an optimized value for the control parameter using the data pertaining to the physiological condition of the patient and a cost function, wherein the cost function disproportionately penalizes for an amount of time that a physiological parameter is below a lower bound of a target range and includes a first weighting factor that is dependent on an amount of time that the physiological parameter is outside of the target range, and a second weighting factor, different from the first weighting factor, dependent on the amount of time that the physiological parameter is below the lower bound of the target range; and

controlling fluid delivery of the medical device to deliver fluid to the patient according to the control parameter at the optimized value.

12. The system of claim 11 , wherein the cost function penalizes for the amount of time the physiological parameter is below the lower bound of the target range more than for an amount of time the physiological parameter is above a higher bound of the target range.

13. The system of claim 11 , wherein the instructions further cause performance of normalizing the amount of time that the physiological parameter is below the lower bound of the target range, wherein the cost function utilizes the normalized amount of time that the physiological parameter is below the lower bound of the target range.

14. The system of claim 13 , wherein normalizing the amount of time that the physiological parameter is below the lower bound of the target range comprises applying a plurality of normalization factors selected to balance a safety risk associated with time that the physiological parameter is below the lower bound of the target range and a safety risk associated with time that the physiological parameter is outside of the target range.

15. The system of claim 11 , wherein determining the optimized value for the control parameter comprises:

determining a plurality of candidate values for the control parameter;

determining a plurality of costs corresponding to the plurality of candidate values using the cost function; and

selecting the optimized value based on the plurality of costs.

16. The system of claim 15 , wherein determining a respective cost associated with a candidate value of the plurality of candidate values comprises determining a simulated profile of the physiological condition.

17. The system of claim 16 , wherein the instructions further cause performance of determining the amount of time the physiological parameter is below the lower bound of the target range and the amount of time the physiological parameter is outside of the target range based on the simulated profile of the physiological condition.

18. The system of claim 16 , wherein determining the simulated profile comprises using a patient-specific physiological simulation model.

19. The system of claim 11 , wherein the optimized value for the control parameter is determined by a device other than the medical device.

20. A method of automatically adjusting a control parameter for an operating mode of a medical device, the method comprising:

determining, by one or more processors, an optimized value for the control parameter using simulated candidate values for the control parameter and a cost function, wherein the cost function disproportionately penalizes for an amount of time that a physiological parameter is below a lower bound of a target range and includes a first weighting factor that is dependent on an amount of time that the physiological parameter is outside of the target range, and a second weighting factor, different from the first weighting factor, dependent on the amount of time that the physiological parameter is below the lower bound of the target range; and

controlling, by the one or more processors, fluid delivery of the medical device to deliver fluid to a patient according to the control parameter at the optimized value.

Assignments (2)
SECURITY INTEREST Recorded Jan 16, 2026
From: MEDTRONIC MINIMED, INC.; COMPANION MEDICAL, INC.
To: CITIBANK, N.A.
Reel/Frame 074394/0237 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 22, 2024
From: WU, DI; GROSMAN, BENYAMIN; LINTEREUR, LOUIS J.; ROY, ANIRBAN; PARIKH, NEHA J.; WEYDT, PATRICK E.; DIANATY, ALI
To: MEDTRONIC MINIMED, INC.
Reel/Frame 067182/0293 →
Continuity (2)
Continuation 16438407 · Jun 11, 2019
Related Publication 20240261505A1 · Aug 8, 2024
References Cited (227)
US 4562751A · Nason et al. · 1986 [cited by applicant]
US 4685903A · Cable et al. · 1987 [cited by applicant]
US 4755173A · Konopka et al. · 1988 [cited by applicant]
US 5080653A · Voss et al. · 1992 [cited by applicant]
US 5097122A · Colman et al. · 1992 [cited by applicant]
US 5391250A · Cheney, II et al. · 1995 [cited by applicant]
US 5485408A · Blomquist · 1996 [cited by applicant]
US 5505709A · Funderburk et al. · 1996 [cited by applicant]
US 5522803A · Teissen-Simony · 1996 [cited by applicant]
US 5665065A · Colman et al. · 1997 [cited by applicant]
US 5800420A · Gross et al. · 1998 [cited by applicant]
US 5807375A · Gross et al. · 1998 [cited by applicant]
US 5925021A · Castellano et al. · 1999 [cited by applicant]
US 5954643A · Vanantwerp et al. · 1999 [cited by applicant]
US 6017328A · Fischell et al. · 2000 [cited by applicant]
US 6088608A · Schulman et al. · 2000 [cited by applicant]
US 6119028A · Schulman et al. · 2000 [cited by applicant]
US 6186982B1 · Gross et al. · 2001 [cited by applicant]
US 6246992B1 · Brown · 2001 [cited by applicant]
US 6248067B1 · Causey, III et al. · 2001 [cited by applicant]
US 6248093B1 · Moberg · 2001 [cited by applicant]
US 6355021B1 · Nielsen et al. · 2002 [cited by applicant]
US 6379301B1 · Worthington et al. · 2002 [cited by applicant]
US 6485465B2 · Moberg et al. · 2002 [cited by applicant]
US 6544212B2 · Galley et al. · 2003 [cited by applicant]
US 6554798B1 · Mann et al. · 2003 [cited by applicant]
US 6558320B1 · Causey, III et al. · 2003 [cited by applicant]
US 6558351B1 · Steil et al. · 2003 [cited by applicant]
US 6589229B1 · Connelly et al. · 2003 [cited by applicant]
US 6591876B2 · Safabash · 2003 [cited by applicant]
US 6641533B2 · Causey, III et al. · 2003 [cited by applicant]
US 6659980B2 · Moberg et al. · 2003 [cited by applicant]
US 6736797B1 · Larsen et al. · 2004 [cited by applicant]
US 6740072B2 · Starkweather et al. · 2004 [cited by applicant]
US 6749587B2 · Flaherty · 2004 [cited by applicant]
US 6752787B1 · Causey, III et al. · 2004 [cited by applicant]
US 6766183B2 · Walsh et al. · 2004 [cited by applicant]
US 6801420B2 · Talbot et al. · 2004 [cited by applicant]
US 6804544B2 · Van et al. · 2004 [cited by applicant]
US 6817990B2 · Yap et al. · 2004 [cited by applicant]
US 6827702B2 · Lebel et al. · 2004 [cited by applicant]
US 6932584B2 · Gray et al. · 2005 [cited by applicant]
US 7003336B2 · Holker et al. · 2006 [cited by applicant]
US 7029444B2 · Shin et al. · 2006 [cited by applicant]
US 7066909B1 · Peter et al. · 2006 [cited by applicant]
US 7137964B2 · Flaherty · 2006 [cited by applicant]
US 7303549B2 · Flaherty et al. · 2007 [cited by applicant]
US 7323142B2 · Pendo et al. · 2008 [cited by applicant]
US 7399277B2 · Saidara et al. · 2008 [cited by applicant]
US 7402153B2 · Steil et al. · 2008 [cited by applicant]
US 7442186B2 · Blomquist · 2008 [cited by applicant]
US 7602310B2 · Mann et al. · 2009 [cited by applicant]
US 7621893B2 · Moberg et al. · 2009 [cited by applicant]
US 7647237B2 · Malave et al. · 2010 [cited by applicant]
US 7699807B2 · Faust et al. · 2010 [cited by applicant]
US 7727148B2 · Talbot et al. · 2010 [cited by applicant]
US 7785313B2 · Mastrototaro · 2010 [cited by applicant]
US 7806886B2 · Kanderian, Jr. et al. · 2010 [cited by applicant]
US 7819843B2 · Mann et al. · 2010 [cited by applicant]
US 7828764B2 · Moberg et al. · 2010 [cited by applicant]
US 7879010B2 · Hunn et al. · 2011 [cited by applicant]
US 7890295B2 · Shin et al. · 2011 [cited by applicant]
US 7892206B2 · Moberg et al. · 2011 [cited by applicant]
US 7892748B2 · Norrild et al. · 2011 [cited by applicant]
US 7901394B2 · Ireland et al. · 2011 [cited by applicant]
US 7942844B2 · Moberg et al. · 2011 [cited by applicant]
US 7946985B2 · Mastrototaro et al. · 2011 [cited by applicant]
US 7955305B2 · Moberg et al. · 2011 [cited by applicant]
US 7963954B2 · Kavazov · 2011 [cited by applicant]
US 7977112B2 · Burke et al. · 2011 [cited by applicant]
US 7979259B2 · Brown · 2011 [cited by applicant]
US 7985330B2 · Wang et al. · 2011 [cited by applicant]
US 8024201B2 · Brown · 2011 [cited by applicant]
US 8100852B2 · Moberg et al. · 2012 [cited by applicant]
US 8114268B2 · Wang et al. · 2012 [cited by applicant]
US 8114269B2 · Cooper et al. · 2012 [cited by applicant]
US 8137314B2 · Mounce et al. · 2012 [cited by applicant]
US 8181849B2 · Bazargan et al. · 2012 [cited by applicant]
US 8182462B2 · Istoc et al. · 2012 [cited by applicant]
US 8192395B2 · Estes et al. · 2012 [cited by applicant]
US 8195265B2 · Goode, Jr. et al. · 2012 [cited by applicant]
US 8202250B2 · Stutz, Jr. · 2012 [cited by applicant]
US 8207859B2 · Enegren et al. · 2012 [cited by applicant]
US 8226615B2 · Bikovsky · 2012 [cited by applicant]
US 8257259B2 · Brauker et al. · 2012 [cited by applicant]
US 8267921B2 · Yodfat et al. · 2012 [cited by applicant]
US 8275437B2 · Brauker et al. · 2012 [cited by applicant]
US 8277415B2 · Mounce et al. · 2012 [cited by applicant]
US 8292849B2 · Bobroff et al. · 2012 [cited by applicant]
US 8298172B2 · Nielsen et al. · 2012 [cited by applicant]
US 8303572B2 · Adair et al. · 2012 [cited by applicant]
US 8305580B2 · Aasmul · 2012 [cited by applicant]
US 8308679B2 · Hanson et al. · 2012 [cited by applicant]
US 8313433B2 · Cohen et al. · 2012 [cited by applicant]
US 8318443B2 · Norrild et al. · 2012 [cited by applicant]
US 8323250B2 · Chong et al. · 2012 [cited by applicant]
US 8343092B2 · Rush et al. · 2013 [cited by applicant]
US 8352011B2 · Van et al. · 2013 [cited by applicant]
US 8353829B2 · Say et al. · 2013 [cited by applicant]
US 8474332B2 · Bente, IV et al. · 2013 [cited by applicant]
US 8674288B2 · Hanson et al. · 2014 [cited by applicant]
US 9526834B2 · Keenan et al. · 2016 [cited by applicant]
US 9907909B2 · Finan et al. · 2018 [cited by applicant]
US 11147919B2 · Parikh et al. · 2021 [cited by applicant]
US 11158413B2 · Grosman et al. · 2021 [cited by applicant]
US 11367526B2 · Chiu et al. · 2022 [cited by applicant]
US 11445981B1 · Fortney · 2022 [cited by examiner]
US 11986629B2 · Wu et al. · 2024 [cited by applicant]
US 12073932B2 · Grosman et al. · 2024 [cited by applicant]
US 20020193679A1 · Malave et al. · 2002 [cited by applicant]
US 20070123819A1 · Mernoe et al. · 2007 [cited by applicant]
US 20080262745A1 · Polidori · 2008 [cited by applicant]
US 20080269714A1 · Mastrototaro et al. · 2008 [cited by applicant]
US 20090036753A1 · King · 2009 [cited by applicant]
US 20090164239A1 · Hayter et al. · 2009 [cited by applicant]
US 20100160861A1 · Causey, III et al. · 2010 [cited by applicant]
US 20100249561A1 · Patek et al. · 2010 [cited by applicant]
US 20110047108A1 · Chakrabarty · 2011 [cited by examiner]
US 20110130746A1 · Budiman · 2011 [cited by applicant]
US 20110208156A1 · Doyle, III et al. · 2011 [cited by applicant]
US 20110282321A1 · Steil · 2011 [cited by examiner]
US 20120172694A1 · Desborough · 2012 [cited by examiner]
US 20130030358A1 · Yodfat et al. · 2013 [cited by applicant]
US 20130102867A1 · Desborough · 2013 [cited by examiner]
US 20130190583A1 · Grosman et al. · 2013 [cited by applicant]
US 20130231642A1 · Doyle, III et al. · 2013 [cited by applicant]
US 20130338630A1 · Agrawal et al. · 2013 [cited by applicant]
US 20140066889A1 · Grosman et al. · 2014 [cited by applicant]
US 20140066892A1 · Keenan et al. · 2014 [cited by applicant]
US 20140128705A1 · Mazlish · 2014 [cited by applicant]
US 20140200559A1 · Doyle, III et al. · 2014 [cited by applicant]
US 20140276554A1 · Finan et al. · 2014 [cited by applicant]
US 20140276555A1 · Morales · 2014 [cited by applicant]
US 20150057807A1 · Mastrototaro et al. · 2015 [cited by applicant]
US 20150352282A1 · Mazlish · 2015 [cited by applicant]
US 20160030339A1 · Muhlen-Bartmer et al. · 2016 [cited by applicant]
US 20160162662A1 · Monirabbasi et al. · 2016 [cited by applicant]
US 20170056591A1 · Breton et al. · 2017 [cited by applicant]
US 20170143899A1 · Gondhalekar et al. · 2017 [cited by applicant]
US 20170189614A1 · Mazlish et al. · 2017 [cited by applicant]
US 20170332952A1 · Desborough et al. · 2017 [cited by applicant]
US 20180020988A1 · Patek · 2018 [cited by applicant]
US 20180099092A1 · Roy · 2018 [cited by applicant]
US 20180174675A1 · Roy et al. · 2018 [cited by applicant]
US 20180200435A1 · Mazlish · 2018 [cited by examiner]
US 20180200439A1 · Mazlish et al. · 2018 [cited by applicant]
US 20180200441A1 · Desborough et al. · 2018 [cited by applicant]
US 20180286518A1 · Raju et al. · 2018 [cited by applicant]
US 20180296757A1 · Finan et al. · 2018 [cited by applicant]
US 20190005195A1 · Peterson et al. · 2019 [cited by applicant]
US 20190258904A1 · Ma et al. · 2019 [cited by applicant]
US 20190336684A1 · O'Connor et al. · 2019 [cited by applicant]
US 20200093988A1 · Zhong et al. · 2020 [cited by applicant]
US 20200098463A1 · Arunachalam et al. · 2020 [cited by applicant]
US 20200098464A1 · Velado et al. · 2020 [cited by applicant]
US 20200098465A1 · Jiang et al. · 2020 [cited by applicant]
US 20200101221A1 · Lintereur et al. · 2020 [cited by applicant]
US 20200101224A1 · Lintereur et al. · 2020 [cited by applicant]
US 20200135311A1 · Mairs · 2020 [cited by applicant]
US 20200246543A1 · Sadeghzadeh et al. · 2020 [cited by applicant]
US 20200282141A1 · Rousson et al. · 2020 [cited by applicant]
US 20200342974A1 · Chen et al. · 2020 [cited by applicant]
US 20200380888A1 · Neumann · 2020 [cited by examiner]
US 20200390973A1 · Wu et al. · 2020 [cited by applicant]
US 20210100486A1 · Romero Ugalde et al. · 2021 [cited by applicant]
US 20220031946A1 · Parikh et al. · 2022 [cited by applicant]
US 20220044785A1 · Grosman et al. · 2022 [cited by applicant]
US 20240371492A1 · Grosman et al. · 2024 [cited by applicant]
AU 2019260574A1 · 2020 [cited by applicant]
CA 3107454A1 · 2019 [cited by applicant]
CN 104520862A · 2015 [cited by applicant]
CN 104667379A · 2015 [cited by examiner]
CN 104756116A · 2015 [cited by applicant]
CN 104769595A · 2015 [cited by applicant]
CN 112005310A · 2020 [cited by applicant]
CN 113646847A · 2021 [cited by applicant]
EP 3785276A1 · 2021 [cited by applicant]
EP 3935646A1 · 2022 [cited by applicant]
JP 2003079723A · 2003 [cited by applicant]
JP 2005508025A · 2005 [cited by applicant]
JP 2008545493A · 2008 [cited by applicant]
JP 2010523167A · 2010 [cited by applicant]
JP 2010532044A · 2010 [cited by applicant]
JP 2011523940A · 2011 [cited by applicant]
JP 2021522582A · 2021 [cited by applicant]
KR 20210004993A · 2021 [cited by applicant]
WO 2014035570A2 · 2014 [cited by applicant]
WO 2014035672A2 · 2014 [cited by applicant]
WO 2016133879A1 · 2016 [cited by applicant]
WO 2018033513A1 · 2018 [cited by applicant]
WO 2019209602A1 · 2019 [cited by applicant]
WO 2020214780A1 · 2020 [cited by applicant]
CN-104667379-A machine translation (Year: 2015). [cited by examiner]
CN Office Action dated Oct. 25, 2024 in CN Application No. 202080027737.6 with English translation. [cited by applicant]
U.S. Non-Final Office Action dated Aug. 29, 2024 in U.S. Appl. No. 17/504,568. [cited by applicant]
AU Office Action dated Oct. 31, 2023 in AU Application No. 2019260574. [cited by applicant]
CN Office Action dated Oct. 12, 2023 in CN Application No. CN201980025847.6 with English translation. [cited by applicant]
EP Office Action dated Jun. 26, 2024 in EP Application No. 20724639.8. [cited by applicant]
Hughes, C., et al., Safety Supervision System Design and Implications for Continuous Subcutaneous Insulin Infusion (Csii) In TIDM, Ph.D. Dissertation, University of Virginia, 2011, 240 pages. [cited by applicant]
International Preliminary Report on Patentability dated Oct. 28, 2021, in PCT Application No. PCT/US2020/028461. [cited by applicant]
International Search Report and Written Opinion dated Jul. 9, 2020, in Application No. PCT/US2020/028461. [cited by applicant]
JP Office Action dated Mar. 14, 2023 in Application No. JP2020-559417 with English translation. [cited by applicant]
Kirchsteiger, H., et al., “Reduced Hypoglycemia Risk in Insulin Bolus Therapy Using Asymmetric Cost Functions,” Proceedings of the 7th Asian Control Conference, Aug. 2009, pp. 751-756. [cited by applicant]
Lee, et al., “Thesis: Personalization and Enhanced Designs for Automated Glucose Control in Artificial Pancreas,” University of California Santa Barbara, 2016, pp. 1-152. [cited by applicant]
U.S. Advisory Action dated Dec. 12, 2023 in U.S. Appl. No. 16/438,407. [cited by applicant]
U.S Advisory Action dated Jun. 20, 2023 in U.S. Appl. No. 16/438,407. [cited by applicant]
U.S Advisory Action dated Sep. 16, 2022 in U.S. Appl. No. 16/438,407. [cited by applicant]
U.S. Final Office Action dated Apr. 4, 2023 in U.S. Appl. No. 16/438,407. [cited by applicant]
U.S. Final Office Action dated Apr. 26, 2024 in U.S. Appl. No. 17/504,568. [cited by applicant]
U.S. Final Office Action dated Jul. 5, 2022 in U.S. Appl. No. 16/438,407. [cited by applicant]
U.S. Final Office Action dated Oct. 24, 2023 in U.S. Appl. No. 16/438,407. [cited by applicant]
U.S. Non-Final Office Action dated Dec. 23, 2022 in U.S. Appl. No. 16/438,407. [cited by applicant]
U.S. Non-Final Office Action dated Jan. 21, 2021, in U.S. Appl. No. 15/960,495. [cited by applicant]
U.S. Non-Final Office Action dated Jul. 19, 2023, in U.S. Appl. No. 16/438,407. [cited by applicant]
U.S. Non-Final Office Action dated Mar. 14, 2022 in U.S. Appl. No. 16/438,407. [cited by applicant]
U.S. Non-Final Office Action dated Mar. 31, 2021, in U.S. Appl. No. 16/386,104. [cited by applicant]
U.S. Non-Final Office Action dated Oct. 6, 2023, in U.S. Appl. No. 17/504,568. [cited by applicant]
U.S. Non-Final Office Action dated Oct. 6, 2023, in U.S. Appl. No. 17/509,670. [cited by applicant]
U.S. Notice of Allowance dated Apr. 16, 2024 in U.S. Appl. No. 17/509,670. [cited by applicant]
U.S. Notice of Allowance dated Apr. 24, 2024 in U.S. Appl. No. 17/509,670. [cited by applicant]
U.S. Notice of Allowance dated Jan. 31, 2024 in U.S. Appl. No. 16/438,407. [cited by applicant]
U.S. Notice of Allowance dated Jul. 20, 2021 in U.S. Appl. No. 16/386,104. [cited by applicant]
U.S. Notice of Allowance dated Jun. 29, 2021 in U.S. Appl. No. 15/960,495. [cited by applicant]
U.S. Appl. No. 18/772,941, inventors Grosman B, et al., filed Jul. 15, 2024. [cited by applicant]
Wang, Q et al., “Model Predictive Control for Type 1 Diabetes Based on Personalized Linear Time-Varying Subject Model Consisting of both Insulin and Meal Inputs: in Silica Evaluation”, American Control Conference, Jul. … [cited by applicant]
Zarkogianni, K., et al., “An Insulin Infusion Advisory System Based on Autotuning Nonlinear Model-Predictive Control,” IEEE Transactions on Biomedical Engineering, 2011, vol. 55 (9), pp. 2467-2477. [cited by applicant]
Zavitsanou, et al., “Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas,” ProQuest, 2016, vol. 4(35), pp. 1-29. [cited by applicant]