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公开(公告)号：US20170181630A1
公开(公告)日：2017-06-29
申请号：US15377850
申请日：2016-12-13
Applicant: DexCom, Inc.
Inventor： Aarthi Mahalingam , Esteban Cabrera, JR. , Basab Dattaray , Rian Draeger , Laura J. Dunn , Derek James Escobar , Thomas Hall , Hari Hampapuram , Apurv Ullas Kamath , Katherine Yerre Koehler , Phil Mayou , Michael Robert Mensinger , Michael Levozier Moore , Andrew Attila Pal , Nicholas Polytaridis , Eli Reihman , Brian Christopher Smith
CPC classification number: A61B5/0205 , A61B5/0004 , A61B5/0022 , A61B5/024 , A61B5/14532 , A61B5/4806 , A61B5/6802 , A61B5/74 , A61B5/746 , A61B2505/07 , A61B2560/0242 , A63B24/0062 , G06Q50/24 , G09B5/02 , G09B5/08 , G16H10/60 , G16H40/67
Abstract: Systems and methods for remote and host monitoring communication are disclosed. In some implementations, monitoring systems can comprise a host monitoring device associated with a Host communicatively coupled to one or more remote monitoring devices associated with Remote Monitors. The host monitoring device can send communications based at least in part on analyte measurements of a Host sensor and/or other contextual data giving such measurements context. Different remote monitoring devices can receive different communications based at least in part on the role of the respective Remote Monitors relative to the Host. These roles can be reflected in classifications of Remote Monitors.
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公开(公告)号：US20240148284A1
公开(公告)日：2024-05-09
申请号：US18521110
申请日：2023-11-28
Applicant: Dexcom, Inc.
Inventor： Derek James Escobar , Naresh C. Bhavaraju , Gary A. Morris , Jorge Valdes
IPC: A61B5/145 , A61B5/00 , A61M5/142 , A61M5/172 , G06N20/00 , G06N20/20 , G16H10/60 , G16H20/17 , G16H40/67 , G16H50/20 , G16H50/30 , H04L9/30 , H04W12/033 , H04W12/037
CPC classification number: A61B5/14532 , A61B5/0004 , A61B5/425 , A61B5/6801 , A61B5/743 , A61B5/7475 , A61M5/14244 , A61M5/1723 , G06N20/00 , G06N20/20 , G16H10/60 , G16H20/17 , G16H40/67 , G16H50/20 , G16H50/30 , H04L9/30 , H04W12/033 , H04W12/037 , A61M2205/18 , A61M2205/3553 , A61M2205/3584 , A61M2205/3592 , A61M2205/502 , A61M2205/52 , A61M2205/581 , A61M2205/582 , A61M2205/583 , A61M2205/8206 , A61M2230/201
Abstract: Machine learning in an artificial pancreas is described. An artificial pancreas system may include a wearable glucose monitoring device, an insulin delivery system, and a computing device. Broadly speaking, the wearable glucose monitoring device provides glucose measurements of a person continuously. The artificial pancreas algorithm, which may be implemented at the computing device, determines doses of insulin to deliver to the person based on a variety of aspects for the purpose of maintaining the person's glucose within a target range, as indicated by those glucose measurements. The insulin delivery system then delivers those determined doses to the person. As the artificial pancreas algorithm determines insulin doses for the person over time and effectiveness of the insulin doses to maintain the person's glucose level in the target range is observed, an underlying model of the artificial pancreas algorithm may be updated to better determine insulin doses.
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公开(公告)号：US20210260287A1
公开(公告)日：2021-08-26
申请号：US17114142
申请日：2020-12-07
Applicant: DexCom, Inc.
Inventor： Apurv Ullas Kamath , Derek James Escobar , Sumitaka Mikami , Hari Hampapuram , Benjamin Elrod West , Nathanael Paul , Naresh C. Bhavaraju , Michael Robert Mensinger , Gary A. Morris , Andrew Attila Pal , Eli Reihman , Scott M. Belliveau , Katherine Yerre Koehler , Nicholas Polytaridis , Rian Draeger , Jorge Valdes , David Price , Peter C. Simpson , Edward Sweeney
Abstract: Machine learning in an artificial pancreas is described. An artificial pancreas system may include a wearable glucose monitoring device, an insulin delivery system, and a computing device. Broadly speaking, the wearable glucose monitoring device provides glucose measurements of a person continuously. The artificial pancreas algorithm, which may be implemented at the computing device, determines doses of insulin to deliver to the person based on a variety of aspects for the purpose of maintaining the person's glucose within a target range, as indicated by those glucose measurements. The insulin delivery system then delivers those determined doses to the person. As the artificial pancreas algorithm determines insulin doses for the person over time and effectiveness of the insulin doses to maintain the person's glucose level in the target range is observed, an underlying model of the artificial pancreas algorithm may be updated to better determine insulin doses.
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公开(公告)号：US20210260289A1
公开(公告)日：2021-08-26
申请号：US17114213
申请日：2020-12-07
Applicant: DexCom, Inc.
Inventor： Apurv Ullas Kamath , Derek James Escobar , Sumitaka Mikami , Hari Hampapuram , Benjamin Elrod West , Nathanael Paul , Naresh C. Bhavaraju , Michael Robert Mensinger , Gary A. Morris , Andrew Attila Pal , Eli Reihman , Scott M. Belliveau , Katherine Yerre Koehler , Nicholas Polytaridis , Rian Draeger , Jorge Valdes , David Price , Peter C. Simpson , Edward Sweeney
Abstract: Machine learning in an artificial pancreas is described. An artificial pancreas system may include a wearable glucose monitoring device, an insulin delivery system, and a computing device. Broadly speaking, the wearable glucose monitoring device provides glucose measurements of a person continuously. The artificial pancreas algorithm, which may be implemented at the computing device, determines doses of insulin to deliver to the person based on a variety of aspects for the purpose of maintaining the person's glucose within a target range, as indicated by those glucose measurements. The insulin delivery system then delivers those determined doses to the person. As the artificial pancreas algorithm determines insulin doses for the person over time and effectiveness of the insulin doses to maintain the person's glucose level in the target range is observed, an underlying model of the artificial pancreas algorithm may be updated to better determine insulin doses.
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公开(公告)号：US20210260286A1
公开(公告)日：2021-08-26
申请号：US17114137
申请日：2020-12-07
Applicant: DexCom, Inc.
Inventor： Apurv Ullas Kamath , Derek James Escobar , Sumitaka Mikami , Hari Hampapuram , Benjamin Elrod West , Nathanael Paul , Naresh C. Bhavaraju , Michael Robert Mensinger , Gary A. Morris , Andrew Attila Pal , Eli Reihman , Scott M. Belliveau , Katherine Yerre Koehler , Nicholas Polytaridis , Rian Draeger , Jorge Valdes , David Price , Peter C. Simpson , Edward Sweeney
Abstract: Machine learning in an artificial pancreas is described. An artificial pancreas system may include a wearable glucose monitoring device, an insulin delivery system, and a computing device. Broadly speaking, the wearable glucose monitoring device provides glucose measurements of a person continuously. The artificial pancreas algorithm, which may be implemented at the computing device, determines doses of insulin to deliver to the person based on a variety of aspects for the purpose of maintaining the person's glucose within a target range, as indicated by those glucose measurements. The insulin delivery system then delivers those determined doses to the person. As the artificial pancreas algorithm determines insulin doses for the person over time and effectiveness of the insulin doses to maintain the person's glucose level in the target range is observed, an underlying model of the artificial pancreas algorithm may be updated to better determine insulin doses.
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Patent Agency Ranking
公开(公告)号：US20170181629A1
公开(公告)日：2017-06-29
申请号：US15377821
申请日：2016-12-13
Applicant: DexCom, Inc.
Inventor： Aarthi Mahalingam , Esteban Cabrera, JR. , Basab Dattaray , Rian Draeger , Laura J. Dunn , Derek James Escobar , Thomas Hall , Hari Hampapuram , Apurv Ullas Kamath , Katherine Yerre Koehler , Phil Mayou , Michael Robert Mensinger , Michael Levozier Moore , Andrew Attila Pal , Nicholas Polytaridis , Eli Reihman , Brian Christopher Smith
CPC classification number: A61B5/0205 , A61B5/0004 , A61B5/0022 , A61B5/024 , A61B5/14532 , A61B5/4806 , A61B5/6802 , A61B5/74 , A61B5/746 , A61B2505/07 , A61B2560/0242 , A63B24/0062 , G06Q50/24 , G09B5/02 , G09B5/08 , G16H10/60 , G16H40/67
Abstract: Systems and methods for remote and host monitoring communication are disclosed. In some implementations, monitoring systems can comprise a host monitoring device associated with a Host communicatively coupled to one or more remote monitoring devices associated with Remote Monitors. The host monitoring device can send communications based at least in part on analyte measurements of a Host sensor and/or other contextual data giving such measurements context. Different remote monitoring devices can receive different communications based at least in part on the role of the respective Remote Monitors relative to the Host. These roles can be reflected in classifications of Remote Monitors.
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公开(公告)号：US20250120594A1
公开(公告)日：2025-04-17
申请号：US19000454
申请日：2024-12-23
Applicant: Dexcom, Inc.
Inventor： Aarthi Mahalingam , Esteban Cabrera, Jr. , Basab Dattaray , Rian Draeger , Laura J. Dunn , Derek James Escobar , Thomas Hall , Hari Hampapuram , Apurv Ullas Kamath , Katherine Yerre Koehler , Phil Mayou , Michael Robert Mensinger , Michael Levozier Moore , Andrew Attila Pal , Nicholas Polytaridis , Eli Reihman , Brian Christopher Smith
IPC: A61B5/0205 , A61B5/00 , A61B5/024 , A61B5/145 , A63B24/00 , G09B5/02 , G09B5/08 , G16H10/60 , G16H40/67
Abstract: Systems and methods for remote and host monitoring communication are disclosed. In some implementations, monitoring systems can comprise a host monitoring device associated with a Host communicatively coupled to one or more remote monitoring devices associated with Remote Monitors. The host monitoring device can send communications based at least in part on analyte measurements of a Host sensor and/or other contextual data giving such measurements context. Different remote monitoring devices can receive different communications based at least in part on the role of the respective Remote Monitors relative to the Host. These roles can be reflected in classifications of Remote Monitors.
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公开(公告)号：US11872034B2
公开(公告)日：2024-01-16
申请号：US17114190
申请日：2020-12-07
Applicant: DexCom, Inc.
Inventor： Derek James Escobar , Naresh C. Bhavaraju , Gary A. Morris , Jorge Valdes
IPC: A61B5/145 , A61B5/00 , A61M5/172 , A61M5/142 , G16H20/17 , G16H40/67 , G06N20/00 , H04W12/037 , H04L9/30 , G06N20/20 , G16H10/60 , G16H50/30 , G16H50/20 , H04W12/033
CPC classification number: A61B5/14532 , A61B5/0004 , A61B5/425 , A61B5/6801 , A61B5/743 , A61B5/7475 , A61M5/14244 , A61M5/1723 , G06N20/00 , G06N20/20 , G16H10/60 , G16H20/17 , G16H40/67 , G16H50/20 , G16H50/30 , H04L9/30 , H04W12/033 , H04W12/037 , A61M2205/18 , A61M2205/3553 , A61M2205/3584 , A61M2205/3592 , A61M2205/502 , A61M2205/52 , A61M2205/581 , A61M2205/582 , A61M2205/583 , A61M2205/8206 , A61M2230/201
Abstract: Machine learning in an artificial pancreas is described. An artificial pancreas system may include a wearable glucose monitoring device, an insulin delivery system, and a computing device. Broadly speaking, the wearable glucose monitoring device provides glucose measurements of a person continuously. The artificial pancreas algorithm, which may be implemented at the computing device, determines doses of insulin to deliver to the person based on a variety of aspects for the purpose of maintaining the person's glucose within a target range, as indicated by those glucose measurements. The insulin delivery system then delivers those determined doses to the person. As the artificial pancreas algorithm determines insulin doses for the person over time and effectiveness of the insulin doses to maintain the person's glucose level in the target range is observed, an underlying model of the artificial pancreas algorithm may be updated to better determine insulin doses.
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公开(公告)号：US20210260288A1
公开(公告)日：2021-08-26
申请号：US17114190
申请日：2020-12-07
Applicant: DexCom, Inc.
Inventor： Apurv Ullas Kamath , Derek James Escobar , Sumitaka Mikami , Hari Hampapuram , Benjamin Elrod West , Nathanael Paul , Naresh C. Bhavaraju , Michael Robert Mensinger , Gary A. Morris , Andrew Attila Pal , Eli Reihman , Scott M. Belliveau , Katherine Yerre Koehler , Nicholas Polytaridis , Rian Draeger , Jorge Valdes , David Price , Peter C. Simpson , Edward Sweeney
Abstract: Machine learning in an artificial pancreas is described. An artificial pancreas system may include a wearable glucose monitoring device, an insulin delivery system, and a computing device. Broadly speaking, the wearable glucose monitoring device provides glucose measurements of a person continuously. The artificial pancreas algorithm, which may be implemented at the computing device, determines doses of insulin to deliver to the person based on a variety of aspects for the purpose of maintaining the person's glucose within a target range, as indicated by those glucose measurements. The insulin delivery system then delivers those determined doses to the person. As the artificial pancreas algorithm determines insulin doses for the person over time and effectiveness of the insulin doses to maintain the person's glucose level in the target range is observed, an underlying model of the artificial pancreas algorithm may be updated to better determine insulin doses.
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公开(公告)号：US20210259591A1
公开(公告)日：2021-08-26
申请号：US17114254
申请日：2020-12-07
Applicant: DexCom, Inc.
Inventor： Apurv Ullas Kamath , Derek James Escobar , Sumitaka Mikami , Hari Hampapuram , Benjamin Elrod West , Nathanael Paul , Naresh C. Bhavaraju , Michael Robert Mensinger , Gary A. Morris , Andrew Attila Pal , Eli Reihman , Scott M. Belliveau , Katherine Yerre Koehler , Nicholas Polytaridis , Rian Draeger , Jorge Valdes , David Price , Peter C. Simpson , Edward Sweeney
Abstract: Machine learning in an artificial pancreas is described. An artificial pancreas system may include a wearable glucose monitoring device, an insulin delivery system, and a computing device. Broadly speaking, the wearable glucose monitoring device provides glucose measurements of a person continuously. The artificial pancreas algorithm, which may be implemented at the computing device, determines doses of insulin to deliver to the person based on a variety of aspects for the purpose of maintaining the person's glucose within a target range, as indicated by those glucose measurements. The insulin delivery system then delivers those determined doses to the person. As the artificial pancreas algorithm determines insulin doses for the person over time and effectiveness of the insulin doses to maintain the person's glucose level in the target range is observed, an underlying model of the artificial pancreas algorithm may be updated to better determine insulin doses.
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