Patents by Inventor Sumitaka Mikami
Sumitaka Mikami has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20240115211Abstract: Disclosed are systems and methods for generating graphical displays of analyte data and/or health information. In some implementations, the graphical displays are generating based on a self-referential dataset that are modifiable based on identified portions of the data. The modified graphical displays can indicate features in the analyte data of a host.Type: ApplicationFiled: December 19, 2023Publication date: April 11, 2024Inventors: Esteban CABRERA, JR., Lauren Danielle ARMENTA, Scott M. BELLIVEAU, Jennifer BLACKWELL, Leif N. BOWMAN, Rian DRAEGER, Arturo GARCIA, Timothy Joseph GOLDSMITH, John Michael GRAY, Andrea Jean JACKSON, Apurv Ullas KAMATH, Katherine Yerre KOEHLER, Paul KRAMER, Aditya Sagar MANDAPAKA, Michael Robert MENSINGER, Sumitaka MIKAMI, Gary A. MORRIS, Hemant Mahendra NIRMAL, Paul NOBLE-CAMPBELL, Philip Thomas PUPA, Eli REIHMAN, Peter C. SIMPSON, Brian Christopher SMITH, Atiim Joseph WILEY
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Patent number: 11931188Abstract: Disclosed are systems and methods for generating graphical displays of analyte data and/or health information. In some implementations, the graphical displays are generating based on a self-referential dataset that are modifiable based on identified portions of the data. The modified graphical displays can indicate features in the analyte data of a host.Type: GrantFiled: September 21, 2021Date of Patent: March 19, 2024Assignee: Dexcom, Inc.Inventors: Esteban Cabrera, Jr., Lauren Danielle Armenta, Scott M. Belliveau, Jennifer Blackwell, Leif N. Bowman, Rian Draeger, Arturo Garcia, Timothy Joseph Goldsmith, John Michael Gray, Andrea Jean Jackson, Apurv Ullas Kamath, Katherine Yerre Koehler, Paul Kramer, Aditya Sagar Mandapaka, Michael Robert Mensinger, Sumitaka Mikami, Gary A Morris, Hemant Mahendra Nirmal, Paul Noble-Campbell, Philip Thomas Pupa, Eli Reihman, Peter C. Simpson, Brian Christopher Smith, Atiim Joseph Wiley
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Patent number: 11766194Abstract: Systems and methods are provided to provide guidance to a user regarding management of a physiologic condition such as diabetes. The determination may be based upon a patient glucose concentration level. The glucose concentration level may be provided to a stored model to determine a state. The guidance may be determined based at least in part on the determined state.Type: GrantFiled: February 6, 2019Date of Patent: September 26, 2023Assignee: Dexcom, Inc.Inventors: Alexandra Elena Constantin, Scott M. Belliveau, Naresh C. Bhavaraju, Jennifer Blackwell, Eric Cohen, Basab Dattaray, Anna Leigh Davis, Rian Draeger, Arturo Garcia, John Michael Gray, Hari Hampapuram, Nathaniel David Heintzman, Lauren Hruby Jepson, Matthew Lawrence Johnson, Apurv Ullas Kamath, Katherine Yerre Koehler, Phil Mayou, Patrick Wile McBride, Michael Robert Mensinger, Sumitaka Mikami, Andrew Attila Pal, Nicholas Polytaridis, Philip Thomas Pupa, Eli Reihman, Peter C. Simpson, Tomas C. Walker, Daniel Justin Wiedeback, Subrai Girish Pai, Matthew T. Vogel
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Patent number: 11723560Abstract: Systems and methods are provided to provide guidance to a user regarding management of a physiologic condition such as diabetes. The determination may be based upon a patient glucose concentration data sensed by a glucose concentration sensor. A host state change associated with the host glucose concentration data may be determined. A guidance message based at least in part on the host state change may also be determined. The guidance message may be delivered through a user interface.Type: GrantFiled: February 6, 2019Date of Patent: August 15, 2023Assignee: Dexcom, Inc.Inventors: Alexandra Elena Constantin, Scott M. Belliveau, Naresh C. Bhavaraju, Jennifer Blackwell, Eric Cohen, Basab Dattaray, Anna Leigh Davis, Rian Draeger, Arturo Garcia, John Michael Gray, Hari Hampapuram, Nathaniel David Heintzman, Lauren Hruby Jepson, Matthew Lawrence Johnson, Apurv Ullas Kamath, Katherine Yerre Koehler, Phil Mayou, Patrick Wile McBride, Michael Robert Mensinger, Sumitaka Mikami, Andrew Attila Pal, Nicholas Polytaridis, Philip Thomas Pupa, Eli Reihman, Peter C. Simpson, Tomas C. Walker, Daniel Justin Wiedeback
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Publication number: 20220273204Abstract: Various examples are directed to systems and methods for measuring a parameter related to patient health. An analyte sensor system may detect that the analyte sensor system has been applied to a host and may store analyte data describing the host. The analyte sensor system may determine that sensor use at the analyte sensor system has terminated and upload stored analyte data to an upload computing device.Type: ApplicationFiled: May 13, 2022Publication date: September 1, 2022Applicant: Dexcom, Inc.Inventors: Apurv Ullas Kamath, Margaret A. Crawford, John Michael Gray, Hari Hampapuram, Matthew Lawrence Johnson, Subrai Girish Pai, Shawn Clay Sanders, Sumitaka Mikami
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Patent number: 11389090Abstract: Various examples are directed to systems and methods for monitoring a patient. For example, patient data of a first type may be monitored with a low-fidelity monitoring technique over a first time period. Patient behavior may be evaluated based on the monitored first type of patient data. Patient data of a second type may be monitored over a second time period with a continuous glucose monitor device. A parameter of the continuous glucose monitor device may be based on the evaluated patient behavior during the monitoring.Type: GrantFiled: December 17, 2019Date of Patent: July 19, 2022Assignee: DexCom, Inc.Inventors: Apurv Ullas Kamath, Margaret A. Crawford, John Michael Gray, Hari Hampapuram, Matthew Lawrence Johnson, Subrai Girish Pai, Shawn Clay Sanders, Sumitaka Mikami
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Publication number: 20220000432Abstract: Disclosed are systems and methods for generating graphical displays of analyte data and/or health information. In some implementations, the graphical displays are generating based on a self-referential dataset that are modifiable based on identified portions of the data. The modified graphical displays can indicate features in the analyte data of a host.Type: ApplicationFiled: September 21, 2021Publication date: January 6, 2022Inventors: Esteban Cabrera, JR., Lauren Danielle Armenta, Scott M. Belliveau, Jennifer Blackwell, Leif N. Bowman, Rian Draeger, Arturo Garcia, Timothy Joseph Goldsmith, John Michael Gray, Andrea Jean Jackson, Apurv Ullas Kamath, Katherine Yerre Koehler, Paul Kramer, Aditya Sagar Mandapaka, Michael Robert Mensinger, Sumitaka Mikami, Gary A. Morris, Hemant Mahendra Nirmal, Paul Noble-Campbell, Philip Thomas Pupa, Eli Reihman, Peter C. Simpson, Brian Christopher Smith, Atiim Joseph Wiley
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Patent number: 11154253Abstract: Disclosed are systems and methods for generating graphical displays of analyte data and/or health information. In some implementations, the graphical displays are generating based on a self-referential dataset that are modifiable based on identified portions of the data. The modified graphical displays can indicate features in the analyte data of a host.Type: GrantFiled: August 10, 2017Date of Patent: October 26, 2021Assignee: DexCom, Inc.Inventors: Esteban Cabrera, Jr., Lauren Danielle Armenta, Scott M. Belliveau, Jennifer Blackwell, Leif N. Bowman, Rian Draeger, Arturo Garcia, Timothy Joseph Goldsmith, John Michael Gray, Andrea Jean Jackson, Apurv Ullas Kamath, Katherine Yerre Koehler, Paul Kramer, Aditya Sagar Mandapaka, Michael Robert Mensinger, Sumitaka Mikami, Gary A. Morris, Hemant Mahendra Nirmal, Paul Noble-Campbell, Philip Thomas Pupa, Eli Reihman, Peter C. Simpson, Brian Christopher Smith, Atiim Joseph Wiley
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Patent number: 11141116Abstract: Disclosed are systems and methods for generating graphical displays of analyte data and/or health information. In some implementations, the graphical displays are generating based on a self-referential dataset that are modifiable based on identified portions of the data. The modified graphical displays can indicate features in the analyte data of a host.Type: GrantFiled: August 10, 2017Date of Patent: October 12, 2021Assignee: DexCom, Inc.Inventors: Esteban Cabrera, Jr., Lauren Danielle Armenta, Scott M. Belliveau, Jennifer Blackwell, Leif N. Bowman, Rian Draeger, Arturo Garcia, Timothy Joseph Goldsmith, John Michael Gray, Andrea Jean Jackson, Apurv Ullas Kamath, Katherine Yerre Koehler, Paul Kramer, Aditya Sagar Mandapaka, Michael Robert Mensinger, Sumitaka Mikami, Gary A. Morris, Hemant Mahendra Nirmal, Paul Noble-Campbell, Philip Thomas Pupa, Eli Reihman, Peter C. Simpson, Brian Christopher Smith, Atiim Joseph Wiley
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Publication number: 20210260289Abstract: 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.Type: ApplicationFiled: December 7, 2020Publication date: August 26, 2021Inventors: 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
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Publication number: 20210260288Abstract: 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.Type: ApplicationFiled: December 7, 2020Publication date: August 26, 2021Inventors: 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
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Publication number: 20210259591Abstract: 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.Type: ApplicationFiled: December 7, 2020Publication date: August 26, 2021Inventors: 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
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Publication number: 20210260286Abstract: 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.Type: ApplicationFiled: December 7, 2020Publication date: August 26, 2021Inventors: 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
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Publication number: 20210260287Abstract: 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.Type: ApplicationFiled: December 7, 2020Publication date: August 26, 2021Inventors: 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
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Publication number: 20210142912Abstract: Systems and methods disclosed provide ways for Health Care Professionals (HCPs) to be involved in initial patient system set up so that the data received is truly transformative, such that the patient not just understands what all the various numbers mean but also how the data can be used. For example, in one implementation, a CGM device is configured for use by a HCP, and includes a housing and a circuit configured to receive a signal from a transmitter coupled to an indwelling glucose sensor. A calibration module converts the received signal into clinical units. A user interface is provided that is configured to display a measured glucose concentration in the clinical units. The user interface is further configured to receive input data about a patient level, where the input data about the patient level causes the device to operate in a mode appropriate to the patient level.Type: ApplicationFiled: January 7, 2021Publication date: May 13, 2021Inventors: Scott M. Belliveau, Naresh C. Bhavaraju, Darin Edward Chum Dew, Eric Cohen, Anna Leigh Davis, Mark Dervaes, Laura J. Dunn, Minda McDorman Grucela, Hari Hampapuram, Matthew Lawrence Johnson, Apurv Ullas Kamath, Steven David King, Katherine Yerre Koehler, Aditya Sagar Mandapaka, Zebediah L. McDaniel, Sumitaka Mikami, Subrai Girish Pai, Philip Mansiel Pellouchoud, Stephen Alan Reichert, Eli Reihman, Peter C. Simpson, Brian Christopher Smith, Stephen J. Vanslyke, Robert Patrick Van Tassel, Matthew D. Wightlin, Richard C. Yang, James Stephen Amidei, David Derenzy, Benjamin Elrod West, Vincent Crabtree, Michael Levozier Moore, Douglas William Burnette, Alexandra Elena Constantin, Nicholas Polytaridis, Dana Charles Cambra, Abhishek Sharma, Kho Braun, Patrick Wile McBride
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Publication number: 20200196923Abstract: Various examples are directed to systems and methods for monitoring a patient. For example, patient data of a first type may be monitored with a low-fidelity monitoring technique over a first time period. Patient behavior may be evaluated based on the monitored first type of patient data. Patient data of a second type may be monitored over a second time period with a continuous glucose monitor device. A parameter of the continuous glucose monitor device may be based on the evaluated patient behavior during the monitoring.Type: ApplicationFiled: December 17, 2019Publication date: June 25, 2020Inventors: Apurv Ullas Kamath, Margaret A. Crawford, John Michael Gray, Hari Hampapuram, Matthew Lawrence Johnson, Subrai Girish Pai, Shawn Clay Sanders, Sumitaka Mikami
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Publication number: 20200203012Abstract: Various examples are directed to patient-management systems and methods. A first application executing a patient-management system may receive, from an analyte sensor, sensor data indicating the analyte concentration of the user. The first application may transmit a sensor message to a second application executing at the patient-management system, the sensor message comprising an indication of the analyte concentration. The second application may deliver a device message to a wearable activity monitor device, the device message based at least in part on the analyte concentration.Type: ApplicationFiled: December 17, 2019Publication date: June 25, 2020Inventors: Apurv Ullas Kamath, Margaret A. Crawford, John Michael Gray, Hari Hampapuram, Matthew Lawrence Johnson, Subrai Girish Pai, Shawn Clay Sanders, Sumitaka Mikami
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Publication number: 20200196922Abstract: Various examples are directed to systems and methods for patient monitoring. An example method comprises receiving an estimated glucose concentration level of the patient from a continuous glucose monitoring (CGM) system for a first time period. The method may also include receiving non-glucose information relating to the patient for the first time period and determining a relationship between the estimated glucose concentration level and the non-glucose information. The method may also include receiving non-glucose information relating to the patient for a second time period and determining diabetic information about the patient for the second time period based upon the determined relationship and the non-glucose information. The method may include electronically delivering a notification about the diabetic information.Type: ApplicationFiled: December 17, 2019Publication date: June 25, 2020Inventors: Apurv Ullas Kamath, Margaret A. Crawford, John Michael Gray, Hari Hampapuram, Matthew Lawrence Johnson, Subrai Girish Pai, Shawn Clay Sanders, Sumitaka Mikami
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Publication number: 20200196865Abstract: Various examples are directed to systems and methods for measuring a parameter related to patient health. An analyte sensor system may detect that the analyte sensor system has been applied to a host and may store analyte data describing the host. The analyte sensor system may determine that sensor use at the analyte sensor system has terminated and upload stored analyte data to an upload computing device.Type: ApplicationFiled: December 17, 2019Publication date: June 25, 2020Inventors: Apurv Ullas Kamath, Margaret A. Crawford, John Michael Gray, Hari Hampapuram, Matthew Lawrence Johnson, Subrai Girish Pai, Shawn Clay Sanders, Sumitaka Mikami
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Publication number: 20190246914Abstract: Systems and methods are provided to provide guidance to a user regarding management of a physiologic condition such as diabetes. The determination may be based upon a patient glucose concentration level. The glucose concentration level may be provided to a stored model to determine a state. The guidance may be determined based at least in part on the determined state.Type: ApplicationFiled: February 6, 2019Publication date: August 15, 2019Inventors: Alexandra Elena Constantin, Scott M. Belliveau, Naresh C. Bhavaraju, Jennifer Blackwell, Eric Cohen, Basab Dattaray, Anna Leigh Davis, Rian Draeger, Arturo Garcia, John Michael Gray, Hari Hampapuram, Nathaniel David Heintzmann, Lauren Hruby Jepson, Matthew Lawrence Johnson, Apurv Ullas Kamath, Katherine Yerre Koehler, Phil Mayou, Patrick Wile McBride, Michael Robert Mensinger, Sumitaka Mikami, Andrew Attila Pal, Nicholas Polytaridis, Philip Thomas Pupa, Eli Reihman, Peter C. Simpson, Tomas C. Walker, Daniel Justin Wiedeback, Subrai Girish Pai, Matthew T. Vogel