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).

  • Patent number: 11389090
    Abstract: 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: Grant
    Filed: December 17, 2019
    Date of Patent: July 19, 2022
    Assignee: 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
  • Publication number: 20220000432
    Abstract: 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: Application
    Filed: September 21, 2021
    Publication date: January 6, 2022
    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
  • Patent number: 11154253
    Abstract: 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: Grant
    Filed: August 10, 2017
    Date of Patent: October 26, 2021
    Assignee: 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
  • Patent number: 11141116
    Abstract: 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: Grant
    Filed: August 10, 2017
    Date of Patent: October 12, 2021
    Assignee: 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
  • Publication number: 20210260289
    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.
    Type: Application
    Filed: December 7, 2020
    Publication date: August 26, 2021
    Inventors: 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
  • Publication number: 20210260286
    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.
    Type: Application
    Filed: December 7, 2020
    Publication date: August 26, 2021
    Inventors: 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
  • Publication number: 20210259591
    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.
    Type: Application
    Filed: December 7, 2020
    Publication date: August 26, 2021
    Inventors: 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
  • Publication number: 20210260287
    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.
    Type: Application
    Filed: December 7, 2020
    Publication date: August 26, 2021
    Inventors: 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
  • Publication number: 20210260288
    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.
    Type: Application
    Filed: December 7, 2020
    Publication date: August 26, 2021
    Inventors: 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
  • Publication number: 20210142912
    Abstract: 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: Application
    Filed: January 7, 2021
    Publication date: May 13, 2021
    Inventors: 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
  • Publication number: 20200203012
    Abstract: 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: Application
    Filed: December 17, 2019
    Publication date: June 25, 2020
    Inventors: Apurv Ullas Kamath, Margaret A. Crawford, John Michael Gray, Hari Hampapuram, Matthew Lawrence Johnson, Subrai Girish Pai, Shawn Clay Sanders, Sumitaka Mikami
  • Publication number: 20200196865
    Abstract: 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: Application
    Filed: December 17, 2019
    Publication date: June 25, 2020
    Inventors: Apurv Ullas Kamath, Margaret A. Crawford, John Michael Gray, Hari Hampapuram, Matthew Lawrence Johnson, Subrai Girish Pai, Shawn Clay Sanders, Sumitaka Mikami
  • Publication number: 20200196922
    Abstract: 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: Application
    Filed: December 17, 2019
    Publication date: June 25, 2020
    Inventors: Apurv Ullas Kamath, Margaret A. Crawford, John Michael Gray, Hari Hampapuram, Matthew Lawrence Johnson, Subrai Girish Pai, Shawn Clay Sanders, Sumitaka Mikami
  • Publication number: 20200196923
    Abstract: 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: Application
    Filed: December 17, 2019
    Publication date: June 25, 2020
    Inventors: Apurv Ullas Kamath, Margaret A. Crawford, John Michael Gray, Hari Hampapuram, Matthew Lawrence Johnson, Subrai Girish Pai, Shawn Clay Sanders, Sumitaka Mikami
  • Publication number: 20190251456
    Abstract: Systems and methods are provided to determine a time to provide guidance to a user regarding management of a physiologic condition such as diabetes. The determination may be based upon a model or pattern. The time to deliver guidance may be calculated to be useful to a user in the management of a glucose concentration level.
    Type: Application
    Filed: February 6, 2019
    Publication date: August 15, 2019
    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 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
  • Publication number: 20190246914
    Abstract: 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: Application
    Filed: February 6, 2019
    Publication date: August 15, 2019
    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 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
  • Publication number: 20190246973
    Abstract: 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: Application
    Filed: February 6, 2019
    Publication date: August 15, 2019
    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 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
  • Publication number: 20190252079
    Abstract: 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: Application
    Filed: February 6, 2019
    Publication date: August 15, 2019
    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 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
  • Publication number: 20180182491
    Abstract: 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: Application
    Filed: December 22, 2017
    Publication date: June 28, 2018
    Inventors: 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
  • Publication number: 20180042559
    Abstract: 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: Application
    Filed: August 10, 2017
    Publication date: February 15, 2018
    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