Patents by Inventor Scott M. Belliveau

Scott M. Belliveau 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: 11222724
    Abstract: Disclosed are systems and methods for secure and seamless set up and modification of bolus calculator parameters for a bolus calculator tool by a health care provider (HCP). In one aspect, a method for enabling HCP set up of a bolus calculator includes providing a server accessible by both an HCP and a patient; upon login by the HCP, displaying, or transmitting for display, a fillable form, the fillable form including one or more fields for entry of one or more bolus calculator parameters; receiving data from the fillable form, the data corresponding to one or more bolus calculator parameters; and upon login by the patient, transmitting data to a device associated with the patient, the transmitted data based on the received data, where the transmitted data corresponds to one or more of the bolus calculator parameters in a format suitable for entry to a bolus calculator.
    Type: Grant
    Filed: September 8, 2017
    Date of Patent: January 11, 2022
    Assignee: DexCom, Inc.
    Inventors: Anna Leigh Davis, Scott M. Belliveau, Esteban Cabrera, Jr., Alexandra Elena Constantin, Rian Draeger, Peter Galuardi, Hari Hampapuram, Matthew Lawrence Johnson, Apurv Ullas Kamath, Katherine Yerre Koehler, Aarthi Mahalingam, Gary A. Morris, Philip Thomas Pupa, Peter C. Simpson, Brian Christopher Smith, Tomas C. Walker
  • 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: 11183298
    Abstract: Disclosed are systems and methods for secure and seamless set up and modification of bolus calculator parameters for a bolus calculator tool by a health care provider (HCP). In one aspect, a method for enabling HCP set up of a bolus calculator includes providing a server accessible by both an HCP and a patient; upon login by the HCP, displaying, or transmitting for display, a fillable form, the fillable form including one or more fields for entry of one or more bolus calculator parameters; receiving data from the fillable form, the data corresponding to one or more bolus calculator parameters; and upon login by the patient, transmitting data to a device associated with the patient, the transmitted data based on the received data, where the transmitted data corresponds to one or more of the bolus calculator parameters in a format suitable for entry to a bolus calculator.
    Type: Grant
    Filed: April 17, 2019
    Date of Patent: November 23, 2021
    Assignee: DexCom, Inc.
    Inventors: Anna Leigh Davis, Scott M. Belliveau, Esteban Cabrera, Jr., Alexandra Elena Constantin, Rian Draeger, Peter Galuardi, Hari Hampapuram, Matthew Lawrence Johnson, Apurv Ullas Kamath, Katherine Yerre Koehler, Aarthi Mahalingam, Gary A. Morris, Philip Thomas Pupa, Peter C. Simpson, Brian Christopher Smith, Tomas C. Walker
  • 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
  • Publication number: 20210316070
    Abstract: Methods, devices and systems are disclosed for inter-app communications between software applications on a mobile communications device. In one aspect, a computer-readable medium on a mobile computing device comprising an inter-application communication data structure to facilitate transitioning and distributing data between software applications in a shared app group for an operating system of the mobile computing device includes a scheme field of the data structure providing a scheme id associated with a target software app to transition to from a source software app, wherein the scheme id is listed on a scheme list stored with the source software app; and a payload field of the data structure providing data and/or an identification where to access data in a shared file system accessible to the software applications in the shared app group, wherein the payload field is encrypted.
    Type: Application
    Filed: March 16, 2021
    Publication date: October 14, 2021
    Inventors: Gary A. Morris, Scott M. Belliveau, Esteban Cabrera, JR., Rian Draeger, Laura J. Dunn, Timothy Joseph Goldsmith, Hari Hampapuram, Christopher Robert Hannemann, Apurv Ullas Kamath, Katherine Yerre Koehler, Patrick Wile McBride, Michael Robert Mensinger, Francis William Pascual, Philip Mansiel Pellouchoud, Nicholas Polytaridis, Philip Thomas Pupa, Anna Leigh Davis, Kevin Shoemaker, Brian Christopher Smith, Benjamin Elrod West, 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: 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: 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: 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: 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
  • Patent number: 10980941
    Abstract: A computer-readable medium on a mobile computing device comprises an inter-application communication data structure to facilitate transitioning and distributing data between software applications in a shared app group for an operating system of the mobile computing device includes a scheme field of the data structure providing a scheme id associated with a target software app to transition to from a source software app, wherein the scheme id is listed on a scheme list stored with the source software app; and a payload field of the data structure providing data and/or an identification where to access data in a shared file system accessible to the software applications in the shared app group, wherein the payload field is encrypted.
    Type: Grant
    Filed: March 30, 2017
    Date of Patent: April 20, 2021
    Assignee: DexCom, Inc.
    Inventors: Gary A. Morris, Scott M. Belliveau, Esteban Cabrera, Jr., Rian Draeger, Laura J. Dunn, Timothy Joseph Goldsmith, Hari Hampapuram, Christopher Robert Hannemann, Apurv Ullas Kamath, Katherine Yerre Koehler, Patrick Wile McBride, Michael Robert Mensinger, Francis William Pascual, Philip Mansiel Pellouchoud, Nicholas Polytaridis, Philip Thomas Pupa, Anna Leigh Davis, Kevin Shoemaker, Brian Christopher Smith, Benjamin Elrod West, Atiim Joseph Wiley
  • Publication number: 20200316296
    Abstract: Systems and methods are disclosed that provide smart alerts to users, e.g., alerts to users about diabetic states that are only provided when it makes sense to do so, e.g., when the system can predict or estimate that the user is not already cognitively aware of their current condition, e.g., particularly where the current condition is a diabetic state warranting attention. In this way, the alert or alarm is personalized and made particularly effective for that user. Such systems and methods still alert the user when action is necessary, e.g., a bolus or temporary basal rate change, or provide a response to a missed bolus or a need for correction, but do not alert when action is unnecessary, e.g., if the user is already estimated or predicted to be cognitively aware of the diabetic state warranting attention, or if corrective action was already taken.
    Type: Application
    Filed: June 17, 2020
    Publication date: October 8, 2020
    Inventors: Anna Leigh Davis, Scott M. Belliveau, Naresh C. Bhavaraju, Leif N. Bowman, Rita M. Castillo, Alexandra Elena Constantin, Rian Draeger, Laura J. Dunn, Gary Brian Gable, Arturo Garcia, Thomas Hall, Hari Hampapuram, Christopher Robert Hannemann, Anna Claire Harley-Trochimczyk, Nathaniel David Heintzman, Andrea Jean Jackson, Lauren Hruby Jepson, Apurv Ullas Kamath, Katherine Yerre Koehler, Aditya Sagar Mandapaka, Samuel Jere Marsh, Gary A. Morris, Subrai Girish Pai, Andrew Attila Pal, Nicholas Polytaridis, Philip Thomas Pupa, Eli Reihman, Ashley Anne Rindfleisch, Sofie Wells Schunk, Peter C. Simpson, Daniel Smith, Stephen J. Vanslyke, Matthew T. Vogel, Tomas C. Walker, Benjamin Elrod West, Atiim Joseph Wiley
  • Patent number: 10737025
    Abstract: Systems and methods are disclosed that provide smart alerts to users, e.g., alerts to users about diabetic states that are only provided when it makes sense to do so, e.g., when the system can predict or estimate that the user is not already cognitively aware of their current condition, e.g., particularly where the current condition is a diabetic state warranting attention. In this way, the alert or alarm is personalized and made particularly effective for that user. Such systems and methods still alert the user when action is necessary, e.g., a bolus or temporary basal rate change, or provide a response to a missed bolus or a need for correction, but do not alert when action is unnecessary, e.g., if the user is already estimated or predicted to be cognitively aware of the diabetic state warranting attention, or if corrective action was already taken.
    Type: Grant
    Filed: August 23, 2017
    Date of Patent: August 11, 2020
    Assignee: DexCom, Inc.
    Inventors: Anna Leigh Davis, Scott M. Belliveau, Naresh C. Bhavaraju, Leif N. Bowman, Rita M. Castillo, Alexandra Elena Constantin, Rian Draeger, Laura J. Dunn, Gary Brian Gable, Arturo Garcia, Thomas Hall, Hari Hampapuram, Christopher Robert Hannemann, Anna Claire Harley-Trochimczyk, Nathaniel David Heintzman, Andrea J. Jackson, Lauren Hruby Jepson, Apurv Ullas Kamath, Katherine Yerre Koehler, Aditya Sagar Mandapaka, Samuel Jere Marsh, Gary A. Morris, Subrai Girish Pai, Andrew Attila Pal, Nicholas Polytaridis, Philip Thomas Pupa, Eli Reihman, Ashley Anne Rindfleisch, Sofie Wells Schunk, Peter C. Simpson, Daniel Smith, Stephen J. Vanslyke, Matthew T. Vogel, Tomas C. Walker, Benjamin Elrod West, Atiim Joseph Wiley
  • Patent number: 10596318
    Abstract: Methods, devices and systems are disclosed for inter-app communications between software applications on a mobile communications device. In one aspect, a computer-readable medium on a mobile computing device comprising an inter-application communication data structure to facilitate transitioning and distributing data between software applications in a shared app group for an operating system of the mobile computing device includes a scheme field of the data structure providing a scheme id associated with a target software app to transition to from a source software app, wherein the scheme id is listed on a scheme list stored with the source software app; and a payload field of the data structure providing data and/or an identification where to access data in a shared file system accessible to the software applications in the shared app group, wherein the payload field is encrypted.
    Type: Grant
    Filed: March 30, 2017
    Date of Patent: March 24, 2020
    Assignee: DexCom, Inc.
    Inventors: Gary A. Morris, Scott M. Belliveau, Esteban Cabrera, Jr., Rian Draeger, Laura J. Dunn, Timothy Joseph Goldsmith, Hari Hampapuram, Christopher Robert Hannemann, Apurv Ullas Kamath, Katherine Yerre Koehler, Patrick Wile McBride, Michael Robert Mensinger, Francis William Pascual, Philip Mansiel Pellouchoud, Nicholas Polytaridis, Philip Thomas Pupa, Anna Leigh Davis, Kevin Shoemaker, Brian Christopher Smith, Benjamin Elrod West, Atiim Joseph Wiley
  • Patent number: 10406287
    Abstract: Systems and methods are disclosed that provide smart alerts to users, e.g., alerts to users about diabetic states that are only provided when it makes sense to do so, e.g., when the system can predict or estimate that the user is not already cognitively aware of their current condition, e.g., particularly where the current condition is a diabetic state warranting attention. In this way, the alert or alarm is personalized and made particularly effective for that user. Such systems and methods still alert the user when action is necessary, e.g., a bolus or temporary basal rate change, or provide a response to a missed bolus or a need for correction, but do not alert when action is unnecessary, e.g., if the user is already estimated or predicted to be cognitively aware of the diabetic state warranting attention, or if corrective action was already taken.
    Type: Grant
    Filed: July 6, 2018
    Date of Patent: September 10, 2019
    Assignee: DexCom, Inc.
    Inventors: Anna Leigh Davis, Scott M. Belliveau, Naresh C. Bhavaraju, Leif N. Bowman, Rita M. Castillo, Alexandra Elena Constantin, Rian Draeger, Laura J. Dunn, Gary Brian Gable, Arturo Garcia, Thomas Hall, Hari Hampapuram, Christopher Robert Hannemann, Anna Claire Harley-Trochimczyk, Nathaniel David Heintzman, Andrea J. Jackson, Lauren Hruby Jepson, Apurv Ullas Kamath, Katherine Yerre Koehler, Aditya Sagar Mandapaka, Samuel Jere Marsh, Gary A. Morris, Subrai Girish Pai, Andrew Attila Pal, Nicholas Polytaridis, Philip Thomas Pupa, Eli Reihman, Ashley Anne Rindfleisch, Sofie Wells Schunk, Peter C. Simpson, Daniel Smith, Stephen J. Vanslyke, Matthew T. Vogel, Tomas C. Walker, Benjamin Elrod West, Atiim Joseph Wiley
  • 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: 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: 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