Patents by Inventor Naresh C. Bhavaraju

Naresh C. Bhavaraju 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).

  • Publication number: 20220137025
    Abstract: Systems and methods for processing sensor data and end of life detection are provided. In some embodiments, a method for determining the end of life of a continuous analyte sensor includes evaluating a plurality of risk factors using an end of life function to determine an end of life status of the sensor and providing an output related to the end of life status of the sensor. The plurality of risk factors may be selected from the list including the number of days the sensor has been in use, whether there has been a decrease in signal sensitivity, whether there is a predetermined noise pattern, whether there is a predetermined oxygen concentration pattern, and error between reference BG values and EGV sensor values.
    Type: Application
    Filed: November 10, 2021
    Publication date: May 5, 2022
    Inventors: Naresh C. Bhavaraju, Arturo Garcia, Hari Hampapuram, Apurv Ullas Kamath, Aarthi Mahalingam, Dmytro Sokolovskyy, Stephen J. Vanslyke
  • Patent number: 11193924
    Abstract: Systems and methods for processing sensor data and end of life detection are provided. In some embodiments, a method for determining the end of life of a continuous analyte sensor includes evaluating a plurality of risk factors using an end of life function to determine an end of life status of the sensor and providing an output related to the end of life status of the sensor. The plurality of risk factors may be selected from the list including the number of days the sensor has been in use, whether there has been a decrease in signal sensitivity, whether there is a predetermined noise pattern, whether there is a predetermined oxygen concentration pattern, and error between reference BG values and EGV sensor values.
    Type: Grant
    Filed: November 27, 2019
    Date of Patent: December 7, 2021
    Assignee: DexCom, Inc.
    Inventors: Naresh C. Bhavaraju, Arturo Garcia, Hari Hampapuram, Apurv Ullas Kamath, Aarthi Mahalingam, Dmytro Sokolovskyy, Stephen J. Vanslyke
  • Publication number: 20210330219
    Abstract: Systems and methods are disclosed which provide for a “factory-calibrated” sensor. In doing so, the systems and methods include predictive prospective modeling of sensor behavior, and also include predictive modeling of physiology. With these two correction factors, a consistent determination of sensitivity can be achieved, thus achieving factory calibration.
    Type: Application
    Filed: July 6, 2021
    Publication date: October 28, 2021
    Inventors: Rui Ma, Naresh C. Bhavaraju, Thomas Stuart Hamilton, Jonathan Hughes, Jeff Jackson, David I-Chun Lee, Peter C. Simpson, Stephen J. Vanslyke
  • 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: 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: 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: 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
  • Patent number: 11051731
    Abstract: Systems and methods are disclosed which provide for a “factory-calibrated” sensor. In doing so, the systems and methods include predictive prospective modeling of sensor behavior, and also include predictive modeling of physiology. With these two correction factors, a consistent determination of sensitivity can be achieved, thus achieving factory calibration.
    Type: Grant
    Filed: June 24, 2020
    Date of Patent: July 6, 2021
    Assignee: DexCom, Inc.
    Inventors: Rui Ma, Naresh C. Bhavaraju, Thomas Stuart Hamilton, Jonathan Hughes, Jeff Jackson, David I-Chun Lee, Peter C. Simpson, Stephen J. Vanslyke
  • Patent number: 11026640
    Abstract: Systems and methods for providing sensitive and specific alarms indicative of glycemic condition are provided herein. In an embodiment, a method of processing sensor data by a continuous analyte sensor includes: evaluating sensor data using a first function to determine whether a real time glucose value meets a first threshold; evaluating sensor data using a second function to determine whether a predicted glucose value meets a second threshold; activating a hypoglycemic indicator if either the first threshold is met or if the second threshold is predicted to be met; and providing an output based on the activated hypoglycemic indicator.
    Type: Grant
    Filed: January 25, 2021
    Date of Patent: June 8, 2021
    Assignee: DexCom, Inc.
    Inventors: Hari Hampapuram, Anna Leigh Davis, Naresh C. Bhavaraju, Apurv Ullas Kamath, Claudio Cobelli, Giovanni Sparacino, Andrea Facchinetti, Chiara Zecchin
  • Publication number: 20210145371
    Abstract: Systems and methods for providing sensitive and specific alarms indicative of glycemic condition are provided herein. In an embodiment, a method of processing sensor data by a continuous analyte sensor includes: evaluating sensor data using a first function to determine whether a real time glucose value meets a first threshold; evaluating sensor data using a second function to determine whether a predicted glucose value meets a second threshold; activating a hypoglycemic indicator if either the first threshold is met or if the second threshold is predicted to be met; and providing an output based on the activated hypoglycemic indicator.
    Type: Application
    Filed: January 25, 2021
    Publication date: May 20, 2021
    Inventors: Hari Hampapuram, Anna Leigh Davis, Naresh C. Bhavaraju, Apurv Ullas Kamath, Claudio Cobelli, Giovanni Sparacino, Andrea Facchinetti, Chiara Zecchin
  • Patent number: 11006903
    Abstract: Systems and methods for providing sensitive and specific alarms indicative of glycemic condition are provided herein. In an embodiment, a method of processing sensor data by a continuous analyte sensor includes: evaluating sensor data using a first function to determine whether a real time glucose value meets a first threshold; evaluating sensor data using a second function to determine whether a predicted glucose value meets a second threshold; activating a hypoglycemic indicator if either the first threshold is met or if the second threshold is predicted to be met; and providing an output based on the activated hypoglycemic indicator.
    Type: Grant
    Filed: December 26, 2019
    Date of Patent: May 18, 2021
    Assignee: DexCom, Inc.
    Inventors: Hari Hampapuram, Anna Leigh Davis, Naresh C. Bhavaraju, Apurv Ullas Kamath, Claudio Cobelli, Giovanni Sparacino, Andrea Facchinetti, Chiara Zecchin
  • 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: 20210085223
    Abstract: Devices, systems, and methods for providing more accurate and reliable sensor data and for detecting sensor failures. Two or more electrodes can be used to generate data, and the data can be subsequently compared by a processing module. Alternatively, one sensor can be used, and the data processed by two parallel algorithms to provide redundancy. Sensor performance, including sensor failures, can be identified. The user or system can then respond appropriately to the information related to sensor performance or failure.
    Type: Application
    Filed: December 1, 2020
    Publication date: March 25, 2021
    Inventors: Thomas A. Peyser, Naresh C. Bhavaraju, Leif N. Bowman, Apurv Ullas Kamath, Aarthi Mahalingam, Jack Pryor, Peter C. Simpson
  • Publication number: 20210090731
    Abstract: Systems and methods that continuously adapt aspects of a continuous monitoring device based on collected information to provide an individually tailored configuration are described. The adaptations may include adapting the user interface, the alerting, the motivational messages, the training, and the like. Such adaptation can allow a patient to more readily identify and understand the information provided by/via the device.
    Type: Application
    Filed: December 8, 2020
    Publication date: March 25, 2021
    Inventors: Phil Mayou, Naresh C. Bhavaraju, Leif N. Bowman, Alexandra Lynn Carlton, Laura J. Dunn, Katherine Yerre Koehler, Aarthi Mahalingam, Eli Reihman, Peter C. Simpson
  • Patent number: 10881339
    Abstract: Devices, systems, and methods for providing more accurate and reliable sensor data and for detecting sensor failures. Two or more electrodes can be used to generate data, and the data can be subsequently compared by a processing module. Alternatively, one sensor can be used, and the data processed by two parallel algorithms to provide redundancy. Sensor performance, including sensor failures, can be identified. The user or system can then respond appropriately to the information related to sensor performance or failure.
    Type: Grant
    Filed: March 7, 2013
    Date of Patent: January 5, 2021
    Assignee: DexCom, Inc.
    Inventors: Thomas A. Peyser, Naresh C. Bhavaraju, Leif N. Bowman, Apurv Ullas Kamath, Aarthi Mahalingam, Jack Pryor, Peter C. Simpson
  • Publication number: 20200337607
    Abstract: Systems and methods for processing sensor data and calibration of the sensors are provided. In some embodiments, the method for calibrating at least one sensor data point from an analyte sensor comprises receiving a priori calibration distribution information; receiving one or more real-time inputs that may influence calibration of the analyte sensor; forming a posteriori calibration distribution information based on the one or more real-time inputs; and converting, in real-time, at least one sensor data point calibrated sensor data based on the a posteriori calibration distribution information.
    Type: Application
    Filed: July 14, 2020
    Publication date: October 29, 2020
    Inventors: Stephen J. Vanslyke, Naresh C. Bhavaraju, Lucas Bohnett, Arturo Garcia, Apurv Ullas Kamath, Jack Pryor
  • Publication number: 20200323470
    Abstract: Systems and methods are disclosed which provide for a “factory-calibrated” sensor. In doing so, the systems and methods include predictive prospective modeling of sensor behavior, and also include predictive modeling of physiology. With these two correction factors, a consistent determination of sensitivity can be achieved, thus achieving factory calibration.
    Type: Application
    Filed: June 24, 2020
    Publication date: October 15, 2020
    Inventors: Rui Ma, Naresh C. Bhavaraju, Thomas Stuart Hamilton, Jonathan Hughes, Jeff Jackson, David I-Chun Lee, Peter C. Simpson, Stephen J. Vanslyke
  • 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
  • Publication number: 20200305804
    Abstract: Systems and methods for providing sensitive and specific alarms indicative of glycemic condition are provided herein. In an embodiment, a method of processing sensor data by a continuous analyte sensor includes: evaluating sensor data using a first function to determine whether a real time glucose value meets a first threshold; evaluating sensor data using a second function to determine whether a predicted glucose value meets a second threshold; activating a hypoglycemic indicator if either the first threshold is met or if the second threshold is predicted to be met; and providing an output based on the activated hypoglycemic indicator.
    Type: Application
    Filed: May 27, 2020
    Publication date: October 1, 2020
    Inventors: Hari Hampapuram, Anna Leigh Davis, Naresh C. Bhavaraju, Apurv Ullas Kamath, Claudio Cobelli, Giovanni Sparacino, Andrea Facchinetti, Chiara Zecchin