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).
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Publication number: 20230210474Abstract: 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: ApplicationFiled: March 7, 2023Publication date: July 6, 2023Inventors: Hari HAMPAPURAM, Anna Leigh DAVIS, Naresh C. BHAVARAJU, Apurv Ullas KAMATH, Claudio COBELLI, Giovanni SPARACINO, Andrea FACCHINETTI, Chiara ZECCHIN
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Publication number: 20230210411Abstract: Systems and methods described provide dynamic and intelligent ways to change the required level of user interaction during use of a monitoring device. The systems and methods generally relate to real time switching between a first or initial mode of user interaction and a second or new mode of user interaction. In some cases, the switching will be automatic and transparent to the user, and in other cases user notification may occur. The mode switching generally affects the user’s interaction with the device, and not just internal processing. The mode switching may relate to calibration modes, data transmission modes, control modes, or the like.Type: ApplicationFiled: February 28, 2023Publication date: July 6, 2023Applicant: Dexcom, Inc.Inventors: Naresh C. Bhavaraju, Michael A. Bloom, Leif N. Bowman, Alexandra Lynn Carlton, Katherine Yerre Koehler, Hari Hampapuram, Jonathan Hughes, Lauren Hruby Jepson, Apurv Ullas Kamath, Anna Leigh Rack-Gomer, Peter C. Simpson, Stephen J. Vanslyke
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Publication number: 20230170090Abstract: Methods and apparatus, including computer program products, are provided for processing analyte data. In some example implementations, a method may include generating glucose sensor data indicative of a host's glucose concentration using a glucose sensor; calculating a glycemic variability index (GVI) value based on the glucose sensor data; and providing output to a user responsive to the calculated glycemic variability index value. The GVI may be a ratio of a length of a line representative of the sensor data and an ideal length of the line. Related systems, methods, and articles of manufacture are also disclosed.Type: ApplicationFiled: February 1, 2023Publication date: June 1, 2023Inventors: Naresh C. BHAVARAJU, Arturo GARCIA, Phil MAYOU, Thomas A. PEYSER, Apurv Ullas KAMATH, Aarthi MAHALINGAM, Kevin SAYER, Thomas HALL, Michael Robert MENSINGER, Hari HAMPAPURAM, David PRICE, Jorge VALDES, Murrad KAZALBASH
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Patent number: 11656195Abstract: Systems and methods are provided that address the need to frequently calibrate analyte sensors, according to implementation. In more detail, systems and methods provide a preconnected analyte sensor system that physically combines an analyte sensor to measurement electronics during the manufacturing phase of the sensor and in some cases in subsequent life phases of the sensor, so as to allow an improved recognition of sensor environment over time to improve subsequent calibration of the sensor.Type: GrantFiled: May 2, 2019Date of Patent: May 23, 2023Assignee: Dexcom, Inc.Inventors: Naresh C. Bhavaraju, Becky L. Clark, Vincent P. Crabtree, Chris W. Dring, Arturo Garcia, Jason Halac, Jonathan Hughes, Jeff Jackson, Lauren Hruby Jepson, David I-Chun Lee, Ted Tang Lee, Rui Ma, Zebediah L. McDaniel, Jason Mitchell, Andrew Attila Pal, Daiting Rong, Disha B. Sheth, Peter C. Simpson, Stephen J. Vanslyke, Matthew D. Wightlin, Anna Leigh Davis, Hari Hampapuram, Aditya Sagar Mandapaka, Alexander Leroy Teeter, Liang Wang
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Publication number: 20230140651Abstract: Systems and methods are provided to calibrate an analyte concentration sensor within a biological system, generally using only a signal from the analyte concentration sensor. For example, at a steady state, the analyte concentration value within the biological system is known, and the same may provide a source for calibration. Similar techniques may be employed with slow-moving averages. Variations are disclosed.Type: ApplicationFiled: October 26, 2022Publication date: May 4, 2023Inventors: Arturo Garcia, Peter C. Simpson, Apurv U. Kamath, Naresh C. Bhavaraju, Stephen J. Vanslyke
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Patent number: 11600384Abstract: Methods and apparatus, including computer program products, are provided for processing analyte data. In some example implementations, a method may include generating glucose sensor data indicative of a host's glucose concentration using a glucose sensor; calculating a glycemic variability index (GVI) value based on the glucose sensor data; and providing output to a user responsive to the calculated glycemic variability index value. The GVI may be a ratio of a length of a line representative of the sensor data and an ideal length of the line. Related systems, methods, and articles of manufacture are also disclosed.Type: GrantFiled: June 18, 2019Date of Patent: March 7, 2023Assignee: Dexcom, Inc.Inventors: Naresh C. Bhavaraju, Arturo Garcia, Phil Mayou, Thomas A. Peyser, Apurv Ullas Kamath, Aarthi Mahalingam, Kevin Sayer, Thomas Hall, Michael Robert Mensinger, Hari Hampapuram, David Price, Jorge Valdes, Murrad Kazalbash
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Publication number: 20230013632Abstract: 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: ApplicationFiled: September 19, 2022Publication date: January 19, 2023Inventors: Anna Leigh DAVIS, Scott M. BELLIVEAU, Naresh C. BHAVARAJU, Leif N. BOWMAN, Rita M. CASTILLO, Alexandra Elena CONSTANTIN, Rian W. 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 S. SMITH, Stephen J. VANSLYKE, Matthew T. VOGEL, Tomas C. WALKER, Benjamin Elrod WEST, Atiim Joseph WILEY
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Patent number: 11504004Abstract: Systems and methods are provided to calibrate an analyte concentration sensor within a biological system, generally using only a signal from the analyte concentration sensor. For example, at a steady state, the analyte concentration value within the biological system is known, and the same may provide a source for calibration. Similar techniques may be employed with slow-moving averages. Variations are disclosed.Type: GrantFiled: February 5, 2020Date of Patent: November 22, 2022Assignee: Dexcom, Inc.Inventors: Arturo Garcia, Peter C. Simpson, Apurv Ullas Kamath, Naresh C. Bhavaraju, Stephen J. Vanslyke
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Patent number: 11450421Abstract: 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: GrantFiled: June 17, 2020Date of Patent: September 20, 2022Assignee: 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 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
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Publication number: 20220137025Abstract: 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: ApplicationFiled: November 10, 2021Publication date: May 5, 2022Inventors: Naresh C. Bhavaraju, Arturo Garcia, Hari Hampapuram, Apurv Ullas Kamath, Aarthi Mahalingam, Dmytro Sokolovskyy, Stephen J. Vanslyke
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Patent number: 11193924Abstract: 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: GrantFiled: November 27, 2019Date of Patent: December 7, 2021Assignee: DexCom, Inc.Inventors: Naresh C. Bhavaraju, Arturo Garcia, Hari Hampapuram, Apurv Ullas Kamath, Aarthi Mahalingam, Dmytro Sokolovskyy, Stephen J. Vanslyke
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Publication number: 20210330219Abstract: 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: ApplicationFiled: July 6, 2021Publication date: October 28, 2021Inventors: Rui Ma, Naresh C. Bhavaraju, Thomas Stuart Hamilton, Jonathan Hughes, Jeff Jackson, David I-Chun Lee, Peter C. Simpson, Stephen J. Vanslyke
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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: 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: 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: 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: 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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Patent number: 11051731Abstract: 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: GrantFiled: June 24, 2020Date of Patent: July 6, 2021Assignee: 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
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Patent number: 11026640Abstract: 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: GrantFiled: January 25, 2021Date of Patent: June 8, 2021Assignee: DexCom, Inc.Inventors: Hari Hampapuram, Anna Leigh Davis, Naresh C. Bhavaraju, Apurv Ullas Kamath, Claudio Cobelli, Giovanni Sparacino, Andrea Facchinetti, Chiara Zecchin
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Publication number: 20210145371Abstract: 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: ApplicationFiled: January 25, 2021Publication date: May 20, 2021Inventors: Hari Hampapuram, Anna Leigh Davis, Naresh C. Bhavaraju, Apurv Ullas Kamath, Claudio Cobelli, Giovanni Sparacino, Andrea Facchinetti, Chiara Zecchin