Patents by Inventor Andrew Attila Pal
Andrew Attila Pal 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: 20220068452Abstract: Provided are systems and methods using which users may learn and become familiar with the effects of various aspects of their lifestyle on their health, e.g., users may learn about how food and/or exercise affects their glucose level and other physiological parameters, as well as overall health. In some cases the user selects a program to try; in other cases, a computing environment embodying the system suggests programs to try, including on the basis of pattern recognition, i.e., by the computing environment determining how a user could improve a detected pattern in some way. In this way, users such as type II diabetics or even users who are only prediabetic or non-diabetic may learn healthy habits to benefit their health.Type: ApplicationFiled: November 11, 2021Publication date: March 3, 2022Inventors: Peter C. Simpson, Robert J. Boock, David DeRenzy, Laura J. Dunn, Matthew Lawrence Johnson, Katherine Yerre Koehler, Apurv Ullas Kamath, Andrew Attila Pal, David Price, Eli Reihman, Mark Wu
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Patent number: 11219413Abstract: Adhesive pad systems that provide longer lasting adherence of the mounting unit to the host's skin are provided. Some systems include a reinforcing overlay that at least partially covers the adhesive pad. The reinforcing overlay may be removable without disturbing the sensor so that the overlay may be replaceable.Type: GrantFiled: August 25, 2015Date of Patent: January 11, 2022Assignee: DexCom, Inc.Inventors: James Jinwoo Lee, Leif N. Bowman, Tim Ray Gackstetter, Jonathan Hughes, Jeff Jackson, Ted Tang Lee, Phong Lieu, Andrew Attila Pal, James R. Petisce, Jack Pryor, Roger Schneider, Peter C. Simpson, George Vigil, Matthew D. Wightlin
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Publication number: 20210294723Abstract: Disclosed are systems, methods, and articles for determining compatibility of a mobile application and operating system on a mobile device. In some aspects, a method includes receiving one or more data values from a mobile device having a mobile medical software application installed thereon, the data value(s) characterizing a version of the software application, a version of an operating system installed on the mobile device, and one or more attributes of the mobile device; determining whether the mobile medical software application is compatible with the operating system by at least comparing the received data value(s) to one or more test values in a configuration file; and sending a message to the mobile device based on the determining, the message causing the software application to operate in one or more of a normal mode, a safe mode, and a non-operational mode.Type: ApplicationFiled: June 3, 2021Publication date: September 23, 2021Inventors: Issa Sami Salameh, Douglas William Burnette, Tifo Vu Hoang, Steven David King, Stephen M. Madigan, Michael Robert Mensinger, Andrew Attila Pal, Michael Ranen Tyler
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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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Publication number: 20210260286Abstract: Machine learning in an artificial pancreas is described. An artificial pancreas system may include a wearable glucose monitoring device, an insulin delivery system, and a computing device. Broadly speaking, the wearable glucose monitoring device provides glucose measurements of a person continuously. The artificial pancreas algorithm, which may be implemented at the computing device, determines doses of insulin to deliver to the person based on a variety of aspects for the purpose of maintaining the person's glucose within a target range, as indicated by those glucose measurements. The insulin delivery system then delivers those determined doses to the person. As the artificial pancreas algorithm determines insulin doses for the person over time and effectiveness of the insulin doses to maintain the person's glucose level in the target range is observed, an underlying model of the artificial pancreas algorithm may be updated to better determine insulin doses.Type: ApplicationFiled: December 7, 2020Publication date: August 26, 2021Inventors: Apurv Ullas Kamath, Derek James Escobar, Sumitaka Mikami, Hari Hampapuram, Benjamin Elrod West, Nathanael Paul, Naresh C. Bhavaraju, Michael Robert Mensinger, Gary A. Morris, Andrew Attila Pal, Eli Reihman, Scott M. Belliveau, Katherine Yerre Koehler, Nicholas Polytaridis, Rian Draeger, Jorge Valdes, David Price, Peter C. Simpson, Edward Sweeney
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Publication number: 20210260287Abstract: Machine learning in an artificial pancreas is described. An artificial pancreas system may include a wearable glucose monitoring device, an insulin delivery system, and a computing device. Broadly speaking, the wearable glucose monitoring device provides glucose measurements of a person continuously. The artificial pancreas algorithm, which may be implemented at the computing device, determines doses of insulin to deliver to the person based on a variety of aspects for the purpose of maintaining the person's glucose within a target range, as indicated by those glucose measurements. The insulin delivery system then delivers those determined doses to the person. As the artificial pancreas algorithm determines insulin doses for the person over time and effectiveness of the insulin doses to maintain the person's glucose level in the target range is observed, an underlying model of the artificial pancreas algorithm may be updated to better determine insulin doses.Type: ApplicationFiled: December 7, 2020Publication date: August 26, 2021Inventors: Apurv Ullas Kamath, Derek James Escobar, Sumitaka Mikami, Hari Hampapuram, Benjamin Elrod West, Nathanael Paul, Naresh C. Bhavaraju, Michael Robert Mensinger, Gary A. Morris, Andrew Attila Pal, Eli Reihman, Scott M. Belliveau, Katherine Yerre Koehler, Nicholas Polytaridis, Rian Draeger, Jorge Valdes, David Price, Peter C. Simpson, Edward Sweeney
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Publication number: 20210251484Abstract: The present disclosure relates to systems, devices and methods for receiving biosensor data acquired by a medical device, e.g., relating to glucose concentration values, and controlling the access and distribution of that data. In some embodiments, systems and methods are disclosed for monitoring glucose levels, displaying data relating to glucose values and metabolic health information, and controlling distribution of glucose data between applications executing on a computer, such as a smart phone. In some embodiments, systems and methods are disclosed for controlling access to medical data such as continuously monitored glucose levels, synchronizing health data relating to glucose levels between multiple applications executing on a computer, and/or encrypting data.Type: ApplicationFiled: March 12, 2021Publication date: August 19, 2021Inventors: Michael Robert Mensinger, Esteban Cabrera, Jr., Eric Cohen, Nathaniel David Heintzman, Apurv Ullas Kamath, Gary A. Morris, Andrew Attila Pal, Eli Reihman, Jorge Valdes
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Patent number: 11055198Abstract: Disclosed are systems, methods, and articles for determining compatibility of a mobile application and operating system on a mobile device. In some aspects, a method includes receiving one or more data values from a mobile device having a mobile medical software application installed thereon, the data value(s) characterizing a version of the software application, a version of an operating system installed on the mobile device, and one or more attributes of the mobile device; determining whether the mobile medical software application is compatible with the operating system by at least comparing the received data value(s) to one or more test values in a configuration file; and sending a message to the mobile device based on the determining, the message causing the software application to operate in one or more of a normal mode, a safe mode, and a non-operational mode.Type: GrantFiled: December 27, 2019Date of Patent: July 6, 2021Assignee: DexCom, Inc.Inventors: Issa Sami Salameh, Douglas William Burnette, Tifo Vu Hoang, Steven David King, Stephen M. Madigan, Michael Robert Mensinger, Andrew Attila Pal, Michael Ranen Tyler
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Patent number: 10945600Abstract: The present disclosure relates to systems, devices and methods for receiving biosensor data acquired by a medical device, e.g., relating to glucose concentration values, and controlling the access and distribution of that data. In some embodiments, systems and methods are disclosed for monitoring glucose levels, displaying data relating to glucose values and metabolic health information, and controlling distribution of glucose data between applications executing on a computer, such as a smart phone. In some embodiments, systems and methods are disclosed for controlling access to medical data such as continuously monitored glucose levels, synchronizing health data relating to glucose levels between multiple applications executing on a computer, and/or encrypting data.Type: GrantFiled: February 9, 2016Date of Patent: March 16, 2021Assignee: DexCom, Inc.Inventors: Michael Robert Mensinger, Esteban Cabrera, Jr., Eric Cohen, Nathaniel David Heintzman, Apurv Ullas Kamath, Gary A. Morris, Andrew Attila Pal, Eli Reihman, Jorge Valdes
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Patent number: 10932672Abstract: Systems and methods for remote and host monitoring communication are disclosed. In some implementations, monitoring systems can comprise a host monitoring device associated with a Host communicatively coupled to one or more remote monitoring devices associated with Remote Monitors. The host monitoring device can send communications based at least in part on analyte measurements of a Host sensor and/or other contextual data giving such measurements context. Different remote monitoring devices can receive different communications based at least in part on the role of the respective Remote Monitors relative to the Host. These roles can be reflected in classifications of Remote Monitors.Type: GrantFiled: December 13, 2016Date of Patent: March 2, 2021Assignee: DexCom, Inc.Inventors: Aarthi Mahalingam, Esteban Cabrera, Jr., Basab Dattaray, Rian Draeger, Laura J. Dunn, Derek James Escobar, Thomas Hall, Hari Hampapuram, Apurv Ullas Kamath, Katherine Yerre Koehler, Phil Mayou, Michael Robert Mensinger, Michael Levozier Moore, Andrew Attila Pal, Nicholas Polytaridis, Eli Reihman, Brian Christopher Smith
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Publication number: 20200316296Abstract: 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: June 17, 2020Publication date: October 8, 2020Inventors: 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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Patent number: 10737025Abstract: 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: August 23, 2017Date of Patent: August 11, 2020Assignee: 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
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Patent number: 10691574Abstract: Disclosed are systems, methods, and articles for determining compatibility of a mobile application and operating system on a mobile device. In some aspects, a method includes receiving one or more data values from a mobile device having a mobile medical software application installed thereon, the data value(s) characterizing a version of the software application, a version of an operating system installed on the mobile device, and one or more attributes of the mobile device; determining whether the mobile medical software application is compatible with the operating system by at least comparing the received data value(s) to one or more test values in a configuration file; and sending a message to the mobile device based on the determining, the message causing the software application to operate in one or more of a normal mode, a safe mode, and a non-operational mode.Type: GrantFiled: October 25, 2016Date of Patent: June 23, 2020Assignee: DexCom, Inc.Inventors: Issa Sami Salameh, Douglas William Burnette, Tifo Vu Hoang, Steven David King, Stephen M. Madigan, Michael Robert Mensinger, Andrew Attila Pal, Michael Ranen Tyler
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Publication number: 20200142804Abstract: Disclosed are systems, methods, and articles for determining compatibility of a mobile application and operating system on a mobile device. In some aspects, a method includes receiving one or more data values from a mobile device having a mobile medical software application installed thereon, the data value(s) characterizing a version of the software application, a version of an operating system installed on the mobile device, and one or more attributes of the mobile device; determining whether the mobile medical software application is compatible with the operating system by at least comparing the received data value(s) to one or more test values in a configuration file; and sending a message to the mobile device based on the determining, the message causing the software application to operate in one or more of a normal mode, a safe mode, and a non-operational mode.Type: ApplicationFiled: December 27, 2019Publication date: May 7, 2020Inventors: Issa Sami Salameh, Douglas William Burnette, Tifo Vu Hoang, Steven David King, Stephen M. Madigan, Michael Robert Mensinger, Andrew Attila Pal, Michael Ranen Tyler
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Patent number: 10545849Abstract: Disclosed are systems, methods, and articles for determining compatibility of a mobile application and operating system on a mobile device. In some aspects, a method includes receiving one or more data values from a mobile device having a mobile medical software application installed thereon, the data value(s) characterizing a version of the software application, a version of an operating system installed on the mobile device, and one or more attributes of the mobile device; determining whether the mobile medical software application is compatible with the operating system by at least comparing the received data value(s) to one or more test values in a configuration file; and sending a message to the mobile device based on the determining, the message causing the software application to operate in one or more of a normal mode, a safe mode, and a non-operational mode.Type: GrantFiled: October 25, 2016Date of Patent: January 28, 2020Assignee: DexCom, Inc.Inventors: Issa Sami Salameh, Douglas William Burnette, Tifo Vu Hoang, Steven David King, Stephen M. Madigan, Michael Robert Mensinger, Andrew Attila Pal, Michael Ranen Tyler
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Publication number: 20200020247Abstract: Provided are systems and methods using which users may learn and become familiar with the effects of various aspects of their lifestyle on their health, e.g., users may learn about how food and/or exercise affects their glucose level and other physiological parameters, as well as overall health. In some cases the user selects a program to try; in other cases, a computing environment embodying the system suggests programs to try, including on the basis of pattern recognition, i.e., by the computing environment determining how a user could improve a detected pattern in some way. In this way, users such as type II diabetics or even users who are only prediabetic or non-diabetic may learn healthy habits to benefit their health.Type: ApplicationFiled: September 12, 2019Publication date: January 16, 2020Inventors: Peter C. Simpson, Robert J. Boock, David DeRenzy, Laura J. Dunn, Matthew Lawrence Johnson, Katherine Yerre Koehler, Apurv Ullas Kamath, Andrew Attila Pal, David Price, Eli Reihman, Mark Wu
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Publication number: 20190339223Abstract: 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: ApplicationFiled: May 2, 2019Publication date: November 7, 2019Inventors: 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: 20190339224Abstract: 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: ApplicationFiled: May 2, 2019Publication date: November 7, 2019Inventors: 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