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公开(公告)号：US20230140143A1
公开(公告)日：2023-05-04
申请号：US17974290
申请日：2022-10-26
Applicant: Dexcom, Inc.
Inventor： Giada Acciaroli , Margaret A. Crawford , Mark Derdzinski , Lauren H. Jepson , Sarah Kate Pickus , Robert J. Dowd , Apurv U. Kamath
Abstract: Glucose measurements are received and features for corresponding time periods over a time window are generated, the features being values indicating whether the user has been engaging in beneficial diabetes management behaviors. Using the aggregated features patterns indicating that beneficial diabetes management behaviors are not being engaged in are identified. Potential behavior modification feedback is generated by including in the potential behavior modification feedback at least one behavior modification feedback, for each of the identified patterns, that a user could take to engage in beneficial diabetes management behavior. At least one of the potential behavior modification feedback is selected and displayed or otherwise presented to the user.
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公开(公告)号：US20220202319A1
公开(公告)日：2022-06-30
申请号：US17556822
申请日：2021-12-20
Applicant: DexCom, Inc.
Inventor： Margaret A. Crawford , Linda Schertzer , Andrea J. Jackson , Douglas Scott Kanter , Giada Acciaroli , Chad Patterson , Apurv Kamath , Alexander Michael Diener , Drew Terry , Mark Derdzinski , Sarah Kate Pickus , Lauren Hruby Jepson , Adam Noar
Abstract: Meal and activity logging with a glucose monitoring interface is described. A glucose monitoring application is configured to display a user interface that includes a glucose graph that plots glucose measurements of a user over time. The glucose measurements, for example, may be obtained from a glucose monitoring device that collects glucose measurements of the user at predetermined intervals, e.g., every five minutes. Unlike conventional event logging approaches, the glucose monitoring application displays representations of logged events in the user interface along with the glucose graph. The logged events, for example, may include meals consumed by the user, and/or various activities performed by the user, such as exercise, meditation, sleep, and so forth. Notably, the glucose monitoring application controls the display of the event representations to be presented at positions on the glucose graph that correspond to times associated with the respective events.
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公开(公告)号：US20210338116A1
公开(公告)日：2021-11-04
申请号：US17114234
申请日：2020-12-07
Applicant: DexCom, Inc.
Inventor： Giada Acciaroli , Mark Derdzinski , Lauren Hruby Jepson , Andrew S. Parker
Abstract: Hypoglycemic event prediction using machine learning is described. A CGM platform includes a machine learning model trained using historical time series glucose measurements of a user population. Once trained, the machine learning model predicts hypoglycemic events for users. When predicting hypoglycemic events, a time series of glucose measurements for a day time interval is received. The glucose measurements of this time series for the day time interval are provided by a CGM system worn by the user. The machine learning model predicts whether a hypoglycemic event will occur during a night time interval that is subsequent to the day time interval by processing the time series of glucose measurements using the trained machine learning model. The hypoglycemic event prediction is then output, such as via communication and/or display of a notification about the hypoglycemic event prediction.
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4.发明申请公开(公告)号：US20200237271A1
公开(公告)日：2020-07-30
申请号：US16779503
申请日：2020-01-31
Applicant: DexCom, Inc.
Inventor： Stephen J. Vanslyke , Giada Acciaroli , Martina Vettoretti , Andrea Facchinetti , Giovanni Sparacino
Abstract: A method for monitoring a blood glucose level of a user is provided. The method includes receiving a time-varying electrical signal from an analyte sensor during a temporal phase of a monitoring session. The method includes selecting a calibration model from a plurality of calibration models, wherein the selected calibration model comprises one or more calibration model parameters. The method includes estimating at least one of the one or more calibration model parameters of the selected calibration model based on at least the time-varying electrical signal during the temporal phase of the monitoring session. The method includes estimating the blood glucose level of the user based on the selected calibration model and using the at least one estimated parameter. An apparatus and non-transitory computer readable medium having similar functionality are also provided.
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公开(公告)号：US20230136188A1
公开(公告)日：2023-05-04
申请号：US17974296
申请日：2022-10-26
Applicant: Dexcom, Inc.
Inventor： Sarah Kate Pickus , Margaret A. Crawford , Mark Derdzinski , Lauren H. Jepson , Robert J. Dowd , Giada Acciaroli , Apurv U. Kamath
Abstract: Glucose level measurements and additional data regarding a user are obtained over time, such as from a wearable glucose monitoring device being worn by the user. This additional data identifies events or conditions that may affect glucose of the user, such as physical activity engaged in by the user. A glucose prediction system analyzes, for example, activity data of the user and determines when a bout of physical activity occurs. The glucose prediction system predicts what the glucose measurements of the user would have been had the physical activity not occurred, and takes various actions based on the predicted glucose measurements (e.g., provides feedback to the user indicating what their glucose would have been had they not engaged in the physical activity).
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Patent Agency Ranking
公开(公告)号：US20210343402A1
公开(公告)日：2021-11-04
申请号：US17114106
申请日：2020-12-07
Applicant: DexCom, Inc.
Inventor： Giada Acciaroli , Mark Derdzinski , Lauren Hruby Jepson , Andrew S. Parker
Abstract: Hypoglycemic event prediction using machine learning is described. A CGM platform includes a machine learning model trained using historical time series glucose measurements of a user population. Once trained, the machine learning model predicts hypoglycemic events for users. When predicting hypoglycemic events, a time series of glucose measurements for a day time interval is received. The glucose measurements of this time series for the day time interval are provided by a CGM system worn by the user. The machine learning model predicts whether a hypoglycemic event will occur during a night time interval that is subsequent to the day time interval by processing the time series of glucose measurements using the trained machine learning model. The hypoglycemic event prediction is then output, such as via communication and/or display of a notification about the hypoglycemic event prediction.
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公开(公告)号：US20230138673A1
公开(公告)日：2023-05-04
申请号：US17974299
申请日：2022-10-26
Applicant: DexCom, Inc.
Inventor： Margaret A. Crawford , Mark Derdzinski , Giada Acciaroli , Robert J. Dowd , Lauren H. Jepson , Sarah Kate Pickus , Apurv U. Kamath
Abstract: Feedback regarding diabetes management by a user is generated, such as feedback identifying improvements in glucose measurements for a given time period over previous days, feedback identifying sustained positive patterns, feedback identifying deviations in glucose measurements between time periods, feedback identifying potential behavior modification that a user could take to engage in beneficial diabetes management behavior, feedback identifying what a user's glucose would have been had the particular events or conditions not occurred or not been present, and so forth. A feedback presentation system analyzes the identified feedback and selects feedback based on various rankings, rules and conditions for display to the user. The selected feedback is provided to the user at various times, such as regular reports (e.g., daily or weekly reports), in real time (e.g., notifying the user what his glucose level would have been had he not just taken a walk), and so forth.
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公开(公告)号：US20230135175A1
公开(公告)日：2023-05-04
申请号：US17974185
申请日：2022-10-26
Applicant: Dexcom, Inc.
Inventor： Lauren H. Jepson , Margaret A. Crawford , Mark Derdzinski , Robert J. Dowd , Giada Acciaroli , Sarah Kate Pickus , Apurv U. Kamath
Abstract: Glucose level measurements or other data regarding a user are obtained over time, such as from a wearable glucose monitoring device being worn by the user. These glucose level measurements or other data are analyzed based on various rules to determine time periods during a day of, for example, good diabetes management by the user and provide feedback indicating such to the user. Good diabetes management is identified in various manners, such as by identifying improvements in glucose measurements for a given time period over one or more previous days, identifying a time period of the day during which glucose measurements were the best, identifying sustained positive patterns (e.g., good diabetes management for a same time period in each of multiple days), and so forth.
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公开(公告)号：US20230134919A1
公开(公告)日：2023-05-04
申请号：US17974190
申请日：2022-10-26
Applicant: Dexcom, Inc.
Inventor： Robert J. Dowd , Margaret A. Crawford , Mark Derdzinski , Lauren H. Jepson , Giada Acciaroli , Sarah Kate Pickus , Apurv U. Kamath
Abstract: Glucose level measurements of a user are obtained over time, such as from a wearable glucose monitoring device being worn by the user. These glucose level measurements can be produced substantially continuously, such that the device may be configured to produce the glucose level measurements at regular or irregular intervals of time, responsive to establishing a communicative coupling with a different device, and so forth. These glucose level measurements are analyzed to detect deviations from past glucose measurements, such as glucose measurements received earlier in the day or glucose measurements received at corresponding times of one or more preceding days. Indications of detected deviations are provided to the user or communicated elsewhere, such as to a healthcare professional.
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公开(公告)号：US20210378563A1
公开(公告)日：2021-12-09
申请号：US17334448
申请日：2021-05-28
Applicant: DexCom, Inc.
Inventor： Mark Derdzinski , Joost van der Linden , Robert Dowd , Lauren Hruby Jepson , Giada Acciaroli
Abstract: Glucose measurement and glucose-impacting event prediction using a stack of machine learning models is described. A CGM platform includes stacked machine learning models, such that an output generated by one of the machine learning models can be provided as input to another one of the machine learning models. The multiple machine learning models include at least one model trained to generate a glucose measurement prediction and another model trained to generate an event prediction, for an upcoming time interval. Each of the stacked machine learning models is configured to generate its respective output when provided as input at least one of glucose measurements provided by a CGM system worn by the user or additional data describing user behavior or other aspects that impact a person's glucose in the future. Predictions may then be output, such as via communication and/or display of a notification about the corresponding prediction.
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