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公开(公告)号：US12048533B2
公开(公告)日：2024-07-30
申请号：US17188675
申请日：2021-03-01
申请人： DexCom, Inc.
发明人： Mark Edward Selander , Alexander Michael Diener , Ryan Richard Ruehl , Kazanna Calais Hames , Mark Douglas Kempkey , Chad Michael Patterson , Apurv Ullas Kamath , Matthew Lawrence Johnson , Jason M. Halac , David A. Price , Peter C. Simpson , Devon M. Headen , Samuel Isaac Epstein
CPC分类号： A61B5/14546 , A61B5/1118 , A61B5/14532 , A61B5/165 , A61B5/7246 , A61B5/7275 , A61B5/7475 , G16H20/60
摘要： Techniques for data analysis and user guidance are provided. One or more current measurements of one or more current analyte levels for the user are received from a sensor. A pattern is generated based on the one or more current measurements and the one or more past measurements. A first alignment with a first user target is then determined based on the pattern, where the first user target relates to one or more of a mental state or physical state of the user. A first result is output to the user, based on the determined first alignment.
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公开(公告)号：US20220354395A1
公开(公告)日：2022-11-10
申请号：US17872823
申请日：2022-07-25
申请人： DexCom, Inc.
摘要： Diabetes prediction using glucose measurements and machine learning is described. In one or more implementations, the observation analysis platform includes a machine learning model trained using historical glucose measurements and historical outcome data of a user population to predict a diabetes classification for an individual user. The historical glucose measurements of the user population may be provided by glucose monitoring devices worn by users of the user population, while the historical outcome data includes one or more diagnostic measurements obtained from sources independent of the glucose monitoring devices. Once trained, the machine learning model predicts a diabetes classification for a user based on glucose measurements collected by a wearable glucose monitoring device during an observation period spanning multiple days. The predicted diabetes classification may then be output, such as by generating one or more notifications or user interfaces based on the classification.
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公开(公告)号：US20240350041A1
公开(公告)日：2024-10-24
申请号：US18760764
申请日：2024-07-01
申请人： Dexcom, Inc.
发明人： Mark Edward Selander , Alexander Michael Diener , Ryan Richard Ruehl , Kazanna Calais Hames , Mark Douglas Kempkey , Chad Michael Patterson , Apurv Ullas Kamath , Matthew Lawrence Johnson , Jason M. Halac , David A. Price , Peter C. Simpson , Devon M. Headen , Samuel Isaac Epstein
CPC分类号： A61B5/14546 , A61B5/1118 , A61B5/14532 , A61B5/165 , A61B5/7246 , A61B5/7275 , A61B5/7475 , G16H20/60
摘要： Techniques for data analysis and user guidance are provided. One or more current measurements of one or more current analyte levels for the user are received from a sensor. A pattern is generated based on the one or more current measurements and the one or more past measurements. A first alignment with a first user target is then determined based on the pattern, where the first user target relates to one or more of a mental state or physical state of the user. A first result is output to the user, based on the determined first alignment.
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公开(公告)号：US11426102B2
公开(公告)日：2022-08-30
申请号：US16917421
申请日：2020-06-30
申请人： DexCom, Inc.
摘要： Diabetes prediction using glucose measurements and machine learning is described. In one or more implementations, the observation analysis platform includes a machine learning model trained using historical glucose measurements and historical outcome data of a user population to predict a diabetes classification for an individual user. The historical glucose measurements of the user population may be provided by glucose monitoring devices worn by users of the user population, while the historical outcome data includes one or more diagnostic measurements obtained from sources independent of the glucose monitoring devices. Once trained, the machine learning model predicts a diabetes classification for a user based on glucose measurements collected by a wearable glucose monitoring device during an observation period spanning multiple days. The predicted diabetes classification may then be output, such as by generating one or more notifications or user interfaces based on the classification.
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公开(公告)号：US20210401330A1
公开(公告)日：2021-12-30
申请号：US16917421
申请日：2020-06-30
申请人： DexCom, Inc.
摘要： Diabetes prediction using glucose measurements and machine learning is described. In one or more implementations, the observation analysis platform includes a machine learning model trained using historical glucose measurements and historical outcome data of a user population to predict a diabetes classification for an individual user. The historical glucose measurements of the user population may be provided by glucose monitoring devices worn by users of the user population, while the historical outcome data includes one or more diagnostic measurements obtained from sources independent of the glucose monitoring devices. Once trained, the machine learning model predicts a diabetes classification for a user based on glucose measurements collected by a wearable glucose monitoring device during an observation period spanning multiple days. The predicted diabetes classification may then be output, such as by generating one or more notifications or user interfaces based on the classification.
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公开(公告)号：US20210267506A1
公开(公告)日：2021-09-02
申请号：US17188675
申请日：2021-03-01
申请人： DexCom, Inc.
发明人： Mark Edward Selander , Alexander Michael Diener , Ryan Richard Ruehl , Kazanna Calais Hames , Mark Douglas Kempkey , Chad Michael Patterson , Apurv Ullas Kamath , Matthew Lawrence Johnson , Jason M. Halac , David A. Price , Peter C. Simpson , Devon M. Headen , Samuel Isaac Epstein
摘要： Techniques for data analysis and user guidance are provided. One or more current measurements of one or more current analyte levels for the user are received from a sensor. A pattern is generated based on the one or more current measurements and the one or more past measurements. A first alignment with a first user target is then determined based on the pattern, where the first user target relates to one or more of a mental state or physical state of the user. A first result is output to the user, based on the determined first alignment.
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