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公开(公告)号：US20240057937A1
公开(公告)日：2024-02-22
申请号：US18467229
申请日：2023-09-14
Applicant: Fitbit, Inc.
Inventor： Hao-Wei Su , Logan Niehaus , Conor Joseph Heneghan , Jonathan David Charlesworth , Subramaniam Venkatraman , Shelten Gee Jao Yuen
CPC classification number: A61B5/4809 , A61B5/02416 , A61B5/1102 , G10L25/51 , A61B5/681 , A61B5/742 , A61B5/0816
Abstract: Approaches described herein can capture an audio signal using at least one microphone while a user of an electronic device is determined to be asleep. At least one audio frame can be determined from the audio signal. The at least one audio frame represents a spectrum of frequencies detected by the at least one microphone over some period of time. One or more sounds associated with the at least one audio frame can be determined. Sleep-related information can be generated. The information identifies the one or more sounds as potential sources of sleep disruption.
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Patent Agency Ranking
公开(公告)号：US11229369B2
公开(公告)日：2022-01-25
申请号：US16431576
申请日：2019-06-04
Applicant: Fitbit, Inc.
Inventor： Hao-Wei Su , Logan Niehaus , Conor Joseph Heneghan , Jonathan David Charlesworth , Subramaniam Venkatraman , Shelten Gee Jao Yuen
Abstract: Approaches described herein can determine one or more breathing phase patterns over a period of time using audio data captured by at least one microphone. The audio data can include one or more snores. A breathing phase pattern included within the period of time can be determined based at least in part on sensor data captured by one or more sensors in the electronic device. A determination can be made that a first breathing phase pattern represented by the audio data and a second breathing phase pattern represented by the sensor data are correlated. A determination can be made that the first breathing phase pattern represented by the audio data and the second breathing phase pattern represented by the sensor data both correspond to a user wearing the electronic device.
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公开(公告)号：US11191466B1
公开(公告)日：2021-12-07
申请号：US16457582
申请日：2019-06-28
Applicant: Fitbit, Inc.
Inventor： Conor Joseph Heneghan , Alexander Statan , Jonathan David Charlesworth
IPC: A61B5/16 , A61B5/0205 , A61B5/00 , A61B5/11 , A61B5/145 , A61B5/01 , G16H50/30 , A61B5/1455 , A61B5/024
Abstract: Physiological variables, metrics, biomarkers, and other data points can be used, in connection with a non-invasive wearable device, to screen for, and predict, mental health issues and cognitive states. In addition to metrics such as heart rate, sleep data, activity level, gamification data, and the like, information such as text message and email data, as well as vocal data obtained through a phone and/or a microphone, may be analyzed, provided user authorization. Applying predictive modeling, one or more of the monitored metrics can be correlated with mental states and disorders. Identified patterns can be used to update the predictive models, such as via machine learning-trained models, as well as to update individual event predictions. Information about the mental state predictions, and updates thereto, can be surfaced to the user accordingly.
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公开(公告)号：US11872041B1
公开(公告)日：2024-01-16
申请号：US17542774
申请日：2021-12-06
Applicant: Fitbit, Inc.
Inventor： Conor Joseph Heneghan , Alexander Statan , Jonathan David Charlesworth
IPC: A61B5/16 , A61B5/0205 , A61B5/00 , A61B5/11 , A61B5/145 , A61B5/01 , G16H50/30 , A61B5/1455 , A61B5/024
CPC classification number: A61B5/165 , A61B5/01 , A61B5/02055 , A61B5/1118 , A61B5/14546 , A61B5/14551 , A61B5/162 , A61B5/4815 , A61B5/6802 , A61B5/7267 , A61B5/742 , G16H50/30 , A61B5/02416
Abstract: Physiological variables, metrics, biomarkers, and other data points can be used, in connection with a non-invasive wearable device, to screen for, and predict, mental health issues and cognitive states. In addition to metrics such as heart rate, sleep data, activity level, gamification data, and the like, information such as text message and email data, as well as vocal data obtained through a phone and/or a microphone, may be analyzed, provided user authorization. Applying predictive modeling, one or more of the monitored metrics can be correlated with mental states and disorders. Identified patterns can be used to update the predictive models, such as via machine learning-trained models, as well as to update individual event predictions. Information about the mental state predictions, and updates thereto, can be surfaced to the user accordingly.
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公开(公告)号：US12201442B2
公开(公告)日：2025-01-21
申请号：US18467229
申请日：2023-09-14
Applicant: Fitbit, Inc.
Inventor： Hao-Wei Su , Logan Alexander Niehaus , Conor Joseph Heneghan , Jonathan David Charlesworth , Subramaniam Venkatraman , Shelten Gee Jao Yuen
Abstract: Approaches described herein can capture an audio signal using at least one microphone while a user of an electronic device is determined to be asleep. At least one audio frame can be determined from the audio signal. The at least one audio frame represents a spectrum of frequencies detected by the at least one microphone over some period of time. One or more sounds associated with the at least one audio frame can be determined. Sleep-related information can be generated. The information identifies the one or more sounds as potential sources of sleep disruption.
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公开(公告)号：US11793453B2
公开(公告)日：2023-10-24
申请号：US16890931
申请日：2020-06-02
Applicant: Fitbit, Inc.
Inventor： Hao-Wei Su , Logan Niehaus , Conor Joseph Heneghan , Jonathan David Charlesworth , Subramaniam Venkatraman , Shelten Gee Jao Yuen
CPC classification number: A61B5/4809 , A61B5/02416 , A61B5/0816 , A61B5/1102 , A61B5/681 , A61B5/742 , G10L25/51
Abstract: Approaches described herein can capture an audio signal using at least one microphone while a user of an electronic device is determined to be asleep. At least one audio frame can be determined from the audio signal. The at least one audio frame represents a spectrum of frequencies detected by the at least one microphone over some period of time. One or more sounds associated with the at least one audio frame can be determined. Sleep-related information can be generated. The information identifies the one or more sounds as potential sources of sleep disruption.
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