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1.发明公开公开(公告)号：US20240120050A1
公开(公告)日：2024-04-11
申请号：US18463673
申请日：2023-09-08
发明人： Michael Griffin , Hailey Kotvis , Josephine Miner , Porter Moody , Kayla Poulsen , Austin Malmin , Sarah Onstad-Hawes , Gloria Solovey , Austin Streitmatter
CPC分类号： G16H15/00 , G06F40/30 , G06T7/0012 , G10L25/66 , G16H10/60 , G16H50/20 , G06T2207/10016 , G06T2207/20081
摘要： Apparatus and associated methods relate to predicting a health outcome of a patient by a machine learning model operating on a video stream of the patient. Video data, audio data, and semantic text data are extracted from a video stream of the patient. The video data are analyzed to identify a first feature set. The audio data are analyzed to identify a second feature set. The semantic text data are analyzed to identify a third feature set. Using a computer-implemented machine-learning model, a health outcome of the patient is predicted based on the first, second, and/or third features sets. The health outcome that is predicted is then reported.
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2.发明公开公开(公告)号：US20240120108A1
公开(公告)日：2024-04-11
申请号：US18463685
申请日：2023-09-08
发明人： Michael Griffin , Hailey Kotvis , Josephine Miner , Porter Moody , Kayla Poulsen , Austin Malmin , Sarah Onstad-Hawes , Gloria Solovey , Austin Streitmatter
IPC分类号： G16H50/30 , G06T7/00 , G06V10/774 , G06V20/40 , G10L15/02 , G10L15/06 , G10L15/18 , G10L25/66
CPC分类号： G16H50/30 , G06T7/0012 , G06V10/774 , G06V20/41 , G06V20/46 , G10L15/02 , G10L15/063 , G10L15/1815 , G10L25/66 , G16H10/60
摘要： Apparatus and associated methods relate to enhancing care of a patient using video and audio analytics. Video data, audio data, and semantic text data are extracted from a video stream of the patient. The video data are analyzed to identify a first feature set. The audio data are analyzed to identify a second feature set. The semantic text data are analyzed to identify a third feature set. Using a computer-implemented machine-learning model, a health outcome of the patient is predicted based on the first, second, and/or third features sets. The health outcome that is predicted is compared with the set of health outcomes of the training patients classified with the patient classification of the patient. Differences are identified between the feature sets corresponding to the patient and feature sets of the training patients who have better health outcomes the patient's predicted health outcome. The differences identified are then reported.
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公开(公告)号：US20240120098A1
公开(公告)日：2024-04-11
申请号：US18463657
申请日：2023-09-08
发明人： Michael Griffin , Hailey Kotvis , Josephine Miner , Porter Moody , Kayla Poulsen , Austin Malmin , Sarah Onstad-Hawes , Gloria Solovey , Austin Streitmatter
IPC分类号： G16H50/20 , G06V10/764 , G06V20/40
CPC分类号： G16H50/20 , G06V10/764 , G06V20/41
摘要： Apparatus and associated methods relate to invoking an alert based upon a behavior of a patient as determined by a machine-learning model operating on a video stream of the patient. Video data, audio data, and semantic text data are extracted from a video stream of the patient. The video data are analyzed to identify first, second, and third features sets of video, audio, and semantic text features, respectively, which have been identified by a computer-implemented machine-learning engine as being indicative of at least one of a set of alerting behaviors corresponding to a patient classification of the patient. Using a computer-implemented machine-learning model, a patient behavior of the patient is determined based on the first, second, and/or third features sets. The patient's behavior is compared with the set of alerting behaviors, and, when the patient's behavior is determined to be included therein, the alert is automatically invoked.
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4.发明公开公开(公告)号：US20240120049A1
公开(公告)日：2024-04-11
申请号：US18463645
申请日：2023-09-08
发明人： Michael Griffin , Hailey Kotvis , Josephine Miner , Porter Moody , Kayla Poulsen , Austin Malmin , Sarah Onstad-Hawes , Gloria Solovey , Austin Streitmatter
IPC分类号： G16H15/00 , G06V10/774 , G06V20/40 , G06V40/10 , G10L15/02 , G10L15/06 , G10L15/18 , G10L25/57 , G16H10/60
CPC分类号： G16H15/00 , G06V10/774 , G06V20/41 , G06V20/46 , G06V40/10 , G10L15/02 , G10L15/063 , G10L15/1815 , G10L25/57 , G16H10/60 , G16H50/20
摘要： Apparatus and associated methods relate to assessing a confidence level of a verbal communication of a person as determined by a machine learning model operating on a video stream of the person. Video data, audio data, and semantic text data are extracted from a video stream of the person. The video data are analyzed to identify a first feature set. The audio data are analyzed to identify a second feature set. The semantic text data are analyzed to identify a third feature set. Using a computer-implemented machine-learning model, a confidence level of the verbal communication of the person is assessed. The confidence level is then associated with a time of the video stream to which the confidence level pertains. The confidence level and the associated time of the video stream are then reported.
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公开(公告)号：US20240119821A1
公开(公告)日：2024-04-11
申请号：US18463690
申请日：2023-09-08
发明人： Michael Griffin , Hailey Kotvis , Josephine Miner , Porter Moody , Kayla Poulsen , Austin Malmin , Sarah Onstad-Hawes , Gloria Solovey , Austin Streitmatter
CPC分类号： G08B21/18 , A61B5/165 , G10L15/02 , G10L15/1822 , G10L25/63
摘要： Apparatus and associated methods relate to invoking an alert based upon a behavior of a patient as determined by a machine learning model operating on an audio stream of the patient. Audio data, and semantic text data are extracted from an audio stream of the patient. The audio data are analyzed to identify a first feature set. The semantic text data are analyzed to identify a second feature set. Using a computer-implemented machine-learning model, a patient behavior of the patient is determined based on the first and/or second features sets. The patient behavior is compared with a set of alerting behaviors corresponding to a patient classification of the patient. The alert is automatically invoked when the patient behavior is determined to be included in the set of alerting behaviors corresponding to the patient classification of the patient.
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