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1. 发明专利

AU2018264140A1 UTILIZING A MACHINE LEARNING MODEL TO IDENTIFY ACTIVITIES AND DEVIATIONS FROM THE ACTIVITIES BY AN INDIVIDUAL 未知
公开(公告)号：AU2018264140A1
公开(公告)日：2019-06-06
申请号：AU2018264140
申请日：2018-11-16
申请人： ACCENTURE GLOBAL SOLUTIONS LTD
发明人： ERIKSSON LAETITIA CAILLETEAU , CHAN KAR LOK , VALLI FAISAL AHMED , ASHLEY CHRISTOPHER PAUL
IPC分类号： G06Q50/22
摘要： A device receives historical information associated with an individual to be monitored, wherein the historical information includes at least one of information associated with a health history of the individual, health histories of other individuals, activities of the individual, or activities of the other individuals. The device receives monitored information associated with the individual from one or more client devices associated with the individual, and pre processes the monitored information to generate pre-processed monitored information that is understood by the trained machine learning model. The device processes the pre processed monitored information, with a trained machine learning model, to identify one or more activities of the individual and one or more deviations from the one or more activities by the individual, and performs one or more actions based on the one or more activities of the individual and/or the one or more deviations from the one or more activities. 0095-0438AU Page 1/12 Co LO co 0 a Cl Lo MC o 0 0 L) 0 4-0 U0 a) 72a) cn-0 co 0 C a) CC_ o) Cc-

2. 发明专利

AU2022204683A1 UTILIZING A MACHINE LEARNING MODEL TO IDENTIFY ACTIVITIES AND DEVIATIONS FROM THE ACTIVITIES BY AN INDIVIDUAL 未知
公开(公告)号：AU2022204683A1
公开(公告)日：2022-07-21
申请号：AU2022204683
申请日：2022-06-30
申请人： ACCENTURE GLOBAL SOLUTIONS LTD
发明人： ERIKSSON LAETITIA CAILLETEAU , CHAN KAR LOK , VALLI FAISAL AHMED , ASHLEY CHRISTOPHER PAUL
IPC分类号： G06Q50/22
摘要： A method, including receiving, by a device, configuration information associated with configuring an application for monitoring an individual, wherein the configuration information includes at least one of information identifying physical characteristics of the individual, information identifying medications taken by the individual, personal information of the individual, or information associated with a caregiver of the individual, receiving, by the device, historical information associated with the individual, wherein the historical information includes at least one of information associated with a health history of the individual, information associated with health histories of other individuals, information associated with activities of the individual, or information associated with activities of the other individuals, creating, by the device, a training set by performing dimensionality reduction to reduce the configuration information and the historical information to a minimum feature set, performing, by the device, binary recursive partitioning to split the configuration and historical information associated with the minimum feature set into partitions and/or branches training, by the device using an unsupervised training procedure and based on the training set, a machine learning model to generate a trained machine learning model, wherein the unsupervised training procedure includes a neural network technique, providing, by the device, third-party application programming interfaces (APIs) to a plurality of client devices associated with the individual, at least one of the third-party APIs enabling an activity service to track physical activity of the individual, receiving, by the device and via the application and the third party APIs, monitored information associated with the individual from the plurality of client devices associated with the individual, the plurality of client devices including a wearable device, an image sensor, and an audio sensor; and the monitored information including video of the individual captured by the image sensor, audio of the individual captured by the audio sensor, a heart rate, a number of steps taken, and blood pressure captured by the wearable device, and the physical activity of the individual tracked using the at least one third party API, pre processing, by the device, the monitored information to generate pre-processed monitored information that is understood by the trained machine learning model, including one or more of performing natural language processing on textual information associated with the monitored information, perform video analytics on video information associated with the monitored information, perform voice or audio recognition on audio information associated with the monitored information, processing, by the device, the pre-processed monitored information, with the trained machine learning model, to identify one or more activities of the individual, determining, by the device, a routine associated with the individual based on identifying the one or more activities of the individual, processing, by the device, the monitored information, using the partitions and/or branches associated with the trained machine learning model, to identify one or more deviations from the routine by the individual; and performing, by the device, one or more actions based on the one or more deviations from the routine by the individual, the one or more actions including one or more of causing a robot to provide medication to the individual based on a first deviation of the one or more deviations indicating that the individual is unable to walk due to an accident, causing an autonomous emergency vehicle to traverse a route to the individual.

3. 发明专利

AU2020203106A1 UTILIZING A MACHINE LEARNING MODEL TO IDENTIFY ACTIVITIES AND DEVIATIONS FROM THE ACTIVITIES BY AN INDIVIDUAL 未知
公开(公告)号：AU2020203106A1
公开(公告)日：2020-05-28
申请号：AU2020203106
申请日：2020-05-12
申请人： ACCENTURE GLOBAL SOLUTIONS LTD
发明人： ERIKSSON LAETITIA CAILLETEAU , CHAN KAR LOK , VALLI FAISAL AHMED , ASHLEY CHRISTOPHER PAUL
IPC分类号： G06Q50/22
摘要： A method, including receiving, by a device, configuration information associated with configuring an application for monitoring an individual, wherein the configuration information includes at least one of: information identifying physical characteristics of the individual, information identifying medications taken by the individual, personal information of the individual, or information associated with a caregiver of the individual, receiving, by the device, historical information associated with the individual, wherein the historical information includes at least one of: information associated with a health history of the individual, information associated with health histories of other individuals, information associated with activities of the individual, or information associated with activities of the other individuals, creating, by the device, a training set using the configuration information and the historical information, training, by the device and using the training set, a machine learning model to generate a trained machine learning model, receiving, by the device and via the application, monitored information associated with the individual from one or more client devices associated with the individual, the one or more client devices including at least one of an image sensor or an audio sensor, the monitored information including first monitored information including at least one of: a first video captured by the image sensor, or first audio captured by the audio sensor, and the monitored information including second monitored information representing information captured at a time subsequent to capture of the first monitored information and including at least one of: a second video captured by the image sensor, or second audio captured by the audio sensor, processing, by the device, the first monitored information, with the trained machine learning model, to identify one or more first activities of the individual, determining, by the device, a routine associated with the individual based on identifying the one or more first activities of the individual, processing, by the device, the second monitored information, with the trained machine learning model, to identify one or more second activities of the individual and one or more deviations from the routine by the individual, the one or more deviations determined based upon analyzing the second video or the second audio and analyzing the first video or the first audio, and performing, by the device, one or more actions based on identifying the one or more second activities of the individual and the one or more deviations, the one or more actions including one or more of: causing a robot to provide medication to the individual based on a first deviation of the one or more deviations, or causing an autonomous emergency vehicle to traverse a route to the individual based on a second deviation of the one or more deviations. Cao 00 , 0 0)a0 LP = c 0) C)l ca aa ca) 0 a) cm 0 0>0 a)a .2 C ' Eca a 0 ca) 'o E 0 l C _2 o >I ca 0 0- U) ) >a) 0 ~ E ca) 0~0 co 0 '

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