Patent AU-2022204683-A1 (AU)
Title: Utilizing a machine learning model to identify activities and deviations from the activities by an individual
Inventor: ERIKSSON LAETITIA CAILLETEAU
IPC: G06Q50/22
Filed: 2022-06-30
Abstract: 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.