Embodiments of the invention present invention provide an approach for governing Internet of Things (IoT) device operation. Specifically, the present invention relates to a method and system for utilizing digital twin-based simulation to selectively govern capabilities of surrounding IoT devices to improve a user experience.

Systems and methods are provided for selecting one or more network-connected devices to provide device functions required by an event. A media guidance application may detect an event in which a user is currently engaged. The media guidance application may identify a set of device capabilities associated with the event. The media guidance application may search, within a plurality of network-connected devices of the user, to identify one or more devices that enable functions corresponding to the set of device capabilities to be perceptible to the user, and the media guidance application may instruct the identified one or more devices to provide the functions corresponding to the set of device capabilities associated with the event.

A computer-assisted method is for remote monitoring of patients. The patient's health condition is monitored by analyzing results of measurement of health parameters entered in a computer system manually or through automated measuring sensors. Once the specific conditions are satisfied, a medical hazard alert is generated. Before monitoring is started, the data on a patient's health condition is downloaded and the patient is allocated to a specific risk group. A notification on the necessity to perform measurement of health parameters is generated with the frequency dependent on the risk group assigned to the patient. The measured health parameters are analyzed taking into account the risk group to which the patient was allocated.

Described herein are systems and methods for modeling complex outcomes using clustering and machine learning algorithms. Machine learning algorithms and models can be implemented on platforms comprising one or more user interfaces and an insight engine. In these embodiments, insight engine comprises a machine learning software algorithm (or module) configured to ingest data and generate insights.

Aspects of the present invention disclose a method for recommending an activity based on a social media profile, IoT devices, and historical engagements of the user. The method includes one or more processors determining a past activity of a user based at least in part on social media posts and internet of things (IoT) enabled devices of the user. The method further includes determining a set of historical conditions corresponding to the past activity, wherein the set of conditions correspond to a positive sentiment of the user. The method further includes identifying a location of the user. The method further includes generating an activity recommendation based on the location of the user and the set of historical conditions corresponding to the past activity, wherein the activity recommendation includes a set of future conditions of a future activity, wherein the set of future conditions correlate with the set of historical conditions.

Disclosed is a method for providing user-customized food information. In the method for providing user-customized food information, when an input of a user is received, the user is classified, history related to the classified user is retrieved from among previously stored food entry/exit history, and information on preferred food of the classified user on the basis of the retrieved entry/exit history, thereby easily providing intuitive information on preferred food having a high usage frequency for each user. The device for providing user-customized food information of the present disclosure is linked to an artificial intelligence module, a robot, an augmented reality (AR) device, a virtual reality (VR) device, a device related to a 5G service, and the like. Can be.

Disclosed is a method for providing user-customized food information. In the method for providing user-customized food information, when an input of a user is received, the user is classified, history related to the classified user is retrieved from among previously stored food entry/exit history, and information on preferred food of the classified user on the basis of the retrieved entry/exit history, thereby easily providing intuitive information on preferred food having a high usage frequency for each user. The device for providing user-customized food information of the present disclosure is linked to an artificial intelligence module, a robot, an augmented reality (AR) device, a virtual reality (VR) device, a device related to a 5G service, and the like. Can be.

A control method of a robot according to an aspect of the present disclosure includes receiving from an external computer information that instructs the robot to encourage a user to exercise; sensing a user's current position; moving the robot into a predetermined area that includes the user's current position; causing the robot to perform a gesture to encourage the user to exercise; monitoring behavior of the user; and performing driving of the robot along with exercise of the user, based on a result of the monitoring.

Implementing methods to provide a shortened textual summary that includes status information that is most pertinent to the user for a plurality of connected smart devices. The method includes determining a list of current statuses for a plurality of enabled smart devices and filtering the list to remove statuses that may not be of interest to the user. The filtering of the list is based on a current context of the requesting user and one or more previous contexts of the user. The resulting filtered statuses are then converted to textual snippets, summarized, and provided to the user via one or more output devices.

Implementing methods to provide a shortened textual summary that includes status information that is most pertinent to the user for a plurality of connected smart devices. The method includes determining a list of current statuses for a plurality of enabled smart devices and filtering the list to remove statuses that may not be of interest to the user. The filtering of the list is based on a current context of the requesting user and one or more previous contexts of the user. The resulting filtered statuses are then converted to textual snippets, summarized, and provided to the user via one or more output devices.