Intelligent monitoring of a health state of a user engaged in operation of a computing device

Patent metadata

Field	Value
Publication number	US-11432762-B2
Application number	US-201916417460-A
Country	US
Kind code	B2
Filing date	May 20, 2019
Priority date	May 20, 2019
Publication date	Sep 6, 2022
Grant date	Sep 6, 2022

Abstract

Embodiments for intelligent monitoring of a health state of a user by a processor. A health state of a user may be learned while engaged in one or more activities associated with a computing device. One or more mitigating actions may be identified and recommended to implement by the user to minimize one or more possible negative impacts upon the health state of the user while engaged in the one or more activities associated with the computing device.

First claim

The invention claimed is:

1. A method for implementing an intelligent monitoring of a health state of a user in a computing environment by a processor, comprising:
receiving health data of the user collected by one or more computing devices in one or more environments;
receiving user activity data of one or more activities performed by the user on the one or more computing devices in the one or more environments;
executing machine learning logic to learn the health state of the user while engaged in the one or more activities associated with the one or more computing devices in the one or more environments by generating a health model trained to correlate the health data to the user activity data, wherein the health model correlates specific sequences of the one or more activities performed by the user on the one or more computing devices at specific times to specific health events impacting the health state of the user;
identifying and recommending one or more mitigating actions to implement by the user to minimize one or more possible negative impacts upon the health state of the user while engaged in the one or more activities associated with the one or more computing devices;
receiving user feedback, as feedback data, with respect to an effectiveness of the one or more mitigating actions implemented by the user; and
executing the machine learning logic to re-train the health model using the feedback data to iteratively optimize the health model to enhance the effectiveness of future mitigating actions recommended be implemented by the user.

2. The method of claim 1 , further including monitoring the health state of the user, the one or more activities of the user associated with the one or more computing devices, the one or more environments, one or more contextual parameters, or a combination thereof.

3. The method of claim 1 , further including learning the health state of the user according to user behavior, contextual information, physical conditions of the user, or a combination thereof.

4. The method of claim 1 , further including identifying those of the one or more activities that negatively impacts the health state of the user.

5. The method of claim 1 , further including predicting the health state of the user according to the one or more activities to identify the one or more health state risks.

6. The method of claim 1 , further including suggesting implementation of the one or more mitigating actions at one or more time periods, upon occurrence of one or more events or activities, or upon detection of one or more physical parameters of the user collected by one or more internet of things (IoT) devices, sensor devices, the computing device, or combination thereof exceeding or falling below a defined threshold.

7. The method of claim 1 , further including executing the machine learning logic to learn, in association with the health data and the user activity data, scheduling data, contextual data, and a current environment of the user.

Assignees

• IBM

Inventors

• LASSOUED Yassine
• Monteil Julien
• Zhuk Sergiy

Classifications

• A61B5/7275

  Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor · CPC title

• A61B5/6897 Primary

  Computer input devices, e.g. mice or keyboards · CPC title

• A61B5/486 Primary

  Biofeedback (using electroencephalography [EEG] A61B5/375) · CPC title

• G16H10/40

  for data related to laboratory analysis, e.g. patient specimen analysis · CPC title

• A61B5/7267

  involving training the classification device · CPC title