Application US20210090710

UTILIZING A MACHINE LEARNING MODEL TO IDENTIFY UNHEALTHY ONLINE USER BEHAVIOR AND TO CAUSE HEALTHY PHYSICAL USER BEHAVIOR
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US20210090710

1. A method, comprising: (6) (3)

receiving, by a device, historical behavior data indicating actions performed via client devices, wherein the actions are associated with online activity;

training, by the device and using the historical behavior data, a machine learning model to determine, based on input behavior data: a type of online activity associated with the input behavior data, and

data indicating whether actions identified by the input behavior data satisfy a behavior threshold associated with the type of online activity; and

providing, by the device and after training the machine learning model, the machine learning model to another device.

2. The method of claim 1, wherein training the machine learning model comprises: (0) (4)

separating the historical behavior data into a training set, a validation set, and a test set;

training the machine learning model using the training set;

validating results of training the machine learning model using the validation set; and

testing the machine learning model using the test set.

3. The method of claim 1, wherein the behavior threshold is at least one of: (0) (2)

4. The method of claim 1, wherein the behavior threshold is based on the type of online activity. (0)

5. The method of claim 1, wherein the historical behavior data includes sensor data associated with the client devices, the sensor data including at least one of: (0) (2)

6. The method of claim 1, wherein the historical behavior data is received from a monitoring application installed on the client devices. (0)

7. The method of claim 1, wherein the actions comprise one or more of: (0) (3)

8. A device, comprising: (6) (2)

one or more memories; and

one or more processors, communicatively coupled to the one or more memories, configured to: receive historical behavior data indicating actions performed via client devices, wherein the actions are associated with online activity;

train, using the historical behavior data, a machine learning model to determine, based on input behavior data: a type of online activity associated with the input behavior data, and

data indicating whether actions identified by the input behavior data satisfy a behavior threshold associated with the type of online activity; and

provide, after training the machine learning model, the machine learning model to another device.

9. The device of claim 8, wherein the one or more processors, when training the machine learning model, are configured to: (0) (4)

separate the historical behavior data into a training set, a validation set, and a test set;

train the machine learning model using the training set;

validate results of training the machine learning model using the validation set; and

test the machine learning model using the test set.

10. The device of claim 8, wherein the behavior threshold is at least one of: (0) (2)

11. The device of claim 8, wherein the behavior threshold is based on the type of online activity. (0)

12. The device of claim 8, wherein the historical behavior data includes sensor data associated with the client devices, the sensor data including at least one of: (0) (2)

13. The device of claim 8, wherein the historical behavior data is received from a monitoring application installed on the client devices. (0)

14. The device of claim 8, wherein the actions comprise one or more of: (0) (3)

15. A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: (5) (1)

one or more instructions that, when executed by one or more processors of a device, cause the device to: receive historical behavior data indicating actions performed via client devices, wherein the actions are associated with online activity;

train, using the historical behavior data, a machine learning model to determine, based on input behavior data: a type of online activity associated with the input behavior data, and

data indicating whether actions identified by the input behavior data satisfy a behavior threshold associated with the type of online activity; and

provide, after training the machine learning model, the machine learning model to another device.

16. The non-transitory computer-readable medium of claim 15, wherein the one or more instructions, that cause the device to train the machine learning model, cause the device to: (0) (4)

separate the historical behavior data into a training set, a validation set, and a test set;

train the machine learning model using the training set;

validate results of training the machine learning model using the validation set; and

test the machine learning model using the test set.

17. The non-transitory computer-readable medium of claim 15, wherein the behavior threshold is at least one of: (0) (2)

18. The non-transitory computer-readable medium of claim 15, wherein the behavior threshold is based on the type of online activity. (0)

19. The non-transitory computer-readable medium of claim 15, wherein the historical behavior data includes sensor data associated with the client devices, the sensor data including at least one of: (0) (2)

20. The non-transitory computer-readable medium of claim 15, wherein the historical behavior data is received from a monitoring application installed on the client devices. (0)

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Application US20210090710

UTILIZING A MACHINE LEARNING MODEL TO IDENTIFY UNHEALTHY ONLINE USER BEHAVIOR AND TO CAUSE HEALTHY PHYSICAL USER BEHAVIOR

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