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Application US20210090710
Capital One

Utilizing A Machine Learning Model To Identify Unhealthy Online User Behavior And To Cause Healthy Physical User Behavior

A device receives, from a client device, behavior data indicating an action of a user of the client device, and processes the behavior data, with a model, to determine whether the action satisfies a behavior threshold. The device determines preventative actions to perform to prevent the action of the user, when the action is determined to satisfy the behavior threshold, and performs the preventative actions to prevent the action of the user. The device provides, to the client device, a request indicating that the user perform a physical activity before the one or more preventative actions are disabled, and monitors a performance of the physical activity by the user. The device determines whether the user satisfies the performance of the physical activity based on the monitoring, and disables the one or more preventative actions when it is determined that the user satisfies the performance of the physical activity.

Much More than Average Length Specification


1 Independent Claims

  • Claim CLM-00001. 1. A method, comprising: 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.
  • Claim CLM-00008. 8. A device, comprising: 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.
  • Claim CLM-00015. 15. A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: 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.


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