BACKGROUND

Some entities utilize a workforce of software developers, network administrators, network technicians, and/or the like to provide service to systems, networks, network devices, software, and/or the like utilized by the entities.

SUMMARY

According to some implementations, a method may include receiving, from a client device associated with a user, user personalized data about the user and user activity data identifying tasks of the user and actions performed by the user in response to the tasks, and performing natural language processing on the user personalized data and the user activity data to generate processed textual data identifying the user personalized data, the tasks, and the actions. The method may include training one or more machine learning models based on the processed textual data to generate one or more trained machine learning models that identify particular actions based on particular tasks, and receiving, from the client device, a command identifying a particular task to be performed. The method may include processing the command and the user activity data, with the one or more trained machine learning models, to determine whether a particular action in the user activity data correlates with the particular task. The method may include performing one or more actions based on the particular action and when the particular action in the user activity data correlates with the particular task.

According to some implementations, a device may include one or more memories, and one or more processors to receive, from a client device associated with a user, a command identifying a particular task to be performed. The one or more processors may process the command and user activity data identifying tasks of the user and actions performed by the user in response to the tasks, with one or more machine learning models, to determine whether a particular action in the user activity data correlates with the particular task. The one or more machine learning models may be trained to identify particular actions based on particular tasks, user personalized data about the user, and the user activity data. The one or more processors may perform one or more actions based on the particular action and when the particular action in the user activity data correlates with the particular task.

According to some implementations, a non-transitory computer-readable medium may store one or more instructions that, when executed by one or more processors of a device, may cause the one or more processors to receive, from a client device associated with a user, user personalized data about the user and user activity data identifying tasks of the user and actions performed by the user in response to the tasks, and perform natural language processing on the user personalized data and the user activity data to generate processed textual data identifying the user personalized data, the tasks, and the actions. The one or more instructions may cause the one or more processors to train one or more machine learning models based on the processed textual data to generate one or more trained machine learning models that identify particular actions based on particular tasks, and receive, from the client device, a command identifying a particular task to be performed. The one or more instructions may cause the one or more processors to process the command, with a first trained machine learning model of the one or more trained machine learning models, to identify an intended task of the command. The one or more instructions may cause the one or more processors to process the intended task and the user activity data, with a second trained machine learning model of the one or more trained machine learning models, to determine whether a particular action in the user activity data correlates with the intended task, and perform one or more actions based on the particular action and when the particular action in the user activity data correlates with the particular task.

