Automated ticket resolution

A method comprising:

• classifying, by a device and using a data model to apply a natural language processing technique, ticket data relating to an issue into a first ticket type;
• modifying, by the device and based on receiving an indication to modify a classification of the ticket data, the classification of the ticket data from the first ticket type to a second ticket type;
• processing, by the device, using the data model, and based on modifying the classification of the ticket data, historical ticket data;
• generating, by the device, using the data model, and based on processing the historical ticket data, a set of recommended resolutions for resolving the issue;
• applying, by the device, a machine learning technique to one or more of the ticket data or the historical ticket data,
• the machine learning technique being applied to predict another issue;
• generating, by the device, additional ticket data based on predicting the other issue,
• the additional ticket data being different than the ticket data;
• and
• generating, by the device and based on processing the additional ticket data using the data model, another set of recommended resolutions for resolving the other issue.

A device, comprising:

• one or more memories;
• and
• one or more processors communicatively coupled to the one or more memories, configured to:
• classify, using a data model to apply a natural language processing technique, ticket data relating to an issue into a first ticket type;
• modify, based on receiving an indication to modify a classification of the ticket data, the classification of the ticket data from the first ticket type to a second ticket type;
• process using the data model, and based on modifying the classification of the ticket data, historical ticket data;
• generate using the data model, and based on processing the historical ticket data, a set of recommended resolutions for resolving the issue;
• apply a machine learning technique to one or more of the ticket data or the historical ticket data,
• the machine learning technique being applied to predict another issue;
• generate additional ticket data based on predicting the other issue,
• the additional ticket data being different than the ticket data;
• and
• generate, based on processing the additional ticket data using the data model, another set of recommended resolutions for resolving the other issue.

A non-transitory computer-readable medium storing instructions, the instructions comprising:

• one or more instructions that, when executed by one or more processors, cause the one or more processors to:
• classify, using a data model to apply a natural language processing technique, ticket data relating to an issue into a first ticket type;
• modify, based on receiving an indication to modify a classification of the ticket data, the classification of the ticket data from the first ticket type to a second ticket type;
• process using the data model, and based on modifying the classification of the ticket data, historical ticket data;
• generate using the data model, and based on processing the historical ticket data, a set of recommended resolutions for resolving the issue;
• apply a machine learning technique to one or more of the ticket data or the historical ticket data,
• the machine learning technique being applied to predict another issue;
• generate additional ticket data based on predicting the other issue,
• the additional ticket data being different than the ticket data;
• and
• generate, based on processing the additional ticket data using the data model, another set of recommended resolutions for resolving the other issue.