Generating priorities for support tickets

A computer-implemented method for generating priorities for support tickets, the computer-implemented method comprising:

• training a machine learning model to determine content data, metadata, context data, and their corresponding weights associated with priorities for support ticket communications, in response to receiving the support ticket communications;
• determining, by the trained machine learning model, content data, metadata, context data and their corresponding weights for a communication associated with a support ticket, in response to receiving the communication;
• converting the content data, the metadata, the context data, and their corresponding weights for the communication into a first information impulse for a first channel and a second information impulse for a second channel;
• determining, by the first channel, a first channel-specific value by executing a first type of channel-specific conversion of the first information impulse and any information impulses for the first channel that are converted from data that is determined for a support ticket event, wherein the first channel periodically increased or periodically decreased a magnitude of an information impulse;
• determining, by the second channel, a second channel-specific value by executing a second type of channel-specific conversion of the second information impulse and any information impulses for the second channel that are converted from the data that is determined for the support ticket event;
• generating, using the first channel-specific value and the second channel-specific value, a priority for the support ticket;
• outputting the priority;
• refining the trained machine-learning model, in response to capturing all variations in incoming data, comprising the content data, the metadata, the context data, to determine data for generating support ticket priorities;
• and
• using the refined machine learning model to determine content data, metadata, context data and their corresponding weights associated with generating a priority for additional support ticket communications, in response to receiving the additional support ticket communications.

A computer program product, comprising a non-transitory computer-readable medium having a computer-readable program code embodied therein to be executed by one or more processors, the program code including instructions to:

• train a machine learning model to determine content data, metadata, context data, and their corresponding weights associated with priorities for support ticket communications, in response to receiving the support ticket communications;
• determine, by the trained machine learning model, content data, metadata, context data and their corresponding weights for a communication associated with a support ticket, in response to receiving the communication;
• convert the content data, the metadata, the context data, and their corresponding weights for the communication into a first information impulse for a first channel and a second information impulse for a second channel;
• determine, by the first channel, a first channel-specific value by executing a first type of channel-specific conversion of the first information impulse and any information impulses for the first channel that are converted from data that is determined for a support ticket event, wherein the first channel periodically increased or periodically decreased a magnitude of an information impulse;
• determine, by the second channel, a second channel-specific value by executing a second type of channel-specific conversion of the second information impulse and any information impulses for the second channel that are converted from the data that is determined for the support ticket event;
• generate, using the first channel-specific value and the second channel-specific value, a priority for the support ticket;
• output the priority;
• refine the trained machine-learning model, in response to capturing all variations in incoming data, comprising the content data, the metadata, the context data, to determine data for generating support ticket priorities;
• and
• using the refined machine learning model to determine content data, metadata, context data and their corresponding weights associated with generating a priority for additional support ticket communications, in response to receiving the additional support ticket communications.