Abstract: Techniques for proactively managing updates in a communication system are disclosed herein. An exemplary computer-implemented method may include retrieving a set of data corresponding to the communication system from one or more sources external to the communication system. The exemplary method may further include determining, by executing a machine learning (ML) chatbot, that the set of data indicates a potential update to at least one component of the communication system. The exemplary method may further include generating, by executing the ML chatbot, an update indication corresponding to the set of data. The exemplary method may further include displaying the update indication on a user interface for viewing by a user.

Abstract: Techniques for improving automated communication system responses are disclosed herein. An exemplary computer-implemented method may include receiving a user query from a user; and determining, by executing a machine learning (ML) chatbot, a stylistic scheme and a contextual scheme for a response to the user query, wherein the ML chatbot is trained with a plurality of training user queries as inputs to generate a plurality of training responses as outputs. The exemplary computer-implemented method may further include generating, by executing the ML chatbot, the response to the user query that is articulated in accordance with the stylistic scheme and the contextual scheme. The exemplary computer-implemented method may further include causing the response to be conveyed to the user.

Abstract: Techniques for diagnosing errors in a communication system are disclosed herein. An exemplary computer-implemented method may include receiving, from the communication system, an error indication representing an error associated with a communication system component. The exemplary method may further include generating, by the one or more processors executing a machine learning (ML) chatbot, an error diagnosis corresponding to the error indication, wherein the ML chatbot is trained with a plurality of training error indications as inputs to generate a plurality of training error diagnoses as outputs. The exemplary method may further include displaying, by the one or more processors, the error diagnosis on a user interface for viewing by a user.