Abstract: Concepts and technologies disclosed herein are directed to interpretation workflows for machine learning-enabled event tree-based diagnostic and customer problem resolution. According to one aspect, a system can receive a workflow construction specification derived from a machine learning-enabled event tree (“MLET”). The MLET can be generated for use by a customer service agent to resolve a customer problem. The workflow construction specification can include a plurality of objects, each of which represents a navigation path through the MLET. The system can traverse the workflow construction specification and can create a set of workflow creation commands based upon at least one policy. The system can generate a workflow visualization interpretation file based upon the set of workflow creation commands. The workflow visualization interpretation file can identify how the MLET derived a root cause of the customer problem.

Abstract: Concepts and technologies disclosed herein are directed to interpretation workflows for machine learning-enabled event tree-based diagnostic and customer problem resolution. According to one aspect, a system can receive a workflow construction specification derived from a machine learning-enabled event tree (“MLET”). The MLET can be generated for use by a customer service agent to resolve a customer problem. The workflow construction specification can include a plurality of objects, each of which represents a navigation path through the MLET. The system can traverse the workflow construction specification and can create a set of workflow creation commands based upon at least one policy. The system can generate a workflow visualization interpretation file based upon the set of workflow creation commands. The workflow visualization interpretation file can identify how the MLET derived a root cause of the customer problem.

Abstract: Concepts and technologies disclosed herein are directed to interpretation workflows for machine learning-enabled event tree-based diagnostic and customer problem resolution. According to one aspect, a system can receive a workflow construction specification derived from a machine learning-enabled event tree (“MLET”). The MLET can be generated for use by a customer service agent to resolve a customer problem. The workflow construction specification can include a plurality of objects, each of which represents a navigation path through the MLET. The system can traverse the workflow construction specification and can create a set of workflow creation commands based upon at least one policy. The system can generate a workflow visualization interpretation file based upon the set of workflow creation commands. The workflow visualization interpretation file can identify how the MLET derived a root cause of the customer problem.

Abstract: Concepts and technologies disclosed herein are directed to interpretation workflows for machine learning-enabled event tree-based diagnostic and customer problem resolution. According to one aspect, a system can receive a workflow construction specification derived from a machine learning-enabled event tree (“MLET”). The MLET can be generated for use by a customer service agent to resolve a customer problem. The workflow construction specification can include a plurality of objects, each of which represents a navigation path through the MLET. The system can traverse the workflow construction specification and can create a set of workflow creation commands based upon at least one policy. The system can generate a workflow visualization interpretation file based upon the set of workflow creation commands. The workflow visualization interpretation file can identify how the MLET derived a root cause of the customer problem.

Abstract: Concepts and technologies disclosed herein are directed to a machine learning-enabled event tree (“MLET”) for rapid and accurate customer problem resolution. According to one aspect disclosed herein, a designer system can receive a customer problem to be modeled. The designer system can create, based upon input from a designer, a plurality of levels and a plurality of nodes for an MLET to be used to resolve the customer problem. The designer system can create, further based upon the input, a plurality of Boolean logic gates between the plurality of levels of the MLET. The designer system can obtain a plurality of machine learning models and, further based upon the input, can create a navigation controller to link the plurality of machine learning models to the plurality of nodes in the MLET. The designer system can save the MLET for the customer problem.

Abstract: Systems and methods to facilitate installation of a digital subscriber line are provided. A particular computer-implemented method includes receiving an installation request for a digital subscriber line. The method includes determining performance data associated with one or more active digital subscriber lines that are located near an installation location of the digital subscriber line. The method includes comparing the performance data to installation criteria. The method also includes sending an installation notice to an installer. The installation notice includes a special installation instruction when the performance data satisfies at least one installation criterion of the installation criteria.