The present application describes a network interface device that acts as an interface between a network service provided by a network service provider and one or more network access devices associated with a particular location and/or entity.

Detect, at a cable/fiber broadband network termination unit of a cable/fiber broadband multi-service operator, an interruption in service to a cable/fiber broadband network customer unit-small cell pair. Responsive to detecting the interruption, the termination unit advises a charging server of the operator of the interruption in service, a corresponding identifier of the customer unit – small cell pair, and a corresponding account identifier. Responsive to termination unit advising the charging server, the charging server advises a backend server of an associated cellular network of a customer identifier corresponding to the account identifier. Responsive to the charging server advising the backend server, the backend server advises a policy control function of the associated cellular network to modify network parameters of the associated cellular network to compensate for the interruption in service.

Detect, at a cable/fiber broadband network termination unit of a cable/fiber broadband multi-service operator, an interruption in service to a cable/fiber broadband network customer unit-small cell pair. Responsive to detecting the interruption, the termination unit advises a charging server of the operator of the interruption in service, a corresponding identifier of the customer unit – small cell pair, and a corresponding account identifier. Responsive to termination unit advising the charging server, the charging server advises a backend server of an associated cellular network of a customer identifier corresponding to the account identifier. Responsive to the charging server advising the backend server, the backend server advises a policy control function of the associated cellular network to modify network parameters of the associated cellular network to compensate for the interruption in service.

Evaluation of a plurality of activities comprising a service includes, for each of the plurality of activities, identification of a network service path of the activity from the source to the user, wherein a network service path of a first activity of the plurality of activities is different from a network service path of at least one other of the plurality of activities, measurement of a service quality metric for each respective network segment of each identified network service path, and, for each of the plurality of activities, determination of a service quality metric based on the service quality metrics measured for each of the network segments of the network service path of the activity.

One exemplary aspect describes systems and methods for determining normal SLE behavior, determining when a SLE exhibits abnormal deterioration, and determining whether to take an action to mitigate what appears to be an indication of an abnormal SLE.

Methods and systems for managing customer encountered issue resolution are disclosed. To manage the customer encountered issue resolution, tickets for the customer encountered issues may be managed to reduce suffering and reduce the rate of ticket churn. To do so, customer pain levels may be identified and used to prioritize resource deployment for ticket resolution. The tickets may be classified into groups for resolution via automated or human-assisted processes, which may be limited in capacity. To ascertain the pain levels of the customers, communications from the customers may be emotionally analyzed to identify emotions present in the customers. The identified emotions may be used to identify whether performed remediation processes for resolving the customer encountered issues were successful, and to what extent they were successful. Information obtained through previous ticket resolutions may be used to prioritize which approach to use to resolve subsequently encountered tickets for customer encountered issues.

Methods and systems for managing customer encountered issue resolution are disclosed. To manage the customer encountered issue resolution, tickets for the customer encountered issues may be managed to reduce suffering and reduce the rate of ticket churn. To do so, customer pain levels may be identified and used to prioritize resource deployment for ticket resolution. The tickets may be classified into groups for resolution via automated or human-assisted processes, which may be limited in capacity. To ascertain the pain levels of the customers, communications from the customers may be emotionally analyzed to identify emotions present in the customers. The identified emotions may be used to identify whether performed remediation processes for resolving the customer encountered issues were successful, and to what extent they were successful. Information obtained through previous ticket resolutions may be used to prioritize which approach to use to resolve subsequently encountered tickets for customer encountered issues.

One exemplary aspect describes systems and methods for determining normal SLE behavior, determining when a SLE exhibits abnormal deterioration, and determining whether to take an action to mitigate what appears to be an indication of an abnormal SLE.

The disclosure herein describes predicting potential impact of issues reported in incident ticket data on infrastructure element. A ticket manager component includes an impact model utilizing machine learning to analyze real-time event and metric data with incident-related data to generate predicted impact data. The predicted impact data identifies potentially impacted infrastructure elements, such as, potentially impacted users, predicted infrastructure components impacted by the issue and/or an updated time-period associated with the issue. The ticket manager component creates labeled incident tickets by updating user-generated incident tickets with additional data generated by the impact model, including predicted impact data and/or additional details associated with the issue. The labeled incident tickets are provided back to the model as training data to further refine predictions generated by the model.

The disclosure herein describes predicting potential impact of issues reported in incident ticket data on infrastructure element. A ticket manager component includes an impact model utilizing machine learning to analyze real-time event and metric data with incident-related data to generate predicted impact data. The predicted impact data identifies potentially impacted infrastructure elements, such as, potentially impacted users, predicted infrastructure components impacted by the issue and/or an updated time-period associated with the issue. The ticket manager component creates labeled incident tickets by updating user-generated incident tickets with additional data generated by the impact model, including predicted impact data and/or additional details associated with the issue. The labeled incident tickets are provided back to the model as training data to further refine predictions generated by the model.