Telecom monitoring and analysis system

A method comprising:

• receiving a request from a user computing device to access a system for monitoring at least one of a session degradation or a session failure of a communication session between subscribers in a telecommunications network;
• requesting, by the system, a user of the user computing device to specify an identifier associated with a subscription to services provided by an operator of the telecommunications network;
• receiving the identifier from the user computing device;
• querying, by the system, multiple dedicated monitoring systems for past session logs associated with the identifier, the multiple dedicated monitoring systems monitoring respective portions of the telecommunications network, individual dedicated monitoring systems of the multiple dedicated monitoring systems being configured to obtain session logs comprising information relating to data packets of the communication session captured by a corresponding dedicated monitoring probe within the respective portion of the telecommunications network for the individual dedicated monitoring systems;
• receiving first past session logs from a first dedicated monitoring system of the multiple dedicated monitoring systems;
• receiving second past session logs from a second dedicated monitoring system of the multiple dedicated monitoring systems;
• sending the first past session logs and the second past session logs to the user computing device;
• and
• generating, using a trained machine learning model, a root cause within the telecommunications network of the at least one of the session degradation or the session failure of the communication session by processing the first past session logs and the second past session logs as input to the trained machine learning model and by performing deep packet inspection before the at least one of the session degradation or the session failure, wherein the trained machine learning model uses training data including additional past session logs and data regarding root causes of session degradations or session failures exhibited in the additional past session logs.

A system comprising:

• one or more processors;
• and
• memory storing computer-executable instructions that, when executed by the one or more processors, cause the system to:
• receive a request from a user computing device to access the system for monitoring at least one of a session degradation or a session failure of a communication session between subscribers in a telecommunications network;
• request, via a user interface of the user computing device, an identifier associated with a subscription to services provided by an operator of the telecommunications network;
• receive the identifier from the user computing device;
• query multiple dedicated monitoring systems for past session logs associated with the identifier, the multiple dedicated monitoring systems monitoring respective portions of the telecommunications network, individual dedicated monitoring systems of the multiple dedicated monitoring systems being configured to obtain session logs comprising information relating to data packets of the communication session captured by a corresponding dedicated monitoring probe within the respective portion of the telecommunications network for the individual dedicated monitoring systems;
• receive first past session logs from a first dedicated monitoring system of the multiple dedicated monitoring systems, the first past session logs being associated with a first portion of the telecommunications network;
• receive second past session logs from a second dedicated monitoring system of the multiple dedicated monitoring systems, the second past session logs being associated with a second portion of the telecommunications network;
• send the first past session logs and the second past session logs to the user computing device;
• and
• generate, using a trained machine learning model, a root cause within the telecommunications network of the session degradation or the session failure of the communication session by processing the first past session logs and the second past session logs as input to the trained machine learning model and by performing deep packet inspection before the at least one of the session degradation or the session failure, wherein the trained machine learning model uses training data including additional past session logs and data regarding root causes of session degradations or session failures exhibited in the additional past session logs.