Abstract: Embodiments relate to analyzing network packets in a telecommunication networks using machine learning models. The network packets are correlated and then labeled to indicate successes or failures in a subtask of communication flow. Features are extracted based on the labels and correlated network packets. The extracted features are applied to a machine learning model to predict or infer success or failure of the entire communication flow. The result from the machine learning model may again be applied to subsequent machine learning models to predict root cause of a failure or to predict or infer the type of success. In this way, more accurate diagnosis of network issues in the telecommunication networks may be made in a more expedient manner.

Abstract: Embodiments relate to generating specialized large language models by performing transfer learning on a base large language model. The base large language model is trained using network traffic capture files as training data to predict information in a network traffic capture file during inference. The base large language model is modified into specialized large language models for including in different applications for performing communication network analysis. In this way, the specialized large language models may be developed in an expedient and efficient manner by leveraging the training performed on the base large language model.

Abstract: Embodiments relate to intelligent entities for providing information service over a network in a telecommunication system. An intelligent element framework manages intelligent entities, which are modular and trained using artificial intelligence or machine learning algorithms to perform prediction or inference for different types of applications. The intelligent entities may communicate with each other via the intelligent element framework. For example, an intelligent entity may generate an output and provide the output for use by one or more other intelligent entities. Thus, the intelligent element framework may distribute portions of tasks for information service across multiple intelligent entities chained together, for example, in a directed graph configuration.

Abstract: A model assessor retrieves a plurality of predicted outputs from a plurality of models, each predicted output generated using one of the models based on one or more data inputs and a regression model. The model assessor generates a candidate model, which includes as input 1) the one or more data inputs of a selected model of the plurality of models and 2) a predictive output of one or more other models of the plurality of models or one or more other data inputs. A correlation is computed between an actual output and a predicted output of the candidate model, and the model assessor determines if the correlation score exceeds a threshold criteria. If so, the selected model is replaced with the candidate model. Otherwise, the candidate model is deleted.

Abstract: A system and a method are disclosed for implementing distributed processing in edge nodes. In an embodiment, a respective edge node receives data from a client of the respective edge node. The respective edge node generates a prediction of a respective activity based on the data, and determines whether the prediction is valid by feeding the prediction into a validator module and receiving a validation response from the validator module. The respective edge node, in response to determining that the prediction is valid, activates a function.

Abstract: Embodiments relate to allocating resources of computing devices for providing information service in a network. The computing devices may be hierarchically structured and may include, for instance, cloud servers, telecommunication servers, edge edges, gateways, and client devices. A system environment may include a hierarchical orchestrator coordinating with one or more local orchestrators to allocate service components (for example, a discrete functional software or hardware component) to computing devices. The orchestrators can automatically reallocate resources responsive to detecting update events such as a change in traffic or payload on the network.