Abstract: The disclosed system obtains KPIs and configuration parameters associated with the mature and the immature network. A physical layer of the mature wireless telecommunication network corresponds to a physical layer of the immature wireless telecommunication network. The system combines KPIs of the immature network and KPIs of the mature network to obtain multiple KPIs. The system predicts an attribute associated with the immature wireless telecommunication network, where the attribute depends on multiple other attributes associated with the wireless telecommunication network. To make the prediction, the system provides the multiple key performance indicators and the configuration parameters to a machine learning model trained on data associated with the mature wireless telecommunication network. The machine learning model predicts the value of the attribute associated with the immature wireless telecommunication network based on the multiple key performance indicators and the configuration parameters.

Abstract: The system obtains multiple KPIs and multiple configuration parameters directly from a wireless telecommunication network. The multiple KPIs indicate an observed performance associated with the wireless telecommunication network. The multiple configuration parameters indicate a configuration of the wireless telecommunication network. The system predicts a value of a difficult to predict attribute of the wireless telecommunication network, where the difficult to predict attribute depends on multiple other attributes associated with the wireless telecommunication network. To make the prediction, the system provides the multiple key performance indicators and the multiple configuration parameters to a machine learning model. The machine learning model predicts the value of the difficult to predict attribute associated with the wireless telecommunication network based on multiple key performance indicators and the multiple configuration parameters.

Abstract: The disclosed system obtains KPIs and configuration parameters associated with the mature and the immature network. A physical layer of the mature wireless telecommunication network corresponds to a physical layer of the immature wireless telecommunication network. The system combines KPIs of the immature network and KPIs of the mature network to obtain multiple KPIs. The system predicts an attribute associated with the immature wireless telecommunication network, where the attribute depends on multiple other attributes associated with the wireless telecommunication network. To make the prediction, the system provides the multiple key performance indicators and the configuration parameters to a machine learning model trained on data associated with the mature wireless telecommunication network. The machine learning model predicts the value of the attribute associated with the immature wireless telecommunication network based on the multiple key performance indicators and the configuration parameters.

Abstract: The system obtains a recording of an interaction between a UE and a representative of a network. The system obtains a summary associated with the interaction, where the summary includes an indication of a topic representing a portion of the interaction, a time when the topic was present in the interaction, and a generator of the portion of the interaction associated with the topic. The system obtains multiple inputs associated with the multiple UEs and multiple representatives. The system provides the summary of the interaction and the multiple inputs to an AI configured to predict a likelihood of a predetermined action associated with the UE. The system receives from the AI the likelihood of the predetermined action. Based on the likelihood of the predetermined action, the system performs an action within a network prior to an occurrence of the predetermined action associated with the UE.

Abstract: The system obtains an undesirable action associated with the UE from multiple undesirable actions, including disassociating from the wireless telecommunication network or seeking another interaction with a representative of the wireless telecommunication network. The system obtains multiple key performance indices (KPIs) associated with the UE or the representative of the wireless telecommunication network. The system obtains multiple groups of representatives configured to connect to the UE, where a first group among the multiple groups is associated with fewer occurrences of the undesirable action than a second group among the multiple groups. Based on the KPIs associated with the UE, the system determines whether a likelihood of the undesirable action occurring is above a predetermined threshold. Upon determining that the likelihood of the undesirable action occurring is above the predetermined threshold, the system connects the UE with a representative from the first group.

Abstract: The disclosed system obtains KPIs and configuration parameters associated with the mature and the immature network. A physical layer of the mature wireless telecommunication network corresponds to a physical layer of the immature wireless telecommunication network. The system combines KPIs of the immature network and KPIs of the mature network to obtain multiple KPIs. The system predicts an attribute associated with the immature wireless telecommunication network, where the attribute depends on multiple other attributes associated with the wireless telecommunication network. To make the prediction, the system provides the multiple key performance indicators and the configuration parameters to a machine learning model trained on data associated with the mature wireless telecommunication network. The machine learning model predicts the value of the attribute associated with the immature wireless telecommunication network based on the multiple key performance indicators and the configuration parameters.

Abstract: The disclosed system obtains KPIs and configuration parameters associated with the mature and the immature network. A physical layer of the mature wireless telecommunication network corresponds to a physical layer of the immature wireless telecommunication network. The system combines KPIs of the immature network and KPIs of the mature network to obtain multiple KPIs. The system predicts an attribute associated with the immature wireless telecommunication network, where the attribute depends on multiple other attributes associated with the wireless telecommunication network. To make the prediction, the system provides the multiple key performance indicators and the configuration parameters to a machine learning model trained on data associated with the mature wireless telecommunication network. The machine learning model predicts the value of the attribute associated with the immature wireless telecommunication network based on the multiple key performance indicators and the configuration parameters.

Abstract: The system obtains multiple KPIs and multiple configuration parameters directly from a wireless telecommunication network. The multiple KPIs indicate an observed performance associated with the wireless telecommunication network. The multiple configuration parameters indicate a configuration of the wireless telecommunication network. The system predicts a value of a difficult to predict attribute of the wireless telecommunication network, where the difficult to predict attribute depends on multiple other attributes associated with the wireless telecommunication network. To make the prediction, the system provides the multiple key performance indicators and the multiple configuration parameters to a machine learning model. The machine learning model predicts the value of the difficult to predict attribute associated with the wireless telecommunication network based on multiple key performance indicators and the multiple configuration parameters.

Abstract: The disclosed system obtains KPIs and configuration parameters associated with the mature and the immature network. A physical layer of the mature wireless telecommunication network corresponds to a physical layer of the immature wireless telecommunication network. The system combines KPIs of the immature network and KPIs of the mature network to obtain multiple KPIs. The system predicts an attribute associated with the immature wireless telecommunication network, where the attribute depends on multiple other attributes associated with the wireless telecommunication network. To make the prediction, the system provides the multiple key performance indicators and the configuration parameters to a machine learning model trained on data associated with the mature wireless telecommunication network. The machine learning model predicts the value of the attribute associated with the immature wireless telecommunication network based on the multiple key performance indicators and the configuration parameters.

Abstract: The disclosure generally describes methods, software, and systems, including a method for generating suggestions for extensions to data sets. Using metadata for a plurality of documents, data relationships are determined between sets of documents of the plurality of documents. Each set of documents originates from different data sources and does not share common ontologies. The metadata includes terms and labels for documents in the plurality of documents. Using the determined data relationships, enhancement suggestions are determined. Each enhancement suggestion identifies pieces of information from at least one first document in the set of documents that can serve as an extension to information contained in a second document in the set of documents. The identified enhancement suggestions are ranked based on enhancement usability ranking factors. The ranked enhancement suggestions are provided.

Abstract: The disclosure generally describes methods, software, and systems, including a method for generating suggestions for extensions to data sets. Using metadata for a plurality of documents, data relationships are determined between sets of documents of the plurality of documents. Each set of documents originates from different data sources and does not share common ontologies. The metadata includes terms and labels for documents in the plurality of documents. Using the determined data relationships, enhancement suggestions are determined. Each enhancement suggestion identifies pieces of information from at least one first document in the set of documents that can serve as an extension to information contained in a second document in the set of documents. The identified enhancement suggestions are ranked based on enhancement usability ranking factors. The ranked enhancement suggestions are provided.