Abstract: This disclosure provides a method and system for automatic creation of software features for a heterogeneous network based on human behavioral analysis. This system tracks user information and the user's behavior over time in order to generate a historical database of their behavior. This system receives a request from the user to access data describing a communications network, via a first screen of a user interface. This request, as well as information about the user which is stored in the historical database and information about network devices associated with the user, is analyzed using statistical or machine learning methods. Based on this analysis, requirements for a software feature are generated. These requirements are made into a software feature that can be a second screen of screen displayed on the user interface, a report sent to the user, or a prompt to the user based on the user's past behavior.

Abstract: This disclosure provides a method and system for an intelligent navigation and interface generation system (INS). This system receives a request to access data describing a communications network, via a first screen of a user interface. A prompt for instructing a large language model (LLM) to generate a structured view descriptor that specifies a second screen of a plurality of screens, of the user interface and a set of data to query for display in the second screen is constructed. The prompt is provided to the LLM to generate the structured view descriptor based on the prompt. The structured view descriptor is provided to the user interface to display a new version of the second screen responsive to the request.

Abstract: Systems and methods are provided for asset utilization and trigger-based automation. A network management system may receive a receive a network management configuration that identifies a configuration of a set of assets to be allocated to a service. The network management system may generate an asset utilization dataset including a set of utilization values associated with the set of assets generated over a time. The asset utilization data set may be used to train a machine-learning model configured to generated predictions corresponding to a future utilization associated with the set of assets. The network management system may then execute the machine-learning model predict utilization of a first asset. Upon determining that the predicted utilization is greater than a threshold, the network management system may execute an action associated with the set of assets.

Abstract: Systems and methods are provided for asset utilization and trigger-based automation. A network management system may receive a receive a network management configuration that identifies a configuration of a set of assets to be allocated to a service. The network management system may generate an asset utilization dataset including a set of utilization values associated with the set of assets generated over a time. The asset utilization data set may be used to train a machine-learning model configured to generated predictions corresponding to a future utilization associated with the set of assets. The network management system may then execute the machine-learning model predict utilization of a first asset. Upon determining that the predicted utilization is greater than a threshold, the network management system may execute an action associated with the set of assets.