A spectrum management method and a spectrum management device are disclosed. The spectrum management method includes adjusting a right of a spectrum block using a blockchain. The spectrum management device includes an adjuster configured to adjust a right of a spectrum block using a blockchain.

Techniques for facilitating connectivity to vPLCs created in a CSP-provided infrastructure in a region. Within the CSP-provided infrastructure in a region, when the destination of a packet is determined to be an endpoint associated with a particular vPLC, the packet is tagged with information related to the particular vPLC. The vPLC-related information for the particular vPLC can include, for example, a vPLC identifier identifying the particular vPLC, an identifier identifying a customer associated with the endpoint, a virtual cloud network identifier identifying a virtual cloud network (VCN) belonging to the particular vPLC and where the endpoint is part of the VCN, and other vPLC-related information. The packet is then routed or communicated within the CSP-provided infrastructure in a region along with the tagged vPLC-related information. The vPLC-related information is used as part of the connectivity and for routing of packets within the CSP-provided infrastructure in a region.

Example embodiments of the present disclosure provide for an example method including maintaining a key values dictionary including a first feature, a first threshold utilization level associated with the first feature, and a second threshold utilization level associated with the first feature. The example method includes obtaining user session initiation data associated with a headless content management service. The example method includes obtaining first utilization level data for the first feature. The example method includes determining that the first utilization level is above the first threshold utilization level. The example method includes performing a first-tier action responsive to determining the first utilization level is high. The example method includes determining a second utilization level is above a second threshold. The example method includes performing a second-tier action responsive to determining the second utilization level is above the second threshold.

A traffic scenario identification method and apparatus, a device, and a storage medium are disclosed. The method may include: constructing a plurality of data features according to user data of a cell to be identified within a set period of time (S110), the data features including cell-level features and sector-level features; selecting, from the plurality of data features, a set number of data features corresponding to a candidate traffic scenario (S120); and inputting the set number of data features into a scenario identification binary classification model corresponding to the candidate traffic scenario to obtain a target traffic scenario of the cell to be identified (S130).

The present disclosure is directed to methods and systems for spectrum leasing. The spectrum leasing system performs a set of functions to provide potential leasing customers with flexible leasing options. The spectrum leasing system can utilize inputs such as: customer problems, size and type of customer premises, list of customer location(s), requested spectrum bands, requested network capability at a customer location, and geographic information system (GIS) information about the customer premises, nearby buildings, and venues The spectrum leasing system can use the input information to identify and select leasing and monetization opportunities for customers. In some implementations, the spectrum leasing system analyzes a network provider spectrum portfolio, proximity of existing and planned public network cell sites, and a set of pre-established business rules for spectrum leasing to identify spectrum bands available for leasing and determine pricing of the lease.

Systems and methods for integrating generative artificial intelligence (AI) capabilities within Software as a Service (SaaS) platforms. One of the computer-implemented methods are for querying a generative AI model about structured data in a SaaS environment, enabling users to interact with and manipulate data through AI-assisted interfaces. One of the systems maintains a generative AI agent configured to interact with SaaS platform data as a virtual team member, capable of understanding context and nuances of project data. Also described are methods for color-context aware data analysis, generation of interactive elements in messaging sessions, and cross-application generative AI agent interactions triggered by user mentions. Also described is facilitating the creation of custom SaaS platform products by combining functionalities from existing products using generative AI. The systems and methods represent advancement in AI-driven SaaS customization and data analysis.

A method includes receiving node-site planning data from a first-party application; validating the node-site planning data based on one or more parameters of validation; pre-loading at least a portion of the node-site planning data on a second-party application for field-survey data gathering; and receiving field-survey data from the second-party application.

A system and method for field service assistance for telecommunications operations are described, which utilize a data acquisition module configured to receive multimodal data inputs including structured and unstructured data from field operations. A preprocessing module normalizes the multimodal data inputs to generate pre-processed data. A vectorization module transforms the pre-processed data into numerical vector representations using domain-specific embedding models trained on telecom equipment data, implementing convolutional neural networks for image feature extraction and transformer-based encoders for text vectorization. A contextual retrieval module retrieves contextually relevant historical data from a vector database by computing similarity metrics between current job vectors and stored job completion vectors. A response generation module processes the numerical vector representations and retrieved contextual data using an evolutionary algorithm engine to generate structured job summaries and real-time field recommendations.

A system and method for field service assistance for telecommunications operations are described, which utilize a data acquisition module configured to receive multimodal data inputs including structured and unstructured data from field operations. A preprocessing module normalizes the multimodal data inputs to generate pre-processed data. A vectorization module transforms the pre-processed data into numerical vector representations using domain-specific embedding models trained on telecom equipment data, implementing convolutional neural networks for image feature extraction and transformer-based encoders for text vectorization. A contextual retrieval module retrieves contextually relevant historical data from a vector database by computing similarity metrics between current job vectors and stored job completion vectors. A response generation module processes the numerical vector representations and retrieved contextual data using an evolutionary algorithm engine to generate structured job summaries and real-time field recommendations.

The present invention relates to a system and method for managing emergency communications and, in particular, to a system and method that implements an alternative wireless communications service such as a satellite-based service operable to automatically connect with user devices in the event that user devices lose access to services normally provided by WIFI or cell phone services, to ensure that the user devices continue to receive notifications regarding a threat situation (e.g. bushfire).