Telecommunication Resource Deployment Through ML Cataloging and Reuse
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Solution Overview
Problem
Telecommunications service providers face challenges in efficiently discovering and reusing existing telecommunication resources due to lack of centralized access, leading to inefficient use and unnecessary duplication.
Innovation Solution
A system utilizing machine learning models to catalog and recommend relevant telecommunication resources based on natural language messages, providing access credentials and metadata for resource ingestion, and generating deployments using user-friendly interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If telecommunication resources are distributed across multiple locations without centralized access, then resource autonomy and flexibility are maintained, but resource discovery efficiency and reuse capability deteriorate
Solution Approach 1:
The patent introduces a machine learning model as an intermediary component that sits between the distributed telecommunication resources and the users. This model catalogs resources from multiple locations and provides centralized access through recommendations, enabling efficient resource discovery without requiring a fully centralized resource management system. The ML model acts as a mediator that bridges the gap between distributed resource storage and centralized resource access needs.
2Productivity
If manual processes are used for resource cataloging and deployment, then system complexity is reduced, but productivity and development time deteriorate
Solution Approach 1:
The machine learning model performs automated resource cataloging, classification, and recommendation without requiring manual intervention. The system self-services by automatically ingesting resource information from multiple locations, organizing it according to predefined taxonomies, and generating deployment recommendations. This automation significantly improves productivity while the modular architecture keeps system complexity manageable.
Solution Approach 2:
The patent replaces manual mechanical processes (human operators physically cataloging and deploying resources) with an automated machine learning system. The ML model uses algorithms to automatically classify resources, generate recommendations, and facilitate deployments, substituting human labor with intelligent automation that improves speed and consistency while reducing operational complexity.
3Adaptability or versatility
If existing resources are not systematically cataloged, then storage and access flexibility are maintained, but resource reuse and efficiency deteriorate
Solution Approach 1:
The system performs preliminary cataloging and classification of telecommunication resources before they are needed for deployment. The machine learning model proactively ingests resource information from multiple locations, organizes it into a structured format with appropriate taxonomies and metadata, and makes it readily available for future reuse. This advance preparation eliminates the need for time-consuming resource discovery and setup during actual deployment activities.
Data Source
AI summary
System and methods for generating a deployment that uses existing telecommunication resources, such as microservices, data sources, and/or communication channels. The deployment can comprise a digital representation of a base station deployment. A plain language message is received that describes a desired deployment of telecommunication resources. One or more entities are extracted from the plain language message. Based on the extracted entities, the system recommends one or more existing telecommunication resources for use in the desired deployment. In some implementations, recommendations are generated using a machine learning model that generates relevance scores for each of multiple existing telecommunication resources. A selection is received from among the recommended telecommunication resources, and the desired deployment is generated using the selected telecommunication resources.