NRF Mediator for AI Service Selection in Communication Networks
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Solution Overview
Problem
Existing communication technologies lack efficient methods for providing artificial intelligence (AI) services across network elements, leading to suboptimal AI service delivery and integration in communication networks.
Innovation Solution
The proposed solution involves an AI service providing method that utilizes a Network Repository Function (NRF) network element to generate and manage a candidate AI service list, process AI service queries from network elements, and feed back AI service lists to select the appropriate AI network elements for service provision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI services are integrated across network elements, then service functionality and adaptability are improved, but system complexity increases
Solution Approach 1:
The patent introduces a service selection mechanism as an intermediary component that mediates between AI network elements and consuming network elements. This mediator manages the complexity of service discovery, selection, and invocation by providing a standardized interface, thereby enabling AI service integration without directly increasing the complexity of individual network elements.
Solution Approach 2:
The patent implements a universal service selection mechanism that can handle multiple AI service types and network element combinations through a single standardized framework. This multi-functional approach allows different network elements to discover and invoke AI services without requiring element-specific integration logic, thus improving adaptability while controlling complexity.
2Ease of operation
If AI service discovery and selection mechanisms are implemented, then service accessibility is improved, but processing time increases
Solution Approach 1:
The patent implements preliminary action by pre-configuring and pre-publishing AI service information in a service registry before actual service invocation is needed. Network elements can query and select from pre-available services, avoiding the need for real-time service discovery and negotiation, thus reducing processing time while maintaining ease of access.
Solution Approach 2:
The patent employs feedback mechanisms where network elements provide feedback about service requirements and selection criteria to the service selection mechanism. This feedback loop enables optimized service matching that reduces selection time by focusing the search on pre-filtered candidates rather than performing exhaustive searches, thereby improving accessibility without significant time loss.
Data Source
AI summary
An artificial intelligence (AI) service implements a method. The method includes: obtaining an AI service provided by an AI network element and generating a candidate AI service list; receiving an AI service query request from a first network element; generating an AI service list corresponding to the first network element based on the AI service query request and the candidate AI service list; and feeding back the AI service list to the first network element, wherein the first network element selects the AI network element from the AI service list to provide the AI service.