Modular AI Entity Chaining for Cloud Task Distribution
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Solution Overview
Problem
In large-scale cloud computing networks, distributing tasks among computing devices for information services is challenging due to inconsistent performance across different functionalities, as a single machine learning algorithm or monolithic AI entity may excel in certain tasks but struggle with others, leading to inefficient automation and data analysis.
Innovation Solution
The intelligent element framework configures multiple intelligent entities in a modular approach, using automation algorithms to distribute tasks and perform predictions or inferences, allowing for complex operations to be executed through chaining of AI entities, enabling scalable, flexible, and automated execution of information services across telecommunication infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single machine learning algorithm or monolithic AI entity is used to perform tasks, then the system is simpler in structure, but the performance becomes inconsistent across different functionalities
Solution Approach 1:
The patent divides a monolithic AI entity into multiple specialized AI entities, each responsible for specific functionalities (e.g., image recognition, natural language processing, predictive analytics). This segmentation allows each entity to be optimized for its specific task, improving performance consistency across different functionalities while maintaining manageable system complexity through modular architecture.
2Reliability
If multiple specialized AI entities are deployed to improve performance consistency, then performance across different functionalities improves, but the system complexity increases
Solution Approach 1:
The patent introduces a unified task management system that serves multiple specialized AI entities, providing common functionalities such as task distribution, resource allocation, and coordination. This multi-functional management layer reduces the need for separate control mechanisms for each AI entity, thereby managing system complexity while enabling performance consistency across diverse functionalities.
3Extent of automation
If manual operation is used to review and analyze data, then the system requires less automation infrastructure, but the efficiency and productivity are reduced
Solution Approach 1:
The patent implements self-service mechanisms where AI entities autonomously perform data analysis, task execution, and performance monitoring without requiring manual intervention. The system automatically distributes tasks to appropriate AI entities, collects results, and optimizes resource allocation, thereby achieving high automation extent and productivity simultaneously through intelligent self-management capabilities.
Data Source
AI summary
Embodiments relate to intelligent entities for providing information service over a network in a telecommunication system. An intelligent element framework manages intelligent entities, which are modular and trained using artificial intelligence or machine learning algorithms to perform prediction or inference for different types of applications. The intelligent entities may communicate with each other via the intelligent element framework. For example, an intelligent entity may generate an output and provide the output for use by one or more other intelligent entities. Thus, the intelligent element framework may distribute portions of tasks for information service across multiple intelligent entities chained together, for example, in a directed graph configuration.