Generative AI Agent Fleets for Service Optimization Automation
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Solution Overview
Problem
Modern services face challenges in optimizing user engagement and operational efficiency due to complex resource combinations and non-intuitive data structures, requiring domain-specific knowledge that is hard for non-experts to comprehend.
Innovation Solution
Utilizing extensible fleets of generative artificial intelligence agents to autonomously identify, propose, and execute optimization tasks based on natural language analysis of dynamically updated data sources, translating high-level tasks into executable operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If complex combinations of resources and tools are used to implement modern services, then service functionality and capabilities are improved, but the complexity of understanding and operating the service increases
Solution Approach 1:
The patent introduces an intermediary layer (service abstraction interface) between the complex service implementation and the user. This intermediary translates high-level user intentions into specific service operations, hiding the underlying complexity of resource combinations and tools while maintaining full service functionality.
Solution Approach 2:
The service system is segmented into distinct modular components: resource management modules, tool execution modules, and a coordination layer. Each module handles specific functions independently, making the overall complex system manageable through clear separation of concerns and standardized interfaces.
2Quantity of substance
If domain-specific knowledge is stored in large natural language knowledge bases, then comprehensive service information is available, but accessibility and comprehension for non-experts deteriorate
Solution Approach 1:
The patent employs natural language processing intermediaries that translate between user-friendly queries and the underlying domain-specific knowledge base. These intermediaries automatically interpret, filter, and present relevant information in accessible formats without requiring users to navigate the full complexity of the knowledge base.
Solution Approach 2:
The system creates simplified copies or representations of complex domain knowledge tailored to different user needs. Instead of presenting the entire knowledge base, it generates customized summaries, FAQs, or step-by-step guides that replicate only the necessary information for specific tasks.
3Ease of operation
If manual task execution is used for service optimization, then flexibility and control are maintained, but productivity and efficiency deteriorate
Solution Approach 1:
The patent implements a dynamic task execution system that can adapt between manual and automated modes based on task characteristics. The system dynamically determines the optimal execution approach, allowing users to maintain control over critical tasks while automatically handling routine optimizations, thus balancing flexibility with productivity.
Solution Approach 2:
The system incorporates self-service capabilities where the service automatically identifies and executes optimization tasks without manual intervention. The service monitors its own performance, detects optimization opportunities, and implements improvements autonomously, freeing users from manual task execution while maintaining oversight through configurable approval workflows.
Data Source
AI summary
Corresponding to individual ones of a plurality of categories of optimization tasks of a service, respective generative artificial intelligence models (GAIMs) are configured. A prompt which instructs a first GAIM to identify a candidate optimization task of a particular category is presented to the first GAIM. The candidate optimization task, identified by the first GAIM, is then initiated using another GAIM.