Conversational Middleware Orchestration for Contextual NLP Routing
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Solution Overview
Problem
Current automated conversational systems face challenges in simulating nuanced conversations with humans, as they struggle to understand contextual cues like mood, intent, and sarcasm. Additionally, natural language processing agents are often domain-specific, leading to limitations in their ability to handle diverse tasks, and there are concerns about privacy and data security.
Innovation Solution
The proposed automated conversation orchestration system interconnects multiple natural language processing agents with different domain specializations. This system uses an orchestration architecture that can control conversational flows, automatically reroute utterances to appropriate agents, and employ additional contextual and behavioral agents to modify utterances and adjust agent selection based on contextual cues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If domain-specific natural language processing agents are used, then the agent performs well at specific tasks, but the agent becomes poor at handling diverse tasks
Solution Approach 1:
The patent implements a hybrid architecture where a general-purpose NLP agent handles diverse conversational tasks while domain-specific agents provide specialized expertise. The system dynamically routes queries between general and specialized agents, allowing the system to maintain versatility across multiple domains while preserving high performance in specific task areas through the specialized agents.
Solution Approach 2:
The patent employs a nested architecture where domain-specific NLP agents are integrated within a broader orchestration system. The general-purpose agent acts as the outer layer handling overall conversation flow, while domain-specific agents are nested within to handle specialized tasks. This nested structure allows the system to combine the versatility of general-purpose processing with the reliability of specialized domain handling.
2Adaptability or versatility
If multiple natural language processing agents are interconnected, then the system handles diverse tasks better, but the system complexity increases
Solution Approach 1:
The patent introduces an orchestration system as an intermediary layer that manages multiple domain-specific NLP agents. This mediator handles the complexity of interconnecting agents by providing standardized interfaces, query routing logic, and coordination mechanisms. The orchestration system abstracts the complexity away from individual agents while enabling them to work together effectively, thus maintaining task diversity without proportionally increasing overall system complexity.
3Extent of automation
If automated conversation technology is implemented, then conversational automation is achieved, but privacy concerns arise regarding personal information
Solution Approach 1:
The patent extracts and separates sensitive personal information processing from the main automated conversation flow. The system identifies and isolates privacy-sensitive operations, handling them through dedicated security mechanisms and data protection protocols. This extraction approach allows the automated conversation system to function while minimizing privacy risks by removing sensitive data handling from the general automation pipeline and subjecting it to enhanced protection measures.
Data Source
AI summary
A de-coupled computing infrastructure is described that is adapted to provide domain specific contextual engines based on conversational flow. The computing infrastructure further includes, in some embodiments, a mechanism for directing conversational flow in respect of a backend natural language processing engine. The computing infrastructure is adapted to control or manage conversational flows using a plurality of natural language processing agents.