Multi-Chatbot Orchestration for Complex Speech Understanding
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Solution Overview
Problem
Conventional chatbots struggle with understanding complex and colloquial language, limiting their scope and requiring human intervention for tasks they cannot handle, leading to inefficiencies and resource overburden.
Innovation Solution
A system utilizing AI tools to parse intents in natural language speech, route statements to appropriate chatbots, and enhance chatbot responsiveness by learning from user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If chatbots use simple language processing, then they can understand commands accurately, but they cannot handle complex or colloquial speech from users
Solution Approach 1:
The system segments the complex task of understanding diverse user statements into multiple specialized chatbots, each trained for specific domains or statement types. The statement parsing module divides complex statements into manageable components, routing them to appropriate specialized chatbots that can handle each segment with high accuracy while collectively covering diverse language patterns.
Solution Approach 2:
The statement parsing module acts as an intermediary between the user's diverse speech inputs and the specialized chatbots. It translates colloquial and complex statements into standardized internal representations that the specialized chatbots can process accurately, bridging the gap between language versatility and processing precision.
2Ease of operation
If a single chatbot handles all tasks, then the system is simple to operate, but the chatbot becomes overburdened and less effective
Solution Approach 1:
The system segments the workload across multiple specialized chatbots instead of overloading a single chatbot. Each chatbot is trained for specific tasks or domains, allowing the system to handle diverse requests efficiently while maintaining simple user interaction through the unified statement parsing module and routing mechanism.
Solution Approach 2:
The statement parsing module and routing system provide universal functionality by handling all types of user statements regardless of complexity or domain, while distributing the actual processing work to specialized chatbots. This maintains ease of operation while improving overall productivity.
3Measurement precision
If chatbots are trained for specific limited topics, then they can provide accurate responses for those topics, but they cannot handle requests outside their training scope
Solution Approach 1:
The system segments the broad knowledge base into multiple specialized chatbots, each trained for specific topics or domains. The statement parsing module identifies the topic of incoming statements and routes them to the most appropriate specialized chatbot, ensuring high response accuracy for each topic while collectively covering a wide range of subjects.
Solution Approach 2:
The statement parsing module serves as an intermediary that bridges the gap between diverse user queries and specialized chatbots. It analyzes the topic of incoming statements and directs them to appropriately trained chatbots, enabling the system to maintain high accuracy for specialized topics while expanding overall topic coverage.
4Device complexity
If chatbots require manual intervention for complex tasks, then they can maintain simple architecture, but human resources become overburdened
Solution Approach 1:
The system segments complex task handling across multiple specialized chatbots that can independently process diverse requests. The statement parsing module automatically identifies and routes complex statements to appropriate chatbots, enabling high automated task handling capacity while maintaining relatively simple architecture through modular design.
Solution Approach 2:
The specialized chatbots are designed to handle complex tasks autonomously within their trained domains without requiring manual human intervention. The statement parsing module automatically routes complex requests to the appropriate chatbots, enabling the system to serve itself and reducing the burden on human resources.
Data Source
AI summary
A computer system for training a plurality of chatbots using artificial intelligence (AI) tools to process statements is provided. The computer system includes an orchestration computing device, and an AI module. The AI module is programmed to: (i) receive a verbal statement of the user including a plurality of words; (ii) translate the verbal statement into a text statement; (iii) augment the text statement by determining at least one intent of the text statement; (iv) provide recommendations for responding to the augmented text statement; (v) analyze the augmented text statement and the recommendations; (vi) generate data representing an audio response to the analyzed augmented text statement; and (vii) present the audio response to the user by causing a selected chatbot to execute the generated data.