AI Chatbot Orchestration for Intent Routing and Reinforcement Training
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Solution Overview
Problem
Conventional chatbots struggle with understanding complex natural language and require manual intervention for tasks beyond their limited scope, leading to inefficiencies and resource overburden.
Innovation Solution
A system utilizing AI tools to parse intents in natural language speech, route utterances to specialized chatbots, and enhance training through user interactions, minimizing the need for live representatives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional chatbots are used to process user requests, then the system can handle simple commands, but it cannot understand complex natural language and requires manual intervention
Solution Approach 1:
The patent introduces an AI module as an intermediary between the user's natural language input and the chatbot's processing. This AI module translates complex natural language into structured intents and parameters, enabling the chatbot to understand and process sophisticated user requests automatically without requiring manual intervention from live representatives.
Solution Approach 2:
The system changes the parameter of language processing by using AI-based intent recognition and natural language understanding capabilities. This transforms the chatbot from handling only simple predefined commands to understanding complex natural language expressions by converting them into structured intent parameters that the chatbot can process.
2Adaptability or versatility
If a single chatbot handles all tasks, then the system structure is simple, but the chatbot cannot handle tasks beyond its limited scope
Solution Approach 1:
The patent segments the chatbot system into multiple specialized chatbots, each trained to handle specific tasks or domains. The AI module acts as a router that directs user requests to the appropriate specialized chatbot based on the detected intent. This segmentation enables each chatbot to excel at its specific task while the overall system maintains versatility through the AI module's routing capability.
Solution Approach 2:
The AI module serves as a universal component that handles multiple functions: translating natural language, recognizing intents, extracting parameters, and routing to appropriate chatbots. This multi-functional AI module enables the system to handle diverse tasks beyond any single chatbot's limited scope while maintaining manageable system structure.
3Productivity
If live representatives manually intervene to process user requests, then complex tasks can be handled, but it creates resource overburden and prevents system learning
Solution Approach 1:
The patent implements a feedback mechanism where interactions between live representatives and users are captured and used to train and improve the AI module. This feedback loop enables the system to learn from real-world complex requests and continuously improve its natural language understanding and intent recognition capabilities, transforming manual interventions into system learning opportunities.
Solution Approach 2:
The system enables self-service by using the AI module to automatically process complex user requests without requiring live representative intervention. The AI module independently translates natural language, recognizes intents, extracts parameters, and routes to appropriate chatbots, freeing live representatives from routine tasks and enabling the system to serve itself while maintaining high productivity.
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.