Chatbot Network Intermediary for Intent Routing
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Solution Overview
Problem
Legacy chatbot systems lack seamless communication between multiple chatbots, leading to cumbersome interactions and limited capabilities, as each chatbot operates independently without sharing knowledge or intents with others.
Innovation Solution
A network of chatbots is built and trained to communicate seamlessly by creating a model that includes a communication protocol, logical intent determination, and data sharing mechanisms, allowing chatbots to understand and respond to a combined set of intents across domains, with 'know it all' and 'delegator' manager bots managing query parsing and data access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple chatbots are maintained at a single entity with each department having its own chatbot, then specialized domain knowledge is improved, but communication between chatbots and overall system capability deteriorates
Solution Approach 1:
An intermediary layer is introduced between multiple domain-specific chatbots to enable seamless communication. This intermediary manages intent routing and knowledge sharing, allowing chatbots to collaborate without direct complex interconnections, thus maintaining specialized domain knowledge while improving overall system communication capability.
Solution Approach 2:
The system implements a universal communication framework that enables chatbots to perform multiple functions including intent determination, knowledge sharing, and collaborative problem-solving. This universal layer allows domain-specific chatbots to maintain their specialized capabilities while participating in broader multi-domain conversations through standardized protocols.
2Device complexity
If each chatbot operates independently without communication, then system simplicity is improved, but capability to handle multi-domain queries deteriorates
Solution Approach 1:
The system is segmented into independent domain-specific chatbots that maintain their simplicity, while a separate coordination layer handles multi-domain query routing. This segmentation allows each chatbot to remain simple and focused on its domain, while the system as a whole gains the capability to handle complex multi-domain queries through the coordination mechanism.
Solution Approach 2:
An intermediary coordination system is introduced that receives user queries, determines appropriate intents, and routes them to the relevant domain-specific chatbots. This intermediary enables the system to handle multi-domain queries effectively while keeping individual chatbots simple and independent in their operation.
3Adaptability or versatility
If chatbots share knowledge and intents across domains, then overall system capability is improved, but information processing complexity deteriorates
Solution Approach 1:
An intermediary layer manages knowledge sharing and intent determination across domain-specific chatbots. This intermediary processes and standardizes information exchange, reducing the information processing complexity for individual chatbots while enabling effective knowledge sharing and collaborative problem-solving across domains.
Solution Approach 2:
The system implements local quality by allowing each domain-specific chatbot to maintain its specialized knowledge structure and processing methods, while the intermediary layer handles the complexity of cross-domain knowledge integration. This approach enables knowledge sharing without requiring each chatbot to process all domain information, thus reducing overall information processing complexity.
Data Source
AI summary
Methods for building and training a network of chatbots are provided. Methods may perform data analysis on a first chatbot in the network. The first chatbot may include a first domain of intents. The data analysis may identify and rank the intents in the first domain. The first domain may represent answers to a first domain of user queries. Methods may select a predetermined number of highest-ranking intents from the first domain based on the data analysis. Methods may input the selected intents into a second chatbot in the network. The second chatbot may include a second domain of intents. The second domain may represent answers to a second domain of user queries. Methods may input a hook into the second chatbot. The hook may include a trigger word and may correspond to the first domain. When the hook is triggered, the second chatbot may invoke the first chatbot.