Parallel Chatbot Network for Intent Resolution
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Solution Overview
Problem
Legacy chatbot systems lack seamless communication between multiple chatbots, limiting their capabilities and requiring cumbersome access to individual chatbots for interactions that span multiple domains, as well as preventing knowledge sharing between them.
Innovation Solution
A network of chatbots is established using a 'bot of bots' model, where chatbots are logically grouped by domain, with a 'know it all' or 'delegator' manager bot handling queries and delegating requests to other bots as necessary, and users are presented with ranked intent options based on accuracy and relevance, allowing for efficient intent selection and user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple chatbots are maintained for different departments or domains, then each chatbot can be specialized in a specific domain of intents, but seamless communication between chatbots is lost and each chatbot must be accessed separately
Solution Approach 1:
An intermediary layer is introduced that sits between the user and multiple specialized chatbots. This intermediary receives user queries, determines which chatbot(s) are appropriate for handling the query, and routes the interaction accordingly. This allows users to access multiple specialized chatbots through a single interface without needing to know which specific chatbot handles which domain, thus maintaining domain specialization while improving access convenience.
Solution Approach 2:
The system creates a universal chatbot interface that can handle multiple domains and intents through a single access point. This universal interface incorporates capabilities to detect user intent, determine appropriate routing, and coordinate between multiple specialized chatbots, allowing users to interact with any domain's chatbot through one unified access method.
2Device complexity
If a single chatbot is maintained for a specific domain, then the chatbot can be focused and simple to manage, but it cannot share knowledge with other chatbots or handle queries outside its domain
Solution Approach 1:
The system segments chatbot functionality into specialized individual chatbots, each maintaining focused knowledge for its specific domain. This segmentation preserves system simplicity and domain expertise while allowing each chatbot to remain relatively simple and manageable. The segmentation is complemented by a coordination layer that enables knowledge sharing without requiring each chatbot to become complex.
Solution Approach 2:
A feedback mechanism is implemented where chatbots can share responses, outcomes, and learned information with the intermediary layer and other chatbots. When one chatbot successfully handles a query or learns new information, this feedback is communicated to the system, allowing other chatbots to benefit from this knowledge. This enables knowledge sharing while maintaining the simplicity of individual specialized chatbots.
3Ease of manufacture
If multiple chatbots operate independently without communication, then each chatbot can be trained and maintained separately, but the system cannot provide comprehensive answers that require multiple domains of knowledge
Solution Approach 1:
The system segments the chatbot network into independently trainable specialized units, each focused on a specific domain. This segmentation allows each chatbot to be trained, maintained, and updated separately without affecting others, preserving training independence. The segmented architecture is complemented by a coordination mechanism that integrates responses from multiple chatbots to provide comprehensive answers.
Solution Approach 2:
The system merges the capabilities of multiple independently trained chatbots through a coordination layer that combines their responses and knowledge. When a query requires multiple domains, the intermediary orchestrates interactions between relevant chatbots, merging their individual contributions into a comprehensive response. This allows the system to achieve versatile comprehensive response capability while maintaining the training independence of individual chatbots.
Data Source
AI summary
Methods and apparatus for parallel intent processing at a network of chatbots may be provided. A query may be received at a user interface. The query may be transmitted to multiple chatbots at the same time. Each chatbot may identify an intent for the query. Each chatbot may also identify a percentage of accuracy that the chatbot considers that the intent corresponds to the query. The chatbots may present the intents to the user interface. The user interface may rank the intents. The user interface may display the ranked intents to the user. The user interface may receive an intent selection from the user. The user interface may direct the user to the chatbot that identified the selected intent.