Distributed Chatbot Network for Natural Language Classification Accuracy
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Solution Overview
Problem
Existing chatbots face scalability, reusability, and interoperability limitations due to the difficulty in managing large training datasets and distinguishing between classes, leading to inaccurate natural language classification and a lack of domain-specific knowledge focus.
Innovation Solution
A network of domain-specific chatbots employing distributed natural language classifiers with local memory caches and sentence vector comparison mechanisms, where chatbots broadcast queries to peers if local confidence is below a threshold, and store responses with high confidence for future reference, allowing for efficient question routing and response generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single chatbot uses a large training dataset to improve classification accuracy, then the accuracy improves, but the device complexity and difficulty of managing training datasets increase
Solution Approach 1:
The patent divides the single large training dataset into multiple domain-specific datasets, with each chatbot specializing in a particular domain. This segmentation allows each chatbot to manage a smaller, more focused dataset while collectively covering a broad range of topics, thereby reducing individual complexity while maintaining overall accuracy.
Solution Approach 2:
The patent merges the capabilities of multiple specialized chatbots into a collaborative network. When a chatbot encounters a query outside its domain expertise, it forwards the query to other chatbots in the network, effectively combining their knowledge to provide comprehensive and accurate responses across all domains.
2Adaptability or versatility
If a chatbot tries to cover all domains with a single model, then versatility improves, but the ability to distinguish between classes and focus on domain-specific knowledge deteriorates
Solution Approach 1:
The patent segments the general chatbot into multiple domain-specific chatbots, each trained on and specialized for a particular domain. This allows each chatbot to achieve high precision in its domain while the network as a whole maintains broad versatility through collaboration.
Solution Approach 2:
The patent creates a universal system through the network of specialized chatbots. Each chatbot is specialized for its domain, but the network collectively provides universal coverage across all domains by routing queries to appropriate specialists, achieving both specificity and versatility.
3Device complexity
If a chatbot processes all queries independently without collaboration, then device complexity remains low, but scalability and reusability are limited
Solution Approach 1:
The patent merges independent chatbots into a collaborative network where each chatbot can leverage the knowledge and capabilities of others. This collaboration enables the system to scale effectively, as new chatbots can be added to the network to handle additional domains or queries without requiring complete redesign of the entire system.
Solution Approach 2:
The patent employs the concept of multiple instances (copies) of chatbots, each specialized for different domains. These copies can be independently trained and deployed, then combined into a network that handles a broader range of queries, improving scalability without requiring a single complex monolithic system.
Data Source
AI summary
An approach is provided for domain-specific chatbots that employ distributed natural language classifiers (NLCs). A NLC included in a first chatbot is executed, which generates a first intent of a question and a first confidence that the first intent is an actual intent of the question. Based on a determination that the first confidence does not exceed a threshold, the question is broadcasted to other chatbots. A second confidence that a second intent is an actual intent is received from a second chatbot in response to the broadcast, and is determined to exceed the threshold. An association among the question, the second chatbot, the second confidence, and the second intent is stored in a memory cache included in the first chatbot. A response to the question is generated based on the second intent. The response is presented to a user from the first chatbot.