Automated Chatbot Generation from Human Chat Logs
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Solution Overview
Problem
Current chatbot systems face challenges in accurately identifying user intent and generating human-like responses due to the complexity of manually creating training data, which is time-consuming, expensive, and prone to errors, leading to reduced reliability and inadequate conversation capabilities.
Innovation Solution
A system comprising a processor with a clusterer, data miner, story generator, and machine learning engine that processes human chat logs to extract attributes, identify intents and actions, generate stories, and train a chatbot to provide responsive templates, thereby automating the chatbot generation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual training data creation is used to train chatbot, then chatbot can be trained with human expertise, but the process becomes time-consuming, expensive, and error-prone
Solution Approach 1:
The system enables self-service by allowing the chatbot to automatically generate its own training data through autonomous interaction with the environment. The chatbot performs self-learning by processing its own conversation logs and generating training examples without human intervention, thus eliminating the time-consuming manual data creation process while maintaining high accuracy through continuous autonomous improvement
Solution Approach 2:
The system applies preliminary action by pre-processing conversation logs and automatically generating training data before the chatbot deployment. The automated pipeline extracts intents, entities, and responses from historical conversations, creating ready-to-use training datasets that eliminate the need for time-consuming manual annotation while ensuring high-quality training data is available in advance
2Reliability
If manual training data creation is used to train chatbot, then chatbot can be trained with human expertise, but the cost increases significantly
Solution Approach 1:
The system eliminates the need for expensive human annotators by enabling the chatbot to generate its own training data autonomously. The automated self-learning process replaces costly manual data creation with algorithm-driven data generation, significantly reducing development costs while maintaining or improving training data quality through systematic processing of conversation logs
Solution Approach 2:
The system creates copies of effective human conversations by analyzing and replicating successful interaction patterns from historical chat logs. Instead of paying experts to create original training data, the system copies and adapts proven conversation structures and responses from existing high-quality interactions, reducing costs while preserving effectiveness
3Reliability
If manual training data creation is used to train chatbot, then chatbot can be trained with human expertise, but errors increase in training data
Solution Approach 1:
The system implements feedback mechanisms where the chatbot continuously monitors its own performance and uses the results to improve training data quality. By analyzing interaction outcomes and user responses, the system identifies errors in training data and automatically corrects them through iterative refinement, ensuring high precision without manual error-prone annotation
Solution Approach 2:
The chatbot performs self-correction by autonomously identifying and fixing errors in its own training data. Through self-learning processes, the system detects inconsistencies and inaccuracies in generated training examples and automatically refines them, eliminating the propagation of human errors while maintaining high data quality through continuous self-improvement
4Reliability
If complex intent-based chatbot is implemented, then conversation quality improves, but system complexity increases
Solution Approach 1:
The system reduces complexity by enabling the chatbot to automatically manage its own intent recognition and response generation through self-learning. Instead of requiring complex manual configuration of intents and responses, the chatbot autonomously learns conversation patterns from logs and adapts its behavior, simplifying the system architecture while maintaining high conversation quality through autonomous adaptation
Data Source
AI summary
The present disclosure relates to automated chatbot generation for different domains from available human-to-human chat logs. The systems and methods may be configured to cluster user utterances as well as agent utterances from the human chat logs. A data miner mines intents and entities from the user utterance clustering and mines actions from agent utterances. The intents, entities and actions mined are used to generate a set of stories or flows which are further used by a machine learning engine to train the chatbot. The stories or flows are also generated automatically by mapping the intents with the actions.