Abstract: A system and method for analyzing collected content from conversations between agents and end users to identify patterns and generate automated responses is disclosed. The system includes a database for storing collected content, a processing module for classifying and analyzing content, a communication interface for retrieving data via an API, and an output module for providing generated responses to users. The processing module employs various techniques, including Natural Language Processing (NLP) methods such as language detection, sentence segmentation, and part-of-speech tagging, as well as Machine Learning (ML) techniques like Naive Bayesian Classifier and clustering algorithms based on cosine similarity. The system can classify conversation content to determine if a message is a question, analyze content based on assumptions such as user-rated helpfulness, and transform the content into vector form using sentence-transformer models.

Abstract: The present disclosure is directed to a method and system for knowledge-based interaction, facilitating efficient and accurate responses to user queries. The system integrates advanced natural language processing, machine learning, and real-time expert input to generate contextually relevant answers. It dynamically adjusts response confidence based on query complexity, ensuring reliable communication. Agents can contribute their expertise via a mobile interface, enriching the system's knowledge base and enhancing its learning capabilities. The system is designed for scalability and integrates seamlessly with other platforms through robust API structures.