Automated Chatbot Generation via Machine Learning Clustering
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Solution Overview
Problem
Current methods for generating chatbots are time-consuming, prone to errors, and biased due to the manual classification and labeling of data by specialists, which increases the effort and time required for creating and validating chatbot accuracy.
Innovation Solution
An automated system using machine learning to cluster and label data for chatbot generation, eliminating the need for manual specialist intervention and enabling unsupervised chatbot creation, validation, and adjustment processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual classification and labeling of data by specialists is used for chatbot generation, then the chatbot can be created with human expertise and oversight, but the process becomes time-consuming and increases the effort required
Solution Approach 1:
The patent segments the chatbot development process into distinct automated phases: data clustering using machine learning algorithms, automatic labeling of clusters, validation through execution scripts, and iterative improvement cycles. This segmentation allows each phase to be handled independently and automatically, reducing manual intervention while maintaining quality through structured validation at each stage.
Solution Approach 2:
The system enables self-service chatbot generation by automatically performing data clustering, labeling, and validation without requiring specialist intervention. The machine learning model autonomously processes the data, generates clusters, applies labels, and executes validation scripts to assess accuracy, allowing the chatbot to be created and validated through automated self-service processes.
2Manufacturing precision
If manual classification and labeling by specialists is used, then expertise can be applied to ensure quality, but the effort and complexity of the process increase
Solution Approach 1:
The patent replaces the mechanical process of manual classification and labeling by specialists with an automated machine learning system. The machine learning model performs data clustering and automatic labeling, substituting human mechanical processes with computational algorithms that execute consistently and scalably without manual intervention.
Solution Approach 2:
The system changes the parameters of the classification process by using machine learning algorithms with configurable parameters such as clustering methods, labeling strategies, and validation thresholds. These parameter changes enable automated processing while maintaining or improving classification accuracy through algorithmic optimization rather than manual adjustment.
3Productivity
If automated machine learning clustering and labeling is used, then development time is reduced and effort is minimized, but manual adjustment may be needed to maintain accuracy
Solution Approach 1:
The patent implements feedback mechanisms through validation scripts that automatically execute against the generated chatbot to assess accuracy. The system provides feedback on classification quality and chatbot performance, enabling automated detection of issues and triggering iterative improvement cycles where the model can be retrained or adjusted based on validation results.
Solution Approach 2:
The system performs preliminary automated clustering and labeling actions before final chatbot deployment. By pre-processing the data through machine learning clustering and automatic labeling, and conducting preliminary validation, the system prepares the chatbot foundation in advance, reducing the need for extensive manual adjustments later while maintaining accuracy through pre-executed quality checks.
Data Source
AI summary
A computer-implemented method, computer system, and computer program product for for generation of a chatbot. The method may include receiving data in a first format. The method may include generating one or more clusters from the received data. The method may include labeling the generated one or more clusters. The method may include exporting the one or more labeled clusters into a cluster database. The method may include generating the chatbot using the one or more labeled clusters exported from the cluster database. The method may include executing a validation script into the chatbot to generate a report. The method may include receiving a determination on an accuracy of the chatbot based on the generated report. In response to determining that the chatbot is not accurate, the method may include determining whether a manual adjustment directly on the chatbot is needed.