Chatbot Training Dialog Visualization via Tree Structure
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Solution Overview
Problem
Creating and updating chatbots is challenging due to the complexity of visualizing logic flows in declarative rules and the 'black box' nature of machine learning models, making it difficult to determine if all possible scenarios are covered and whether conflicts exist in the chatbot logic.
Innovation Solution
A method and system for visualizing and editing chatbot training dialogs and rules using a graphical user interface (GUI) that displays training dialogs in a tree form, allowing developers to modify and update them, and distinguishing between decisions based on declarative rules and machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If declarative rules are used to create a chatbot, then the chatbot can provide structured responses, but it becomes difficult to visualize the logic flow as the chatbot becomes more sophisticated
Solution Approach 1:
The patent segments the complex logic flow into individual decision nodes and training dialogs, each representing a specific decision point. This segmentation allows developers to visualize and manage complex chatbot logic by breaking it down into discrete, manageable units that can be individually examined and modified.
Solution Approach 2:
The patent implements a nested structure where training dialogs are organized within a tree form that contains multiple levels of decision nodes. Each decision node can contain nested training dialogs, creating a hierarchical visualization that accommodates sophisticated chatbot logic while maintaining visual clarity through nested organization.
2Adaptability or versatility
If machine learning is used to train the chatbot, then the chatbot can handle unexpected inputs, but the model becomes a black box that is difficult to understand
Solution Approach 1:
The patent introduces an intermediary visualization layer (the tree form with decision nodes) that bridges the gap between machine learning models and human understanding. This intermediary representation makes the black box transparent by showing the actual training dialogs and decision points, allowing developers to inspect and understand the machine learning model's logic without losing the ability to handle unexpected inputs.
3Adaptability or versatility
If the chatbot is updated frequently, then the chatbot can adapt to new scenarios, but the updating process becomes time-consuming
Solution Approach 1:
The patent enables preliminary action by allowing developers to pre-organize training dialogs and decision nodes in a tree structure before deployment. This preliminary organization facilitates rapid updates, as developers can easily add, remove, or modify specific training dialogs and decision nodes without restructuring the entire chatbot logic, significantly reducing update time while maintaining comprehensive scenario coverage.
Data Source
AI summary
This document relates to creating and/or updating a chatbot using a graphical user interface. For example, training dialogs for a chatbot can be displayed in a tree form on a graphical user interface. Based at least on interactions between a developer and the graphical user interface, the training dialogs can be modified in the tree form, and training dialogs can be updated based on the modifications provided on the tree form via the graphical user interface.