Chatbot GUI for Interactive Model Training
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Solution Overview
Problem
Creating and updating chatbots is a time-consuming and expensive process, requiring domain expertise and manual code updates, especially when users interact unexpectedly or new functionality is needed.
Innovation Solution
The development of graphical user interface (GUI) features that facilitate interactive training and updating of chatbots, including an entity extractor module and response model, using tools like recurrent neural networks, allowing developers to customize entities, add responses, and retrain models based on user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a chatbot is created using hard-coded rules and traditional programming methods, then the chatbot can provide functional services, but the process of creating and updating the chatbot becomes time-consuming and expensive
Solution Approach 1:
The patent replaces traditional mechanical programming methods with machine learning models. Instead of manually coding rules and responses, the system uses trained neural networks to automatically generate chatbot responses, significantly reducing creation and update time while maintaining functionality.
Solution Approach 2:
The chatbot system enables self-service through automated training pipelines. The model can be retrained with new data automatically, and the system self-updates its knowledge base without requiring extensive manual reprogramming, allowing rapid adaptation to new scenarios.
2Device complexity
If a chatbot is created using hard-coded rules, then the system structure remains simple, but updating the chatbot requires domain expertise and manual code updates
Solution Approach 1:
The patent transforms the static, hard-coded structure into a dynamic system where the chatbot model can be continuously retrained and updated. The system adapts to new requirements by incorporating new training data and retraining the model, enabling versatile updates without complex manual interventions.
Solution Approach 2:
The patent segments the chatbot system into modular components: the trained model, the training pipeline, and the inference engine. This modular architecture allows independent updates of the model without affecting the overall system structure, simplifying maintenance while enhancing adaptability.
3Manufacturing precision
If traditional programming methods are used to update the chatbot, then code accuracy can be maintained, but the process becomes expensive and requires domain expertise
Solution Approach 1:
The patent replaces manual code editing with automated machine learning model retraining. The system maintains response accuracy through controlled training processes and validation, while eliminating the need for domain experts to manually update code, significantly easing the update process.
Solution Approach 2:
The patent implements feedback mechanisms where the chatbot's performance is continuously evaluated, and training data is refined based on actual usage patterns. This feedback loop ensures code accuracy is maintained through data-driven improvements rather than manual verification.
4Ease of operation
If the chatbot uses predefined responses and rules, then the system is easy to manage, but it cannot handle unexpected user interactions or provide new functionality
Solution Approach 1:
The patent creates a dynamic response system where the chatbot adapts to unexpected interactions through continuous learning. The model can generate novel responses based on patterns learned from training data, handling unforeseen user inputs while maintaining operational simplicity through automated processes.
Solution Approach 2:
The patent makes the chatbot system multi-functional by enabling it to handle both predefined scenarios and unexpected interactions through a single unified model. The same training infrastructure supports diverse functionalities, from simple Q&A to complex multi-turn conversations, without requiring separate management systems.
Data Source
AI summary
Various technologies pertaining to creating and/or updating a chatbot are described herein. Graphical user interfaces (GUIs) are described that facilitate updating a computer-implemented response model of the chatbot based upon interaction between a developer and features of the GUIs, wherein the GUIs depict dialogs between a user and the chatbot.