Intelligent Tutor Visual Dialog System
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Solution Overview
Problem
Intelligent tutor systems have isolated text-based communication interfaces from graphical learning interfaces, limiting seamless interaction and integration between user inputs across different components of the system.
Innovation Solution
A method and system that utilize machine learning classifiers to translate text inputs into visual actions or objects, generating program code statements to modify the visualization interface, and vice versa, enabling dynamic integration and enhanced communication between text-based and graphical components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If text-based communication interface and graphical learning interface are kept isolated, then system simplicity is maintained, but interaction seamlessness and integration are limited
Solution Approach 1:
The patent merges the text-based communication interface and graphical learning interface into a unified system where both interfaces can interact seamlessly. The natural language processing module bridges these interfaces by translating user intents from text input into graphical actions and vice versa, allowing users to interact with graphical elements through natural language while maintaining system manageability through modular architecture.
2Adaptability or versatility
If machine learning classifiers are used to translate text inputs to visual actions, then interaction functionality is enhanced, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and understanding user intents before full execution. The natural language processing module analyzes and categorizes user intents in advance, preparing the translation to graphical actions or code statements beforehand, which reduces the actual execution time when users interact with the system.
3Adaptability or versatility
If text-based interface is integrated with graphical components, then user interaction capability improves, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary natural language processing module that mediates between the text-based communication interface and graphical learning components. This intermediary translates user intents from text into graphical actions and vice versa, enabling seamless integration without directly coupling the text and graphical systems, thus managing complexity through abstraction.
Data Source
AI summary
Systems, methods, and computer program products to perform an operation comprising receiving text input via a chat interface of a tutor application, identifying, by at least one classifier applied to the text input, a concept in the text input, mapping the concept in the text input to at least one of a visual action and a first visual object, generating, based on a first machine learning (ML) model, a first program code statement corresponding to the at least one of the visual action and the first visual object, and executing the first program code statement to modify a visualization interface of the tutor application based on the text input received via the chat interface.