User Interaction Visualization Framework for AI Chat Agents
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Solution Overview
Problem
Existing approaches fail to provide visualization of user interactions with AI chat agents in the context of user understanding, experience, and progression through tasks and subtasks, lacking intelligence to generate visual representations that improve user experiences and learning objectives.
Innovation Solution
A framework for visualizing user transcripts that automatically learns features from interactions, such as emotions, responses, and engagement levels, to present graphical representations of user interactions, tasks, and subtasks, enabling teachers to monitor progress and identify successful paths for improved learning experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If user interactions are transmitted for backend processing with language processing and AI response formulation, then the chat agent can provide intelligent responses, but the system lacks visualization capability to show user progression through tasks
Solution Approach 1:
The patent introduces an intermediary visualization system that sits between the backend processing and the user interface. This intermediary layer captures user interactions, processes them through machine learning models to extract features like emotions and engagement levels, and presents them as visual representations showing user progression through tasks. This mediator resolves the contradiction by adding visualization capability without requiring complete system redesign.
Solution Approach 2:
The visualization system segments user interactions into distinct analyzable units, processing different aspects (emotions, engagement, task progression) separately through specialized machine learning models. Each segment is then reassembled into a comprehensive visual representation, allowing the system to handle complex visualization requirements through modular processing steps.
2Measurement precision
If the system provides detailed visualization of user interactions and features, then teachers can monitor progress and identify successful paths, but the complexity of the visualization system increases
Solution Approach 1:
The system applies partial action by selectively visualizing only the most relevant features of user interactions based on predefined criteria and machine learning predictions. Rather than attempting to visualize every aspect of user behavior, the system focuses on key dimensions like task progression, emotional states, and engagement levels, achieving effective monitoring without overwhelming complexity.
Solution Approach 2:
The visualization system dynamically adjusts which parameters are displayed and how they are represented based on the context, user role, and interaction stage. Machine learning models predict which features are most relevant and adjust the visualization accordingly, allowing high measurement precision while managing complexity through adaptive parameter selection.
3Extent of automation
If machine learning models are applied to classify interactions and identify features, then intelligent visualization is achieved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models offline to recognize patterns in user interactions. During actual user sessions, these pre-trained models quickly classify interactions and extract features without requiring extensive real-time computation. This preliminary preparation resolves the contradiction by shifting computational burden from real-time processing to offline model training.
Solution Approach 2:
The visualization system skips detailed analysis of certain interaction aspects when they are not relevant to the current context or when patterns are already well-established. Machine learning models quickly identify and skip over routine or predictable interaction patterns, focusing computational resources only on novel or significant events, thereby reducing overall processing time while maintaining automation.
Data Source
AI summary
Classification and visualization of user interactions with an interactive computing platform is provided by building and providing a graphical user interface (GUI) of graphical elements for display on a display device. The graphical elements present visualizations of user interactions between users and an interactive computing platform in progression of the users through tasks based on the user interactions. The graphical elements also present identified features of the user interactions relative to the tasks and progression of the users therethrough. The building and providing includes building and providing, for a task of the tasks, a task element with first features corresponding to subtasks of the task and second features providing relations between the first features.