Intent Visualization in Virtual Agents via Dimensional Projection
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Solution Overview
Problem
Current virtual agents struggle with intent management, including identifying and understanding user intents, handling intent changes, and discerning correct versus incorrect intents, leading to user frustration and unresolved problems.
Innovation Solution
The implementation of enhanced intent visualization techniques that project user sentences into higher-dimensional spaces, reducing them to lower-dimensional vectors for graphical representation, allowing users to easily identify and manage intents, detect new or obsolete intents, and train intent classifiers accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional virtual agents use linear predefined question and answer paths, then the system structure is simple and easy to implement, but the agent cannot handle intent changes or unexpected user responses, leading to user frustration
Solution Approach 1:
The patent transforms the linear, one-dimensional conversation flow into a multi-dimensional intent space where user sentences are projected into higher-dimensional vectors and then visualized in lower-dimensional space. This dimensional transformation allows the system to capture multiple intents simultaneously and detect deviations from expected conversation paths, enabling the agent to handle intent changes while maintaining a manageable system structure through visualization tools.
2Measurement precision
If virtual agents continue along predefined paths when encountering unexpected responses, then the system operates reliably according to design, but it misunderstands user intent and fails to solve problems
Solution Approach 1:
The patent implements feedback mechanisms through visualization tools that display user intents in semantic space. Developers can observe when user sentences deviate from expected intent clusters, providing feedback about misclassified intents. This allows continuous improvement of intent classification accuracy while maintaining reliable conversation flows by identifying and correcting classification errors.
Solution Approach 2:
The system performs preliminary projection of user sentences into higher-dimensional spaces and visualization in lower-dimensional spaces before final intent classification decisions are made. This preliminary action allows the system to detect potential misclassifications and intent changes early in the processing pipeline, enabling corrective actions before the conversation flow diverges incorrectly.
3Difficulty of detecting and measuring
If developers manually review interaction sessions to understand user intents, then intent classification can be improved, but it is time-consuming and difficult to discern correct versus incorrect intents
Solution Approach 1:
The patent introduces visualization tools as an intermediary between raw interaction data and developer analysis. By projecting user sentences into semantic spaces and displaying them as visual patterns, the tools automatically perform the initial detection and measurement of intents, making it easier for developers to identify correct versus incorrect classifications without manually reviewing every interaction session.
Solution Approach 2:
The patent replaces the manual, mechanical process of reviewing text-based interaction sessions with automated visualization systems that project data into semantic spaces. This substitution transforms the tedious task of reading and analyzing text into an efficient visual inspection process, significantly reducing the time and difficulty of intent detection while improving accuracy.
Data Source
AI summary
Generally discussed herein are devices, systems, and methods for visualization of user intent in accessing a virtual agent. A method can include receiving sentences from the respective interaction sessions, projecting the sentences to a higher-dimensional space to create respective higher-dimensional vectors, projecting the higher-dimensional vectors to a lower-dimensional space to create respective lower-dimensional vectors, the lower-dimensional space including either two dimensions or three dimensions, plotting the lower-dimensional vectors as respective points on a graph, encoding the respective points consistent with the respective intents determined to be associated with the sentences by an intent classifier to create encoded points, and generating a visual representation of the encoded points.