Information Visualization UX Benchmarking with NLP-Derived Scores
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Solution Overview
Problem
Existing methods fail to provide a holistic, diagnostic, and objective manner to measure and benchmark the user experience (UX) of information visualizations, considering subjective and qualitative factors such as complexity, usability, and emotional responses, which are crucial for evaluating their effectiveness.
Innovation Solution
A system and method that utilizes natural language processing (NLP) to quantify qualitative parameters, determine complexity, and calculate pragmatic and hedonic scores for information visualizations, plotting them on a scatter chart to identify scenarios and profile user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If qualitative parameters are used to evaluate user experience, then the evaluation can capture subjective aspects like visual appeal and emotional response, but the evaluation becomes non-comparable and non-quantifiable
Solution Approach 1:
The patent replaces manual qualitative evaluation with automated NLP-based analysis. Text feedback from users is processed through natural language processing algorithms that automatically extract sentiments and convert them into quantitative scores, eliminating the need for manual scoring while maintaining the ability to capture subjective user experiences
Solution Approach 2:
The patent transforms qualitative parameters into quantitative parameters by using NLP to convert text feedback into numerical sentiment scores. This parameter transformation enables comparison and benchmarking while preserving the essence of subjective user experience evaluation
2Adaptability or versatility
If multiple factors like complexity, usability, and emotional responses are considered, then a holistic view of user experience is achieved, but the measurement becomes subjective and non-comparable
Solution Approach 1:
The patent divides the holistic user experience evaluation into distinct segments: pragmatic score for usability and functional performance, and hedonic score for emotional and aesthetic aspects. This segmentation allows each dimension to be measured and analyzed separately while maintaining overall holistic evaluation capability
Solution Approach 2:
The patent uses NLP-based automated analysis to objectively measure all multiple factors including complexity, usability, and emotional responses. The system processes user feedback text through computational algorithms that consistently quantify each factor, eliminating subjectivity while maintaining comprehensive measurement
3Quantity of substance
If traditional user testing data is collected, then some quantitative data is obtained, but business, task, and emotional factors cannot be considered together
Solution Approach 1:
The patent creates a universal evaluation framework that simultaneously considers business factors, task performance, and emotional responses through a single integrated system. The NLP-based analysis processes all types of feedback (functional, emotional, contextual) through the same processing pipeline, enabling comprehensive multi-factor evaluation that traditional separate methods cannot achieve
Data Source
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AI summary
The quality of user experience (UX) of an information visualization depends on multiple diverse aspects. These include qualitative, quantitative, and contextual parameters that are unmeasurable and incomparable. Hence, measuring the UX of a visualization is challenging. The disclosure herein relates to a system and method that collects, processes, and analyzes a multiple diverse parameters to measure and profile the UX of a visualization. To accomplish this, the system collects data regarding the usage, effectiveness, and user perception of the visualization. The system creates a quantitative and comparable version of all these parameters to measure holistically the UX of the visualization. This involves quantifying qualitative values, considering context by objectively computing the complexity of charts, complexity based benchmarking, summarizing individual parameters into hedonic and pragmatic scores, plotting the visualization on a scatter chart, dividing the scatter chart based on low-high hedonic and pragmatic scores, labeling each section and labeling the visualization.