Abstract
This idea relates to a machine learning algorithm that optimizes workflow through user interface modifications based on the user’s detected emotion. Emotion detection is done through the notebook computer’s sensor hardware and user inputs to the system. The algorithm analyzes the user’s facial expressions, voice, eye movements, keyboard strokes, mouse clicks, and other indicators of emotion and adjusts the user interface accordingly. The algorithm aims to enhance the user’s productivity, satisfaction, and well-being by providing personalized and adaptive user interface elements, such as colors, fonts, layouts, menus, notifications, app management, and feedback mechanisms. The algorithm also learns the user’s responses to the user interface modifications and improves its performance over time. The solution provides an effective way of improving human-computer interaction and optimizing workflow.
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Recommended Citation
INC, HP, "Reactive Workflow Optimization Methods through Emotion Detection and Machine Learning", Technical Disclosure Commons, (January 07, 2025)
https://www.tdcommons.org/dpubs_series/7711