Project

ThoughtSort

Search engines often return millions of results to a query. Organizing these results is a challenge, particularly for visual imagery. Search engines typically use textual annotations rather than the visual characteristics of the images to perform the search. ThoughtSort uses gaze detection and inferred points of interest to dynamically adjust the results of a search query. The user implicitly steers the system by showing more visual interest in some results than others. With ThoughtSort, search becomes a more dynamic experience as results self-adjust before the user's eyes. This application is part of a framework which aims to provide developers with the necessary tools to create dynamic and considerate content that can adjust to the natural responses of the user.

Search engines often return millions of results to a query. Organizing these results is a challenge, particularly for visual imagery. Search engines typically use textual annotations rather than the visual characteristics of the images to perform the search. ThoughtSort uses gaze detection and inferred points of interest to dynamically adjust the results of a search query. The user implicitly steers the system by showing more visual interest in some results than others. With ThoughtSort, search becomes a more dynamic experience as results self-adjust before the user's eyes. This application is part of a framework which aims to provide developers with the necessary tools to create dynamic and considerate content that can adjust to the natural responses of the user.