Virginia Tech Intellectual Properties Inc

Ran Jin

Xiaoyu Chen

Nathan Lau

Adapting user interface designs for specific tasks performed by different users is a challenging yet important problem. Automatically adapting visualization designs to users and contexts (e.g., tasks, display devices, environments, etc.) can theoretically improve human-computer interaction to acquire insights from complex datasets. However, effectiveness of any specific visualization is moderated by individual differences in knowledge, skills, and abilities for different contexts. A modeling framework called Personalized Recommender System for Information visualization Methods via Extended matrix completion (PRIME) is described for recommending the optimal visualization designs for individual users in different contexts.

Personalized recommender system for information visualization

Disclosed are various embodiments for a visual language processing modeling framework via an attention-on-attention mechanism, which may be employed for object identification, classification, and the like. In association with a display of a user interface, an eye tracking via images captured by an imaging device is performed to programmatically detect eye movement and fixation relative to sub-regions of the user interface. Eye fixations on at least one of the sub-regions from the eye tracking. Visual cues are extracted from the user interface based at least in part on the eye fixations, the visual cues being in a sequence of identification. A visual language sentence is generated based at least in part on the visual cues as extracted. The visual language sentence of the visual cues in the sequence of identification is correlated to at least one decision using a visual language understanding routine.

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Blacksburg, VA, United States of America

Ran Jin