International Business Machines Corporation

Anil U Joshi

Kuntal Dey

Puthukode G Ramachandran

Willie L Scott, Ii

A content clarification server receives at least one language element entered by a user into a client computer, where the user works in a first area of specialization. The content clarification server extracts a set of concepts found in the at least one language element, and launches an auction bidding process for replacing original language in the at least one language element to content clarification providers who provide replacement language that clarifies a meaning of the at least one language element. The content clarification server filters out replacement language from content clarification providers that work in a second area of specialization that is different from the first area of specialization in which the user works, and identifies winning replacement language, from the filtered out replacement language, for the original language from one of the content clarification providers. The content clarification server replaces the original language with the winning replacement language.

Real-time on-demand auction based content clarification

A method modifies a user interface based on a user having nystagmus. One or more processors collect eye gaze data points of a viewer of a user interface that displays content. The processor(s) determine, based on the eye gaze data points, that the viewer of the user interface has nystagmus. Responsive to determining that the viewer of the user interface has nystagmus based on the eye gaze data points, the processor(s) modifies the user interface.

Modifying a computer-based interaction based on eye gaze

A method modifies a computer-based interaction based on gaze data. One or more processors collect eye gaze data points to create an eye gaze corpus of information, where the eye gaze data points describe an eye gaze of viewers of a first set of at least one user interface. The processor(s) generate a plurality of clusters of viewers, and determine a target action performance for each of the plurality of clusters. The processor(s) collect, from a device having eye tracking technology, real time eye gaze data from a plurality of current users who are viewing a second set of at least one user interface, and segment the plurality of current users. The processor(s) then modify a computer-based interaction for at least one segment in order to maximize target action performance of the second set of at least one user interface.

A method identifies and filters winning clarifications of content. A content clarification server receives a language element from a client computer, and extracts and marks for clarification a concept among the language element. The content clarification server launches an auction bidding process for replacing original language associated with the concept, and receives sponsored alternatives to the original language from multiple content clarification provider servers. The content clarification server generates an organic alternative, and then assigns a score to each of the sponsored alternatives and the organic alternative. Based upon the respective assigned scores, the content clarification server identifies a winning alternative, which is delivered to the client computer as a replacement for the original language associated with the at least one concept.

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New Delhi, India

Anil U Joshi