View source for 20240053964. AUTOMATIC GENERATION OF USER EXPERIENCE MOCKUPS USING ADAPTIVE GAZE TRACKING simplified abstract (Kyndryl, Inc.)

=AUTOMATIC GENERATION OF USER EXPERIENCE MOCKUPS USING ADAPTIVE GAZE TRACKING=


==Organization Name==

[[:Category:Kyndryl, Inc.|Kyndryl, Inc.]]


[[Category:Kyndryl, Inc.]]

==Inventor(s)==

[[:Category:Mouleswara Reddy Chintakunta of Allagadda (IN)|Mouleswara Reddy Chintakunta of Allagadda (IN)]][[Category:Mouleswara Reddy Chintakunta of Allagadda (IN)]]

[[:Category:Omar Odibat of Cedar Park TX (US)|Omar Odibat of Cedar Park TX (US)]][[Category:Omar Odibat of Cedar Park TX (US)]]

[[:Category:Pritpal S. Arora of Bangalore (IN)|Pritpal S. Arora of Bangalore (IN)]][[Category:Pritpal S. Arora of Bangalore (IN)]]

==AUTOMATIC GENERATION OF USER EXPERIENCE MOCKUPS USING ADAPTIVE GAZE TRACKING - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240053964 titled 'AUTOMATIC GENERATION OF USER EXPERIENCE MOCKUPS USING ADAPTIVE GAZE TRACKING

==Simplified Explanation==

The abstract of the patent application describes a method that uses a generative adversarial network (GAN) to generate multiple user experience designs based on a new feature image set. These designs are displayed on an electronic display device with an eye gaze tracking system. The positions of user interface elements are identified based on the user's eye gaze, and the positions and types of these elements are compared between a desired user interface design and a generated user interface design. Feedback from the comparison is used to enhance the results of the GAN.

* A generative adversarial network (GAN) is used to generate multiple user experience designs.
* The designs are based on a new feature image set and a theme description.
* An electronic display device with an eye gaze tracking system is used to display the designs.
* User interface elements and their positions are identified based on the user's eye gaze.
* The positions and types of user interface elements are compared between a desired design and a generated design.
* Feedback from the comparison is used to improve the results of the GAN.

Potential applications of this technology:
* User experience design in various industries such as software development, website design, and mobile app development.
* Designing personalized user interfaces based on individual preferences and eye gaze patterns.

Problems solved by this technology:
* Streamlining the user experience design process by automating the generation of multiple design options.
* Improving the accuracy of identifying user interface elements based on eye gaze tracking.
* Enhancing the efficiency of user interface design by comparing desired and generated designs.

Benefits of this technology:
* Increased creativity and diversity in user experience design.
* Improved user satisfaction by designing interfaces based on individual preferences.
* Time and cost savings in the user experience design process.


==Original Abstract Submitted==

a user experience theme description is obtained, along with a new user experience feature image set. the theme description and new user experience feature image set are input into a generative adversarial network (gan). the gan is trained to output multiple user experience designs based on the new feature image set. the multiple designs are displayed on an electronic display device that includes an eye gaze tracking system. user interface elements and their corresponding positions within a user interface are identified based on eye gaze of a user towards the electronic display device. the position and type of user interface elements are compared between a desired user interface design and a generated user interface design. errors between the desired user interface design and the generated user interface design are input as feedback into the gan to further enhance the results.



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