Visual content optimization system using artificial intelligence (AI) based design generation and validation

1. A system for providing visual content optimization, comprising:

a processor; and

a memory storing instructions, which when executed by the processor, cause the processor to:

identify a plurality of discrete design elements from a design or graphical layout of received data;

create a new design or graphical layout based on the plurality of discrete design elements and at least one priority parameter associated with each of the plurality of discrete design elements;

evaluate the new design or graphical layout based on an evaluation technique comprising: a scan path evaluation that sorts the plurality of discrete design elements based on a likelihood of being seen first by a viewer; and a visual attention prediction using at least one of artificial intelligence (AI), machine learning (ML), or a neural network (NN); and

select the new design or graphical layout based on a selection technique for presentation at a user device or publishable medium.

2. The system of claim 1 , wherein the received data associated with the design or graphical layout comprises at least one of: a uniform resource locator (URL) directed to a webpage comprising the design or graphical layout, an image comprising the design or graphical layout, and a list comprising the plurality of discrete design elements.

3. The system of claim 1 , wherein the received data is from at least one of a web or online source, a mobile device or application, an enterprise resource planning (ERP) system, and a document.

4. The system of claim 1 , wherein the plurality of discrete design elements comprise at least one of a textual element, a graphical element, a visual or audio element, and a hyperlink.

5. The system of claim 1 , wherein the at least one priority parameter associated with each of the plurality of discrete design elements comprises at least one of the following: grouping rules, scan path rules, overlapping rules, hierarchy, size, position, color, brightness, transformation, value, and adjustment increment.

6. The system of claim 1 , wherein identifying the plurality of discrete design elements from the design or graphical layout is based on at least one of: source code, page structure, and stylesheets.

7. The system of claim 1 , wherein the instructions further cause the processor to:

provide feedback on the new design or graphical layout based on the evaluation of the new design or graphical layout; and

adjust the new design or graphical layout based on the feedback.

8. The system of claim 1 , wherein the scan path evaluation sorts the plurality of discrete design elements based on a score that represents a likelihood of one of the plurality of discrete design elements being seen first by a viewer.

9. The system of claim 8 , wherein the score that represents the likelihood of one of the plurality of discrete design elements being, seen first by a viewer is determined based on a surface and a distance from a center to a top left corner or top right corner of the new design or graphical layout.

10. The system of claim 1 , wherein the neural network is a convolutional neural network (CNN) for visual saliency prediction trained with generative adversarial networks.

11. A method for providing visual content optimization, comprising:

identifying, at a processor in communication with one or more data stores and one or more servers of a network, a plurality of discrete design elements from a first design or graphical layout of received data;

creating, by the processor, a new design or graphical layout based on the plurality of discrete design elements and at least one priority parameter associated with each of the plurality of discrete design elements;

evaluating, by the processor, the new design or graphical layout based on an evaluation technique, wherein the evaluation technique comprises a scan path evaluation that sorts the plurality of discrete design elements based on scores that represent likelihoods of the plurality of discrete design elements being seen first by a viewer; and a visual attention prediction using at least one of artificial intelligence (AI), machine learning (ML), or a neural network (NN); and

selecting, by the processor, the new design or graphical layout based on a selection technique for presentation at a user device or publishable medium.

12. The method of claim 11 , wherein the received data associated with the first design or graphical layout comprises at least one of: a uniform resource locator (URL) directed to a webpage comprising the first design or graphical layout, an image comprising the first design or graphical layout, and a list comprising the plurality of discrete design elements.

13. The method of claim 11 , wherein the received data is from at least one of a web or online source, a mobile device or application, an enterprise resource planning (ERP) system, and a document.

14. The method of claim 11 , wherein the plurality of discrete design elements comprise at least one of a textual element, a graphical element, a visual or audio element, and a hyperlink.

15. The method of claim 11 , wherein the at least one priority parameter associated with each of the plurality of discrete design elements comprises at least one of the following: grouping rules, scan path rules, overlapping rules, hierarchy, size, position, color, brightness, transformation, value, and adjustment increment.

16. The method of claim 11 , wherein identifying the plurality of discrete design elements from the first design or graphical layout is based on at least one of: source code, page structure, and stylesheets.

17. The method of claim 11 , further comprising:

providing feedback on the new design or graphical layout based on the evaluation of the new design or graphical layout; and

adjusting the new design or graphical layout based on the feedback.

18. The method of claim 11 , wherein each of the scores is determined based on a surface and a distance from a center to a top left corner or top right corner of the new design or graphical layout.

19. The method of claim 11 , wherein the visual attention prediction is determined using a convolutional neural network (CNN) for visual saliency prediction trained with generative adversarial network.

20. A non-transitory computer-readable storage medium storing instructions, which when executed by a processor cause the processor to:

identify a plurality of discrete design elements from a first design or graphical layout of received data;

create a new design or graphical layout based on the plurality of discrete design elements and at least one priority parameter associated with each of the plurality of discrete design elements;

evaluate the new design or graphical layout based on an evaluation technique, wherein the evaluation technique comprises a scan path evaluation that sorts the plurality of discrete design elements based on a likelihood of being seen first by a viewer; and an artificial-intelligence-based model predicting a most visually appealing design;

select the new design or graphical layout based on a selection technique; and

transmit the new design or graphical layout for presentation at a user device or publishable medium.