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The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Patent No.:

US 11579850 B1

PDFFull Text
Date of Patent:
Feb. 14, 2023

Filed:

Apr. 21, 2020

Method for generating web code for ui based on a generative adversarial network and a convolutional neural network

Applicant:

Yangzhou University, Jiangsu, CN;

Inventors:

Xiaobing Sun, Jiangsu, CN;

Yong Xu, Jiangsu, CN;

Bin Li, Jiangsu, CN;

Assignee:

YANGZHOU UNIVERSITY;

Attorney:

Dentons Cohen & Grigsby P.C.

Primary Examiner:

Shahid K Khan

Assistant Examiner:

Ahamed I Nazar

Int. Cl.
CPC ...
G06F 8/38 (2018.01); G06F 40/143 (2020.01); G06T 7/73 (2017.01); G06N 3/08 (2006.01); G06V 10/25 (2022.01); G06V 10/82 (2022.01); G06V 10/77 (2022.01); G06V 10/74 (2022.01); G06N 3/04 (2023.01);
U.S. Cl.
CPC ...
G06F 8/38 (2013.01); G06F 40/143 (2020.01); G06N 3/0454 (2013.01); G06N 3/0481 (2013.01); G06T 7/73 (2017.01); G06V 10/25 (2022.01); G06V 10/761 (2022.01); G06V 10/7715 (2022.01); G06V 10/82 (2022.01); G06T 2207/20084 (2013.01); G06T 2207/30176 (2013.01);
Abstract

Provided is a method for generating web codes for a user interface (UI) based on a generative adversarial network (GAN) and a convolutional neural network (CNN). The method includes steps described below. A mapping relationship between display effects of a HyperText Markup Language (HTML) element and source codes of the HTML element is constructed. A location of an HTML element in an image I is recognized. Complete HTML codes of the image I are generated. The similarity between manually-written HTML codes and the generated complete HTML codes and the similarity between the image I and an image Igenerated by the generated complete HTML codes are obtained. After training, an image-to-HTML-code generation model M is obtained. A to-be-processed UI image is input into the model M so as to obtain corresponding HTML codes. According to the method of the present disclosure, an image-to-HTML-code generation model M can be obtained.

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