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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 12443420 B1

PDFFull Text
Date of Patent:
Oct. 14, 2025

Filed:

Jan. 26, 2022

Automatic image conversion

Applicant:

Salesforce.com, Inc., San Francisco, CA (US);

Inventors:

Michael Sollami, Cambridge, MA (US);

Sönke Rohde, San Francisco, CA (US);

Alan Martin Ross, San Francisco, CA (US);

David James Woodward, Bozeman, MT (US);

Jessica Lundin, Seattle, WA (US);

Owen Winne Schoppe, Orinda, CA (US);

Brian J. Lonsdorf, Soquel, CA (US);

Aashish Jain, Cambridge, MA (US);

Assignee:

Salesforce, Inc., San Francisco, CA (US);

Attorneys:

Kowert, Hood, Munyon, Rankin & Goetzel, P.C.

Catherine L. Gerhardt

Dean M. Munyon

Primary Examiner:

Kieu D Vu

Assistant Examiner:

Blaine T Basom

Int. Cl.
CPC ...
G06N 3/08 (2023.01); G06F 3/04845 (2022.01); G06F 8/38 (2018.01); G06F 9/451 (2018.01); G06F 9/54 (2006.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06V 10/762 (2022.01); G06V 10/771 (2022.01); G06V 10/82 (2022.01); G06N 3/047 (2023.01); G06N 3/088 (2023.01); G06N 7/01 (2023.01); G06V 30/19 (2022.01);
U.S. Cl.
CPC ...
G06F 9/451 (2018.02); G06F 3/04845 (2013.01); G06F 9/547 (2013.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06V 10/763 (2022.01); G06V 10/771 (2022.01); G06V 10/82 (2022.01); G06F 8/38 (2013.01); G06N 3/044 (2023.01); G06N 3/047 (2023.01); G06N 3/088 (2013.01); G06N 7/01 (2023.01); G06V 30/19173 (2022.01);
Abstract

Techniques are disclosed for automatically converting a layout image to a text-based representation. In the disclosed techniques, a server computer system receives a layout image that includes a plurality of portions representing a plurality of user interface (UI) elements included in a UI design. The server computer system transforms, via executed of a trained residual neural network (ResNet), the layout image to a text-based representation of the layout image that specifies coordinates of bounding regions of the plurality of UI elements included in the UI design, where the text-based representation is usable to generate program code executable to render the UI design. The disclosed techniques may advantageously automate one or more portions of a UI design process and, as a result save time and computing resources via the execution of an image to text-based conversion ResNet machine learning model.

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