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.

Date of Patent:
Sep. 09, 2025

Filed:

Nov. 07, 2022
Applicant:

Rezolve Ltd., London, GB;

Inventors:

Salman Ahmad, Leicester, GB;

Daniel Maurice Wagner, London, GB;

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06T 9/00 (2006.01); G06F 9/30 (2018.01); G06F 11/00 (2006.01); G06F 11/08 (2006.01); G06F 17/16 (2006.01); G06K 7/14 (2006.01); G06N 3/0464 (2023.01); G06N 20/00 (2019.01); G06T 1/00 (2006.01); G06T 5/60 (2024.01); G06T 7/11 (2017.01); G06T 7/90 (2017.01); G06V 10/70 (2022.01); G06V 10/74 (2022.01); G06V 10/82 (2022.01); H03M 13/00 (2006.01); H03M 13/11 (2006.01); H03M 13/37 (2006.01);
U.S. Cl.
CPC ...
G06T 9/00 (2013.01); G06F 9/30036 (2013.01); G06F 11/00 (2013.01); G06F 11/08 (2013.01); G06F 17/16 (2013.01); G06K 7/1473 (2013.01); G06N 3/0464 (2023.01); G06N 20/00 (2019.01); G06T 1/0021 (2013.01); G06T 5/60 (2024.01); G06T 7/11 (2017.01); G06T 7/90 (2017.01); G06V 10/70 (2022.01); G06V 10/761 (2022.01); G06V 10/82 (2022.01); H03M 13/00 (2013.01); H03M 13/1105 (2013.01); H03M 13/37 (2013.01); H03M 13/61 (2013.01); H03M 13/616 (2013.01); G06T 2207/10024 (2013.01); H04L 2209/34 (2013.01);
Abstract

Provided herein are systems and methods of encoding messages into images. At least one server can identify a first image having a first plurality of pixels in a color space having a set of channels. The at least one server can generate, using a message to encode in the first image, a data matrix identifying a plurality of values. The at least one server can apply a machine learning (ML) model comprising a plurality of convolutional layers to the first image and to the data matrix to generate a second image having a second plurality of pixels in the color space. The second image can correspond to the first image encoded with the data matrix across the set of channels in the color space.


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