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:
Jul. 16, 2024

Filed:

Jan. 04, 2023
Applicant:

Sap SE, Walldorf, DE;

Inventors:

Suchitra Sundararaman, Bellevue, WA (US);

Jesper Lind, Bellevue, WA (US);

Juliy Broyda, Bat Yam, IL;

Lev Sigal, Carmiel, IL;

Anton Ioffe, Kfar Saba, IL;

Yuri Arshavski, Natenya, IL;

Assignee:

SAP SE, Walldorf, DE;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 18/24 (2023.01); G06F 40/284 (2020.01); G06N 3/02 (2006.01); G06N 3/08 (2023.01); G06N 20/00 (2019.01); G06Q 20/04 (2012.01); G06Q 20/38 (2012.01); G06Q 20/40 (2012.01); G06Q 40/12 (2023.01); G06T 7/00 (2017.01); G06T 7/73 (2017.01); G06V 30/224 (2022.01); G06V 30/40 (2022.01); G06V 30/413 (2022.01); G06V 30/414 (2022.01); G06V 30/418 (2022.01); G06F 16/2455 (2019.01);
U.S. Cl.
CPC ...
G06Q 40/12 (2013.12); G06F 18/24 (2023.01); G06F 40/284 (2020.01); G06N 3/02 (2013.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); G06Q 20/045 (2013.01); G06Q 20/389 (2013.01); G06Q 20/40 (2013.01); G06Q 20/4016 (2013.01); G06T 7/0002 (2013.01); G06T 7/74 (2017.01); G06V 30/224 (2022.01); G06V 30/40 (2022.01); G06V 30/413 (2022.01); G06V 30/414 (2022.01); G06V 30/418 (2022.01); G06F 16/24564 (2019.01); G06T 2207/20061 (2013.01); G06T 2207/30176 (2013.01);
Abstract

The present disclosure involves systems, software, and computer implemented methods for transaction auditing. One example method includes training at least one machine learning model to determine features that can be used to determine whether an image is an authentic image of a document or an automatically generated document image, using a training set of authentic images and a training set of automatically generated document images. A request to classify an image as either an authentic image of a document or an automatically generated document image is received. The machine learning model(s) are used to classify the image as either an authentic image of a document or an automatically generated document image, based on features included in the image that are identified by the machine learning model(s). A classification of the image is provided. The machine learning model(s) are updated based on the image and the classification of the image.


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