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. 15, 2025

Filed:

Aug. 05, 2022
Applicant:

Naver Corporation, Gyeonggi-do, KR;

Inventors:

Matthias Galle, Eybens, FR;

Hady Elsahar, Grenoble, FR;

Joseph Rozen, Grenoble, FR;

German Kruszewski, Saint Mandé, FR;

Assignee:

Naver Corporation, Gyeonggi-do, KR;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 40/279 (2020.01); G06F 16/903 (2019.01); G06F 16/93 (2019.01); G06N 5/022 (2023.01);
U.S. Cl.
CPC ...
G06F 40/279 (2020.01); G06F 16/90344 (2019.01); G06F 16/93 (2019.01); G06N 5/022 (2013.01);
Abstract

There is disclosed a computer-implemented method for detecting machine-generated documents in a collection of documents including machine-generated and human-authored documents. The computer-implemented method includes computing a set of long-repeated substrings (such as super-maximal repeats) with respect to the collection of documents and using a subset of the long-repeated substrings to designate documents containing the subset of the repeated substrings as machine-generated. The documents designated as machine-generated serve as positive examples of machine-generated documents and a set of documents including at least one human-authored document serves as negative examples of machine-generated documents. A plurality of classifiers are trained with a dataset including both the positive and negative examples of machine-generated documents. Classified output of the classifiers is then used to detect an extent to which a given document of the dataset is machine-generated.


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