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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 11971983 B1

PDFFull Text
Date of Patent:
Apr. 30, 2024

Filed:

Jan. 06, 2022

Systems and methods for rapid natural language based message categorization

Applicant:

Fortinet, Inc., Sunnyvale, CA (US);

Inventor:

Sameer Khanna, Cupertino, CA (US);

Assignee:

Fortinet, Inc., Sunnyvale, CA (US);

Attorney:

Jaffery Watson Mendonsa & Hamilton LLP

Primary Examiner:

Ibrahim Siddo

Int. Cl.
CPC ...
G06F 40/242 (2020.01); G06F 18/24 (2023.01); G06F 21/31 (2013.01); G06F 21/55 (2013.01); G06F 21/62 (2013.01); G06F 40/279 (2020.01); G06F 40/284 (2020.01); G06V 10/56 (2022.01); G06V 10/764 (2022.01); G06V 10/776 (2022.01); G06V 40/20 (2022.01); H04L 9/40 (2022.01); H04L 43/045 (2022.01); G06F 40/205 (2020.01);
U.S. Cl.
CPC ...
G06F 21/552 (2013.01); G06F 18/24 (2023.01); G06F 21/316 (2013.01); G06F 21/6218 (2013.01); G06F 40/242 (2020.01); G06F 40/279 (2020.01); G06F 40/284 (2020.01); G06V 10/56 (2022.01); G06V 10/764 (2022.01); G06V 10/776 (2022.01); G06V 40/20 (2022.01); H04L 43/045 (2013.01); H04L 63/1416 (2013.01); H04L 63/1425 (2013.01); G06F 40/205 (2020.01);
Abstract

Systems, devices, and methods are disclosed in relation to a system for natural language based message categorization designed to identify text from a particular topic from a potentially inexhaustible set of potential topics. In one of many possible implementations, a vector space model is first used to translate text into a vector representation. This vector is used to determine if the text can be recreated by swapping words and phrases from a training corpus of documents. This is done by determining if the vector is within the conical span of the vector representations of the text in the training corpus of documents. Span composition is evaluated by a two vector boolean comparison, enabling great computational complexity and short-circuiting enabling fast real-time topic determination.

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