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:
Dec. 10, 2024

Filed:

Jan. 06, 2022
Applicant:

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

Inventor:

Sameer Khanna, Cupertino, CA (US);

Assignee:

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

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 40/10 (2020.01); G06F 18/24 (2023.01); G06F 21/31 (2013.01); G06F 21/55 (2013.01); G06F 21/62 (2013.01); G06F 40/20 (2020.01); 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 9/40 (2022.01); H04L 43/045 (2022.01); G06F 40/157 (2020.01); 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 40/157 (2020.01); G06F 40/205 (2020.01);
Abstract

Systems, devices, and methods are disclosed in relation to a vector space model that may be used to characterize a category of messages. In one of many possible implementations, the frequency of words found within a piece of text is determined. These frequencies are compared against the frequencies of words within a given corpus like the Oxford English Corpus by first converting the frequencies to probabilities via the inverse cumulative distribution function assuming a normal distribution of frequencies then via taking the absolute difference in frequencies. A small difference reduces the weight of the given word whereas a large weight increases the weight of the word, leading to excellent word ranking for automated feature selection filtering without the need for a negative corpus.


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