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:
Oct. 19, 2021

Filed:

Nov. 19, 2018
Applicant:

International Business Machines Corporation, Armonk, NY (US);

Inventors:

Lijun Mei, Beijing, CN;

Qi Cheng Li, Beijing, CN;

Xin Zhou, Beijing, CN;

Ya Bin Dang, Beijing, CN;

Hao Chen, Beijing, CN;

Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
H04L 12/58 (2006.01); G06N 3/08 (2006.01); G06N 3/04 (2006.01); G06N 20/00 (2019.01); G06N 5/02 (2006.01);
U.S. Cl.
CPC ...
H04L 51/12 (2013.01); G06N 3/0454 (2013.01); G06N 3/084 (2013.01); G06N 5/02 (2013.01); G06N 20/00 (2019.01); G06N 3/088 (2013.01);
Abstract

A computer implemented method of pre-emptively blocking an electronic communication is provided. The computer implemented method includes inputting an electronic communication history, wherein the electronic communication history includes a plurality of electronic communications and a set of corresponding recipients for each of the plurality of electronic communications. The computer implemented method further includes normalizing the plurality of electronic communications, and extracting a topic from each of the plurality of electronic communications. The computer implemented method further includes clustering the plurality of electronic communications according to the extracted topics, and digesting the plurality of electronic communication to form a positive learning data set and a negative learning data set to train a neural network. The computer implemented method further includes training the neural network on the positive learning data set and the negative learning data set, and preparing a positive neutral network model and a negative neural network model.


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