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:
Jun. 07, 2022

Filed:

Dec. 03, 2018
Applicant:

Bank of America Corporation, Charlotte, NC (US);

Inventors:

Pankaj Panging, Frisco, TX (US);

Patrick N. Lawrence, Plano, TX (US);

Assignee:

Bank of America Corporation, Charlotte, NC (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06K 9/62 (2022.01); G06N 20/00 (2019.01); G06F 9/30 (2018.01); G06F 16/35 (2019.01); G06F 16/28 (2019.01); G06N 10/00 (2022.01);
U.S. Cl.
CPC ...
G06K 9/6223 (2013.01); G06F 9/30029 (2013.01); G06F 16/285 (2019.01); G06F 16/355 (2019.01); G06K 9/6215 (2013.01); G06K 9/6232 (2013.01); G06N 10/00 (2019.01); G06N 20/00 (2019.01);
Abstract

A device that includes a model training engine implemented by a processor. The model training engine is configured to obtain a set of data values associated with a feature vector. The model training engine is further configured to transform a first data value and a second data value from the set of data value into sub-string correlithm objects. The model training engine is further configured to compute a Hamming distance between the first sub-string correlithm object and the second sub-string correlithm object and to identify a boundary in response to determining that the Hamming distance exceeds a bit difference threshold value. The model training engine is further configured to determine a number of identified boundaries, to determine a number of clusters based on the number of identified boundaries, and to train the machine learning model to associate the determined number of clusters with the feature vector.


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