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:
Sep. 28, 2021

Filed:

Oct. 29, 2019
Applicant:

Capital One Services, Llc, McLean, VA (US);

Inventors:

Mohammad Shami, San Mateo, CA (US);

Bogdan Nedanov, Newark, DE (US);

Conor Anstett, Washington, DC (US);

Joshua Edwards, Philadelphia, PA (US);

Assignee:

CAPITAL ONE SERVICES, LLC, McLean, VA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
H04L 29/06 (2006.01); G06Q 20/40 (2012.01); G06F 16/332 (2019.01); G06N 3/08 (2006.01); G06N 3/04 (2006.01); G06N 5/04 (2006.01); G06N 5/02 (2006.01);
U.S. Cl.
CPC ...
G06Q 20/4016 (2013.01); G06F 16/332 (2019.01); G06N 3/0427 (2013.01); G06N 3/08 (2013.01); G06N 3/084 (2013.01); G06Q 20/405 (2013.01); G06Q 20/4014 (2013.01); H04L 63/1408 (2013.01); G06N 3/0481 (2013.01); G06N 5/022 (2013.01); G06N 5/046 (2013.01); H04L 2463/102 (2013.01);
Abstract

Computer-implemented methods and systems are provided for generating a distributed representation of electronic transaction data. Consistent with disclosed embodiments, generation may include receiving electronic transaction data including first and second entity identifiers. Generation may also include generating an output distributed representation by iteratively updating a distributed representation using the electronic transaction data. The distributed representation may include rows corresponding to first entity identifiers and rows corresponding to second entity identifiers. An iterative update may include generating a training sample and an embedding vector using the components and the distributed representation; determining, by a neural network, a predicted category from the embedding vector; and updating the distributed representation using the predicted category and the training sample. The embodiments may also include outputting the output distributed representation to determine authorization of electronic transactions. Disclosed embodiments may also receive an electronic transaction and determine whether to authorize the electronic transaction based on a distributed representation of electronic transaction data.


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