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:
Feb. 06, 2024

Filed:

Feb. 16, 2021
Applicant:

Tripleblind, Inc., Kansas City, MO (US);

Inventors:

Greg Storm, Parkville, MO (US);

Riddhiman Das, Lenexa, KS (US);

Babak Poorebrahim Gilkalaye, Kansas City, MO (US);

Assignee:

TripleBlind, Inc., Kansas City, MO (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04L 9/00 (2022.01); G06F 17/16 (2006.01); H04L 9/40 (2022.01); H04L 9/06 (2006.01); G06Q 20/40 (2012.01); G06Q 30/0601 (2023.01); G06Q 20/12 (2012.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 10/44 (2022.01); G06N 3/04 (2023.01); G06N 3/082 (2023.01); G06F 18/24 (2023.01); G06F 18/2113 (2023.01); G06F 18/2413 (2023.01);
U.S. Cl.
CPC ...
H04L 9/008 (2013.01); G06F 17/16 (2013.01); G06Q 20/1235 (2013.01); G06Q 20/401 (2013.01); G06Q 30/0623 (2013.01); G06V 10/454 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); H04L 9/0625 (2013.01); H04L 63/0428 (2013.01); G06F 18/2113 (2023.01); G06F 18/24 (2023.01); G06F 18/24133 (2023.01); G06N 3/04 (2013.01); G06N 3/082 (2013.01); G06Q 2220/00 (2013.01); H04L 2209/46 (2013.01);
Abstract

A method includes dividing a plurality of filters in a first layer of a neural network into a first set of filters and a second set of filters, applying each of the first set of filters to an input of the neural network, aggregating, at a second layer of the neural network, a respective one of a first set of outputs with a respective one of a second set of outputs, splitting respective weights of specific neurons activated in each remaining layer, at each specific neuron from each remaining layer, applying a respective filter associated with each specific neuron and a first corresponding weight, obtaining a second set of neuron outputs, for each specific neuron, aggregating one of the first set of neuron outputs with one of a second set of neuron outputs and generating an output of the neural network based on the aggregated neuron outputs.


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