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. 04, 2024

Filed:

Mar. 31, 2021
Applicant:

Toast, Inc., Boston, MA (US);

Inventors:

Alan Z. Zhao, Cambridge, MA (US);

Benjamin C. W. Tang, Lexington, MA (US);

Assignee:

Toast, Inc., Boston, MA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 20/10 (2012.01); G06N 20/00 (2019.01); G06Q 20/24 (2012.01); G06Q 20/26 (2012.01); G06Q 20/40 (2012.01);
U.S. Cl.
CPC ...
G06Q 20/102 (2013.01); G06N 20/00 (2019.01); G06Q 20/24 (2013.01); G06Q 20/26 (2013.01); G06Q 20/4093 (2013.01);
Abstract

A computer-implemented method for predicting interchange charges includes: retrieving a historical transactions set, where each completed transaction in the set includes transaction features, a bank identification number (BIN), and a corresponding true interchange code; transforming all BINs in the set into a corresponding plurality of BIN features that comprise probabilities; creating a first training set including all transaction features, all pluralities of BIN features, and all true interchange codes associated with the historical transactions set; training a random forest model using the first training set and generating a second training set including rounded BIN features, rounded transaction features, discrete ones of the transaction features, and the true interchange codes; training the random forest model using the second training set to generate a trained random forest model for prediction of the interchange codes; and executing the trained random forest model for new transactions to generate corresponding predicted interchange codes.


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