US 11,734,558 B2
Machine learning module training using input reconstruction techniques and unlabeled transactions
Moein Saleh, Campbell, CA (US); Chiara Poletti, San Jose, CA (US); Sina Modaresi, San Jose, CA (US); Yang Chen, San Jose, CA (US); and Xing Ji, San Jose, CA (US)
Assigned to PayPal, Inc., San Jose, CA (US)
Filed by PayPal, Inc., San Jose, CA (US)
Filed on Jun. 12, 2020, as Appl. No. 16/900,129.
Prior Publication US 2021/0390385 A1, Dec. 16, 2021
Int. Cl. G06Q 40/00 (2023.01); G06N 3/08 (2023.01); G06Q 20/30 (2012.01); G06N 3/04 (2023.01)
CPC G06N 3/08 (2013.01) [G06N 3/04 (2013.01); G06Q 20/30 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method for training machine learning models using input reconstruction results for unlabeled, incomplete transactions, comprising:
training, by a computing system, a machine learning module to classify electronic transactions, such that the machine learning module learns a characteristic of the electronic transactions that is indicative of different error data for those transactions, wherein the training uses a set of labeled transactions with labels indicating designated classifications for those transactions and a set of unlabeled transactions, and wherein the training includes:
generating first error data based on classification results generated by the machine learning module for the set of labeled transactions;
generating second, different error data based on reconstruction results generated via reconstruction of both the set of labeled transactions and the set of unlabeled transactions input into the machine learning module; and
updating the machine learning module based on the first and second error data; and
determining, by the computing system in response to receiving a request for authorization of a newly initiated transaction, to authorize the newly initiated transaction,
wherein the determining is performed using the machine learning module that is trained based on reconstruction results for both labeled and unlabeled transactions.