US 12,217,306 B2
Method for identifying anomalous transactions using machine learning
Ramesh Natarajan, Pleasantville, NY (US)
Assigned to Google LLC, Mountain View, CA (US)
Filed by Google LLC, Mountain View, CA (US)
Filed on Oct. 24, 2022, as Appl. No. 17/972,134.
Claims priority of provisional application 63/273,360, filed on Oct. 29, 2021.
Prior Publication US 2023/0137892 A1, May 4, 2023
Int. Cl. G06Q 40/00 (2023.01); G06Q 40/02 (2023.01); G06T 11/00 (2006.01); G06T 11/20 (2006.01)
CPC G06Q 40/02 (2013.01) [G06T 11/001 (2013.01); G06T 11/206 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system for detecting anomalous activity related to potential fraud or criminal activity among a plurality of transactions, comprising: memory; and one or more processors in communication with the memory, the one or more processors configured to: retrieve the plurality of transactions from a sliding time window; review the plurality of transactions in one or more data structures identifying the transactions; identify one or more sets of potentially related transactions occurring within the sliding time window; receive, via a graphical user interface, a parameter related to one or more transformations; generate, based on application of a machine learning model to the one or more sets of potentially related transactions and using the received parameter, results of the one or more various transformations; identify potentially anomalous activity corresponding to the one or more sets of potentially related transactions; flag the identified potentially anomalous activity using one or more perceptible indicia; and output a table containing the results of the one or more various transformations for display via the graphical user interface.