US 12,086,874 B2
Intelligent alert system
Yuh-shen Song, Porter Ranch, CA (US); Catherine Lew, Porter Ranch, CA (US); Alexander Song, Porter Ranch, CA (US); and Victoria Song, Porter Ranch, CA (US)
Filed by Yuh-shen Song, Porter Ranch, CA (US); Catherine Lew, Porter Ranch, CA (US); Alexander Song, Porter Ranch, CA (US); and Victoria Song, Porter Ranch, CA (US)
Filed on Jan. 14, 2020, as Appl. No. 16/742,766.
Claims priority of provisional application 62/805,085, filed on Feb. 13, 2019.
Prior Publication US 2020/0258147 A1, Aug. 13, 2020
Int. Cl. G06Q 40/02 (2023.01); G06F 16/22 (2019.01); G06F 16/28 (2019.01); G06Q 10/10 (2023.01); G06Q 20/40 (2012.01); G06Q 30/018 (2023.01); G06Q 40/12 (2023.01); G06Q 50/26 (2024.01)
CPC G06Q 40/02 (2013.01) [G06F 16/22 (2019.01); G06F 16/285 (2019.01); G06Q 10/10 (2013.01); G06Q 20/4016 (2013.01); G06Q 30/0185 (2013.01); G06Q 40/125 (2013.12); G06Q 50/26 (2013.01); G06Q 50/265 (2013.01)] 14 Claims
OG exemplary drawing
 
1. A method for electronically detecting money laundering activity via a machine learning model associated with a first computer system in a financial computer network, comprising:
learning a reporting threshold based on historical filings of a group of reports associated with one or more cause vectors, the historical filings being manually reported by a user to a third computer system based on a value of each of the one or more cause vectors being greater than a certain value, the reporting threshold being equal to the certain value;
monitoring electronic transaction data associated with one or more transactions performed by a party, the electronic transaction data including one or more of a transaction amount, a transaction date, or a transaction location for each of the one or more transactions;
flagging one or more scenarios of a plurality of scenarios based on background information of the party and monitoring the electronic transaction data, the one or more flagged scenarios associated with a first cause vector of the group of cause vectors;
detecting a first potential case for money laundering in response to the one or more flagged scenarios satisfying detection criteria;
determining a first ratio of a first value associated with the first cause vector to a second value associated with the first cause vector, the first value indicating a number of true positives associated with the first cause vector during a time period, the second value indicating a number of potential cases associated with the first cause vector during the time period;
learning a writing style of the user based on one or more edits by the user to one or more writing prompts presented to the user via the machine learning model;
generating a first report associated with the first potential case in accordance with the first ratio being greater than or equal to the reporting threshold, the first report including a narrative comprising:
one or more facts associated with the background information and the electronic transaction data, and
one or more linking words associated with the learned writing style of the user, the one or more linking words linking the one or more facts together; and
transmitting the first report to the third computer system based on generating the first report.