US 12,450,618 B2
Reducing false positives using customer feedback and machine learning
Timothy Kramme, Parker, TX (US); Elizabeth A. Flowers, Bloomington, IL (US); Reena Batra, Alpharetta, GA (US); Miriam Valero, Bloomington, IL (US); Puneit Dua, Bloomington, IL (US); Shanna L. Phillips, Bloomington, IL (US); Russell Ruestman, Minonk, IL (US); and Bradley A. Craig, Normal, IL (US)
Assigned to State Farm Mutual Automobile Insurance Company, Bloomington, IL (US)
Filed by State Farm Mutual Automobile Insurance Company, Bloomington, IL (US)
Filed on Apr. 16, 2024, as Appl. No. 18/636,886.
Application 18/636,886 is a continuation of application No. 17/993,758, filed on Nov. 23, 2022, granted, now 11,989,740.
Application 17/993,758 is a continuation of application No. 17/080,476, filed on Oct. 26, 2020, granted, now 11,687,938, issued on Jun. 27, 2023.
Application 17/080,476 is a continuation of application No. 15/465,832, filed on Mar. 22, 2017, granted, now 10,872,339, issued on Dec. 22, 2020.
Claims priority of provisional application 62/365,699, filed on Jul. 22, 2016.
Claims priority of provisional application 62/331,530, filed on May 4, 2016.
Claims priority of provisional application 62/318,423, filed on Apr. 5, 2016.
Claims priority of provisional application 62/313,196, filed on Mar. 25, 2016.
Prior Publication US 2024/0265405 A1, Aug. 8, 2024
Int. Cl. G06Q 30/01 (2023.01); G06N 5/046 (2023.01); G06N 20/00 (2019.01); G06Q 20/10 (2012.01); G06Q 20/20 (2012.01); G06Q 20/24 (2012.01); G06Q 20/32 (2012.01); G06Q 20/34 (2012.01); G06Q 20/40 (2012.01); G06Q 30/018 (2023.01); G06Q 30/0207 (2023.01); G06V 30/194 (2022.01); G06V 30/41 (2022.01); G06Q 30/0241 (2023.01)
CPC G06Q 30/0185 (2013.01) [G06N 5/046 (2013.01); G06N 20/00 (2019.01); G06Q 20/102 (2013.01); G06Q 20/20 (2013.01); G06Q 20/24 (2013.01); G06Q 20/3224 (2013.01); G06Q 20/34 (2013.01); G06Q 20/401 (2013.01); G06Q 20/4016 (2013.01); G06Q 20/407 (2013.01); G06Q 20/409 (2013.01); G06Q 30/0225 (2013.01); G06V 30/194 (2022.01); G06V 30/41 (2022.01); G06Q 30/0248 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method of predicting a fraud classification using fraud classification rules, comprising:
training, by a processor, a machine learning program using training data labeled with fraud classification labels, wherein:
the fraud classification labels identify types of fraud, of a set of different predetermined types of fraud, associated with respective historical transactions;
generating, by the processor, the fraud classification rules using the trained machine learning program; and
predicting, by the processor, and by applying the fraud classification rules to account data associated with a particular financial account, the fraud classification that:
corresponds to a transaction associated with the particular financial account, and
identifies a particular type of fraud, included in the set of different predetermined types of fraud, that is predicted to be associated with the transaction.