US 11,893,634 B2
Method and system for correcting errors in consumer reporting
Arthur McWain, Bloomington, IL (US); and James J. Dunn, 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 Dec. 9, 2021, as Appl. No. 17/546,448.
Application 17/546,448 is a continuation of application No. 16/870,588, filed on May 8, 2020, granted, now 11,232,518.
Application 16/870,588 is a continuation of application No. 15/700,394, filed on Sep. 11, 2017.
Claims priority of provisional application 62/554,938, filed on Sep. 6, 2017.
Prior Publication US 2022/0101428 A1, Mar. 31, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 40/02 (2023.01); G06Q 40/03 (2023.01); G06F 11/00 (2006.01); G06Q 40/04 (2012.01)
CPC G06Q 40/03 (2023.01) 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for correcting errors in consumer credit reporting, the method executed by one or more processors programmed to perform the method, the method comprising:
training, by the one or more processors, a first machine learning model for identifying incorrect value errors in consumer credit reporting;
training, by the one or more processors, a second machine learning model for correcting incorrect value errors in consumer credit reporting using original values and changed values for previously corrected errors;
obtaining, at the one or more processors, a secure data file including a set of consumer credit information for a consumer related to one or more products;
receiving, at the one or more processors from a user, a request for the set of consumer credit information of the consumer;
applying, by the one or more processors, the set of consumer credit information for each particular data field to the first machine learning model to identify an incorrect value error in the set of consumer credit information; and
applying, by the one or more processors, the incorrect value error to the second machine learning model to automatically correct the incorrect value error.