US 11,694,277 B2
Credit eligibility predictor
Guilherme Gomes, Porto Alegre (BR); Roberto Silveira, Sao Paulo (BR); Stefan Zanona, Porto Alegre (BR); Wagner Peres, Porto Alegre (BR); Leonardo Santos, Porto Alegre (BR); Mallie Griffin, Florence, SC (US); and Anjo Costa, Porto Alegre (BR)
Assigned to ADP, Inc., Roseland, NJ (US)
Filed by ADP, LLC, Roseland, NJ (US)
Filed on Apr. 15, 2019, as Appl. No. 16/383,984.
Prior Publication US 2020/0327621 A1, Oct. 15, 2020
Int. Cl. G06Q 30/00 (2012.01); G06Q 40/12 (2023.01); G06Q 10/105 (2023.01); G06N 3/08 (2023.01)
CPC G06Q 40/123 (2013.12) [G06N 3/08 (2013.01); G06Q 10/105 (2013.01)] 19 Claims
OG exemplary drawing
 
1. A computer-implemented method, comprising:
integrating computer-readable program code into a computer system comprising a processor, a computer readable memory in circuit communication with the processor, and a computer readable storage medium in circuit communication with the processor;
the processor executing program code instructions stored on the computer-readable storage medium via the computer readable memory and thereby performing steps of:
extracting, from payroll data of each of plurality of employees of an organization, data that is historically associated to previous instances of certified tax credit eligibility;
normalizing the extracted data with respect to data type and data value;
generating, via a neural network classifier, from an input of the normalized extracted data, multi-class outputs for each employee that indicate strengths of likelihood that each employee is currently eligible for each of a plurality of different tax credits, wherein the neural network classifier is trained on normalized data values and historic success rates for each of a plurality of persons that applied for the tax credits;
filtering the normalized extracted data by removing portions of the normalized extracted data that are associated to ones of the employees that are indicated within the multi-class outputs as having no likelihood that they are currently eligible for any of the plurality of different tax credits, thereby generating a remainder set of normalized extracted data that is associated to the remainder eligible ones of the employees; and
prioritizing an order of application for each of the tax credits for the remainder eligible ones of the employees as a function of respective values and likelihoods of eligibility of the tax credits indicated within the remainder set of normalized extracted data.