US 11,756,040 B2
System and method for generating a contention scheme
Kevin Wayne Marcum, South Hero, VT (US)
Assigned to Kevin Wayne Marcum, South Hero, VT (US)
Filed by Kevin Wayne Marcum, South Hero, VT (US)
Filed on Aug. 9, 2021, as Appl. No. 17/397,360.
Prior Publication US 2023/0042823 A1, Feb. 9, 2023
Int. Cl. G06Q 20/40 (2012.01); G06Q 20/38 (2012.01); G06N 20/00 (2019.01); G06Q 20/42 (2012.01); G06F 18/2113 (2023.01)
CPC G06Q 20/403 (2013.01) [G06F 18/2113 (2023.01); G06N 20/00 (2019.01); G06Q 20/3821 (2013.01); G06Q 20/3825 (2013.01); G06Q 20/42 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system for generating a contention scheme, the system comprising a computing device, the computing device configured to:
obtain a solvency signature associated with a user from a solvency entity, wherein the solvency signature comprises the user's iris patterns in conjunction with a bank record, wherein obtaining the solvency signature comprises encrypting the solvency signature as a function of a cryptographic function, wherein the cryptographic function includes a commitment cryptographic primitive;
determine a solvency grouping based on the solvency signature;
identify a null element based on the solvency grouping, wherein identifying the null element further comprises:
receiving a regulation element from a regulation database, wherein receiving the regulation element further comprises obtaining a regulation input, wherein the regulation input includes a user-entered input including information on the solvency signature; and
identifying the null element based on the regulation element and the solvency grouping;
produce a weighted vector based on the null element, wherein producing the weighted vector further comprises:
training a weighted machine-learning model based on a weighted training set that correlates a predictive outcome to the null element; and
producing the weighted vector based on the weighted machine-learning model, wherein the weighted machine-learning model receives the null element as an input and outputs the weighted vector;
generate a contention scheme based on the weighted vector with a success parameter, wherein the contention scheme includes a success probability of resolving the null element; and
presenting the contention scheme on a graphical user interface (GUI).