US 12,131,392 B2
System and method for recommending matches using machine learning
Timothy Dirk Stevens, Littleton, CO (US)
Filed by Timothy Dirk Stevens, Littleton, CO (US)
Filed on Feb. 3, 2022, as Appl. No. 17/592,454.
Application 17/592,454 is a continuation of application No. 17/192,845, filed on Mar. 4, 2021, granted, now 11,276,127.
Prior Publication US 2022/0284520 A1, Sep. 8, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 3/08 (2023.01); G06F 18/214 (2023.01); G06N 3/04 (2023.01); G06Q 10/105 (2023.01); G06Q 30/0203 (2023.01); G06Q 50/00 (2012.01); G06V 40/16 (2022.01)
CPC G06Q 50/01 (2013.01) [G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06F 18/214 (2023.01); G06Q 10/105 (2013.01); G06Q 30/0203 (2013.01); G06V 40/168 (2022.01)] 132 Claims
OG exemplary drawing
 
1. A method for recommending persons for a dating interpersonal relationship, comprising:
training a supervised machine learning engine from a database of multiple existing relationships;
wherein the database of data describing multiple existing relationships comprises successful relationships and unsuccessful relationships;
wherein the data describing each relationship in the database of data describing multiple existing relationships comprises attributes of a first person in the relationship, attributes of a second person in the relationship and an evaluation of the relationship;
wherein the attributes of the first person in the relationship and the second person in the relationship comprise respective answers provided by the first person and the second person to survey questions;
wherein the supervised machine learning engine comprises a first neural network, the first neural network processing the answers to survey questions provided by the first person and the second person in each relationship, where the first neural network has multiple hidden layers;
wherein the supervised machine learning engine comprises a second neural network, different from the first neural network, the second neural network processing additional attributes provided by the first person and the second person in each relationship, where the second neural network has multiple hidden layers;
inputting data into the trained supervised machine learning engine attributes of a first person and attributes of a plurality of candidate persons for the relationship;
utilizing the trained supervised machine learning engine to predict a numerical evaluation of a potential relationship between the first person and each candidate person in the plurality of candidate persons;
wherein the prediction is performed by a third neural network based on a concatenation of outputs of the first and second neural networks;
determining a likelihood of a successful relationship for each candidate person by comparing the numerical evaluation against a threshold; and
recommending at least one candidate person of the plurality of candidate persons to the first person based on results of the comparing.