US 12,118,439 B2
Mixed-initiative machine learning systems and methods for determining segmentations
Liang Gou, San Jose, CA (US); and Hao Yang, San Jose, CA (US)
Assigned to Visa International Service Association, San Francisco, CA (US)
Filed by Visa International Service Association, San Francisco, CA (US)
Filed on May 21, 2021, as Appl. No. 17/326,688.
Application 17/326,688 is a continuation of application No. 15/462,675, filed on Mar. 17, 2017, granted, now 11,042,814.
Prior Publication US 2021/0279642 A1, Sep. 9, 2021
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 5/04 (2023.01); G06N 20/00 (2019.01); G06Q 30/0204 (2023.01)
CPC G06N 20/00 (2019.01) [G06N 5/04 (2013.01); G06Q 30/0204 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
obtaining, by a computer, input data associated with a plurality of individuals;
obtaining, by the computer, label data associated with a subset of the plurality of individuals, the label data assigning one or more labels to each individual of the subset;
performing, by the computer, a semi-supervised machine learning process using the input data and the label data;
generating, by the computer, visualization data that is based on the semi-supervised machine learning process and is displayed to a user, the visualization data representing one or more segmentations of the plurality of individuals;
receiving, by the computer, user feedback from the user corresponding to the visualization data; and
performing, by the computer, the semi-supervised machine learning process, using the user feedback, the input data, and the label data, and wherein the method further comprises:
determining, by the computer, sets of initial features based on the input data, the sets of initial features including a set of initial features for each individual of the plurality of individuals,
wherein the semi-supervised machine learning process includes determining sets of updated features based on the sets of initial features and the label data, the sets of updated features including a set updated features for each individual of the plurality of individuals,
wherein the generating of the visualization data is based on the sets of updated features and the label data, and
wherein the sets of updated features are different than the sets of initial features, the sets of updated features more consistently characterizing individuals to specific labels than the sets of initial features.