US 11,704,598 B2
Machine-learning techniques for evaluating suitability of candidate datasets for target applications
Kourosh Modarresi, Sunnyvale, CA (US); Hongyuan Yuan, San Jose, CA (US); and Charles Menguy, New York, NY (US)
Assigned to ADOBE INC., San Jose, CA (US)
Filed by Adobe Inc., San Jose, CA (US)
Filed on Sep. 2, 2022, as Appl. No. 17/929,394.
Application 17/929,394 is a continuation of application No. 16/274,954, filed on Feb. 13, 2019, granted, now 11,481,668.
Prior Publication US 2023/0004869 A1, Jan. 5, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 20/00 (2019.01); G06F 16/22 (2019.01); G06F 16/28 (2019.01)
CPC G06N 20/00 (2019.01) [G06F 16/2264 (2019.01); G06F 16/285 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A method for applying machine-learning techniques to evaluate candidate datasets for use by software applications, the method comprising performing, by one or more processing devices, operations including:
identifying, in a candidate dataset identifying first entities associated with first features, first unique candidate entities that are absent from a reference dataset identifying second entities associated with second features that include a baseline feature of a target population and that are associated with the baseline feature in the candidate dataset;
forming, in a multi-dimensional space and based on a subset of the second features lacking the baseline feature, a cluster of data points representing the second entities;
mapping a subset of the first entities that are absent from the reference dataset and that are not in the first unique candidate entities to additional data points, respectively in the multi-dimensional space;
identifying, from the subset of the first entities, second unique candidate entities corresponding to a subset of the additional data points within a threshold distance of the cluster;
determining a merit attribute of the candidate dataset based on a first weight for each first unique candidate entity, a second weight for each second unique candidate entity, a number of the first unique candidate entities in the candidate dataset, and a number of the second unique candidate entities in the candidate dataset; and
selecting the candidate dataset as input data for a target software application based on the merit attribute of the candidate dataset being greater than a threshold value.