US 12,073,343 B2
Machine learning models with accurate data labeling
Prateek Baranwal, Dallas, TX (US); Jami Daly, Brookhaven, GA (US); Martin Patrick McEnroe, Plano, TX (US); and Kelly Dowd, Dallas, TX (US)
Assigned to AT&T Intellectual Property I, L.P., Atlanta, GA (US)
Filed by AT&T Intellectual Property I, L.P., Atlanta, GA (US)
Filed on May 18, 2021, as Appl. No. 17/324,016.
Prior Publication US 2022/0374930 A1, Nov. 24, 2022
Int. Cl. G06Q 10/0631 (2023.01); G06N 20/00 (2019.01); G06Q 10/0639 (2023.01); G06Q 30/0207 (2023.01)
CPC G06Q 10/063112 (2013.01) [G06N 20/00 (2019.01); G06Q 10/06393 (2013.01); G06Q 10/06398 (2013.01); G06Q 30/0215 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
receiving, by a processing system comprising at least one processor, data from a data source;
routing, by the processing system, the data to a plurality of different data labelers to label the data;
determining, by the processing system, that an inflection point associated with an accuracy of the plurality of different data labelers is reached, wherein the inflection point comprises when the accuracy of the plurality of different data labelers relative to a consensus reaches an accuracy threshold for the data;
re-routing, by the processing system, additional data from the data source to a machine learning model when the inflection point is reached; and
activating, by the processing system, the machine learning model to label the additional data.