US 12,407,780 B2
Training modeling engines to predict contact center agent demand
Qiumin Dong, SuZhou (CN); Periyaven Naiken Gopalla, Burlingame, CA (US); Tao Huang, Hangzhou (CN); Wei Ji, San Jose, CA (US); Nicholas Troy Johnson, Argyle, TX (US); Eunkyung Kim, Glendale, CA (US); Bilung Lee, Los Angeles, CA (US); Vijay Venkataswamy Parthasarathy, San Jose, CA (US); and Amarakota Madhu Vamsi, Andhra Pradesh (IN)
Assigned to Zoom Communications, Inc., San Jose, CA (US)
Filed by Zoom Communications, Inc., San Jose, CA (US)
Filed on Apr. 30, 2023, as Appl. No. 18/309,807.
Prior Publication US 2024/0364815 A1, Oct. 31, 2024
Int. Cl. H04M 3/523 (2006.01); G06N 20/20 (2019.01)
CPC H04M 3/5238 (2013.01) [G06N 20/20 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
obtaining historical contact center data of a contact center by tracking contact center conditions at a contact center server;
training, based on the historical contact center data, multiple modeling engines to generate agent demand data representing a number of agents working at a given time;
training, based on the historical center contact center data and performance data of the multiple modeling engines, a combination engine to generate a combination of one or more modeling engines from the multiple modeling engines, wherein training the combination engine leverages an error metric corresponding to an error between a measured average user wait time and an average user wait time calculated based on at least one of the multiple modeling engines; and
providing an output representing the trained combination engine and the trained multiple modeling engines.