US 12,094,458 B2
Multi-channel conversation processing
Vivek Kumar, Redwood Shores, CA (US)
Assigned to Oracle International Corporation, Redwood Shores, CA (US)
Filed by Oracle International Corporation, Redwood Shores, CA (US)
Filed on Feb. 10, 2022, as Appl. No. 17/668,968.
Prior Publication US 2023/0252980 A1, Aug. 10, 2023
Int. Cl. G10L 15/16 (2006.01); G06F 40/30 (2020.01); G10L 15/18 (2013.01); H04L 51/212 (2022.01); G06F 40/289 (2020.01); G06F 40/35 (2020.01); G10L 15/183 (2013.01); G10L 15/22 (2006.01)
CPC G10L 15/1815 (2013.01) [G06F 40/30 (2020.01); G10L 15/16 (2013.01); G10L 15/1822 (2013.01); H04L 51/212 (2022.05); G06F 40/289 (2020.01); G06F 40/35 (2020.01); G10L 15/183 (2013.01); G10L 2015/225 (2013.01)] 20 Claims
OG exemplary drawing
 
9. A method comprising:
obtaining a first training data set of historical conversation data for a plurality of conversations, the first training data set of historical conversation data comprising:
a first conversation;
a communication channel type, of a plurality of communication channel types, over which the first conversation was conducted;
a label identifying a first portion of the first conversation that is to be extracted;
training a first machine learning model based on the first training data set to extract portions of conversations based at least in part on communication channel types over which the conversations were conducted;
identifying a second conversation conducted over a first communication channel type of the plurality of communication channel types;
applying the first machine learning model to the second conversation and the first communication channel type to identify a first portion of the second conversation to be extracted;
extracting the first portion of the second conversation; and
populating a database with the extracted first portion of the second conversation without populating the database with a second portion of the second conversation.