US 12,192,409 B2
Categorizing audio calls based on machine learning models
Anh Burton, Seattle, WA (US); Hans-Martin Will, Marina del Rey, CA (US); James Tian, Redondo Beach, CA (US); Andreas Kurt Pursche, Santa Ana, CA (US); Jeff Dixon, Yorktown, VA (US); Matthew Berg, Minneapolis, MN (US); and Thomas Obermeyer, Alsbach (DE)
Assigned to SAP SE, Walldorf (DE)
Filed by SAP SE, Walldorf (DE)
Filed on Jun. 16, 2020, as Appl. No. 16/903,082.
Claims priority of provisional application 62/980,030, filed on Feb. 21, 2020.
Prior Publication US 2021/0266407 A1, Aug. 26, 2021
Int. Cl. H04M 3/00 (2024.01); G06N 20/00 (2019.01); G06Q 30/016 (2023.01); G10L 15/26 (2006.01); H04M 3/523 (2006.01)
CPC H04M 3/5235 (2013.01) [G06N 20/00 (2019.01); G06Q 30/016 (2013.01); G10L 15/26 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A non-transitory machine-readable medium 1 storing a program executable by at least one processing unit of a device, the program comprising sets of instructions for: receiving a set of audio files, each audio file in the set of audio files comprising audio from an audio call; truncating each audio file in the set of audio files to a defined call length; for each audio call in the set of audio calls, receiving a transcript of the audio call based on the truncated audio file of the audio call; and for each audio call in the set of audio calls, using the transcript of the audio call as input to a machine learning model for the machine learning model to predict a category from a plurality of categories that is associated with the audio call.