US 12,230,248 B2
Unsupervised clustering of intents for natural language understanding
Prakash Chandra, Fremont, CA (US); Santosh Gupta, San Jose, CA (US); Ajay Nandanwar, Campbell, CA (US); Sanjay Verma, Atherton, CA (US); and Devendra Vidhani, Pleasanton, CA (US)
Assigned to PwC Product Sales LLC, New York, NY (US)
Filed by PwC Product Sales LLC, New York, NY (US)
Filed on Dec. 8, 2021, as Appl. No. 17/545,555.
Claims priority of provisional application 63/131,515, filed on Dec. 29, 2020.
Prior Publication US 2022/0208177 A1, Jun. 30, 2022
Int. Cl. G10L 15/06 (2013.01); G06N 3/045 (2023.01); G06N 3/088 (2023.01); G10L 25/30 (2013.01)
CPC G10L 15/063 (2013.01) [G06N 3/045 (2023.01); G06N 3/088 (2013.01); G10L 25/30 (2013.01)] 19 Claims
OG exemplary drawing
 
1. A computer-enabled method for providing a natural-language-processing system, the method comprising:
receiving first utterance data corresponding to a first plurality of intents;
identifying, based on the first utterance data, a first plurality of intent clusters using an unsupervised machine-learning algorithm, wherein each intent cluster of the first plurality of intent clusters comprises a respective subset of the first plurality of intents;
training, based on the first utterance data, an intent cluster classification model, wherein the intent cluster classification model is configured to receive a user utterance and identify an intent cluster of the first plurality of intent clusters; and
training, based on the first utterance data, an intent classification model for each intent cluster of the first plurality of intent clusters to obtain a plurality of intent classification models, wherein each intent classification model is configured to receive the user utterance and identify an intent from the respective intent cluster.