US 12,224,969 B2
Machine learning-based prediction of message responses using a corpus of response templates
João Francisco Homrich Da Jornada, Porto Alegre (BR); Walter Cristian Bruck Mundstock, Porto Alegre (BR); and Daniel Carvalho Liedke, Porto Alegre (BR)
Assigned to Dell Products L.P., Round Rock, TX (US)
Filed by Dell Products L.P., Round Rock, TX (US)
Filed on May 13, 2022, as Appl. No. 17/743,553.
Prior Publication US 2023/0370393 A1, Nov. 16, 2023
Int. Cl. H04L 51/02 (2022.01); G06F 18/21 (2023.01); G06N 20/00 (2019.01); G06Q 30/016 (2023.01)
CPC H04L 51/02 (2013.01) [G06F 18/2178 (2023.01); G06Q 30/016 (2013.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
obtaining at least one user message sent by at least one user;
applying the at least one user message of the at least one user to at least one machine learning model that predicts one or more response templates, from a corpus of response templates, that are responsive to the at least one user message, wherein the at least one machine learning model is trained using training data comprising at least one historical user message sent by one or more users and at least one historical agent message sent by at least one customer service agent, wherein the at least one historical agent message: (i) has a similarity score with respect to at least one of the response templates, from the corpus of response templates, that satisfies one or more similarity criteria, (ii) is sent in response to the at least one historical user message sent by the one or more users; and (iii) is based at least in part on one or more of the at least one response template from the corpus of response templates, wherein an identifier of the at least one response template is used as a classification label in a supervised learning of the at least one machine learning model and wherein the at least one response template and one or more respective variations, that satisfy the one or more similarity criteria, of the at least one response template are considered together during the training of the at least one machine learning model; and
automatically generating at least one response to the at least one user message using the predicted one or more response templates;
wherein the method is performed by at least one processing device comprising a processor coupled to a memory.