US 11,954,443 B1
Complaint prioritization using deep learning model
Abhishek Kumar, Bangalore (IN); Dipanjan Deb, Bangalore (IN); and Amit Agarwal, Bangalore (IN)
Assigned to Wells Fargo Bank, N.A., San Francisco, CA (US)
Filed by Wells Fargo Bank, N.A., San Francisco, CA (US)
Filed on Jun. 3, 2021, as Appl. No. 17/338,167.
Int. Cl. G06F 40/30 (2020.01); G06N 20/00 (2019.01); G06V 40/20 (2022.01)
CPC G06F 40/30 (2020.01) [G06N 20/00 (2019.01); G06V 40/20 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A computing system comprising:
a memory; and
one or more processors in communication with the memory and configured to:
receive a set of emotion factor values for communication data of a current service inquiry associated with a customer, wherein the set of emotion factor values comprises a determination value for the current service inquiry, an inquisitiveness value for the current service inquiry, a valence value for the current service inquiry, and an aggression value for the current service inquiry, wherein each emotion factor value indicates a measure of a particular emotion factor in the current service inquiry;
generate, using an emotion priority model running on the one or more processors, an emotional priority score for the current service inquiry based on the set of emotion factor values for the current service inquiry associated with the customer; and
determine a response priority order for the current service inquiry based on at least the emotional priority score for the current service inquiry.