US 12,462,211 B2
Method and system for determination of personality traits of agents in a contact center
Vishal Manchanda, Bangalore (IN); Amit Kumar, Bangalore (IN); Venugopal Subbarao, Bangalore (IN); and Janupalli Pranay, Bangalore (IN)
Assigned to Infosys Limited, Bangalore (IN)
Filed by INFOSYS LIMITED, Bangalore (IN)
Filed on Mar. 27, 2023, as Appl. No. 18/126,868.
Prior Publication US 2024/0320595 A1, Sep. 26, 2024
Int. Cl. H04M 3/51 (2006.01); G06F 40/40 (2020.01); G06Q 10/0639 (2023.01)
CPC G06Q 10/0639 (2013.01) [G06F 40/40 (2020.01); H04M 3/51 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A method for determination of personality traits of agents in a contact center, comprising:
retrieving, by a server, textual data corresponding to a conversation between a first agent and a first customer, wherein the textual data comprises a transcript of the conversation;
training, by the server, a first Machine Learning (ML) model using a training dataset that comprises training textual data and corresponding ground truth data, wherein each element of the ground truth data comprises a ground truth value and an associated ground truth reasoning for each of a set of personality traits of the first agent, and wherein the training of the first ML model comprises:
inputting, by the server, the training textual data to the first ML model;
receiving, by the server, a value corresponding to each of the set of personality traits and a natural language justification associated with the value, from the first ML model;
determining, by the server, a binary cross entropy loss between the received value and the corresponding ground truth value;
determining, by the server, a text similarity score between the received natural language justification and the ground truth reasoning using a sentence encoder;
performing, by the server, backpropagation using a weighted loss of the binary cross entropy loss and the text similarity score;
calculating, by the server, an accuracy score based on a comparison between the received value and the ground truth value for the first ML model; and
modifying, by the server, one or more parameters of the first ML model based on the accuracy score, the text similarity score, and the backpropagation;
generating, by the server, a natural language justification corresponding to the set of personality traits of the first agent based on the textual data through the first ML model, wherein the natural language justification comprises one or more sentences, and wherein the one or more sentences comprise a mapping of the textual data with the set of personality traits and a qualitative label associated with each of the set of personality traits; and
determining, by the server, a value corresponding to each of the set of personality traits of the first agent through the first ML model based on the natural language justification and the associated qualitative label.