US 2023/0053913 A1 · TAILORING A MULTI-CHANNEL HELP DESK ENVIRONMENT BASED ON MACHINE LEARNING MODELS

Abstract

Computer-implemented methods of training machine learning models and using the machine learning models for tailoring a multi-channel help desk environment. One or more computers train a machine learning model of selecting best attendance channels for respective customer clusters and for respective issue clusters. One or more computers train machine learning models of tailoring respective attendance channel types. One or more computers employ the machine learning models to determine a best attendance channel for resolving an information technology problem of a user and to predict channel tailoring characteristics for the best attendance channel. One or more computers employ genetic algorithm operators to determine a random attendance channel with random tailoring characteristics. One or more computer use random routing to route the user to one of the best attendance channel and the random attendance channel, avoiding undesired bias favorable toward the best attendance channel.

Claims20

Dependent claims

A computer-implemented method for training machine learning models for tailoring a multi-channel help desk environment, the method comprising:

analyzing customer feedback on attendance channels to obtain parameterized feedback metrics;
assembling a dataset including ticket records associated with the attendance channels and the parameterized feedback metrics;
augmenting the dataset by replacing customer identifications in the dataset with respective cluster centroids of customer clusters;
augmenting the dataset by replacing information technology (IT) descriptions in the dataset with respective cluster centroids of issue clusters;
training a machine learning model of selecting best attendance channels for respective ones of the customer clusters and for respective ones of the issue clusters, using an augmented dataset;
enhancing the augmented dataset by adding channel tailoring characteristics to the augmented dataset;
dividing an enhanced dataset into respective partitions for respective attendance channel types;
and
training machine learning models of tailoring the respective attendance channel types, using the respective partitions of the enhanced dataset.

6 more elements

A computer-implemented method for tailoring a multi-channel help desk environment based on machine learning models, the method comprising:

in response to receiving a request from a user for resolving an information technology (IT) problem, determining a customer cluster of the user;
determining an issue cluster of the IT problem;
employing a machine learning model of selecting best attendance channels to determine a best attendance channel for resolving the IT problem of the user;
employing a machine learning model of tailoring respective attendance channel types to predict channel tailoring characteristics for the best attendance channel;
employing genetic algorithm operators to determine a random attendance channel with random tailoring characteristics;
using random routing to route the user to one of the best attendance channel and the random attendance channel, routing the user to the random attendance channel with a predetermined probability;
and
wherein using the random routing avoids undesired bias favorable toward the best attendance channel.

5 more elements

Dependencies are inferred from the claim text.

Tailoring a multi-channel help desk environment based on machine learning models

Life of this patent

Tailoring a multi-channel help desk environment based on machine learning models

Life of this patent

Abstract

Claims20