INTELLIGENT SUPPORT FRAMEWORK
A method comprises training at least one machine learning model with training data from a plurality of support cases, and receiving an input comprising data associated with at least one support case. The input is analyzed using the at least one machine learning model to determine one or more resolution options for the at least one support case, and the one or more resolution options are transmitted to an agent.
1 . A method comprising:
training at least one machine learning model with training data from a plurality of support cases;
receiving an input comprising data associated with at least one support case;
analyzing the input using the at least one machine learning model to determine one or more resolution options for the at least one support case; and
transmitting the one or more resolution options to an agent;
wherein the steps of the method are executed by at least one processing device operatively coupled to a memory.
2 . The method of claim 1 further comprising performing natural language processing (NLP) on the training data from the plurality of support cases and the data associated with the at least one support case.
3 . The method of claim 1 wherein the at least one machine learning model comprises a linear support vector machine (LSVM) classifier.
4 . The method of claim 1 wherein the training data comprises for respective ones of the plurality of support cases at least one of a case title, a case description, affected device details and one or more elements requiring at least one of replacement and installation.
5 . The method of claim 1 further comprising:
computing a degree of confidence for respective ones of the one or more resolution options; and
transmitting the computed degrees of confidence with the one or more resolution options to the agent.
6 . The method claim 1 wherein the one or more resolution options comprise one or more recommendations for at least one of a replacement and an installation of an element.
7 . The method of claim 6 wherein:
the element comprises a device part; and
the method further comprises recommending one or more alternative parts to be used instead of the device part, wherein the recommending is performed using at least one other machine learning model.
8 . The method of claim 7 further comprising training the at least one other machine learning model with data comprising attributes and configurations for respective ones of a plurality of parts.
9 . The method of claim 7 wherein the recommending comprises comparing the device part to a plurality of alternative parts to determine a level of similarity between the device part and respective ones of the plurality of alternative parts.
10 . The method of claim 9 wherein the comparing is performed using at least one of a k-nearest neighbor (KNN) algorithm and a Euclidean distance algorithm.
11 . The method of claim 1 further comprising evaluating performance of the at least one machine learning model, wherein the evaluating is performed using K-folds cross validation.
12 . The method of claim 11 further comprising generating a visualization of the performance of the at least one machine learning model, wherein the visualization comprises a confusion matrix comprising dispatched resolutions versus recommended resolutions for a plurality of received support cases.
13 . An apparatus comprising:
a processing device operatively coupled to a memory and configured:
to train at least one machine learning model with training data from a plurality of support cases;
to receive an input comprising data associated with at least one support case;
to analyze the input using the at least one machine learning model to determine one or more resolution options for the at least one support case; and
to transmit the one or more resolution options to an agent.
14 . The apparatus of claim 13 wherein the one or more resolution options comprise one or more recommendations for at least one of a replacement and an installation of an element.
15 . The apparatus of claim 14 wherein:
the element comprises a device part; and
the processing device is further configured to recommend one or more alternative parts to be used instead of the device part, wherein the recommending is performed using at least one other machine learning model.
16 . The apparatus of claim 15 wherein the processing device is further configured to train the at least one other machine learning model with data comprising attributes and configurations for respective ones of a plurality of parts.
17 . The apparatus of claim 15 wherein in performing the recommending, the processing device is configured to compare the device part to a plurality of alternative parts to determine a level of similarity between the device part and respective ones of the plurality of alternative parts.
18 . An article of manufacture comprising a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes said at least one processing device to perform the steps of:
training at least one machine learning model with training data from a plurality of support cases;
receiving an input comprising data associated with at least one support case;
analyzing the input using the at least one machine learning model to determine one or more resolution options for the at least one support case; and
transmitting the one or more resolution options to an agent.
19 . The article of manufacture of claim 18 wherein the one or more resolution options comprise one or more recommendations for at least one of a replacement and an installation of an element.
20 . The article of manufacture of claim 19 wherein:
the element comprises a device part; and
the program code further causes said at least one processing device to perform the step of recommending one or more alternative parts to be used instead of the device part, wherein the recommending is performed using at least one other machine learning model.