Automatic problem extraction and analysis from unstructured text in IT tickets

Malhotra

et al. 2019

AAAI

Ticket assignment/dispatch is a crucial part of service delivery business with lot of scope for automation and optimization. In this paper, we present an end-to-end automated helpdesk email ticket assignment system, which is also offered as a service. The objective of the system is to determine the nature of the problem mentioned in an incoming email ticket and then automatically dispatch it to an appropriate resolver group (or team) for resolution.The proposed system uses an ensemble classifier augmented with a configurable rule engine. While design of a classifier that is accurate is one of the main challenges, we also need to address the need of designing a system that is robust and adaptive to changing business needs. We discuss some of the main design challenges associated with email ticket assignment automation and how we solve them. The design decisions for our system are driven by high accuracy, coverage, business continuity, scalability and optimal usage of computational resources.Our system has been deployed in production of three major service providers and currently assigning over 90,000 emails per month, on an average, with an accuracy close to 90% and covering at least 90% of email tickets. This translates to achieving human-level accuracy and results in a net saving of more than 50000 man-hours of effort per annum. Till date, our deployed system has already served more than 700,000 tickets in production.

Section: Related Workmentioning

confidence: 99%

Automated Dispatch of Helpdesk Email Tickets: Pushing the Limits with AI

Malhotra

et al. 2019

AAAI

“…Many systems proposed in the past provide a solution for automated problem determination and resolution e.g. [6], [1] talk about auto-remediation by first categorizing the ticket into a problem category and then recommending a solution for the problem category identified. They have used text based classification.…”

Section: Related Workmentioning

confidence: 99%

Improving IT Support by Enhancing Incident Management Process with Multi-modal Analysis

Lecture Notes in Computer Science

Malhotra³

et al. 2019

3 IT support services industry is going through a major transformation with AI becoming commonplace. There has been a lot of effort in the direction of automation at every human touchpoint in the IT support processes. Incident management is one such process which has been a beacon process for AI based automation. The vision is to automate the process from the time an incident/ticket arrives till it is resolved and closed. While text is the primary mode of communicating the incidents, there has been a growing trend of using alternate modalities like image to communicate the problem. A large fraction of IT support tickets today contain attached image data in the form of screenshots, log messages, invoices and so on. These attachments help in better explanation of the problem which aids in faster resolution. Anybody who aspires to provide AI based IT support, it is essential to build systems which can handle multi-modal content. In this paper we present how incident management in IT support domain can be made much more effective using multi-modal analysis. The information extracted from different modalities are correlated to enrich the information in the ticket and used for better ticket routing and resolution. We evaluate our system using about 25000 real tickets containing attachments from selected problem areas. Our results demonstrate significant improvements in both routing and resolution with the use of multi-modal ticket analysis compared to only text based analysis.

“…The work in [9] attempts to classify the incoming change requests into one of the fine-grained activities in a catalog. Some other works [10] and [11] talk about a holistic approach of ticket category classification, cause analysis and resolution recommendation. However, they do not automate the process of assignment.…”

Section: Related Workmentioning

confidence: 99%

Automated dispatch of helpdesk email tickets: Pushing the limits with AI

Malhotra

et al. 2018

EasyChair Preprints

Ticket assignment/dispatch is a crucial part of service delivery business with lot of scope for automation and optimization. In this paper, we present an end-to-end automated helpdesk email ticket assignment system, which is also offered as a service. The objective of the system is to determine the nature of the problem mentioned in an incoming email ticket and then automatically dispatch it to an appropriate resolver group (or team) for resolution.<br/>The proposed system uses an ensemble classifier augmented with a configurable rule engine. While design of classifier that is accurate is one of the main challenges, we also need to address the need for designing a system that is robust and adaptive to changing business needs. We discuss some of the main design challenges associated with email ticket assignment automation and how we solve them. The design decisions for our system are driven by high accuracy, coverage, business continuity, scalability and optimal usage of computational resources.<br/>Our system has been deployed in production of three major service providers and currently assigning over 90,000 emails per month, on an average, with an accuracy close to 90% and covering at least 90% of email tickets. This translates to achieving human-level accuracy and results in a net saving of more than 50000 man-hours of effort per annum.