Problem Statement:

In many organizations, managing customer service tickets and defect reports efficiently is crucial for maintaining customer satisfaction and product quality. However, manually identifying duplicate tickets or defects can be a time-consuming and error-prone task. Traditional methods often rely on keyword matching or manual inspection, leading to inconsistencies and missed duplicates.

Input:

The input to our NLP-based solution consists of user-created service tickets and defect reports submitted by customers or internal teams. Each ticket contains text describing the issue or request, along with relevant metadata such as ticket ID, date, and category.

Output:

Our solution aims to automate the detection of duplicate tickets by identifying sets of tickets that exhibit similar content and characteristics. The output includes a list of detected duplicate tickets, allowing organizations to streamline their ticketing systems and prioritize their response efforts more effectively.

Challenges Faced:

One of the main challenges in this project is the inherent variability and complexity of natural language text. Service tickets and defect reports can vary widely in terms of language, syntax, and semantics, making it difficult to develop a robust duplicate detection algorithm. Additionally, ensuring scalability and real-time performance in handling large volumes of tickets presents another significant challenge.

Proposed Solution:

Our solution leverages state-of-the-art natural language processing (NLP) techniques, including text embedding models and similarity measures, to compare and identify similarities between tickets. By transforming ticket text into high-dimensional semantic representations, we can effectively capture and quantify the semantic similarity between tickets, regardless of variations in language and expression. Furthermore, we employ efficient indexing and search algorithms to enable real-time duplicate detection, ensuring scalability and responsiveness even with large ticket datasets.

Summary:

Our NLP-based solution for automated duplicate ticket detection offers organizations a powerful tool to streamline their ticketing processes and improve operational efficiency. By automating the tedious task of identifying duplicate tickets, we enable teams to focus their resources on addressing unique customer issues and resolving defects promptly. With our scalable and accurate solution, organizations can enhance their customer service operations, reduce ticket resolution times, and ultimately deliver a superior experience to their customers.