Automated Trending Issue Detection in Text Streams
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for identifying trending issues in text streams are inefficient, often leading to delayed escalation and resolution, as they rely on manual voting processes that fail to quickly detect issues affecting multiple users.
Innovation Solution
The implementation of textual analysis techniques, including natural language processing, machine learning models, and time series anomaly detection, to automatically identify and flag trending issues in text streams, filtering out malicious content and surfacing relevant information to support teams for timely action.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual voting processes are used to identify trending issues, then system complexity is reduced, but issue detection speed and productivity deteriorate
Solution Approach 1:
The patent replaces manual voting processes with automated text stream analysis using machine learning models. The system automatically processes text streams, extracts issues, determines trends, and generates notifications without human intervention, thereby increasing detection speed while managing complexity through algorithmic automation.
Solution Approach 2:
The system performs self-service by automatically analyzing text streams, identifying issues, determining if they are trending, and notifying relevant users without requiring manual input. The automated pipeline includes text stream reception, issue extraction, trend determination, and notification generation, all executed autonomously by the system.
2Extent of automation
If manual voting processes are used, then automation extent is minimized, but resolution time increases
Solution Approach 1:
The system performs preliminary action by continuously analyzing text streams in real-time before issues become widespread. The automated analysis detects emerging trends early, allowing support teams to prepare and respond proactively, reducing overall resolution time through advance detection and notification.
Solution Approach 2:
Manual voting and issue tracking processes are replaced with automated machine learning-based analysis. The system automatically processes text streams, identifies issues, determines trending status, and sends notifications, eliminating delays associated with manual processes and significantly reducing resolution time.
3Productivity
If automated textual analysis is implemented, then issue detection speed improves, but system complexity increases
Solution Approach 1:
The automated analysis system is segmented into distinct functional modules: text stream reception, issue extraction using machine learning, trend determination through comparison logic, and notification generation. This modular segmentation manages complexity by organizing functions into separate, manageable components that can be developed and maintained independently.
Solution Approach 2:
The system employs universal machine learning models that can process various types of text streams (emails, chat logs, support tickets) and extract different types of issues. This multi-functionality reduces overall system complexity by using a single automated analysis framework rather than separate systems for each text stream type.
4Measurement precision
If comprehensive text stream analysis is performed, then measurement precision of trending issues improves, but processing time increases
Solution Approach 1:
The system performs partial analysis by focusing on extracting and analyzing only the most relevant features and keywords from text streams using machine learning. Rather than processing every aspect of each text stream in depth, the system identifies key indicators of trending issues, achieving sufficient precision for trend detection while maintaining efficient processing speeds.
Solution Approach 2:
Manual review processes are replaced with automated machine learning analysis that can rapidly process text streams with high precision. The automated system uses trained models to accurately identify issues and determine trends without the time constraints of human analysis, achieving both high measurement precision and efficient processing.
Data Source
AI summary
This application relates to a systems and methods for trending issue identification in text streams. In one embodiment, a method for improving resolution of a trending issue identified in a set of text streams includes presenting a user interface of an application that is being executed by a computing device. The method also includes receiving a notification including the trending issue that has been identified in the set of text streams based at least in part on textual analysis performed on the set of text streams, and presenting the trending issue on the user interface of the application to enable an action to be performed to resolve the trending issue.