Predictive Issue Content Suggestion Engine
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Solution Overview
Problem
Conventional issue tracking systems require users to input duplicate or similar data repeatedly, leading to time and resource inefficiencies.
Innovation Solution
A networked issue tracking system that includes a client device and a host service with a predictive model to analyze and suggest issue content, such as issue types, complexities, and time estimates, based on user input, by correlating it with existing issue records, thereby reducing the need for repetitive data entry.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually input issue request data in conventional issue tracking systems, then complete issue records can be created, but users must repeatedly enter duplicate or similar data consuming time and resources
Solution Approach 1:
The system performs preliminary actions by analyzing historical issue records and pre-computing correlated content items before they are needed. When a user creates a new issue, the system has already prepared suggestions based on patterns from previous issues, eliminating the need for users to manually input duplicate information.
Solution Approach 2:
The system creates copies of relevant content from historical issue records and presents them as suggestions for the new issue. Instead of requiring users to re-enter data, the system copies and adapts proven content from similar past issues, such as correlated fields, descriptions, or metadata.
2Ease of operation
If the system analyzes and suggests issue content based on predictive models, then data entry time is reduced, but system complexity increases
Solution Approach 1:
The patent introduces a predictive model server as an intermediary component that handles the complex analysis between the issue tracking system and historical data. This mediator performs sentiment analysis, pattern recognition, and content correlation, shielding the user interface from complexity while providing intelligent suggestions.
Solution Approach 2:
The system segments the issue tracking functionality into distinct modules: the client application for user interaction, the host service for core tracking operations, and the predictive model server for intelligent analysis. This segmentation allows each component to specialize in specific tasks, managing overall system complexity through modular design.
3Reliability
If the predictive model correlates issue content with existing records, then suggestion accuracy improves, but processing time increases
Solution Approach 1:
The system applies partial action by not analyzing all historical issue records equally. Instead, it focuses on correlating with the most relevant recent or similar issues, performing sentiment analysis only when necessary, and using selective pattern matching to maintain accuracy while reducing processing time through intelligent sampling.
Data Source
AI summary
An issue tracking system configured to determine similarity between issue content items (e.g., title, type, description, and the like). Based on a determined similarity satisfying a threshold and/or using a predictive model, the issue tracking system may provide a user with a suggested supplemental content item to be submitted to the issue tracking system.