Issue Queue Grouping With Generative Assignment Recommendations

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Solution Overview

Problem

Existing issue tracking systems face challenges in efficiently accessing and compiling large volumes of dispersed information, making it difficult to manage and resolve technical issues effectively.

Innovation Solution

Implement a generative output engine to generate content for a graphical user interface of an issue tracking platform, including a recommendation panel with links to relevant resources, subject matter experts, and suggested actions, using semantic analysis and machine learning to identify similar issues and provide actionable insights.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional issue tracking interfaces are used, then system simplicity is maintained, but information access efficiency deteriorates when managing large volumes of dispersed data

Engineering Contradiction:

Improveinformation access efficiencyVSAvoidinterface complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

A generative AI assistant acts as an intermediary between users and the issue tracking system. The assistant processes natural language queries, retrieves relevant information from dispersed data sources, and presents synthesized results through conversational interfaces, reducing the complexity burden on users while improving information access efficiency

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

Traditional mechanical search and filtering mechanisms are replaced with generative AI models that understand semantic relationships between issues, automatically compile relevant information from disparate locations, and generate contextualized responses, thereby improving productivity without requiring complex user interactions

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Loss of time

If manual compilation of dispersed information is required, then system simplicity is maintained, but time consumption increases when managing large quantities of issues

Engineering Contradiction:

Improvetime to compile and access informationVSAvoidautomation level

Core Design Contradiction:

Loss of timeVSExtent of automation

Solution Approach 1:

The system performs preliminary actions by continuously indexing and pre-processing issue data from multiple sources before queries are made. When users need information, pre-organized data structures and semantic relationships enable rapid retrieval without manual compilation, significantly reducing time loss

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The generative AI assistant provides self-service by automatically compiling, filtering, and presenting relevant information from dispersed issue data without requiring user intervention in the data gathering process. The system serves itself by maintaining updated knowledge bases and generating responses autonomously

Inventive Principle:

Principle #25Self-service

3Loss of information

If detailed issue data is displayed, then information completeness is improved, but interface complexity and difficulty of operation increase

Engineering Contradiction:

Improveinformation completenessVSAvoiduser interaction ease

Core Design Contradiction:

Loss of informationVSEase of operation

Solution Approach 1:

Instead of displaying all issue data uniformly, the system applies local quality by dynamically filtering and presenting only the most relevant information for each specific query context. The generative AI identifies and highlights locally important details while omitting irrelevant data, maintaining information completeness where needed while simplifying the interface

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS20260003481A1Issue tracking platform having bulk action operations using a generative output engine

Publication Date: 2026.01.01 ATLASSIAN PTY LTD

AI summary

Embodiments described herein are directed to systems and methods for assigning issues in a graphical user interface of an issue tracking platform. An issue dashboard graphical user interface can include a first set of issue tickets. A prompt can be generated and include predetermined prompt text including grouping instructions, data extracted from the first set of issue tickets displayed in the issue dashboard graphical user interface, and data extracted from a second set of issues each having at least one value of a defined set of values assigned to the particular field. A generative response can be analyzed and cause display of a field assignment interface, where the field assignment interface includes the first set of issue tickets arranged into multiple groups of issue tickets and each group of the multiple groups is associated with a respective value. In response to a second user input, the respective value can be assigned to a corresponding group of the multiple groups of issue tickets.