Generative Intake Request Types for Issue Clustering and Routing

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Solution Overview

Problem

Existing issue tracking systems face challenges in efficiently managing large volumes of issues due to difficulties in accessing dispersed information and creating appropriate request types, leading to inefficient issue resolution and longer resolution times.

Innovation Solution

An issue tracking system that utilizes a generative output engine to analyze issue data and suggest new request types, including intake forms and workflows, based on clustering analysis and user feedback, to improve issue management efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If manual creation of request types is used in traditional issue tracking systems, then users can create customized request types, but the process is time-consuming and complex requiring manual analysis of issue patterns

Engineering Contradiction:

Improverequest type customizationVSAvoidtime to create request types

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system performs self-service by automatically analyzing issue data patterns and generating request type definitions without requiring manual user intervention. The generative AI model autonomously clusters issues, identifies patterns, and creates customized request types based on the analyzed data, eliminating the need for users to manually create and configure request types.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary action by proactively analyzing issue patterns and pre-generating request type definitions before users need them. The continuous monitoring and automatic clustering of issues allows the system to prepare customized request types in advance, making them immediately available when needed without requiring time-consuming manual creation.

Inventive Principle:

Principle #10Preliminary action

2Ease of operation

If traditional issue tracking interfaces are used, then basic issue tracking is possible, but accessing and compiling dispersed information from multiple locations is difficult and inefficient

Engineering Contradiction:

Improveinformation accessibilityVSAvoidinformation dispersion

Core Design Contradiction:

Ease of operationVSLoss of information

Solution Approach 1:

The system merges dispersed information from multiple locations and sources into a unified view. By integrating data from various issue tracking locations and combining them through AI-powered analysis, the system presents consolidated information in a single accessible interface, eliminating the need for users to manually search and compile information from disparate sources.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The generative AI model acts as an intermediary between dispersed information sources and users. It automatically retrieves, analyzes, and synthesizes information from multiple locations, then presents the compiled results to users in a unified format, serving as a mediator that bridges the gap between scattered data and user needs.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Productivity

If generic request types are used in issue tracking, then setup is simpler, but misclassification of issues occurs leading to longer resolution times

Engineering Contradiction:

Improveissue resolution speedVSAvoidissue classification accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system applies local quality by creating customized request types tailored to specific issue patterns and characteristics. Instead of using uniform generic categories, the AI analyzes local patterns in issue data and generates specialized request type definitions that match the specific qualities and nuances of different issue types, improving classification precision for each local context.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system dynamically changes parameters by automatically adjusting request type definitions based on analyzed issue patterns. The generative AI model modifies classification criteria, field requirements, and routing parameters according to the specific characteristics discovered in the issue data, allowing the system to adapt its classification parameters to achieve both high accuracy and efficient resolution.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12530696B1Issue tracking platform with a generative service for suggesting and creating new intake request types for issue processing

Publication Date: 2026.01.20 ATLASSIAN PTY LTD

AI summary

Embodiments described herein relate to systems and methods for generating new request types for an intake portal of an issue tracking platform. The methods include determining that a threshold number of issues satisfy a clustering criteria and causing generation of a prompt. The prompt includes issue data extracted from the request, predetermined prompt text including example data fields corresponding to one or more defined request types and a request to identify one or more data fields for a new request type corresponding to the issues. The prompt can be provided to a generative output engine. The methods can include receiving a generative response, analyzing the generative response and causing display of the recommendation panel. The recommendation panel can include a suggested new request type generated using the suggested data fields and an option to create the suggested new request type for the intake portal of the issue tracking platform.