Context-Aware Task Segmentation for Multi-Layer Software Features

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Solution Overview

Problem

Existing issue tracking systems struggle to accurately and efficiently segment software feature development tasks into smaller, manageable units due to the complexity and interdependence of services and microservices in multi-layer service-oriented platforms, making it difficult to navigate and gather relevant context data.

Innovation Solution

A system leveraging context-aware artificial intelligence using large language models to generate and refine software feature development sub-tasks by aggregating data from various sources, including issue documents and external applications, and generating child issue documents based on user input.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual task segmentation is performed in existing issue tracking systems, then users can create sub-tasks, but the process becomes time-consuming and inefficient due to the complexity of multi-layer service-oriented platforms

Engineering Contradiction:

Improvetask segmentation efficiencyVSAvoidtime required for task segmentation

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system enables self-service task segmentation by automatically analyzing issue documents and generating candidate sub-tasks using AI models, allowing the system to serve itself rather than requiring manual user intervention for each segmentation decision

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary action by pre-generating candidate sub-tasks and presenting them to users before finalization, reducing the time needed for actual task creation by having the groundwork prepared in advance

Inventive Principle:

Principle #10Preliminary action

2Reliability

If users navigate multiple resources to gather context data, then comprehensive information can be obtained, but the complexity of navigation and data gathering increases

Engineering Contradiction:

Improvecompleteness of context dataVSAvoidsystem navigation complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system merges multiple context data sources including issue documents, external applications, and domain-specific knowledge bases into a unified context window, allowing comprehensive information gathering without requiring users to navigate multiple separate resources

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The AI model acts as an intermediary that automatically retrieves and synthesizes context data from various sources based on the issue document content, eliminating the need for users to manually navigate and gather information from multiple resources

Inventive Principle:

Principle #24Intermediary (Mediator)

3Ease of operation

If generic task segmentation methods are used, then simple issues can be handled, but accuracy and relevancy of sub-tasks decrease for complex software feature development

Engineering Contradiction:

Improvesimplicity of segmentation processVSAvoidaccuracy of task segmentation

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The system applies local quality by using domain-specific knowledge bases and entity relation graphs tailored to software feature development contexts, ensuring that the segmentation process and generated sub-tasks are accurately aligned with domain-specific requirements rather than using generic methods

Inventive Principle:

Principle #3Local quality

4Measurement precision

If comprehensive context data is aggregated from multiple sources, then task segmentation accuracy improves, but data processing time and computational resources increase

Engineering Contradiction:

Improvetask segmentation accuracyVSAvoiddata processing time

Core Design Contradiction:

Measurement precisionVSDuration of action of moving object

Solution Approach 1:

The system extracts only the most relevant context data from multiple sources based on the specific issue document being analyzed, rather than processing all available data, thus maintaining high accuracy while reducing unnecessary computational overhead

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS20260003579A1Intelligent and context-aware software feature development task segmentation in a multi-layer service-oriented platform

Publication Date: 2026.01.01 ATLASSIAN PTY LTD

AI summary

Intelligent and context-aware software feature development task segmentation in a multi-layer service-oriented platform is provided. A task segmentation request for an issue document hosted by a software application may be received. One or more context data sources for the issue document may be identified. Context data for the issue document may be aggregated based on the one or more context data sources. One or more candidate software feature development sub-tasks may be generated for the issue document using a large language model and based on the context data. One or more software feature development sub-tasks may be selected from the one or more candidate software feature development sub-tasks in response to receiving an indication of a candidate software feature development sub-task selection. One or more child issue documents corresponding to the one or more software feature development sub-tasks that is selected may be generated within the software application.