AI-Generated Application Architecture for Faster Framework Creation
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Solution Overview
Problem
Application architecture design is a time-consuming process that requires extensive manual effort and resources, hindering the efficiency of software development.
Innovation Solution
Utilizing generative artificial intelligence (GenAI) to automate the creation of a development framework that documents the application architecture, reducing the time and resources needed for software development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual application architecture design is used, then design quality and completeness can be ensured, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The patent replaces manual mechanical design processes with an automated system that uses machine learning models and algorithms to generate application architecture. The system automatically processes input requirements, generates architectural components, and produces design documentation without manual intervention, thereby reducing time consumption while maintaining design quality through computational precision.
Solution Approach 2:
The patent introduces an intermediary automated architecture generation system that acts as a mediator between requirements input and final architecture design. This intermediary system processes requirements through multiple AI models and generation steps, transforming raw requirements into structured architecture documentation, thereby reducing direct manual effort while preserving design completeness.
2Loss of information
If manual application architecture design is used, then detailed technical components can be thoroughly documented, but extensive resources and effort are required
Solution Approach 1:
The patent replaces manual documentation processes with automated generation systems that use template-based approaches and AI-driven content creation. The system automatically generates comprehensive architecture documentation including technical components, data models, and operational procedures, ensuring completeness while eliminating manual effort through computational automation.
Solution Approach 2:
The patent employs preliminary action by pre-defining architecture templates, documentation structures, and component libraries before the actual design process. These pre-prepared elements are then automatically instantiated and customized based on input requirements, enabling rapid generation of complete documentation without manual creation of each element from scratch.
3Productivity
If automated methods are used to reduce design time, then productivity improves, but design complexity and precision may be compromised
Solution Approach 1:
The patent segments the architecture generation process into multiple distinct stages, each handled by specialized AI models or algorithms. The process is divided into requirement analysis, component generation, relationship definition, and documentation creation phases. This segmentation allows each stage to be optimized independently for both speed and precision, maintaining high design quality while achieving automation-driven productivity.
Solution Approach 2:
The patent incorporates feedback mechanisms where the automated system evaluates generated architecture designs against predefined quality criteria and requirements. The system iteratively refines its output based on this feedback, ensuring that automated generation maintains design precision and completeness while achieving high productivity through reduced manual revision cycles.
Data Source
AI summary
Disclosed are various embodiments for automating the creation of an application architecture. A description is received for an artifact in a first layer of a development framework. Then, a second set of artifacts in a second layer of the development framework is identified based at least in part on the artifact in the first layer of the development framework. Next, a prompt is generated that comprises the description for the artifact and the set of artifacts. Subsequently, the prompt is sent to a generative AI service.