Generative AI Software Application Prototyping
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Solution Overview
Problem
Software application development is time-consuming and costly, requiring significant expertise and resources, and existing methods do not efficiently simplify the process to accommodate multiple hardware configurations and online server connections.
Innovation Solution
A system and method that uses a generative AI to converse with users to determine software application features and generate a prototype based on customer inputs, including estimating linkages between features and using application information to create a buildcard, which can be used to recommend launch screens and generate instant applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional software application development methods are used, then the application can be developed with full functionality and compatibility, but the development time and cost increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically generating application prototypes, build cards, and development specifications before actual development begins. The generative AI creates functional specifications, UI/UX designs, and code structures in advance, allowing developers to start with pre-prepared frameworks rather than building from scratch.
Solution Approach 2:
The system enables self-service by allowing non-technical users to describe their application needs in natural language, and the generative AI automatically transforms these descriptions into detailed development specifications, reducing dependency on technical expertise and manual development processes.
2Reliability
If traditional software application development methods are used, then the application can be developed with full functionality and compatibility, but the development cost increases significantly
Solution Approach 1:
The system uses copying by generating application templates and prototypes that can be reused across multiple projects. The generative AI creates standardized code structures, UI components, and integration frameworks that serve as reusable assets, reducing the need to develop everything from scratch for each new application.
Solution Approach 2:
The system achieves universality by creating a multi-functional generative AI platform that can handle various aspects of application development including specification generation, UI design, code generation, and testing. This single platform performs multiple functions that traditionally required separate tools and expert personnel.
3Adaptability or versatility
If manual processes are used to determine customer needs and generate application prototypes, then the process can be customized, but the time and resources required increase
Solution Approach 1:
The system implements feedback loops where the generative AI continuously refines application specifications and prototypes based on user input and validation. The system analyzes customer requirements, generates initial prototypes, receives feedback, and automatically iterates to improve the design, maintaining customization while accelerating the process through automated iteration.
Solution Approach 2:
The system enables rapid parameter changes by allowing users to modify application specifications through natural language descriptions, and the generative AI automatically adjusts the generated code, UI designs, and configurations. This allows easy customization without manual reprogramming or lengthy modification processes.
Data Source
AI summary
Aspects of the present disclosure involve a computer system and method for generating a software application. The system and method engage in a conversation with a user via a chat module about an idea for a software application, identify one or more features of the software application based on the conversation with the user, and convert the one or more features into a machine-readable specification for generating the software application.