Natural-Language Application Generation for Serverless Functions
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Solution Overview
Problem
Existing low-code environments for serverless computing require significant knowledge of underlying systems and infrastructure, making it difficult for non-developers to create complex applications without coding expertise.
Innovation Solution
A computer-implemented method using large language models (LLM) to automate application generation, enabling users to define application functionality through natural language conversations, integrating with serverless computing for seamless deployment and testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If visual compositors and low-code environments are used to compose serverless functions, then application composition capability is improved, but the knowledge requirement for users increases
Solution Approach 1:
The patent introduces a natural language processing intermediary that mediates between the user's simple text input and the complex serverless function composition system. The LLM translates natural language descriptions into function compositions, configurations, and deployments, eliminating the need for users to directly interact with complex visual compositors or understand underlying infrastructure details.
Solution Approach 2:
The patent replaces the mechanical interaction with visual compositors and manual configuration with an automated linguistic system. Instead of requiring users to manually drag-and-drop components or configure parameters through visual interfaces, the system uses natural language processing to automatically generate and deploy function compositions based on text descriptions.
2Adaptability or versatility
If manual configuration of constraints and parameters is required, then application functionality is improved, but development time increases
Solution Approach 1:
The patent performs preliminary action by having the LLM pre-process and automatically generate function compositions, configurations, and parameter settings based on natural language input. This preliminary automated generation eliminates the need for manual configuration steps, significantly reducing development time while maintaining full application functionality.
Solution Approach 2:
The system enables self-service by allowing the LLM to automatically handle function composition, configuration generation, and deployment based on user input. The system serves itself by translating natural language requirements into executable serverless function compositions without requiring manual intervention for each configuration step.
3Extent of automation
If existing low-code platforms are used, then application deployment capability is improved, but the learning curve increases
Solution Approach 1:
The patent replaces the mechanical learning process required for visual compositors with a natural language interface. Users no longer need to learn platform-specific visual tools, drag-and-drop interfaces, or configuration syntax - they simply describe their application needs in natural language, which the LLM translates into deployed serverless functions.
Solution Approach 2:
The patent creates a universal natural language interface that works across different serverless platforms and function types. Instead of requiring users to learn platform-specific visual compositors and their unique features, the LLM provides a single unified interface that handles diverse function composition, configuration, and deployment tasks across multiple platforms.
Data Source
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AI summary
The computer-implemented method for generating an application (A), comprises the steps of receiving an application task (DDA), splitting the application task into at least two or more functions using a first large language model (LDE), generating the functions using a second large language model (LFB) and generating the application (GFWFCET) involving linking the functions together. The application generator is configured to carry out this method.