Large Language Modules for Cross-Environment Visual Workflows
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Solution Overview
Problem
Existing artificial intelligence systems require significant memory, time, and specialized expertise to develop and deploy, hindering their widespread adoption in practical applications.
Innovation Solution
A system utilizing a configuration server with a processing element, large language models, and a shared memory with a language bridge to facilitate the creation of customizable functionality modules, enabling drag-and-drop visual programming and efficient memory usage across different operational environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If artificial intelligence systems are implemented in traditional systems, then system capabilities and functionality are improved, but memory consumption and development time increase significantly
Solution Approach 1:
The patent segments the AI system into modular functional blocks that can be independently deployed and executed. Each functional block performs a specific AI-related task (e.g., image recognition, natural language processing) and can be selectively activated based on application needs, reducing overall memory consumption while maintaining system capabilities.
Solution Approach 2:
The patent implements a nested architecture where functional blocks are contained within a container system that provides shared resources and coordination. The container structure allows multiple functional blocks to share common infrastructure (memory, processing resources), enabling efficient resource utilization and reduced overall memory footprint.
2Adaptability or versatility
If artificial intelligence systems are implemented in traditional systems, then system capabilities are improved, but development time and deployment complexity increase
Solution Approach 1:
By dividing the AI system into pre-defined functional blocks with standardized interfaces, the patent enables rapid assembly and deployment. Developers can select and configure needed functional blocks without building entire AI systems from scratch, significantly reducing development time while maintaining system capabilities.
Solution Approach 2:
The patent creates universal functional blocks that can be applied across multiple applications and domains. These standardized blocks with consistent interfaces can be reused and reconfigured for different purposes, eliminating the need to develop custom AI solutions for each application and thereby reducing overall development time.
3Adaptability or versatility
If artificial intelligence systems are implemented in traditional systems, then system capabilities are improved, but expertise requirements and operational complexity increase
Solution Approach 1:
The patent introduces a container system as an intermediary layer between the functional blocks and the end application. This container manages the complexity of AI system operations, providing standardized interfaces and abstraction that hide implementation details, thereby reducing expertise requirements for operators while maintaining advanced system capabilities.
Solution Approach 2:
The functional blocks are designed to be self-contained with built-in configuration and management capabilities. Each block can operate independently with predefined parameters, reducing the need for specialized expertise in system integration and operation while maintaining high system capabilities.
Data Source
AI summary
A system for creating functionality modules for deployment in a workflow for use in visual programming including a configuration server with a processing element operable to implement the functionality modules and workflow, at least one large language model, a customizable functionality module in a workflow including at least one interface defining one or more customizable properties, and wherein the workflow executes a first operational environment different from a second operational environment executed by the large language models.