AI Interface Layer for Legacy Expert Software Access
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional domain expert software applications require extensive UI and training to access and interface with their underlying expertise, making it difficult to evolve with new paradigms without modifying the access surface.
Innovation Solution
Utilizing a generative Artificial Intelligence (AI), specifically a Large Language Model (LLM), to analyze and index the source code of expert systems, generating annotated component descriptions and enabling interaction through dynamic natural language interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional domain expert software applications are used, then domain specific knowledge and expertise are preserved in source code, but extensive UI and training are required to access and interface with the expertise
Solution Approach 1:
The patent introduces an AI intermediary layer that mediates between the user and the expert system's source code. The AI model analyzes the source code, understands the domain expertise embedded within it, and provides natural language interfaces that allow users to query and interact with the system without needing to understand the underlying complex code or undergo extensive training.
2Adaptability or versatility
If traditional expert systems are evolved over long periods, then domain specific knowledge is distilled into source code, but access surface modification (UI and API changes) is required to access new paradigms
Solution Approach 1:
The patent creates a virtual copy or representation of the expert system's functionality through AI-generated natural language interfaces. Instead of modifying the actual UI and APIs of the legacy system, the AI model creates an accessible copy that allows users to interact with the system's capabilities through natural language, preserving the original system while providing new access paradigms.
3Ease of operation
If AI analysis is performed on expert system source code, then annotated component descriptions are generated, but processing time and computational resources are required
Solution Approach 1:
The patent performs preliminary analysis of the expert system source code to generate annotated component descriptions and build the AI model's understanding of the system beforehand. This preprocessing step creates a knowledge base that enables rapid natural language interaction later, trading initial processing time for faster user interaction in the operational phase.
Data Source
AI summary
Embodiments of the present disclosure are directed to facilitating access and use of existing domain expert software applications through the use of Artificial Intelligence (AI) to analyze, distill and index source code of expert software systems and expose information through new and evolving fully dynamic natural language interfaces. More specifically, embodiments described herein are directed to dynamically building an interface to existing expert systems through recursive summarization of expert system logic (potentially decades of it) into condensed AI-generated summaries from the unlabeled source code. These summaries, describing the different parts of the expert system, can then be used to generate new means of interacting with the existing system without having to fully understand or write the interface.