AI Modernization Disposition System for Legacy Applications
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Solution Overview
Problem
Legacy software and hardware environments become obsolete, making them difficult to maintain and integrate with modern systems, often requiring expensive consultants for modernization, and current methods lack explainability and efficiency in recommending migration paths.
Innovation Solution
An AI-based system uses neural word segmentation and machine learning to extract structured information from natural language problem statements, generating standardized entities and dispositions based on business constraints, recommending modernization paths and providing explainable recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods are used for legacy application modernization, then expertise and guidance are available, but cost is high and efficiency is low
Solution Approach 1:
The patent replaces manual expert consultation (mechanical human expertise) with an AI-based natural language processing system. The system automatically analyzes legacy application code, generates modernization recommendations, and provides guidance without requiring human experts, thereby maintaining reliability while dramatically improving efficiency and reducing costs.
Solution Approach 2:
The system enables organizations to perform self-service modernization analysis by automatically processing their own legacy codebases. The AI system extracts technical entities, business constraints, and disposition information directly from the code and documentation, allowing organizations to generate modernization roadmaps independently without external consultant intervention.
2Measurement precision
If manual analysis of legacy applications is performed, then detailed understanding is achieved, but time consumption is high
Solution Approach 1:
The patent replaces manual code analysis (mechanical human review) with automated natural language processing and machine learning algorithms. The system rapidly parses legacy code, identifies technical entities, understands business logic, and generates modernization recommendations automatically, achieving both high accuracy and fast processing speeds simultaneously.
Solution Approach 2:
The system performs preliminary automated analysis of the entire codebase before detailed modernization planning. By pre-identifying technical entities, dependencies, and modernization opportunities through automated scanning, the system prepares structured information that accelerates subsequent detailed analysis and recommendation generation.
3Reliability
If comprehensive modernization recommendations are generated, then decision quality is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex modernization recommendation system into distinct functional modules: natural language processing for extracting technical entities, business constraint identification, disposition information analysis, and recommendation generation. Each module handles a specific aspect of the analysis, making the overall complex system manageable and maintainable while delivering comprehensive recommendations.
Solution Approach 2:
The system introduces an intermediary AI processing layer between the legacy application code and the modernization recommendations. This intermediary automatically translates complex code analysis into structured, interpretable recommendations, simplifying the interface between technical analysis and decision-making without reducing recommendation quality.
4Measurement precision
If expert consultants are engaged for modernization, then guidance accuracy is high, but cost is high
Solution Approach 1:
The patent replaces expensive human expert consultants with an automated AI system that provides equivalent or superior guidance accuracy. The system leverages machine learning models trained on extensive modernization knowledge bases to deliver accurate, consistent recommendations at a fraction of the cost of human expert engagement.
Solution Approach 2:
The system captures and codifies expert knowledge into reusable AI models and knowledge bases. By copying and formalizing modernization expertise into structured algorithms and reference data, the system makes expert-level guidance available at scale without requiring actual experts, dramatically reducing cost while maintaining accuracy.
Data Source
AI summary
A method includes receiving a natural language problem statement corresponding to application modernization needs of a user, the natural language problem statement including at least one technical entity, business constraint and disposition information; providing structured information by extracting information from the natural language problem statement using a neural word segmentation method; generating standardized technical entities, standardized business entities, and standardized dispositions by inputting the structured information to at least one machine learning model; and generating at least one recommended disposition of at least one technical entity to a second technical entity based at least on a business constraint corresponding to the natural language problem statement using the standardized technical entities, business entities, and dispositions. Optionally, the at least one recommended disposition corresponds to one or more possible target environments along with explanation generated based on the business constraints and mentions of technical entities present in the natural language problem statement.