AI Engine Assembling Application Modernization Solutions

The patent creates a digital twin or virtual representation of SME knowledge through an AI engine that has been trained on extensive modernization solution data. This virtual expert system copies the decision-making patterns, knowledge base, and problem-solving approaches of multiple SMEs into a single automated system, allowing it to provide expert-level solutions without requiring actual SME involvement in each case.

The system performs preliminary action by pre-processing and organizing modernization solutions into structured data formats before they are needed. The AI engine is trained in advance on comprehensive datasets containing modernization patterns, best practices, and historical solutions, so that when a modernization request occurs, the system can quickly retrieve and apply pre-analyzed solutions rather than starting from scratch.

The system implements self-service by enabling automated assembly of modernization solutions without requiring human intervention. The AI engine autonomously analyzes the input modernization requirements, selects appropriate solutions from its knowledge base, assembles them into coherent plans, and presents them to users. This self-assembly capability eliminates the need for complex manual coordination between multiple SMEs and systems.

The patent creates a universal modernization assembly system that can handle multiple types of modernization tasks through a single platform. The AI engine is designed to process diverse modernization requirements (application modernization, infrastructure modernization, technology stack updates, etc.) using the same underlying architecture and solution assembly mechanisms, making the system adaptable to various modernization scenarios without requiring separate specialized systems.

The system applies dynamics by continuously updating and evolving its knowledge base of modernization solutions. Rather than using static historical data, the AI engine is designed to ingest new modernization patterns, best practices, and technology updates continuously, allowing the solution recommendations to adapt and remain current. This dynamic update mechanism ensures that while the system reuses historical solutions for efficiency, it also incorporates latest practices to maintain reliability.

The patent implements feedback mechanisms where the system learns from the outcomes of deployed modernization solutions. When modernization solutions are implemented in production environments, their performance and success metrics are fed back into the AI engine, allowing it to refine its recommendations and update its knowledge base. This feedback loop ensures that historical solutions are continuously validated and improved, maintaining both resource efficiency through reuse and reliability through currency.

The patent replaces the mechanical system of human SME consultation with an automated AI-based system. The AI engine uses machine learning algorithms, pattern recognition, and data analysis to perform modernization solution assembly, substituting human cognitive processes with computational ones. This substitution maintains high automation levels while preserving accuracy through the use of sophisticated algorithms that can analyze vast amounts of data and identify optimal solutions.

The system applies segmentation by breaking down the complex modernization solution assembly process into distinct modular components. The AI engine processes different aspects of modernization (application analysis, technology selection, implementation planning, risk assessment) as separate but coordinated tasks. This segmented approach allows each component to be optimized independently while maintaining overall solution accuracy, and enables the system to leverage specialized algorithms for different modernization challenges.