AI Form Field Mapping for Legacy Configuration Migration

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Solution Overview

Problem

Legacy software systems often lack flexibility in handling database fields, necessitating the use of generic fields that require manual mapping and programming to adapt forms across different client systems, leading to inefficiencies and potential errors.

Innovation Solution

An AI-based automatic form conversion system using machine learning to analyze client form structures, determine relational maps between generic and standard fields, and convert forms dynamically without human intervention, enabling standardized form creation across multiple client systems.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If generic fields are used to store common items in legacy software, then database entries shortage is overcome, but manual mapping and programming are required to adapt forms across different client systems

Engineering Contradiction:

Improveflexibility in storing and retrieving dataVSAvoidmanual mapping and programming requirements

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system enables self-service by allowing the form conversion process to automatically map generic fields to standard fields without requiring manual programming or configuration. The AI-based system autonomously analyzes client form structures and generates conversion mappings, eliminating the need for developers to manually program adaptation logic for each client system.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical manual mapping process with an AI-based automated system. Instead of requiring developers to manually analyze and map generic fields to standard fields for each client, the system uses machine learning models to automatically perform this transformation, substituting human expertise with an automated intelligent system.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If manual mapping is performed for generic fields, then form adaptation across client systems is achieved, but processing time and labor increase

Engineering Contradiction:

Improveform adaptation capabilityVSAvoidprocessing time for form conversion

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system performs preliminary action by pre-processing and analyzing client form structures before actual form conversion is needed. The AI model analyzes and stores mapping relationships between generic and standard fields in advance, so that when form conversion is required, the system can quickly retrieve and apply pre-established mappings without time-consuming manual analysis.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces time-consuming manual mapping operations with an automated AI-based system that instantly generates form conversions. The machine learning model processes form structures and generates standardized outputs much faster than manual programming, dramatically reducing processing time while maintaining adaptability across client systems.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Adaptability or versatility

If generic fields are used in legacy software, then database flexibility is improved, but errors may occur during manual mapping processes

Engineering Contradiction:

Improvedatabase field flexibilityVSAvoidaccuracy of form conversion

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent replaces error-prone manual mapping processes with an automated AI-based system that consistently applies learned mapping rules. The machine learning model analyzes patterns in client form structures and generates accurate, repeatable conversions to standard fields, eliminating the variability and errors that occur during manual programming and mapping operations.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system incorporates feedback mechanisms where the AI model continuously learns from and refines its mapping based on analyzed client form structures. This feedback loop ensures that the mapping accuracy improves over time, and the system can adapt to nuanced variations in client forms while maintaining high reliability in the conversion process.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250363287A1Systems and methods for form generation using artificial intelligence based data configuration migration

Publication Date: 2025.11.27 CDK GLOBAL LLC

AI summary

Methods and systems for automatic form conversion are disclosed. An example method includes: receiving a plurality of client forms from a plurality of client systems; evaluating a client form structure comprising one or more generic fields for a client system of the plurality of client systems in a form specific language by an artificial intelligence (AI) model, including analyzing one or more client forms from the client system by the AI model; looking up a client configuration of the client system by the AI model; and determining a relational map between the one or more generic fields in the client form structure for the client system and one or more standard fields in a standard form structure.