AI ORM Validation Framework for Real-Time Error Detection

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Solution Overview

Problem

Existing Object-Relational Mapping (ORM) frameworks do not effectively identify mapping issues between objects and relational databases until runtime, leading to inefficiencies in code development and manual overhead during compilation.

Innovation Solution

The use of artificial intelligence (AI)-based approaches to validate ORM metadata during the compilation of source code, utilizing natural language processing (NLP) models to compare ORM metadata with database attributes and identify potential mapping issues.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If ORM frameworks are used to map objects to relational databases, then code development flexibility is improved, but mapping errors can only be detected at runtime causing delays

Engineering Contradiction:
Improvecode development flexibilityVSAvoidcode development time
Core Design Contradiction:
Ease of operationVSLoss of time

Solution Approach 1:

The patent applies preliminary action by performing ORM mapping validation during the code compilation phase rather than at runtime. The validation system checks ORM configurations, database schema compatibility, and mapping correctness before the code is executed, thereby detecting errors early in the development process and eliminating runtime delays.

Inventive Principle:
Principle #10Preliminary action

2Manufacturing precision

If manual review of ORM framework is performed, then mapping accuracy is improved, but additional manual overhead is introduced

Engineering Contradiction:
Improvemapping accuracyVSAvoidmanual overhead
Core Design Contradiction:
Manufacturing precisionVSDevice complexity

Solution Approach 1:

The patent implements self-service by creating an automated validation system that performs ORM mapping checks without requiring manual reviewer intervention. The system automatically analyzes ORM configurations, compares them against database schemas, and identifies mapping errors, thereby maintaining high mapping accuracy while eliminating the complexity of manual review processes.

Inventive Principle:
Principle #25Self-service

3Reliability

If ORM validation is performed at runtime, then error detection capability is improved, but code execution performance deteriorates

Engineering Contradiction:
Improveerror detection capabilityVSAvoidcode execution performance
Core Design Contradiction:
ReliabilityVSProductivity

Solution Approach 1:

The patent resolves this contradiction by performing all validation activities during the compilation phase before code execution. The validation system checks ORM mappings, database connectivity configurations, and schema compatibility in advance, ensuring high error detection capability while keeping the runtime execution path clean and performant without additional validation overhead.

Inventive Principle:
Principle #10Preliminary action

Data Source

PatentUS12327092B2Artificial intelligence (AI) framework to identify object-relational mapping issues in real-time
Publication Date: 2025.06.10 BANK OF AMERICA CORP
  • US12327092B2 patent drawing
  • US12327092B2 patent drawing
  • US12327092B2 patent drawing

AI summary

An artificial intelligence (AI) model may be trained to identify errors in mapping between relational databases and objects during code compilation. Multiple AI models may be used, with different models being associated with different programming frameworks, thereby making this technique framework agnostic. Errors in mapping between relational databases and objects may be identified based on comparison between object relational mapping (ORM) metadata (e.g., as identified using an AI natural language processing (NLP) model applied to one or more ORM files) and attributes associated with the relational databases.