AI Code Update Engine Using Embeddings for Data Source Compliance

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Solution Overview

Problem

Updating software applications to comply with changes in data sources, such as tax laws and regulations, is a complex and error-prone task that requires significant manual labor and is prone to human error due to the complexity and length of the task, and existing automated methods lack the semantic and syntactic insight needed for accurate code updates.

Innovation Solution

A method using embeddings and machine learning models to compare versions of data sources and software application code, generating data source difference summaries and application code change instructions, and updating the code to ensure compliance with the new data sources, utilizing techniques like BERT and GANs to ensure semantic and syntactic correctness.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If manual updating of software code is performed to comply with data source changes, then accuracy and semantic understanding can be maintained, but the time required and manual labor increase significantly

Engineering Contradiction:

Improvecode update accuracyVSAvoidcode update time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The patent introduces an intermediary system consisting of embedding models and language models that act as a mediator between the data source changes and the software code. The embedding model converts code and data into semantic representations, and the language model generates update instructions, thereby automating the code update process while maintaining accuracy through semantic understanding.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces the mechanical manual process of code review and updating with an automated system using machine learning models. The system automatically compares embeddings, identifies changes, and generates code update instructions, substituting human manual labor with an automated computational process that maintains high accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Speed

If automated diff operations are performed on data sources, then comparison speed increases, but semantic insight and understanding of code implications are lost

Engineering Contradiction:

Improvedata source comparison speedVSAvoidsemantic insight

Core Design Contradiction:

SpeedVSLoss of information

Solution Approach 1:

The patent transforms the data source and code into a different parameter space using embedding models. Instead of comparing raw text or code directly, the system compares semantic embeddings, which preserves semantic information while enabling efficient automated comparison. This parameter transformation allows both speed and semantic understanding to coexist.

Inventive Principle:

Principle #35Parameter changes

3Reliability

If comprehensive code updates are performed to ensure full compliance with data source changes, then reliability improves, but the complexity of the update process increases

Engineering Contradiction:

Improvecompliance reliabilityVSAvoidupdate process complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent segments the code base into modular units (functions, classes, modules) and processes each segment independently by generating update instructions for specific code portions. This segmentation reduces the overall complexity of the update process while ensuring comprehensive compliance coverage across the entire code base.

Inventive Principle:

Principle #1Segmentation

4Adaptability or versatility

If existing code is updated to match new data sources, then compliance is achieved, but the existing code structure and logic may be disrupted

Engineering Contradiction:

Improvedata source adaptabilityVSAvoidcode structure stability

Core Design Contradiction:

Adaptability or versatilityVSStability of the object's composition

Solution Approach 1:

The patent incorporates a feedback mechanism where the generated code updates are evaluated against the original code structure and requirements. The system can iterate and refine update instructions to maintain code structure stability while achieving adaptability to new data sources, ensuring that updates are both compliant and structurally sound.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20260003609A1Generative ai system for automated programming and accelerated code modification

Publication Date: 2026.01.01 INTUIT INC

AI summary

Aspects of the present disclosure relate to automatically updating a software application to ensure compliance with an updated data source. Embodiments include using an embedding of a first version of a data source and an embedding of a second version of the data source to generate a data source difference summary. Embodiments further include providing the data source difference summary to a code update engine configured to generate an updated version of the software application code module based on the data source difference summary. Embodiments further include updating code of the software application using the updated version of the software application code module.