NLP Translation of Program Modifications for Change Request Clarity

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Solution Overview

Problem

Existing systems struggle to accurately, efficiently, and dynamically determine natural language from computer programming language, making it difficult to fix and describe program issues to laypeople.

Innovation Solution

A system utilizing a trained NLP engine to identify change requests and modifications, generate natural language interpretations, and transmit them through a GUI, reducing resource usage and improving accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional methods are used to determine natural language from computer programming language, then the process is simple, but the accuracy is insufficient and resource consumption is high

Engineering Contradiction:

Improveaccuracy of natural language determinationVSAvoidcomputing resource consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The NLP engine is trained in advance using historical change requests, modifications, and natural language interpretations to build a knowledge base before actual use. This preliminary training enables the system to make accurate determinations during operation without consuming excessive computing resources in real-time

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system introduces an NLP engine as an intermediary component between computer programming language and natural language interpretation. This specialized intermediary processes the translation task efficiently, reducing the need for complex traditional parsing methods and associated resource consumption

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If dynamic natural language determination is implemented, then the system adapts to program changes, but the complexity of the system increases

Engineering Contradiction:

Improvedynamic adaptation to program changesVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system continuously receives feedback from change requests, modifications, and their natural language interpretations. This feedback loop enables the NLP engine to learn and adapt to new programming patterns and language styles dynamically, improving versatility without requiring complete system redesign

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The NLP engine performs self-training and self-improvement by automatically learning from historical data and evolving programming patterns. This self-service capability allows the system to adapt dynamically while minimizing the need for manual intervention and complex external management

Inventive Principle:

Principle #25Self-service

3Productivity

If efficient processing is implemented, then resource consumption is reduced, but the difficulty of accurately interpreting complex modifications increases

Engineering Contradiction:

Improveprocessing efficiencyVSAvoiddifficulty of interpreting complex modifications

Core Design Contradiction:

ProductivityVSDifficulty of detecting and measuring

Solution Approach 1:

The system segments complex modifications into smaller, manageable units of change. By breaking down complex programming modifications into individual elements, the NLP engine can process each segment efficiently while maintaining accurate interpretation of the overall modification

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system adds a natural language dimension to the interpretation process, transforming complex programming language modifications into comprehensible natural language explanations. This dimensional transformation makes complex modifications easier to detect and interpret without sacrificing processing efficiency

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

Data Source

PatentUS12547845B2Systems, methods, and apparatuses for implementing natural language processing to determine natural language from computer programming language in an electronic environment

Publication Date: 2026.02.10 BANK OF AMERICA CORP

AI summary

Systems, computer program products, and methods are described herein for implementing natural language processing to determine natural language from computer programming language in an electronic environment. The present disclosure is configured to identify at least one change request associated with at least one computer program to: identify at least one modification to the at least one computer program; apply the at least one change request and the at least one modification to a natural language processor (NLP); generate a natural language interpretation of the at least one modification; generate a modification interpretation interface component, wherein the modification interpretation interface component comprises a data packet of the natural language interpretation; and transmit the modification interpretation interface component to a user device associated with the at least one change request and configure a graphical user interface (GUI) of the user device with the modification interpretation interface component.