Code Change Scheduling Conflict Detection Using ML Impact Analysis

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Solution Overview

Problem

Existing systems face challenges in efficiently scheduling code changes to avoid conflicts that can cause delays, disruptions, or failures in electronic ecosystems due to overlapping changes, especially when they occur during non-operational hours, making it difficult to determine the impact on interconnected elements.

Innovation Solution

Utilizing advanced computation models, particularly machine learning, to analyze and automate decision-making for code change scheduling, identifying conflicts and optimizing change times to minimize ecosystem disruption by comparing proposed changes with planned changes, determining affected operations, and adjusting schedules accordingly.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If multiple code changes are scheduled at similar times due to operational and schedule constraints, then scheduling efficiency is improved, but conflicts and incidents occur from multiple code changes occurring simultaneously

Engineering Contradiction:

Improvescheduling efficiencyVSAvoidcode change execution reliability

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system performs preliminary analysis of code change dependencies and potential conflicts before scheduling changes. By evaluating the impact of proposed changes against the electronic ecosystem and existing scheduled changes in advance, the system identifies potential conflicts and adjusts schedules proactively, preventing simultaneous execution of conflicting changes while maintaining efficient scheduling.

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If advanced computation models are used to determine affected operations and detect conflicts, then accuracy of conflict detection is improved, but computing resource usage increases

Engineering Contradiction:

Improveconflict detection accuracyVSAvoidcomputing resource usage

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system segments the electronic ecosystem into distinct elements and groups code changes by their affected elements. By dividing the analysis into smaller, element-specific subsets rather than analyzing all changes against the entire ecosystem, the system maintains high detection accuracy while significantly reducing computing resource requirements through localized analysis.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs partial analysis by focusing computational resources only on code changes that have overlapping time ranges and affect the same ecosystem elements. Rather than analyzing all possible change combinations, the system applies computation selectively to relevant subsets, achieving accurate conflict detection with reduced resource consumption.

Inventive Principle:

Principle #16Partial or excessive action

3Device complexity

If manual input and analysis are used for code change scheduling, then system complexity is reduced, but process efficiency and accuracy deteriorate

Engineering Contradiction:

Improvesystem complexityVSAvoidprocess efficiency

Core Design Contradiction:

Device complexityVSProductivity

Solution Approach 1:

The system automatically analyzes code change proposals, determines affected operations, detects conflicts, and adjusts schedules without requiring manual input. The automated computation models independently evaluate changes against the electronic ecosystem and existing schedules, eliminating manual analysis while maintaining low system complexity through rule-based automated decision-making.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS12541362B2System and method of using advanced computation models for data analysis and automated decision-making for code change scheduling conflicts

Publication Date: 2026.02.03 BANK OF AMERICA CORP

AI summary

Systems, computer program products, and methods are described herein for advanced computation models for data analysis and automated decision-making for code change scheduling conflicts. The present disclosure is configured to receive a proposed code change associated with an electronic ecosystem from an end user; compile a set of planned code changes within a code change database; compare the proposed code change against the set of planned code changes; determine a set of affected operations by the proposed code change and the set of planned code changes within the electronic ecosystem using the advanced computation models for data analysis and automated decision-making; determine if a code change scheduling conflict exists between the proposed code change and the set of planned code; and transmit a notification to the end user upon determination of the code change scheduling conflict indicating the proposed code change conflicts with the set of planned code changes.