Cloud Financial Operations Module Automating Code Generation

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Solution Overview

Problem

Current methods for automating cloud financial operations management are inefficient due to drift issues and manual errors, particularly when translating optimizations into code across multiple cloud providers.

Innovation Solution

A platform-agnostic cloud financial operations management module utilizing Generative AI to automatically generate and implement code specific to cloud environments, dynamically correcting drift issues and reducing manual intervention.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual translation of cloud financial operations management optimizations into code is performed, then code accuracy can be verified, but development time and effort increase significantly

Engineering Contradiction:

Improvecode accuracyVSAvoiddevelopment time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system enables self-service automated code generation where the AI model automatically translates cloud financial operations management optimizations into infrastructure code without requiring manual intervention from developers, thus reducing development time while maintaining accuracy through automated verification processes

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical manual coding process with an AI-based automated system that uses machine learning models to generate, validate, and deploy infrastructure code, substituting human manual translation with intelligent automated translation that operates faster and with consistent accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If multiple manual steps are involved in translating optimizations into code, then each step can be quality checked, but error susceptibility increases

Engineering Contradiction:

Improvequality controlVSAvoidmanual errors

Core Design Contradiction:

ReliabilityVSObject-generated harmful factors

Solution Approach 1:

The system merges multiple separate manual quality check steps into a single integrated automated validation process where the AI model performs generation, validation, and deployment in one unified workflow, eliminating the accumulation of errors that occur across multiple manual handoff points

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The patent introduces an AI model as an intermediary between the optimization requirements and the infrastructure code, acting as a intelligent mediator that automatically translates and validates the code without requiring multiple manual intervention steps, thus reducing error susceptibility while maintaining quality control

Inventive Principle:

Principle #24Intermediary (Mediator)

3Productivity

If cloud financial operations management is automated without IaaC process, then deployment speed increases, but environment drift and inconsistencies occur

Engineering Contradiction:

Improvedeployment speedVSAvoidenvironment consistency

Core Design Contradiction:

ProductivityVSStability of the object's composition

Solution Approach 1:

The system performs preliminary action by generating and validating the infrastructure code before deployment, ensuring that the code is ready for immediate deployment without requiring manual review steps, thus maintaining fast deployment speed while ensuring environment consistency through pre-deployment validation

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements feedback mechanisms where the AI model continuously monitors deployment status and environment state, providing real-time feedback to detect and correct any drift or inconsistencies, thus maintaining both fast deployment speed and environment consistency through automated monitoring and correction

Inventive Principle:

Principle #23Feedback

4Loss of time

If AI LLM is used for automated code generation, then development time reduces, but code specificity to cloud platforms must be ensured

Engineering Contradiction:

Improvecode generation timeVSAvoidcloud platform specificity

Core Design Contradiction:

Loss of timeVSAdaptability or versatility

Solution Approach 1:

The system applies local quality by training the AI model on platform-specific infrastructure code patterns and conventions, enabling the model to generate code with the appropriate local characteristics for each cloud platform (AWS, Azure, GCP) while maintaining fast automated generation, thus ensuring both speed and platform specificity

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS12306838B2System and method for automating cloud financial operations management optimizations

Publication Date: 2025.05.20 JPMORGAN CHASE BANK NA

AI summary

Various methods and processes, apparatuses/systems, and media for automating cloud financial operations management optimizations are disclosed. A processor implements a cloud financial operations management module (CFOMM) and establishes a communication link among the CFOMM, a plurality of infrastructure observability systems, and a plurality of Infrastructure as a Code (Iaac) systems of record (SOR) via a communication interface. The processors triggers the CFOMM through events from the infrastructure observability systems on cloud financial operations management based metrics and alerts data identifying environments to be corrected associated with a cloud; identifies IaaC code's originating code repository and related materials through corresponding SOR; implements an AI LLM to automatically generate new code specific to the environments to be corrected based on predefined rules received from a rule database and the code received from the originating code repository; and automatically corrects the environments associated with the cloud by implementing the new code.