Description:FIELD OF INVENTION
The present invention relates to the field of cloud computing. More particularly, the invention relates to a system and method to process organization invoices from cloud providers to identify, manage and optimize cloud expenses.

BACKGROUND
Cloud computing refers to a system which delivers computing resources, including storage, servers, and applications over the internet, thereby facilitating remote work and global collaboration as it allows users to access and utilize such resources without owning or managing the underlying infrastructure. Moreover, cloud computing enables scaling of resources to meet changing demands, thereby ensuring optimal performance and efficient resource utilization. Further, cloud-based solutions offer robust disaster recovery capabilities which ensure business continuity in the event of unforeseen circumstances.

A cloud computing system encompasses costs for storage, computing, networking, and other services, all of which require careful management as such expenses can quickly escalate if not monitored and optimized. Organizations frequently overprovision, underutilize, or misclassify resources which leads to unnecessary expenditures. For example, teams may allocate resources which are larger than necessary, leading to unused capacity and higher costs, thereby adversely affecting financial stability.

The effective management of cloud computing expenses presents various challenges, such as static cost reports which lack visibility into complex pricing models and forecasting costs. While various dashboard tools exist which perform real-time data monitoring, decision-making, and performance tracking, they often lack contextual awareness, predictive modelling, and explainability. In this context, organization invoices, which refer to structured records of financial transactions maintained by a business to ensure accurate bookkeeping, timely payments, financial reporting, and operational efficiency, play a crucial role. To overcome the drawbacks of the existing dashboard tools and static cost reports, it becomes essential to have an intelligent processing system whereby such invoices are processed in a manner that enables delivery of actionable recommendations for cost-saving opportunities within cloud computing systems.
Prior Arts:

US9785983B2 discloses a system which receives and pre-processes billing information. The system applies one or more predictive models to the pre-processed billing information to identify billing errors. The results could be optionally sent to, and reviewed by, third party auditors, whereby their feedback could be incorporated into the results. A final report is generated by the system which indicates billing errors that require correction, thereby allowing an entity to correct such errors and prevent revenue leakage.

US20180197161A1 introduces a system for multi-layered billing in a cloud service brokerage is provided to apply the service vendor pricing rules and the service vendor billing rules, and to calculate settlements and perform reconciliations.

TW201432593A introduces an invoice cloud management system includes a CPU unit, a data-reading unit, a data-identifying unit, a data storage unit and a data transmission unit. The system identifies and classifies the account datum to obtain at least one identified account datum; transmitting the identified account datum to a cloud computing center; comparing the identified account datum with predetermined data so as to decide generating a winner datum or not.

Prior arts hereinabove deal with systems for pre-processing billing information, structuring billing, and cloud-based invoice management. While the existing systems deal with processing invoices in a cloud management system, there lacks a system that utilizes the said billing information or organization invoices to analyse spending, complex pricing models, and forecasting costs to assist effective resource optimization. Thus, there is a need to introduce a system that transcends existing dashboard tools and static cost reports by utilizing such organisation invoices in an intelligent manner so that there is contextual awareness, predictive modelling, and explainability while identifying cost-saving opportunities within cloud management systems.

DEFINITIONS:
The expression “system” used hereinafter in this specification refers to an ecosystem comprising, but is not limited to a system with a user, input and output devices, processing unit, plurality of mobile devices, a mobile device-based application to identify dependencies and relationships between diverse businesses, a visualization platform, and output; and is extended to computing systems like mobile, laptops, computers, PCs, etc.
The expression “input unit” used hereinafter in this specification refers to, but is not limited to, mobile, laptops, computers, PCs, keyboards, mouse, pen drives or drives.
The expression “output unit” used hereinafter in this specification refers to, but is not limited to, an onboard output device, a user interface (UI), a display kit, a local display, a screen, a dashboard, or a visualization platform enabling the user to visualize, observe or analyse any data or scores provided by the system.
The expression “processing unit” refers to, but is not limited to, a processor of at least one computing device that optimizes the system.
The expression “API” used hereinafter in this specification stands for Application Programming Interface. It is a set of protocols, routines, and tools for building software and applications. An API specifies how software components should interact and allows different software systems to communicate with each other.
The expression “SHAP values” used hereinafter in this specification stands for SHAP (SHapley Additive exPlanations). These values are a powerful tool for interpreting machine learning models. They provide a way to understand the contributions of each input feature to the model’s predictions.
The expression “Seasonal and Trend Decomposition using Loess (STL)” used hereinafter in this specification refers to a popular method for time series decomposition, widely used in forecasting and econometrics.

OBJECTS OF THE INVENTION:
The primary object of the invention is to provide a system and method for processing organization invoices to identify cloud cost-saving opportunities.
Another object of the invention is to transcend existing dashboard tools and static cost reports by introducing a system and method which is context-aware, predictive and explainable while identifying cost-saving opportunities within cloud management systems.
Yet another object of the invention is to eliminate unnecessary organization expenditures due to overprovision, underutilization, or misclassification of resources.

SUMMARY
Before the present invention is described, it is to be understood that the present invention is not limited to specific methodologies and materials described, as these may vary as per the person skilled in the art. It is also to be understood that the terminology used in the description is for the purpose of describing the particular embodiments only and is not intended to limit the scope of the present invention.

The present invention describes a system and method for processing organization invoices to identify cloud cost savings opportunities. The system processing cloud provider invoices in conjunction with usage data and rate cards to extract optimization opportunities through a unique, explainable, and predictive algorithm referred to as SILO-MAPEX. The system integrates invoice ingestion, real-time telemetry analysis, statistical modelling, and machine learning to deliver high-confidence, explainable recommendations for reducing cloud infrastructure costs. The SILO-MAPEX algorithm represents a unique, statistically rigorous, and business-context-aware approach that transcends existing cloud dashboards and static cost reports.
According to an aspect of the present invention, the system comprises of an input unit, a processing unit configured to execute instructions, a memory storing invoice data, rate cards, and usage logs; and output unit, wherein the processing unit further comprises of invoice ingestion module, rate card and usage integration layer module, temporal and behavioural modelling engine module, silo-Mapex algorithm engine module, optimization report generator module, API & dashboard layer module, and feedback loop module.
According to an aspect of the present invention, the system and method for intelligent analysis of cloud infrastructure billing data processes cloud invoices, usage metrics, and rate card information to discover cost inefficiencies. Using a proprietary algorithm, SILO-MAPEX, the system performs intent mapping, load modelling, overlap detection, time-series forecasting, anomaly detection, and predictive ROI analysis. Optimization actions are explained using SHAP-based rationale for maximum transparency.

BRIEF DESCRIPTION OF DRAWINGS
A complete understanding of the present invention may be made by reference to the following detailed description which is to be taken in conjugation with the accompanying drawing. The accompanying drawing, which is incorporated into and constitutes a part of the specification, illustrates one or more embodiments of the present invention and, together with the detailed description, it serves to explain the principles and implementations of the invention.

FIG.1 illustrates a system architecture flow diagram showing key components as described in the present invention
FIG. 2 illustrates the workflow of the method of the present invention.

DETAILED DESCRIPTION OF INVENTION:
Before the present invention is described, it is to be understood that this invention is not limited to methodologies described, as these may vary as per the person skilled in the art. It is also to be understood that the terminology used in the description is for the purpose of describing the particular embodiments only and is not intended to limit the scope of the present invention. Throughout this specification, the word “comprise”, or variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps. The use of the expression “at least” or “at least one” suggests the use of one or more elements or ingredients or quantities, as the use may be in the embodiment of the invention to achieve one or more of the desired objects or results. Various embodiments of the present invention are described below. It is, however, noted that the present invention is not limited to these embodiments, but rather the intention is that modifications that are apparent are also included.
The present invention describes a system and method for processing organization invoices to identify cloud cost savings opportunities. The system processing cloud provider invoices in conjunction with usage data and rate cards to extract optimization opportunities through a unique, explainable, and predictive algorithm referred to as SILO-MAPEX. The system integrates invoice ingestion, real-time telemetry analysis, statistical modelling, and machine learning to deliver high-confidence, explainable recommendations for reducing cloud infrastructure costs. The SILO-MAPEX algorithm represents a unique, statistically rigorous, and business-context-aware approach that transcends existing cloud dashboards and static cost reports.
According to the embodiment of the present invention, the system comprises of an input unit, a processing unit configured to execute instructions, a memory storing invoice data, rate cards, and usage logs; and output unit, wherein the processing unit further comprises of invoice ingestion module, rate card and usage integration layer module, temporal and behavioural modelling engine module, silo-Mapex algorithm engine module, optimization report generator module, API & dashboard layer module, and feedback loop module.
According to the embodiment of the present invention, the function of the modules as described in FIG.1 are described hereunder:
1. Invoice Ingestion Module
Invoice ingestion module is responsible for extracting, parsing, and structuring invoice data received from various cloud service providers. It accepts formats including CSV, JSON, and PDF (using OCR where necessary). The module maps raw billing lines into a unified schema with fields such as resource_id, usage_hours, billing_period, service_type, tags, and cost. It may include logic to reconcile billing line-items across multi-account structures or linked billing accounts.
2. Rate Card and Usage Integration Layer module:
This module communicates with cloud provider APIs to fetch the latest pricing and consumption data for compute, storage, network, and managed services. The module also maps service identifiers from invoices to corresponding usage logs, aligning resource lifecycles, configurations, and time-based usage patterns. Examples include AWS Cost Explorer, Azure Cost Management API, and GCP Billing Catalog APIs.
3. Temporal and Behavioural Modelling Engine module:
This module performs deep temporal analysis of historical usage and cost behaviour. It applies methods such as Seasonal-Trend decomposition using LOESS (STL), moving averages, and entropy scoring to extract behavioural patterns. This module enables detection of cyclic usage patterns (e.g., batch jobs), seasonal spikes, and idle trends. Behavioural labels (e.g., bursty, idle, stable) are generated for each resource.
4. SILO-MAPEX Algorithm Engine module:
The core of the system, this module sequentially applies the SILO-MAPEX logic:
• Intent Classification using BERT or LDA over tags, resource names, and descriptions.
• Load Elasticity Modelling to compute provisioning vs. actual usage gaps.
• Overlap Graph Construction using cosine similarity and graph metrics.
• Time-Series Forecasting using Prophet, LSTM, or Holt-Winters for anomaly detection.
• Predictive Classification (e.g., Random Forest, XGBoost) to score actionability.
• Explainability using SHAP values to provide reasons for each recommendation.
5. Optimization Report Generator module:
This module compiles output from the algorithm engine module, filters actionable items based on confidence and business rules, and generates a structured report. It provides JSON, PDF, or interactive UI formats with the following fields:
• Resource Identifier
• Recommended Action
• Estimated Savings (USD)
• Confidence Level
• Explanation (Human-readable and SHAP-based)
6. API & Dashboard Layer module
This optional module provides RESTful endpoints (RESTful endpoints are URLs that act as points of contact between an API client and an API server) and a user interface for viewing recommendations, exploring historical optimization decisions, adjusting thresholds, and exporting reports. It may integrate with tools like Jira or ServiceNow to trigger optimization workflows.
7. Feedback Loop Module
This module captures historical actions taken on recommendations and their realized outcomes (e.g., actual savings). Retrains the predictive models periodically using this data, increasing precision and relevance over time.
According to an embodiment of the present invention, the SILO-MAPEX Algorithm comprises of the following sub modules:
• Intent Classification: Maps services to business functions using NLP.
• Load Elasticity Modelling: Identifies under- or over-provisioned resources.
• Overlap Detection: Uses graph analytics and embeddings to flag redundant deployments.
• Modelling & Anomaly Detection: Forecasts expected consumption using Prophet, LSTM, and detects outliers with residual analysis.
• Predictive Optimization Estimator: Applies machine learning to rank savings recommendations by likelihood and ROI.
• Explainability Layer: Applies SHAP values to explain each action.
According to an embodiment of the present invention, the method for identifying optimization opportunities in cloud infrastructure costs as described in FIG. 2, comprising the steps of:
• ingesting cloud invoices and normalizing them into a structured format;
• integrating pricing data and time-series usage metrics;
• classifying cloud resources by operational intent using natural language processing techniques;
• computing load elasticity scores to determine underutilized or overprovisioned resources;
• constructing a graph to identify overlapping or redundant cloud services;
• predicting expected usage using time-series models and identifying deviations indicative of inefficiencies;
• ranking identified optimization opportunities using a predictive model trained on historical data;
• generating human-readable and explainable recommendations for cost-saving actions.

According to the embodiment of the present invention, the classification of operational intent includes unsupervised and supervised learning models trained on metadata, tags, and naming conventions. The predictive model of the present invention uses a random forest, gradient-boosted decision tree, or logistic regression ensemble trained on features such as usage variance, billing volatility, and service type. The explainability of each recommendation is derived from SHAP values or similar model interpretation techniques. The recommendations of the system are exported via APIs, integrated into workflow automation tools, or presented through an interactive dashboard. The system also comprises of a feedback loop module configured to retrain the predictive model using historical optimization actions and realized savings.
Therefore, the present invention provides a system and method for intelligent analysis of cloud infrastructure billing data. The system processes cloud invoices, usage metrics, and rate card information to discover cost inefficiencies. Using the proprietary algorithm, SILO-MAPEX, the system performs intent mapping, load modelling, overlap detection, time-series forecasting, anomaly detection, and predictive ROI analysis. Optimization actions are explained using SHAP-based rationale for maximum transparency.
The system and method of the present invention outlines a novel, machine learning-powered, explainable system for discovering and implementing cost optimizations within cloud infrastructures. The SILO-MAPEX algorithm represents a unique, statistically rigorous, and business-context-aware approach that transcends existing cloud dashboards and static cost reports.
While considerable emphasis has been placed herein on the specific elements of the preferred embodiment, it will be appreciated that many alterations can be made and that many modifications can be made in preferred embodiment without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the invention and not as a limitation.
, Claims:We claim,

1. A system and method for processing organization invoices to identify cloud cost savings opportunities
characterised in that
the system comprises of an input unit, a processing unit configured to execute instructions, a memory storing invoice data, rate cards, and usage logs; and output unit, wherein the processing unit further comprises of invoice ingestion module, rate card and usage integration layer module, temporal and behavioural modelling engine module, silo-Mapex algorithm engine module, optimization report generator module, API and dashboard layer module, and feedback loop module;
and
the method for identifying optimization opportunities in cloud infrastructure costs, comprising the steps of:
• ingesting cloud invoices and normalizing them into a structured format;
• integrating pricing data and time-series usage metrics;
• classifying cloud resources by operational intent using natural language processing techniques;
• computing load elasticity scores to determine underutilized or overprovisioned resources;
• constructing a graph to identify overlapping or redundant cloud services;
• predicting expected usage using time-series models and identifying deviations indicative of inefficiencies;
• ranking identified optimization opportunities using a predictive model trained on historical data;
• generating human-readable and explainable recommendations for cost-saving actions.

2. The system and method as claimed in claim 1, wherein the system integrates invoice ingestion, real-time telemetry analysis, statistical modelling, and machine learning to deliver high-confidence, explainable recommendations for reducing cloud infrastructure costs.

3. The system and method as claimed in claim 1, wherein invoice ingestion module is responsible for extracting, parsing, and structuring invoice data received from various cloud service providers, and rate card and usage integration layer module communicates with cloud provider APIs to fetch the latest pricing and consumption data for compute, storage, network, and managed services and maps service identifiers from invoices to corresponding usage logs, aligning resource lifecycles, configurations, and time-based usage patterns.

4. The system and method as claimed in claim 1, wherein temporal and behavioural modelling engine module performs deep temporal analysis of historical usage and cost behaviour and enables detection of cyclic usage patterns, seasonal spikes, and idle trends and behavioural labels are generated for each resource.

5. The system and method as claimed in claim 1, wherein SILO-MAPEX Algorithm engine module sequentially applies the work flow of
• intent classification over tags, resource names, and descriptions;
• load elasticity modelling to compute provisioning vs. actual usage gap;
• overlap graph construction using cosine similarity and graph metrics;
• time-series forecasting for anomaly detection;
• predictive classification to score actionability;
• explainability using SHAP values to provide reasons for each recommendation.

6. The system and method as claimed in claim 1, wherein optimization report generator module compiles output from the algorithm engine module, filters actionable items based on confidence and business rules, and generates a structured report.

7. The system and method as claimed in claim 1, wherein API and dashboard layer module is an optional module and provides URLs and user interface for viewing recommendations, exploring historical optimization decisions, adjusting thresholds, and exporting reports and it can integrate with tools to trigger optimization workflows.

8. The system and method as claimed in claim 1, wherein feedback loop module captures historical actions taken on recommendations and their realized outcomes and retrains the predictive models periodically using this data, increasing precision and relevance over time.

9. The system and method as claimed in claim 1, wherein the SILO-MAPEX Algorithm comprises of the following sub modules:
• intent classification maps services to business functions using NLP;
• load elasticity modelling identifies under- or over-provisioned resources;
• overlap detection uses graph analytics and embeddings to flag redundant deployments;
• modelling & anomaly detection forecasts expected consumption and detects outliers with residual analysis;
• predictive optimization estimator applies machine learning to rank savings recommendations;
• explainability layer applies SHAP values to explain each action.

10. The system and method as claimed in claim 1, wherein the classification of operational intent includes unsupervised and supervised learning models trained on metadata, tags, and naming conventions; the predictive model uses a random forest, gradient-boosted decision tree, or logistic regression ensemble trained on features such as usage variance, billing volatility, and service type and the explainability of each recommendation is derived from SHAP values or similar model interpretation techniques and the recommendations of the system are exported via APIs, integrated into workflow automation tools, or presented through an interactive dashboard.