Description:FORM 2
THE PATENTS ACT 1970
(39 of 1970)
&
The Patents Rules, 2003
COMPLETE SPECIFICATION
(See section 10 and rule 13)
1. TITLE OF THE INVENTION: “A DIGITAL SYSTEM FOR DATA MANAGEMENT AND ANALYSIS ACROSS VARIOUS ORGANIZATIONAL FUNCTIONS USING ARTIFICIAL INTELLIGENCE AND ITS METHODOLOGY”
2. APPLICANTS:
(a) Name : WELSPUN TRANSFORMATION SERVICES LIMITED
(b) Nationality : Indian
(c) Address : WELSPUN HOUSE, 7TH FLOOR, KAMALA CITY, SENAPATI BAPAT MARG, LOWER PAREL, MUMBAI-400013.MAHARASHTRA-INDIA

PROVISIONAL
The following specification describes the invention. ?COMPLETE
The following specification particularly describes the invention and the manner in which it is to be performed.

Field of Invention:

The present invention relates to a digital system for data management and analysis across various organizational functions using artificial intelligence and its methodology. More specifically, the invention pertains to a system and module that facilitates automated verification of employee details and associated key performance indicators using advanced artificial intelligence techniques across diverse organizational functions.

Background of Invention

Traditional approaches to Analytics have historically centered around intuitive assessments of individuals and their potential impact on an organization's productivity and profitability. The emergence of Data Analytics challenges this traditional paradigm by introducing data-driven insights to reshape the dynamics of workforce management and decision-making across various functions within an organization.

Data Analytics involves the systematic analysis of data patterns and correlations to develop predictive models that optimize decision-making and resource allocation. Similar to general analytics, Data Analytics strives to establish models based on analyzed data, using algorithms to forecast future outcomes based on the manipulation of model variables.

This innovative approach prompts organizations to recognize analytics as a strategic partner, particularly given the evolution of disruptive technologies. Analytics, within this context, elevates the significance of effective decision-making, enabling organizations to adapt and thrive amidst shifting trends and practices.

One application of the present invention is evident during the on boarding of prospective employees within organizations or enterprises. Existing practices often entail third-party background checks, which can lead to process inefficiencies and compromises in data integrity. The current methodologies employed by third parties may result in lengthy verification periods and potential falsification of crucial information, such as employment history and salary details.

There is a need of the invention that represents a pioneering advancement in data analytics and management, reshaping the landscape of analytics across diverse organizational functions. By leveraging artificial intelligence, the invention offers a comprehensive and efficient solution for verifying employee details and key performance indicators, thus enhancing data integrity and streamlining essential processes.

Summary of Invention
This invention introduces an integrated digital system and method for effectively managing and analyzing data across diverse organizational sectors, employing the capabilities of artificial intelligence. The innovation offers a comprehensive solution applicable not only to the HR department but also spanning domains such as Procurement, Finance, and more. It empowers decision-making processes in personnel recruitment and facilitates the evaluation of key performance indicators within the existing workforce. Additionally, the invention furnishes an interactive dashboard that provides insights into data analyses concerning employees within the organization. The system is engineered to efficiently handle substantial volumes of employee-related data, resulting in streamlined outputs for crucial performance indicators like moonlighting, employee poaching, attrition rates, and data currency. Equipped with source codes that enable periodic updates, the invention ensures the perpetually refreshed status of data. Ultimately, this innovation equips organizations with the tools to make well-informed decisions during recruitment, enabling proactive and comprehensive hiring choices.

Detailed Description

The digital system, as outlined in this invention, incorporates advanced data analytics and management mechanisms. It employs artificial intelligence algorithms to analyze and verify employee information, ensuring the accuracy and authenticity of critical details. The system operates seamlessly across various organizational functions, offering a unified approach to data analytics and management.

The present invention presents an innovative solution that transcends traditional Data Analytics, offering a comprehensive data analytics and management system that extends to diverse organizational functions, including but not limited to procurement, finance, HR, and various other operational areas. Employing advanced artificial intelligence techniques, the invention provides an automated and efficient platform for verifying employee details and key performance indicators.

The primary objective of the invention is to streamline the data analytics and management processes, eliminating the need for third-party services in the context of background verification. By doing so, the invention enhances data integrity, reduces processing times, and minimizes the risk of compromised information.

The present innovation introduces a comprehensive digital system and methodology for data management and analysis across diverse organizational functions, utilizing the power of artificial intelligence. The scope of this invention extends beyond any specific area and finds relevance in various fields such as HR, Procurement, Finance, and other key functions within the organization.

This invention aims to offer an integrated solution that aids not only in the decision-making process during the recruitment of personnel but also in the examination of key performance indicators of existing employees. By providing an interactive dashboard, the invention facilitates the analysis of data related to employees within the organization.

The system and method presented by this invention provide an efficient solution in terms of time and cost, enabling the analysis of a vast amount of employee-related data. It simplifies the process of assessing key performance indicators, encompassing aspects such as moonlighting, employee poaching, employee attrition, and data currency.

Central to this invention is the provision of source codes that continuously update and refresh data, ensuring that the information remains current at all times. This capability empowers organizations and enterprises to make well-informed decisions early in the employment process, utilizing accurate information to proactively shape comprehensive hiring strategies.

The invention's application in the context of employee on boarding involves the real-time verification of information such as employment history, salary records, and other pertinent data points. Through a combination of data sources and algorithmic analysis, the system rapidly and reliably validates candidate information, minimizing the possibility of falsification or misinformation.

Voluntary attrition of employees within an enterprise, for any reason, poses a challenge to project delivery schedules and the seamless continuity of business operations across diverse functions. The proactive identification of potential attrition among employees in various fields such as HR, Procurement, Finance, and other organizational functions, aids personnel in estimating suitable retention strategies.

Employee attrition across different functions of an organization encompasses the reduction of personnel due to natural transitions, like retirement and resignation, or external factors such as changes in target demographics. The issue of high attrition rates is a significant concern, affecting organizations at large. Departing employees take with them invaluable tacit knowledge, often a source of competitive advantage. Attrition necessitates the allocation of resources for business disruptions, new hires, and training. Conversely, a higher retention rate translates to reduced hiring and training costs, along with an influx of experienced professionals into the workforce over time. Contemporary organizations recognize the need to understand the drivers of attrition to curtail its impact, making the prediction and identification of these factors a vital organizational objective.

Employee attrition manifests as the natural process through which personnel depart from an organization, often via resignations. Diverse factors contribute to attrition, potentially leading to a scenario where vacancies exceed new hires. Consequently, organizations face operational gaps and losses. Monitoring the attrition rate provides insights into organizational progress, with higher rates indicating frequent departures and potential losses of organizational benefits. Effective management of attrition is paramount for sustained organizational advancement across various functions.

In the business realm, replacing attriting associates across different functions poses challenges due to specific service requirements. For instance, the IT sector encompasses diverse areas like data centers, storage, security, and networking, while application or development support may pertain to other domains. The departure of highly skilled individuals necessitates external hiring or specialized cross-training, particularly in response to specific customer needs. External hiring entails replacement time and costs for recruitment, induction, and shadowing. Retention emerges as a critical organizational priority, emphasizing the need for an attrition risk analysis system across diverse functions.

Modern technology, particularly Machine Learning within Artificial Intelligence (AI), equips machines to learn from historical data and make predictions. This capability is pivotal in data science, aiming to surpass human accuracy. Machine learning models automate learning from refined datasets, aiming to derive insights and patterns.

Machine learning finds applications in diverse technological domains, resolving real-world challenges such as image recognition, traffic prediction, speech analysis, text categorization, social dynamics, stock market trends, healthcare, e-commerce, agriculture, and more. The prediction of attrition across various organizational functions leverages machine learning techniques, demonstrating its potential to address contemporary challenges.

Advancements in 21st-century technology necessitate integrated platforms or frameworks to facilitate interactions across various systems and applications, irrespective of computing environments. Such platforms offer unified architectures, scalable to incorporate evolving requirements and multiple systems.

Thus, an integrated platform, framework, or system is required to ensure robust verification of employment and/or background across various organizational functions. This system should withstand tampering, falsification, ensure reliability, expedite verification, and furnish accurate information, reducing processing time and enhancing the efficiency of employment and/or background verification across diverse functions.

Objective of the Invention

The primary objective of the present innovation is to introduce a digital system and methodology for comprehensive data management and analysis utilizing artificial intelligence.

Another key intention of this invention is to establish a system and module that automates and digitalizes the process of verifying employee particulars and their associated crucial performance indicators, all accomplished through the utilization of artificial intelligence.

Furthermore, a significant aim of this invention is to establish interactive dashboards accessible to stakeholders within the platform, alongside a business intelligence tool that empowers teams in various organizational functions like Procurement, Finance, and more, to monitor, scrutinize, and report on pivotal key performance indicators.

Additionally, a fundamental aspiration of this invention is to provide a human resource tool that seamlessly amalgamates data from diverse systems, enabling an in-depth exploration of this data directly within the dashboard, thereby enhancing the ease of decision-making in employee-related activities.

Moreover, the invention seeks to achieve real-time extraction and analysis of data from government databases, exemplified by its interaction with the EPFO portal.

Yet another integral goal of this innovation is to equip the human resource department with a tool to cultivate data-driven insights that influence talent-related choices, enhance workforce processes, and foster a positive employee experience.

Furthermore, the invention aims to realize a system that conducts real-time data tracking, promptly raising alerts for potential fraud instances such as dual employment or moonlighting, which contravene the organization's employment policies.

In addition, the invention aspires to endow each stakeholder with user rights for live application tracking at every stage of the process, thereby enhancing coordination amongst stakeholders and expediting application processing.

Another noteworthy objective of this invention is to furnish the human resource management sphere with a tool that aids in comprehending risks and implementing preemptive measures in association with various organizational functions.

This invention also strives to establish a methodology that performs diverse Key Performance Indicators (KPIs) tasks on system components, culminating in tailored object-specific analyses.

Furthermore, the innovation addresses present challenges concerning employer-employee engagement, encompassing issues like moonlighting, employee poaching, employee attrition, and the duplication of Universal Account Numbers (UANs), presenting a robust solution to mitigate these concerns.

Another pivotal aim of this invention involves furnishing a system capable of delivering Actionable Business Intelligence (Actionable BI). This entails the initiation of structured actions within a Business Intelligence analysis application, directly linked to predefined business processes. This empowers users to swiftly identify problems and take immediate actions within a single BI application.

Lastly, this invention introduces a digital system that curtails the reliance on manual verification and minimizes the likelihood of human error in background checks, streamlining the process in accordance with established Standard Operating Procedures (SOPs).

Furthermore, the invention encompasses a software platform featuring a Graphical User Interface (GUI) component, adept at presenting the disparities between business target parameters and realization parameters while facilitating user commands.

Brief description of drawings:

The invention's embodiment is succinctly depicted in the following description, accompanied by illustrative figures:

Fig. 1 showcases a network system in the form of a block diagram, embodying the preferred embodiment of the present innovation.

Fig. 2 provides a detailed system block diagram, featuring modules that embody the preferred embodiment of this invention.

Fig. 3 visually presents an exemplary dashboard, aligning with the preferred embodiments of this invention, offering a user-friendly interface to assess outcomes derived from employee data analysis.

Fig. 4 delineates the architecture of the python sub-module, an integral component embodying the present innovation.

Fig. 5 elaborates on the architecture of the service sub-module, specifically the POWER BI sub-module, which constitutes an essential aspect of the preferred embodiment of this invention.

Fig. 6 illustrates the integration of the embedment sub-module within the system, showcasing how this embodiment of the present invention functions seamlessly.

These figures collectively contribute to a deeper understanding of the invention's architecture and functionality, highlighting its capacity to provide an integrated solution for data analysis and management across diverse organizational functions.

Detail Description of Invention
The nature of the invention and the manner in which it works is clearly described in the complete specification. The invention has various embodiments and they are clearly described in the following pages of the complete specification. Before explaining the present invention, it is to be understood that the invention is not limited in to any specific embodiment.
The present invention covers one digital module with right based access to all key stakeholders involved in the process of establishing the feasibility of recruitment and/or status change and/or maintenance/ management of the data pertaining to the employees.
Reference throughout this specification to “one embodiment”, “this embodiment” and similar phrases, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one of the one or more embodiments. Thus, the appearances of these phrases in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
In the following text, a detailed description of embodiments and/or examples will be given with reference to the drawings. It should be understood that various modifications to the embodiments and/or examples may be made. In particular, one or more elements of one embodiment and/or example may be combined and used in other embodiments and/or examples to form new embodiments and/or examples.
The following terminology is used for consistency and should in no way limit the scope of the invention.
The present invention discloses a system which allows the users to interactively access and retrieves on demand extensive information on databases throughout the world regardless of time zone differences. The application of the instant disclosure described herein applies to the human resource management; however, the disclosed system can be used for other applications. The system of the present invention uses the latest technology to continuously improve data collection, enhance service experience, service capabilities and control costs as the marketplace framework. The system preferably uses a mobile information-collecting device that is equipped with a GPS system and a link to the platform of the system of the present invention.
A ‘User device’ may include mobile phone, tablet, desktop and laptop. The device supports a variety of applications, such as one or more of the following: a telephone application, a video conferencing application, an e-mail application, an instant messaging application, a blogging application, a photo management application, a digital camera application, a digital video camera application, a web browsing application, and/or a digital video player application.
A ‘network server’ can include a central processing unit (“CPU”), at least one read-only memory (“ROM”), at least one random access memory (“RAM), at least one hard drive (“HD”), at least one network card and one or more input/output (“I/O”) device(s).
A ‘network’ is known for communication and interaction between user devices on the network server. The network must contain transmission media, routers, repeaters, gateways, network adapters and cables. The network is placed in (Local Area Network (L.A.N.), Wide Area Network (W.A.N.) and/or Virtual Private Network (V.P.N.) in which network server is storing and giving data to user device.
An ‘End User’ defines the party viewing the database files. Typically an End User of the present system is person who works in management or human resource department. The End User generally has access to only limited files and can optionally have input capability to alter the files they are viewing.
A ‘Server’ is a computer program or device that provides a service to another computer program and its user (Client or Third party). The Server's computer alternatively can be a workstation, minicomputer or microcomputer or other device. Although reference herein is made to information transfer via modem, it should be noted that cable, satellite, fibre optics, or other means for transferring information can also be utilized. The method of transferring the information is based on the current availability within the communities.
The term ‘business intelligence (BI)’ refers to technologies, applications, and practices for the collection, integration, analysis, and presentation (i.e. reporting) of business information and also sometimes to the information itself. The purpose of business intelligence is to Support better business decision-making. BI describes a set of concepts and methods to improve business decision making by using fact-based Support systems. Business Intelligence systems are data-driven DSS. BI systems provide historical, current, and predictive views of business operations, most often using data that has been gathered into a data warehouse.
A ‘Document or Data’ defines a record of some information that can be used as an authority or for reference, further analyses or study. Documentation refers to the on-going process of creating, disseminating, managing and using documents.
A ‘Data storage’ defines the use of recording media to retain data using computers or other infrastructure (cloud). The most prevalent forms of data storage are file storage, block storage and object storage, with each being ideal for different purposes. Data storage is the recording (storing) of information (data) in a storage medium. Handwriting, phonographic recording, hard drive, magnetic tape, and optical discs are all examples of storage media.
As used herein, a ‘processor’ includes any programmable system including systems using micro-controllers, reduced instruction set circuits (RISC), application specific integrated circuits (ASICs), logic circuits, and any other circuit or processor capable of executing the functions described herein. The above examples are for illustration only, and are thus not intended to limit in any way the definition and/or meaning of the term “processor”.
A ‘Machine learning (ML)’ is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values that resembles to human brains’ decisive capabilities.
A Key Performance Indicator (KPI) generally accepted as a way to measure performance or progress based on specific business goals and objectives. These show organizations how well they are performing and meeting objectives, as well as the areas that need improvement or monitored. For the purpose of present invention, the KPIs need to be interpreted as indicators for management or maintenance of employee data for the organization.
A ‘Rules Engine’ is a set of instruction designed in programme that uses business rules or instructions during the course of operations. Those rules may have a legal base or come from the company’s policy.
As used herein, the term “platform” is interchangeable with module and generally includes any tools, components or any device program stored in memory for execution by processor. It is not intended to limit in any way the definition and/or meaning of the term “platform”. As used herein, the term "database" refers to either a body of data, a relational database management system (RDBMS), or to both. The database includes integration and collection of data including hierarchical databases, relational databases, flat file databases, object-relational databases, object oriented databases, and any other structured collection of records or data that is stored in a computer system. The above examples are illustration only, and thus are not intended to limit in any way the definition and/or meaning of the term database. The present invention provides system, which runs on platform that is designed with AI (Artificial Intelligence) and machine learning tools.
Unless specifically stated otherwise, throughout this specification, terms such as "processing," "computing," "calculating," "selecting," "forming," "enabling," "inhibiting," "identifying," "initiating," "querying," "obtaining," "hosting," "maintaining," "representing," "modifying," "receiving," "transmitting," "storing," "authenticating," "authorizing," "hosting," "determining" and/or the like refer to actions and/or processes that may be performed by a system, such as a computer and/or other computing platform, capable of manipulating and/or transforming data which may be represented as electronic, magnetic and/or other physical quantities within the system's processors, memories, registers, and/or other information storage, transmission, reception and/or display devices.
A ‘Method’ describes flow diagrams or otherwise, may also be executed and/or controlled, in whole or in part, by a computing platform. Method herein described as flow diagram is graphical or visual representation using a standardized set of symbols and notations to describe a business's operations through data movement.
A ‘credential’ refers to the verification of identity or tools for authentication. They may be part of authentication process that helps confirm a user’s identity in relation to a network address or other system ID.
As used herein, a ‘module’ refers to sections of large software packages to define its functionality. In the present invention module is used to make the software easier to use for a specific purpose or to define where boundaries exist in a program. The modules described minutely with sub-modules which merely differentiates detailed aspect of said module work to serve the purpose.
In this description, the terms components, software components, software model or model, module, engine, systems, etc., may be used interchangeably, may depend on implementation. The implementation of the above is not limited to applications, software code, programming scripts, etc., that may be executed by the processor of the system or the framework or the platform or the computer on which it is implemented.
Figure 1 diagrammatically illustrates a schematic representation of a system for providing integrated solution to end users in process of decision making for hiring candidate or verifying available data of employee for the purpose of compliance and decision making.
As illustrated in Fig. 1, the environment comprises four major components: Web-portal, Server with storage and software, cloud computing infrastructure, and a dashboard at the end terminal or user device. These components are securely interconnected through a network.

The Web portal is linked to the server via a secure network, where the EPFO (Employees' Provident Fund Organization) database is connected through an encrypted pathway. The Structured Query Language (SQL) database undergoes rule-based data processing within the business intelligence module to generate the desired Key Performance Indicators (KPIs) output.

The resultant output may contain structured data, which is then visualized in customized charts within an embedded dashboard. These graphical representations of KPIs offer direct insights into data queries posed by stakeholders within the system.

Fig. 2 showcases a block diagram of information processing in the preferred embodiment of the present invention. The data source, represented at one end, undergoes sequential processing through various modules to generate a specific output at the user's end device.

The preferred embodiment employs several sub-modules to achieve the objectives of the invention:
i. Python BOT
ii. SQL Server
iii. POWER BI
iv. Report embedment

These modules work in tandem with data storage and retrieval components, enhancing data exchange.

The Python sub-module, being an integral part, extracts data from the source and facilitates tasks like data cleaning, buffer memory storage, handling missing data, and restructuring it to align with other modules' requirements.

The SQL server structures data as per the present invention's needs, ensuring less reliance on the EPFO database by storing relevant structured employee data for future analysis using tools like POWER BI.

Business Intelligence (BI) in this context encompasses technologies, applications, and practices for gathering, integrating, analyzing, and presenting business information. It aids in better decision-making, especially in the realm of human resources and associated compliance. The BI modules work with historical, current, and predictive data, often loaded into a data warehouse through Extract, Transform, Load (ETL) tools. Although POWER BI is the preferred data visualization tool, other options can also complement the system.

The present system empowers the HR department to efficiently analyze employee data, providing a visual representation for decision-making and compliance cross-checks.

Fig. 3 presents an exemplary dashboard based on the preferred embodiments. This dashboard lists active and past employees, along with associated KPIs for both categories.

The BI modules' predefined rules or logics offer users real-time analysis of KPIs based on EPFO data. Key performance indicators, including dual employment, missing data, attrition trends, and more, assist organizations in overcoming HR-related limitations.

The versatile system can be deployed on-premise or on-cloud, gathering data from various sources and enabling users to consolidate, visualize, and share data seamlessly.

This disclosure encompasses a central repository facilitating shared access for multiple users. It optimizes security, resource management, and access to information between the application server and user devices. The system's interface allows users to register and access resources at any time, providing real-time services using the latest web and mobile technology.

The dashboard, a core feature, leverages business intelligence to help HR teams track, analyze, and report on various KPIs. This interactive tool amalgamates data from multiple systems and enables deep exploration directly within the dashboard.

The integrated framework enables unified and secure data communication, fostering reusability of system components, and optimizing performance. This framework can be deployed in various computing environments, including cloud platforms like Amazon Web Services, POWER BI, and Tableau.

In a specific embodiment, when the integrated frameworks or systems are deployed within cloud computing environments, they can be configured to collaborate and utilize the features, benefits, functionalities, and operations offered by the cloud computing environments. For instance, these environments may provide functions such as data encryption, network access control, data backup and restoration services, monitoring and logging of API calls, access to third-party applications hosted on the cloud, a secure communication and data transfer platform (e.g., secured hyper-text transfer protocol (https)), load balancing mechanisms with high availability of service, and more. Additionally, third-party applications may also be integrated into the cloud computing environment to work in synergy with the integrated frameworks or systems. Examples of these applications include web application firewalls, tools to enforce internet privacy and security, and protection against computer viruses and malware.

In the preferred embodiment, the background dataset refresh feature functions as an independent event that can be triggered by users through the tool portal. This dataset refresh feature leverages Power BI APIs to connect with Power BI services. The report refresh occurs subsequent to a successful execution of the BOT on the server. Presently, there exists no direct trigger between the BOT run and the refresh of the Power BI report.

In another embodiment, the aforementioned AI engine or rule-based analysis can incorporate instructions related to executing multiple rules for tasks such as determining, extracting, parsing, and more, within various sections and forms as specified or described within the EPFO database.

Within yet another embodiment, the defined rules or obligations may involve processes such as validating information or data through explicit user consent, adhering to predetermined rules and time intervals for data storage, deleting stored data as per predetermined rules and time intervals, and more. This mechanism for employment and/or background verification reinforces compliance and ethics by furnishing legitimate information or data about the user or candidate.

Broadly in line with the embodiments of the invention, program modules encompass routines, programs, components, data structures, and other structures that execute specific tasks or implement particular abstract data types. Furthermore, embodiments of the invention can be implemented within various computer system configurations, including handheld devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, and the like. These embodiments can also be practiced within distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In such distributed environments, program modules can be located and processed in both local and remote devices. The processes and methodologies described herein can be executed through fully digitized flows or even manually, with or without the use of different technologies and devices, in a phased or distributed manner.

As illustrated in Figure 2, a comprehensive framework outlines the essential components of the system, functioning on the collected data. The present system encompasses sub-modules detailed within the subsequent specification:

Figure 4 reveals the block diagram of the Python sub-module responsible for synchronizing data obtained from the EPFO portal. This module extracts data from government records and organizes it as Structured Query Language (SQL) data. The sub-module's operations progress through the following steps:

a. Extraction of the active employee list from the EPFO (source data),
b. Data cleaning of the extracted data in step (a),
c. Storage of data from step (b) in a memory buffer data frame,
d. Sequential data extraction for each PF number in accordance with the KPIs required for real-time dashboard data update,
e. Data cleaning for the data processed in step (d),
f. Storage of data from step (e) in the memory buffer data frame in SQL format,
g. Extraction of the list of exited employees (those who have resigned from the organization) from the EPFO (source data),
h. Storage of data from step (f) in the memory buffer data frame,
i. Extraction of missing details related to specific KPIs from the data in step (h),
j. Transmission of data from step (h) to the SQL server.

Figure 5 unveils the block diagram of the service python
sub-module that synchronizes data received from the previous Python sub-module. This module gathers data from government records and stores it. The sub-module operates through the subsequent steps:
a. Establishment of a secured SQL sub-module connection to work with data from the preceding sub-module,
b. Loading of data received from the previous Python sub-module into the service module, preferably the POWER BI data model,
c. Execution of Extract, Transform, and Load (ETL) operations within the POWER BI data module,
d. Computation of Key Performance Indicators (KPIs) in accordance with rule-based queries set up by the system administrator in DAX format,
e. Preparation of data visualizations based on the data from step d, to generate required charts for users,
f. Periodic data refreshing for real-time analysis of EPFO data, synchronizing with requested KPIs in the system,
g. Publishing of reports or analyzed data from step d to the subsequent sub-module.

Figure 6 discloses the block diagram of the sub-module responsible for embedding reports onto end terminals, which synchronizes data received from the previous service sub-module, i.e., POWER BI. This sub-module operates as follows:
a. Establishment of a secure connection to work with data received from the previous sub-module, i.e., the POWER BI service module,
b. Incorporation of dashboards onto a portal,
c. Retrieval of APIs relevant to KPIs from the preceding POWER BI sub-module.

Moreover, the embodiments of the invention can be applied within an electrical circuit containing discrete electronic elements, integrated electronic chips with logic gates, circuits utilizing microprocessors, or on a single chip with electronic elements or microprocessors. Quantum, mechanical, optical, and fluidic technologies, among others, are also capable of executing logical operations such as AND, OR, and NOT, to perform embodiments of the invention.

Furthermore, embodiments of the invention can be realized as computer processes, computing systems, or as articles of manufacture, such as computer program products or computer-readable media. These embodiments may exist in hardware and/or software forms, and may be embodied in general-purpose computers or other circuits and systems.

The computer-usable or computer-readable medium can include electronic, optical, magnetic, electromagnetic, infrared, semiconductor, or any other medium capable of containing, storing, communicating, propagating, or transporting computer program instructions. The present invention may be embodied in hardware and/or software, and the details of the specific embodiments described herein are not exhaustive.

While specific embodiments have been detailed, it is evident that modifications and adaptations may occur to those skilled in the art. Such modifications and adaptations remain within the scope and spirit of the present invention as defined in the subsequent claims.
, Claims:We claim,

1. A digital system for data management and analysis across various organizational functions using artificial intelligence and its methodology comprises,

At least one end user device,
At least one application server,
At least one python web scrip-driven sub-module,
At least one service sub-module,
At least one data embedment sub-module,
At least one network, and,
At least one memory unit,

Wherein, python sub-module is configured to extract and store data in SQL language from data sources or open-source websites.

Wherein, service sub-module is configured to perform rule-based data extraction, transformation, and loading to achieve specific Key Performance Indicators (KPIs).

Wherein, data embedment sub-module is configured to display processed data on end user devices.

Wherein, storage is configured for data storage and retrieval between system modules.

Wherein, memory is configured to store an object-oriented management service-based operating system for monitoring, configuring, and controlling system elements, services, and applications.

Wherein, the network is configured to securely connect end user devices, module storage, and end devices within the system.

2. The digital system for data management and analysis across various organizational functions using artificial intelligence as claimed in claim 1, wherein a schema for a python sub-module managed by the object-oriented service of the operating system, the python sub-module being configured to provide output of a SQL database system for the object-oriented management service and to allows data manipulation for modeling objects representing system services and applications.

3. The digital system for data management and analysis across various organizational functions using artificial intelligence as claimed in claim 1, wherein the schema for the service sub-module is managed by the object-oriented service of the operating system, the service sub-module provides rule-based data output where each KPI undergoes extraction, transformation, and loading using data from the previous python sub-module, presenting analysis as the output.

4. The digital system for data management and analysis across various organizational functions using artificial intelligence as claimed in claim 1, wherein KPIs encompass Duel Employment, Date of Leaving Not Updated, Employee Left but in Active List, Critical Details Missing, Employee Poaching, Duplicate UAN, Employee Tenure Aging, and Employee Attrition Trend.

5. The digital system for data management and analysis across various organizational functions using artificial intelligence as claimed in claim 1, wherein the application server and associated modules or sub-modules provide right-based access to key stakeholders engaged in tasks like recruitment feasibility evaluation, status changes, and data management.

6. The digital system for data management and analysis across various organizational functions using artificial intelligence as claimed in claim 1, wherein
various functions including but not limited to human resource, Procurement, Finance within an organization.

7. The digital system for data management and analysis across various organizational functions using artificial intelligence and its methodology as claimed in claim 1, wherein the process steps include:
a. extracting data from the Government database or source by web scrapping python sub-module,
b. Feeding the data from step (a) to the service sub-module for Extraction, Transformation, and Loading (ETL), producing the required format output,
c. feeding data, received from step (b), to embed on web-portal into end user device with requisite visual format.

8. The digital system for data management and analysis across various organizational functions using artificial intelligence and its methodology as claimed in claim 1, wherein the process within the python web scrapping sub-module involves the following steps:
a. Extracting the active employee list from the EPFO (source data),
b. Cleaning the data received in step (a),
c. Storing the cleaned data from step (b) in a memory buffer data frame,
d. Extracting and processing data for each PF number according to the required KPIs or real-time dashboard data updates,
e. Cleaning the data from step (d),
f. Storing the data from step (e) in a buffer data frame in SQL format,
g. Extracting the list of exited employees from the EPFO (source data),
h. Storing the data from step (g) in a buffer data frame,
i. Extracting missing details for specific KPIs from the data received in step (h),
j. Pushing the data from step (h) to the SQL server.

9. The digital system for data management and analysis across various organizational functions using artificial intelligence and its methodology as claimed in claim 1, wherein the process within the service sub-module involves the following steps:
a. Establishing a secured SQL sub-module connection to work with data from the previous sub-module,
b. Loading data from the previous python sub-module into the service module, preferably the POWER BI data model,
c. Performing ETL operations within the POWER BI data module,
d. Calculating Key Performance Indicators (KPIs) based on rule-based queries set by the system administrator in DAX format,
e. Preparing data visualizations based on the data from step d, in accordance with user requirements,
f. Regularly refreshing data to analyze real-time EPFO data and synchronize with requested KPIs in the system,
g. Publishing reports or analyzed data from step d to the subsequent sub-module.

10. The digital system for data management and analysis across various organizational functions using artificial intelligence and its methodology as claimed in claim 1, wherein the process within the embedment sub-module involves the following steps:
a. Establishing a secure connection to work with data received from the previous sub-module, i.e., the Service POWER-BI module
b. Embedding dashboard on web-portal,
c. Fetching API to relevant KPIs from the previous POWER BI sub module.
Dated this: 13th October 2023