DESC:
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
METHOD AND SYSTEM OF INTERCONNECTING WORKFLOWS
2. APPLICANT(S)
NAME NATIONALITY ADDRESS
JIO PLATFORMS LIMITED INDIAN OFFICE-101, SAFFRON, NR. CENTRE POINT, PANCHWATI 5 RASTA, AMBAWADI, AHMEDABAD 380006, GUJARAT, INDIA
3.PREAMBLE TO THE DESCRIPTION

THE FOLLOWING SPECIFICATION PARTICULARLY DESCRIBES THE NATURE OF THIS INVENTION AND THE MANNER IN WHICH IT IS TO BE PERFORMED.

FIELD OF THE INVENTION
[0001] The present invention relates generally to electronic systems and more specifically to workflow optimization in electronic systems.
BACKGROUND OF THE INVENTION
[0002] In electronic systems, data is processed by a sequence of tasks, commonly known as workflow. A workflow may include one or more sub-workflows that descend all the way into a network or a physical hardware.
[0003] In conventional electronic systems, a single workflow is executed by the electronic system. That is, for a process having multiple workflows, the workflows are executed sequentially. Consequently, multiple processes require multiple requests for each workflow. This results in delayed processing and decreased throughput of the electronic system.
[0004] Conventionally, the electronic systems are deployed with a main workflow and optionally with sub-workflows. When a user chooses to execute the workflow, the deployed workflow and the sub-workflows therein are accessed and executed. Such electronic systems do not allow dynamic nesting of sub-workflows or branching out to other workflows during the execution of the deployed workflow. That is, the workflow is designed and deployed either during production or when the electronic system is offline.
[0005] Furthermore, while designing nested workflows for deployment, the user may not know which workflows or sub-workflows exist in the electronic systems. This often results in duplication of workflows and of the code thereof. Such code repetition reduces the efficiency and throughput of the system. Therefore, there is a need for a method of automatically nesting the workflows without causing code repetition.
SUMMARY OF THE INVENTION
[0006] One or more embodiments of the present disclosure provide a method and a system of interconnecting workflows.
[0007] In one aspect of the present invention, the system of interconnecting the workflows is disclosed. The system includes a transceiver configured to receive one or more requests from a Northbound Interface (NBI) to execute a first workflow. The system further includes a dynamic activator unit configured to determine whether a second workflow requires to be implemented to complete execution of the first workflow based on at least one of, a trained model, nature of one or more requests received from the NBI and one or more responses received from one or more interacting nodes associated with the first workflow. The dynamic activator unit is configured to interconnect the first workflow with the second workflow based on determining that the second workflow is required to be implemented to complete the execution of the first workflow. Further the dynamic activator unit is configured to execute the first workflow and the second workflow in tandem based on interconnecting the first workflow with the second workflow.
[0008] In an embodiment, the model is at least one of an Artificial Intelligence/Machine Learning (AI/ML) model. In one embodiment, the model is trained with historical data pertaining to a plurality of workflows in an environment.
[0009] In an embodiment, the dynamic activator unit determines whether the second workflow requires to be implemented to complete execution of the first workflow based on the model trained with historical data by checking, whether execution of similar workflows have occurred in relation to the first workflow as per the historical data fed to the model. In response to checking that similar workflows have occurred in relation to the first workflow as per the historical data, performing, utilizing the model, at least one of, trends/patterns analysis and immersive action on data metrics of the historical data. Determining, utilizing the model, whether the second workflow requires to be implemented to complete execution of the first workflow based on the at least one of, the performed trends/patterns analysis and immersive action.
[0010] In another aspect of the present invention, the method of interconnecting workflows is disclosed. The method includes the step of receiving one or more requests from a Northbound Interface (NBI) to execute a first workflow. The method further includes the step of determining whether a second workflow requires to be implemented to complete execution of the first workflow based on at least one of, a trained model, nature of the one or more requests received from the NBI and one or more responses received from one or more interacting nodes of the first workflow. The method further includes the step of interconnecting the first workflow with the second workflow based on determining that the second workflow is required to be implemented to complete the execution of the first workflow. The method further includes the step of executing the first workflow and the second workflow in tandem based on interconnecting the first workflow with the second workflow.
[0011] Other features and aspects of this invention will be apparent from the following description and the accompanying drawings. The features and advantages described in this summary and in the following detailed description are not all-inclusive, and particularly, many additional features and advantages will be apparent to one of ordinary skill in the relevant art, in view of the drawings, specification, and claims hereof. Moreover, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes and may not have been selected to delineate or circumscribe the inventive subject matter, resort to the claims being necessary to determine such inventive subject matter.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings includes disclosure of electrical components, electronic components or circuitry commonly used to implement such components.
[0013] FIG. 1 is an exemplary block diagram of an environment for interconnecting workflows, according to one or more embodiments of the present invention;
[0014] FIG. 2 is an exemplary block diagram of a system for interconnecting the workflows, according to one or more embodiments of the present invention;
[0015] FIG. 3 is an exemplary block diagram of an architecture of the system of FIG. 2, according to one or more embodiments of the present invention;
[0016] FIG. 4 is a schematic representation of a method of interconnecting the workflows, according to one or more embodiments of the present invention.
[0017] The foregoing shall be more apparent from the following detailed description of the invention.
DETAILED DESCRIPTION OF THE INVENTION
[0018] Some embodiments of the present disclosure, illustrating all its features, will now be discussed in detail. It must also be noted that as used herein and in the appended claims, the singular forms "a", "an" and "the" include plural references unless the context clearly dictates otherwise.
[0019] Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure including the definitions listed here below are not intended to be limited to the embodiments illustrated but is to be accorded the widest scope consistent with the principles and features described herein.
[0020] A person of ordinary skill in the art will readily ascertain that the illustrated steps detailed in the figures and here below are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
[0021] As per various embodiments depicted, the present invention discloses a system and a method of interconnecting workflows. Conventionally, the system is deployed with a main workflow and optionally with sub-workflows. When a user chooses to execute the workflow, the deployed workflow and the sub-workflows therein are accessed and executed. As such, the system does not allow dynamic nesting of sub-workflows or branching out to other workflows during the execution of the deployed workflow. In the present disclosure, one or more requests are received from a Northbound Interface (NBI) to execute the first workflow. Upon receiving the one or more requests, the system executes the first workflow. Thereafter the execution of the first workflow is transmitted to the Artificial Intelligence/Machine Learning (AI/ML) model. The AI/ML model performs the analysis of whether a second workflow is required to implement the completion of the execution of the first workflow. Based on the analysis, if the second workflow is required the AI/ML model interconnects the first workflow with the second workflow. Thus, the method and system intelligently and connects multiple workflows to execute a process.
[0022] FIG. 1 illustrates an exemplary block diagram of an environment 100 for interconnecting workflows according to one or more embodiments of the present disclosure. In this regard, the environment 100 includes a User Equipment (UE) 102, a server 104, a network 106 and a system 108 communicably coupled to each other for interconnecting workflows.
[0023] As per the illustrated embodiment and for the purpose of description and illustration, the UE 102 includes, but not limited to, a first UE 102a, a second UE 102b, and a third UE 102c, and should nowhere be construed as limiting the scope of the present disclosure. In alternate embodiments, the UE 102 may include a plurality of UEs as per the requirement. For ease of reference, each of the first UE 102a, the second UE 102b, and the third UE 102c, will hereinafter be collectively and individually referred to as the “User Equipment (UE) 102”.
[0024] In an embodiment, the UE 102 is one of, but not limited to, any electrical, electronic, electro-mechanical or an equipment and a combination of one or more of the above devices such as virtual reality (VR) devices, augmented reality (AR) devices, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, mainframe computer, or any other computing device.
[0025] The environment 100 includes the server 104 accessible via the network 106. The server 104 may include, by way of example but not limitation, one or more of a standalone server, a server blade, a server rack, a bank of servers, a server farm, hardware supporting a part of a cloud service or system, a home server, hardware running a virtualized server, one or more processors executing code to function as a server, one or more machines performing server-side functionality as described herein, at least a portion of any of the above, some combination thereof. In an embodiment, the entity may include, but is not limited to, a vendor, a network operator, a company, an organization, a university, a lab facility, a business enterprise side, a defence facility side, or any other facility that provides service.
[0026] The network 106 includes, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, or some combination thereof. The network 106 may include, but is not limited to, a Third Generation (3G), a Fourth Generation (4G), a Fifth Generation (5G), a Sixth Generation (6G), a New Radio (NR), a Narrow Band Internet of Things (NB-IoT), an Open Radio Access Network (O-RAN), and the like.
[0027] The network 106 may also include, by way of example but not limitation, at least a portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, etc. one or more messages, packets, signals, waves, voltage or current levels, some combination thereof, or so forth. The network 106 may also include, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, a VOIP or some combination thereof.
[0028] The environment 100 further includes the system 108 communicably coupled to the server 104 and the UE 102 via the network 106. The system 108 is configured to interconnect workflows. As per one or more embodiments, the system 108 is adapted to be embedded within the server 104 or embedded as an individual entity. However, for the purpose of description, the system 108 is described as an integral part of the server 104, without deviating from the scope of the present disclosure.
[0029] Operational and construction features of the system 108 will be explained in detail with respect to the following figures.
[0030] FIG. 2 is an exemplary block diagram of the system 108 of interconnecting workflows, according to one or more embodiments of the present invention.
[0031] As per the illustrated embodiment, the system 108 includes one or more processors 202, a memory 204, a user interface 206, and a database 208. For the purpose of description and explanation, the description will be explained with respect to one processor 202 and should nowhere be construed as limiting the scope of the present disclosure. In alternate embodiments, the system 108 may include more than one processors 202 as per the requirement of the network 106. The one or more processors 202, hereinafter referred to as the processor 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, single board computers, and/or any devices that manipulate signals based on operational instructions.
[0032] As per the illustrated embodiment, the processor 202 is configured to fetch and execute computer-readable instructions stored in the memory 204. The memory 204 may be configured to store one or more computer-readable instructions or routines in a non-transitory computer-readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory 204 may include any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as disk memory, EPROMs, FLASH memory, unalterable memory, and the like.
[0033] In an embodiment, the user interface 206 includes a variety of interfaces, for example, interfaces for a graphical user interface, a web user interface, a Command Line Interface (CLI), and the like. The user interface 206 facilitates communication of the system 108. In one embodiment, the user interface 206 provides a communication pathway for one or more components of the system 108. Examples of such components include, but are not limited to, the UE 102 and the database 208.
[0034] The database 208 is one of, but not limited to, a centralized database, a cloud-based database, a commercial database, an open-source database, a distributed database, an end-user database, a graphical database, a No-Structured Query Language (NoSQL) database, an object-oriented database, a personal database, an in-memory database, a document-based database, a time series database, a wide column database, a key value database, a search database, a cache databases, and so forth. The foregoing examples of database 208 types are non-limiting and may not be mutually exclusive e.g., a database can be both commercial and cloud-based, or both relational and open-source, etc.
[0035] In order for the system 108 to interconnect workflows, the processor 202 includes one or more modules. In one embodiment, the one or more modules includes, but not limited to, a transceiver 210, and a dynamic activator unit 212communicably coupled to each other to interconnect workflows.
[0036] The transceiver 210, and the dynamic activator unit 212in an embodiment, may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processor 202. In the examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processor 202 may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processor may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the memory 204 may store instructions that, when executed by the processing resource, implement the processor. In such examples, the system 108 may comprise the memory 204 storing the instructions and the processing resource to execute the instructions, or the memory 204 may be separate but accessible to the system 108 and the processing resource. In other examples, the processor 202 may be implemented by electronic circuitry.
[0037] In one embodiment, the transceiver 210 of the system 108 is configured to receive one or more requests from a Northbound Interface (NBI) 302 (shown in FIG. 3) to execute a plurality of workflows. The NBI 302 is an application programming interface (API) or protocol that allows a lower-level network component to communicate with a higher-level or more central component. The lower-level network component includes but not limited to, switches, routers, Access Points (APs), Network Interface Cards (NICs), hubs, firewalls and network load balancers. The higher-level or more central component includes but not limited to, Software-Defined Networking (SDN) controller, Network Management System (NMS), orchestration platform, policy management server, network analytics platform, Service Function Chaining (SFC) controller, and cloud orchestrator.
[0038] The one or more requests include, but not limited to, service provisioning requests, network slice management requests, Quality of Service (QoS) configuration requests, user authentication and authorization requests, policy enforcement requests, and network management and monitoring requests.
[0039] The plurality of workflows is a series of steps or tasks that are executed in a specific sequence to achieve a particular goal or objective. The plurality of workflows includes, but is not limited to, policy-based workflow, service provisioning workflow, traffic engineering workflow, event-driven workflow, and analytics-driven workflow. In an embodiment, the plurality of workflows includes a first workflow and a second workflow.
[0040] Upon receiving the one or more requests from the NBI 302, the dynamic activator unit 212 is configured to determine whether the second workflow requires to be implemented to complete execution of the first workflow. The determination is based on at least one of, a trained model, a nature of one or more requests received from the NBI 302 and one or more responses received from one or more interacting nodes associated with the first workflow. The nature of one or more requests refers to characteristics, attributes or specifics of the requests received from the NBI 302. The nature of one or more requests includes, but is not limited to, type of operation, priority level, complexity, dependencies, data requirements, expected outputs, contextual information, resource requirements.
[0041] The trained model is at least one of an Artificial Intelligence/Machine Learning (AI/ML) model 304 (as shown in FIG. 3). The AI/ML models is at least one of, but not limited to, supervised learning models, unsupervised learning models, reinforcement learning models, deep learning models, and Natural Language Processing (NLP) models. The model is trained with historical data pertaining to a plurality of workflows in an environment.
[0042] The historical data includes at least, but not limited to, detailed records of past workflow executions, encompassing various aspects such as inputs, processes, outputs, timings, errors, resource usage, and outcomes. For example, the inputs include, but not limited to, types of data processed (e.g., text files, images etc.) and sizes of data batches (e.g., small, medium, large). The processes include, but are not limited to, specific operations performed (e.g., data cleaning, transformation, analysis) and sequences of tasks with workflows. The output includes, but is not limited to, results produced by the workflows (e.g., reports, visualizations, predictions), quality metrics of outputs (e.g., accuracy, completeness). The timings include, but are not limited to, start and end times of workflows, time taken for each task within the workflow. The errors include, but are not limited to, types and frequencies of errors encountered (e.g., data format errors, processing failure, error handling and resolution actions. The resource usage includes, but is not limited to, Central processing Unit (CPU), memory and network bandwidth used by workflows, resource allocation efficiency and bottlenecks. The outcomes include, but are not limited to, success rates of workflows (e.g., percentage of workflows completed without issues), feedback or satisfaction ratings from users or systems relying on workflow outputs. The one or more interacting nodes includes, but not limited to, input node, task node, decision node, gateway node, output node, event node, service node, and error handling node.
[0043] In one embodiment, the dynamic activator unit 212 determines whether the second workflow is required to be implemented to complete execution of the first workflow based on the trained model. In order to determine, the dynamic activator unit 212 checks whether execution of similar workflows have occurred in relation to the first workflow as per the historical data fed to the model. In order to check for execution of similar workflows, the dynamic activator unit 212 includes, but not limited to identifying workflow attributes such as type of operation, inputs, expected outputs resource requirements and other relevant characteristics, retrieving the historical data, feature extraction, similarity analysis, pattern matching, contextual factor. In response to checking, the dynamic activator unit 212 is configured to perform at least one of, trends/patterns analysis and an immersive action on data metrics of the historical data by utilizing the model.
[0044] The trends/pattern analysis involves using the trained AI/ML model to identify recurring themes, behaviors, or sequences in the historical data that relate to the execution of workflows. The trend/pattern analysis helps in predicting future occurrences and making decisions about workflow execution. The trend/ pattern analysis includes, but not limited to, identifying recurring themes, behavioral analysis, sequence recognition. The immersive action involves taking proactive steps based on the insights gained from trends/patterns analysis to ensure smooth and efficient workflow execution. The immersive action includes preemptively interconnecting workflows, allocating resources, or adjusting processes. The immersive action includes, but not limited to, proactive workflow interconnection, resources allocation, process adjustment. For example, the AI/ML model identifies a pattern that large transaction volumes frequently trigger fraud detection workflows, based on this pattern, the system preemptively links the fraud detection workflow to the current customer data processing workflow.
[0045] Upon performing at least one of the trends/patterns analysis and immersive action, the dynamic activator unit 212 determines the requirement of the second workflow. More specifically, the dynamic activator unit 212 determines whether the second workflow requires to be implemented to complete execution of the first workflow.
[0046] Upon determining the requirement of the second workflow to complete the execution of the first workflow, the first workflow is interconnected with the second workflow. On receipt of interconnecting the first workflow with the second workflow, the dynamic activator unit 212 executes the first and the second workflow. The first and the second workflow is executed in tandem based on interconnecting the first and the second workflow. Thus, the plurality of workflows is interconnected based on the requirement. By doing so, the system 108 improves the processing speed of the processor 202. In particular, the processing speed is improved on integration and development to expedite operations. Further, the processing speed is improved by configuring the workflow using the user interface 206 without requiring any change at code base level by the user.
[0047] FIG. 3 is an exemplary block diagram of an architecture 300 of the system 108 of FIG. 2, according to one or more embodiments of the present invention.
[0048] According to the exemplary embodiment, the NBI 302 transmits the one or more requests to execute the first workflow through the system 108. The first workflow includes a state 1 and a state 2. The state 1 and the state 2 are basically the Application Programming Interface (API) invocation which gets executed sequentially to different network nodes such as Policy Control Function (PCF), Unified Data Management (UDM) etc. The first workflow is said to be executed when the state 1 and the state 2 is executed.
[0049] More specifically, as explained in FIG. 2, upon execution of the first workflow, the response is transmitted to the AI/ML model 304 of the system 108. The AI/ML model 304 will analyze the response received from the execution of the first workflow. The analysis is performed based on checking whether execution of similar workflows have occurred in relation to the first workflow as per the historical data fed in the AI/ML model 304. In response to checking that similar workflows occurred in relation to the first workflow as per the historical data, the AI/ML model 304 performs at least one of, trends/patterns analysis and an immersive action on data metrics of the historical data. Upon performing, the AI/ML model 304 determines whether the second workflow requires to be implemented to complete execution of the first workflow based on the at least one of the performed trends/patterns analysis and immersive action.
[0050] Thereafter, if the AI/ML model 304 identifies that the second workflow is required to complete the execution of the first workflow, then the AI/ML model 304 interconnects the second workflow with the first workflow. The first and the second workflows are interconnected to complete the execution of the first workflow. Upon completion of the execution of the first workflow, the response is transmitted back to the NBI 302 through the system 108.
[0051] FIG. 4 is a flow diagram of a method 400 of interconnecting workflows, according to one or more embodiments of the present invention. For the purpose of description, the method 400 is described with the embodiments as illustrated in FIG. 2 and should nowhere be construed as limiting the scope of the present disclosure.
[0052] At step 402, the method 400 includes the step of receiving the one or more requests from the NBI 302 to execute the first workflow by the transceiver 210.
[0053] At step 404, the method 400 includes the step of determining whether the second workflow requires to be implemented to complete execution of the first workflow by the dynamic activator unit 212. The determination is performed based on at least one of, the trained model, the nature of the one or more requests received from the NBI 302 and one or more responses received from one or more interacting nodes of the first workflow. The trained model is at least one of the AI/ML model 304. The AI/ML model 304 is trained with historical data pertaining to a plurality of workflows in an environment.
[0054] The step of determining by the dynamic activator unit 214 further includes the steps of checking whether execution of similar workflows have occurred in relation to the first workflow as per the historical data fed to the model. In response to checking that similar workflows have occurred in relation to the first workflow as per the historical data, performing at least one of, trends/patterns analysis and immersive action on data metrics of the historical data by utilizing the trained model. Further, the dynamic activator unit 214 determines by utilizing the trained model whether the second workflow requires to be implemented to complete execution of the first workflow based on the at least one of, the performed trends/patterns analysis and immersive action.
[0055] At step 406, the method 400 includes the step of interconnecting the first workflow with the second workflow based on determining that the second workflow is required to be implemented to complete the execution of the first workflow by the dynamic activator unit 212.
[0056] At step 408, the method 400 includes the step of executing the first workflow and the second workflow in tandem based on interconnecting the first workflow with the second workflow by the dynamic activator unit 212.
[0057] The present invention further discloses a non-transitory computer-readable medium having stored thereon computer-readable instructions. The computer-readable instructions are executed by the processor 202. The processor 202 is configured to receive one or more requests from the NBI 302 to execute the first workflow. The processor 202 is further configured to determine, whether the second workflow requires to be implemented to complete execution of the first workflow based on at least one of, a trained model trained, nature of one or more requests received from the NBI and one or more responses received from one or more interacting nodes associated with the first workflow. The processor 302 is further configured to interconnect the first workflow with the second workflow based on determining that the second workflow is required to be implemented to complete the execution of the first workflow. The processor 302 is further configured to execute the first workflow and the second workflow in tandem based on interconnecting the first workflow with the second workflow.
[0058] A person of ordinary skill in the art will readily ascertain that the illustrated embodiments and steps in description and drawings (FIG.1-4) are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
[0059] The present disclosure incorporates technical advancement of feasibility to reuse the workflows. The reusing of the workflows saves time spent on integration and development time and expedites operations. Further, the reusing of the workflows also saves time by configuring the workflow using UE without requiring any change at code base level by the user using UE. Further, the present disclosure eliminates the need for manual intervention and code level changes to connect the processes. Further, the present disclosure facilitates code reusability.
[0060] The present invention offers multiple advantages over the prior art and the above listed are a few examples to emphasize on some of the advantageous features. The listed advantages are to be read in a non-limiting manner.

REFERENCE NUMERALS

[0061] Environment- 100
[0062] User Equipment (UE)- 102
[0063] Server- 104
[0064] Network- 106
[0065] System -108
[0066] Processor- 202
[0067] Memory- 204
[0068] User Interface- 206
[0069] Database- 208
[0070] Transceiver- 210
[0071] Dynamic Activator Unit- 212
[0072] Northbound Interface (NBI)- 302
[0073] Artificial Intelligence/Machine learning (AI/ML) model- 304

,CLAIMS:CLAIMS:
We Claim:
1. A method (400) of interconnecting workflows, the method (400) comprising the steps of:
receiving, by one or more processors (202), one or more requests from a Northbound Interface (NBI) (302) to execute a first workflow;
determining, by the one or more processors (202), whether a second workflow requires to be implemented to complete execution of the first workflow based on at least one of, a trained model , nature of the one or more requests received from the NBI (302) and one or more responses received from one or more interacting nodes of the first workflow;
interconnecting, by the one or more processors (202), the first workflow with the second workflow based on determining that the second workflow is required to be implemented to complete the execution of the first workflow; and
executing, by the one or more processors (202), the first workflow and the second workflow in tandem based on interconnecting the first workflow with the second workflow.

2. The method (400) as claimed in claim 1, wherein the trained model is at least one of, an Artificial Intelligence/Machine Learning (AI/ML) model (304).

3. The method (400) as claimed in claim 1, wherein the model is trained with historical data pertaining to a plurality of workflows in an environment.

4. The method (400) as claimed in claim 1, wherein the step of, determining, whether the second workflow requires to be implemented to complete execution of the first workflow based on the model trained with historical data, includes the steps of:
checking, by the one or more processors (202), whether execution of similar workflows have occurred in relation to the first workflow as per the historical data fed to the model;
in response to checking that similar workflows have occurred in relation to the first workflow as per the historical data, performing, by the one or more processors, utilizing the model, at least one of, trends/patterns analysis and immersive action on data metrics of the historical data; and
determining, by the one or more processors (202), utilizing the model, whether the second workflow requires to be implemented to complete execution of the first workflow based on the at least one of, the performed trends/patterns analysis and immersive action.

5. A system (108) of interconnecting workflows, the system (108) comprising:
a transceiver (210) configured to, receive, one or more requests from a Northbound Interface (NBI) (302) to execute a first workflow;
a dynamic activator unit (212), configured to:
determine, whether a second workflow requires to be implemented to complete execution of the first workflow based on at least one of, a trained model, nature of one or more requests received from the NBI (302) and one or more responses received from one or more interacting nodes associated with the first workflow;
interconnect, the first workflow with the second workflow based on determining that the second workflow is required to be implemented to complete the execution of the first workflow; and
execute, the first workflow and the second workflow in tandem based on interconnecting the first workflow with the second workflow.

6. The system (108) as claimed in claim 5, wherein the model is at least one of, an Artificial Intelligence/Machine Learning (AI/ML) model (304).

7. The system (108) as claimed in claim 5, wherein the model is trained with historical data pertaining to a plurality of workflows in an environment.

8. The system (108) as claimed in claim 5, wherein the dynamic activator unit (212), determines, whether the second workflow requires to be implemented to complete execution of the first workflow based on the model trained with historical data, by:
checking, whether execution of similar workflows have occurred in relation to the first workflow as per the historical data fed to the model;
in response to checking that similar workflows have occurred in relation to the first workflow as per the historical data, performing, utilizing the model, at least one of, trends/patterns analysis and immersive action on data metrics of the historical data; and
determining, utilizing the model, whether the second workflow requires to be implemented to complete execution of the first workflow based on the at least one of, the performed trends/patterns analysis and immersive action.