FORM 2
THE PATENTS ACT, 1970 (39 OF 1970) & THE PATENT RULES, 2003
COMPLETE SPECIFICATION
(See section 10 and rule 13)
“METHOD AND SYSTEM FOR MITIGATING WORKFLOW FAILURES IN A FULFILMENT MANAGEMENT SYSTEM”
We, Jio Platforms Limited, an Indian National, of Office - 101, Saffron, Nr. Centre Point, Panchwati 5 Rasta, Ambawadi, Ahmedabad - 380006, Gujarat, India.
The following specification particularly describes the invention and the manner in which it is to be performed.

METHOD AND SYSTEM FOR MITIGATING WORKFLOW FAILURES IN A FULFILMENT MANAGEMENT SYSTEM
FIELD OF INVENTION
5
[0001] Embodiments of the present disclosure generally relate to network performance management systems. More particularly, embodiments of the present disclosure relate to mitigating workflow failures in a fulfilment management system. 10
BACKGROUND
[0002] The following description of the related art is intended to provide
background information pertaining to the field of the disclosure. This section may
15 include certain aspects of the art that may be related to various features of the
present disclosure. However, it should be appreciated that this section is used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of the prior art.
20 [0003] Wireless communication technology has rapidly evolved over the past few
decades, with each generation bringing significant improvements and advancements. The first generation of wireless communication technology was based on analog technology and offered only voice services. However, with the advent of the second-generation (2G) technology, digital communication and data
25 services became possible, and text messaging was introduced. The third-generation
(3G) technology marked the introduction of high-speed internet access, mobile video calling, and location-based services. The fourth-generation (4G) technology revolutionized wireless communication with faster data speeds, better network coverage, and improved security. Currently, the fifth-generation (5G) technology is
30 being deployed, promising even faster data speeds, low latency, and the ability to
connect multiple devices simultaneously. With each generation, wireless
2

communication technology has become more advanced, sophisticated, and capable of delivering more services to its users.
[0004] Workflow failures in a 5G network can be detrimental and disrupt the
5 seamless operation of the network. One common cause of such failures is incorrect
request, due to fulfillment management services (FMS) provisioning or some issue related to network node. In a complex 5G network environment, various stakeholders interact to request and provision network resources and services. If there are errors in the requests made, such as incorrect resource requirements or
10 incompatible service configurations, it can lead to inefficient allocation of resources
and subsequent workflow failures. Likewise, if the fulfillment management services responsible for provisioning and delivering the requested resources are not properly aligned with the network's capabilities and operational constraints, it can result in inefficiencies, delays, and even service outages.
15
[0005] However, there are certain challenges with existing solutions as implementing regular monitoring and performance analysis of the workflow leads to user interference may lead to delay in identifying bottlenecks or anomalies, hence defeating the purpose of allowing for timely corrective actions. Thereby, it is
20 imperative to nullify the user interference in failure case handling by fulfilment
management system. Further, over the period of time a few solutions have been developed to improve the performance of communication devices and to create automated systems and intelligent algorithms for fulfillment management that can minimize human errors and improve the efficiency of resource allocation and
25 service provisioning.
[0006] Thus, there exists an imperative need in the art to ensure accurate and
precise communication between stakeholders, meticulous planning and
provisioning of network resources, and effective management of fulfillment
30 services to prevent such workflow failures and ensure the smooth operation of a 5G
network, which the present disclosure aims to address.
3

SUMMARY
[0007] This section is provided to introduce certain aspects of the present
5 disclosure in a simplified form that are further described below in the detailed
description. This summary is not intended to identify the key features or the scope of the claimed subject matter.
[0008] An aspect of the present disclosure relates to a method for mitigating
10 workflow failures in a fulfilment management system, the method comprising
identifying, by an identification unit, one or more workflow failures at one or more
network nodes, wherein the one or more workflow failures arise due to any or a
combination of an issue in fulfilment management service provisioning, an
incorrect request reception, or network node-related issue. The method comprises
15 analysing, by an analysis unit, the identified one or more workflow failures. The
method comprises rectifying and executing, by an executing unit, the analysed one
or more workflow failures using a trained learning model. The method comprises
storing, by a storage unit, data associated with the rectified one or more workflow
failures.
20
[0009] In an exemplary aspect of the present disclosure, the method further comprises rectifying, by the execution unit using the trained model, the one or more workflow failures based on error codes identified in a request-response schema.
25 [0010] In another exemplary aspect of the present disclosure, the request-response
schema includes mandatory and optional string identifiers for customer details and error identifications.
[0011] In yet another exemplary aspect of the present disclosure, the method
30 further comprises retrying and completing, by the execution unit, the workflow
automatically after rectifying the identified one or more workflow failures.
4

[0012] In yet another exemplary aspect of the present disclosure, the one or more
workflow failures comprises failures arising due to receiving incorrect requests and
issues related to at least one of a network node and fulfilment management service
5 provisioning.
[0013] In yet another exemplary aspect of the present disclosure, the data stored by the storage unit is utilized for continual learning and improvement of the trained model, thereby progressively enhancing accuracy of rectification steps over time.
10
[0014] Another aspect of the present disclosure relates to a system for mitigating workflow failures in a fulfilment management system, the system comprises an identification unit, configured to identify one or more workflow failures at one or more network nodes, wherein the one or more workflow failures arise due to any
15 or a combination of an issue in fulfilment management service provisioning, an
incorrect request reception, or network node-related issue. The system comprises an analysis unit connected to the identification unit, wherein the analysis unit configured to analyse the identified one or more workflow failures. The system comprises an execution unit connected to the analysis unit wherein the execution
20 unit configured to rectify and execute the analysed one or more workflow failures
using a trained learning model. The system comprises a storage unit connected to the execution unit, wherein the storage unit configured to store, data associated with the rectified one or more workflow failures.
25 [0015] In an exemplary aspect of the present disclosure, the execution unit may
be configured to rectify, using the trained model, the one or more workflow failures based on error codes identified in a request-response schema.
[0016] In another exemplary aspect of the present disclosure, the request-response
30 schema includes mandatory and optional string identifiers for customer details and
error identifications.
5

[0017] In yet another exemplary aspect of the present disclosure, the execution unit may be configured to retry and complete the workflow automatically after rectifying the identified one or more workflow failures. 5
[0018] In yet another exemplary aspect of the present disclosure, the one or more workflow failures includes failures arising due to receiving incorrect requests and issues related to at least one of a network node and fulfilment management service provisioning.
10
[0019] In yet another exemplary aspect of the present disclosure, the data stored by the storage unit may be utilized for continual learning and improvement of the trained model, thereby progressively enhancing accuracy of rectification steps over time.
15
[0020] Yet another aspect of the present disclosure may relate to a non-transitory computer readable storage medium storing instructions for mitigating workflow failures in a fulfilment management system, the instructions include executable code which, when executed by a one or more units of a system, causes: an
20 identification unit of the system to identify one or more workflow failures at one or
more network nodes, wherein the one or more workflow failures arise due to any or a combination of an issue in fulfilment management service provisioning, an incorrect request reception, or network node-related issue, an analysis unit of the system to analyse the identified one or more workflow failures, an execution unit
25 of the system to rectify and execute the analysed one or more workflow failures
using a trained learning model, a storage unit of the system to store, data associated with the rectified one or more workflow failures.
30
6

OBJECTS OF THE INVENTION
[0021] Some of the objects of the present disclosure, which at least one embodiment disclosed herein satisfies are listed herein below. 5
[0022] It is an object of the present disclosure to provide a system and a method to analyse, rectify and execute workflow failure by nullifying user/manual interference.
10 [0023] It is another object of the present disclosure to provide a solution that
addresses workflow failures at any state of a network node using trained learning models.
[0024] It is yet another object of the present disclosure to provide a solution to
15 efficiently analyse and rectify workflow failures.
DESCRIPTION OF THE DRAWINGS
[0025] The accompanying drawings, which are incorporated herein, and
20 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. Also, the embodiments shown in the figures are
25 not to be construed as limiting the disclosure, but the possible variants of the method
and system according to the disclosure are illustrated herein to highlight the
advantages of the disclosure. It will be appreciated by those skilled in the art that
disclosure of such drawings includes disclosure of electrical components or
circuitry commonly used to implement such components.
30
7

[0026] FIG. 1 illustrates an exemplary block diagram of a computing device upon which the features of the present disclosure may be implemented in accordance with exemplary implementation of the present disclosure;
5 [0027] FIG. 2 illustrates an exemplary block diagram of a system for mitigating
workflow failures in a fulfilment management system, in accordance with exemplary implementations of the present disclosure;
[0028] FIG. 3 illustrates a method flow diagram for mitigating workflow failures
10 in a fulfilment management system, in accordance with exemplary implementations
of the present disclosure; and
[0029] FIG. 4 illustrates a flow diagram for mitigating workflow failures in a
fulfilment management system, in accordance with exemplary implementations of
15 the present disclosure.
[0030] The foregoing shall be more apparent from the following more detailed description of the disclosure.
20 DETAILED DESCRIPTION
[0031] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that
25 embodiments of the present disclosure may be practiced without these specific
details. Several features described hereafter may each be used independently of one another or with any combination of other features. An individual feature may not address any of the problems discussed above or might address only some of the problems discussed above.
30
8

[0032] The ensuing description provides exemplary embodiments only, and is not
intended to limit the scope, applicability, or configuration of the disclosure. Rather,
the ensuing description of the exemplary embodiments will provide those skilled in
the art with an enabling description for implementing an exemplary embodiment.
5 It should be understood that various changes may be made in the function and
arrangement of elements without departing from the spirit and scope of the disclosure as set forth.
[0033] Specific details are given in the following description to provide a
10 thorough understanding of the embodiments. However, it will be understood by one
of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. 15
[0034] Also, it is noted that individual embodiments may be described as a
process which is depicted as a flowchart, a flow diagram, a data flow diagram, a
structure diagram, or a block diagram. Although a flowchart may describe the
operations as a sequential process, many of the operations may be performed in
20 parallel or concurrently. In addition, the order of the operations may be re-arranged.
A process is terminated when its operations are completed but could have additional steps not included in a figure.
[0035] The word “exemplary” and/or “demonstrative” is used herein to mean
25 serving as an example, instance, or illustration. For the avoidance of doubt, the
subject matter disclosed herein is not limited by such examples. In addition, any
aspect or design described herein as “exemplary” and/or “demonstrative” is not
necessarily to be construed as preferred or advantageous over other aspects or
designs, nor is it meant to preclude equivalent exemplary structures and techniques
30 known to those of ordinary skill in the art. Furthermore, to the extent that the terms
“includes,” “has,” “contains,” and other similar words are used in either the detailed
9

description or the claims, such terms are intended to be inclusive—in a manner similar to the term “comprising” as an open transition word—without precluding any additional or other elements.
5 [0036] As used herein, a “processing unit” or “processor” or “operating
processor” includes one or more processors, wherein processor refers to any logic circuitry for processing instructions. A processor may be a general-purpose processor, a special purpose processor, a conventional processor, a digital signal processor, a plurality of microprocessors, one or more microprocessors in
10 association with a (Digital Signal Processing) DSP core, a controller, a
microcontroller, Application Specific Integrated Circuits, Field Programmable Gate Array circuits, any other type of integrated circuits, etc. The processor may perform signal coding data processing, input/output processing, and/or any other functionality that enables the working of the system according to the present
15 disclosure. More specifically, the processor or processing unit is a hardware
processor.
[0037] As used herein, “a user equipment”, “a user device”, “a smart-user-device”, “a smart-device”, “an electronic device”, “a mobile device”, “a handheld
20 device”, “a wireless communication device”, “a mobile communication device”, “a
communication device” may be any electrical, electronic and/or computing device or equipment, capable of implementing the features of the present disclosure. The user equipment/device may include, but is not limited to, a mobile phone, smart phone, laptop, a general-purpose computer, desktop, personal digital assistant,
25 tablet computer, wearable device or any other computing device which is capable
of implementing the features of the present disclosure. Also, the user device may contain at least one input means configured to receive an input from at least one of a transceiver unit, a processing unit, a storage unit, a detection unit and any other such unit(s) which are required to implement the features of the present disclosure.
30
10

[0038] As used herein, “storage unit” or “memory unit” refers to a machine or
computer-readable medium including any mechanism for storing information in a
form readable by a computer or similar machine. For example, a computer-readable
medium includes read-only memory (“ROM”), random access memory (“RAM”),
5 magnetic disk storage media, optical storage media, flash memory devices or other
types of machine-accessible storage media. The storage unit stores at least the data that may be required by one or more units of the system to perform their respective functions.
10 [0039] As used herein “interface” or “user interface refers to a shared boundary
across which two or more separate components of a system exchange information or data. The interface may also be referred to a set of rules or protocols that define communication or interaction of one or more modules or one or more units with each other, which also includes the methods, functions, or procedures that may be
15 called.
[0040] Fulfilment management system (FMS) is a robust and flexible solution for
managing complex inter-system communications, translating requests into
actionable tasks, and ensuring efficient execution of these tasks based on predefined
20 workflows. The FMS orchestrates and manages requests and responses between
different systems or interfaces.
[0041] An application programming interface (API) is a set of protocols, rules, and tools that specifies how software components should interact and communicate
25 with each other. APIs are used in all kinds of digital environments such as Web
APIs, for example, HTTP APIs or REST APIs; Operating System APIs define how different software applications interact with the operating system. For example, if a software program needs to display a window on your screen, it uses an API provided by the operating system to do so; and database APIs enable
30 communication between an application and a database. For example, if an
application needs to retrieve some data from a database, it uses a database API to
11

send a query to the database and receive the results. In the context of the Fulfilment Management System, APIs would be used to send requests between different systems or interfaces (northbound and southbound interfaces), allowing them to communicate and share data. 5
[0042] All modules, units, components used herein, unless explicitly excluded
herein, may be software modules or hardware processors, the processors being a
general-purpose processor, a special purpose processor, a conventional processor,
a digital signal processor (DSP), a plurality of microprocessors, one or more
10 microprocessors in association with a DSP core, a controller, a microcontroller,
Application Specific Integrated Circuits (ASIC), Field Programmable Gate Array circuits (FPGA), any other type of integrated circuits, etc.
[0043] As used herein the transceiver unit include at least one receiver and at least
15 one transmitter configured respectively for receiving and transmitting data, signals,
information or a combination thereof between units/components within the system and/or connected with the system.
[0044] As discussed in the background section, the current known solutions for
20 executing workflow failures by fulfilment management services have several
shortcomings such as involves a lot human intervention and hence, not allowing timely action for rectification of the workflow failure.
[0045] The present disclosure aims to overcome the above-mentioned and other
25 existing problems in this field of technology by nullifying human intervention and
employing automated systems and intelligent algorithms for fulfilment management to improve the efficiency of resource allocation and service provisioning.
30 [0046] FIG. 1 illustrates an exemplary block diagram of a computing device [100]
upon which the features of the present disclosure may be implemented in
12

accordance with exemplary implementation of the present disclosure. In an
implementation, the computing device [100] may also implement a method for
mitigating workflow failures in a fulfilment management system utilising the
system. In another implementation, the computing device [100] itself implements
5 the method for mitigating workflow failures in a fulfilment management system
using one or more units configured within the computing device [100], wherein said one or more units are capable of implementing the features as disclosed in the present disclosure.
10 [0047] The computing device [100] may include a bus [102] or other
communication mechanism for communicating information, and a hardware processor [104] coupled with bus [102] for processing information. The hardware processor [104] may be, for example, a general purpose microprocessor. The computer system [100] may also include a main memory [106], such as a random
15 access memory (RAM), or other dynamic storage device, coupled to the bus [102]
for storing information and instructions to be executed by the processor [104]. The main memory [106] also may be used for storing temporary variables or other intermediate information during execution of the instructions to be executed by the processor [104]. Such instructions, when stored in non-transitory storage media
20 accessible to the processor [104], render the computer system [100] into a special-
purpose machine that is customized to perform the operations specified in the instructions. The computer system [100] further includes a read only memory (ROM) [108] or other static storage device coupled to the bus [102] for storing static information and instructions for the processor [104].
25
[0048] A storage device [110], such as a magnetic disk, optical disk, or solid-state drive is provided and coupled to the bus [102] for storing information and instructions. The computer system [100] may be coupled via the bus [102] to a display [112], such as a cathode ray tube (CRT), Liquid crystal Display (LCD),
30 Light Emitting Diode (LED) display, Organic LED (OLED) display, etc. for
displaying information to a computer user. An input device [114], including
13

alphanumeric and other keys, touch screen input means, etc. may be coupled to the
bus [102] for communicating information and command selections to the processor
[104]. Another type of user input device may be a cursor controller [116], such as
a mouse, a trackball, or cursor direction keys, for communicating direction
5 information and command selections to the processor [104], and for controlling
cursor movement on the display [112]. This input device typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allow the device to specify positions in a plane.
10 [0049] The computer system [100] may implement the techniques described
herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware and/or program logic which in combination with the computer system [100] causes or programs the computer system [100] to be a special-purpose machine. According to one implementation, the techniques herein are performed by the computer system
15 [100] in response to the processor [104] executing one or more sequences of one or
more instructions contained in the main memory [106]. Such instructions may be read into the main memory [106] from another storage medium, such as the storage device [110]. Execution of the sequences of instructions contained in the main memory [106] causes the processor [104] to perform the process steps described
20 herein. In alternative implementations of the present disclosure, hard-wired
circuitry may be used in place of or in combination with software instructions.
[0050] The computer system [100] also may include a communication interface
[118] coupled to the bus [102]. The communication interface [118] provides a two-
25 way data communication coupling to a network link [120] that is connected to a
local network [122]. For example, the communication interface [118] may be an
integrated services digital network (ISDN) card, cable modem, satellite modem, or
a modem to provide a data communication connection to a corresponding type of
telephone line. As another example, the communication interface [118] may be a
30 local area network (LAN) card to provide a data communication connection to a
compatible LAN. Wireless links may also be implemented. In any such
14

implementation, the communication interface [118] sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.
5 [0051] The computer system [100] can send messages and receive data, including
program code, through the network(s), the network link [120] and the
communication interface [118]. In the Internet example, a server [130] might
transmit a requested code for an application program through the Internet [128], the
ISP [126], the local network [122] and the communication interface [118]. The
10 received code may be executed by the processor [104] as it is received, and/or stored
in the storage device [110], or other non-volatile storage for later execution.
[0052] Referring to FIG. 2, an exemplary block diagram of a system [200] for mitigating workflow failures in a fulfilment management system is shown, in
15 accordance with the exemplary implementations of the present disclosure. The
system [200] comprises at least one identification unit [202], at least one analysis unit [204], at least one execution unit [206] and at least one storage unit [208]. Also, all of the components/ units of the system [200] are assumed to be connected to each other unless otherwise indicated below. As shown in the figures, all units
20 shown within the system should also be assumed to be connected to each other.
Also, in FIG. 2 only a few units are shown, however, the system [200] may comprise multiple such units or the system [200] may comprise any such numbers of said units, as required to implement the features of the present disclosure. Further, in an implementation, the system [200] may be in communication with the
25 user device (may also referred herein as a UE). In another implementation, the
system [200] may reside in a server or a network entity. In yet another implementation, the system [200] may reside partly in the server/ network entity and partly in the user device.
15

[0053] The system [200] is configured for mitigating workflow failures in a fulfilment management system, with the help of the interconnection between the components/units of the system [200].
5 [0054] The fulfilment management service provisioning refers to the processes
and activities involved in setting up, maintaining, and delivering fulfilment services. These fulfilment services encompass the various stages of processing orders, managing inventory, shipping products, and handling customer service related to order fulfilment.
10
[0055] In operation, in one example, the system is used for mitigating workflow failures in a fulfilment management system. The system comprises an identification unit [202], configured to identify one or more workflow failures at one or more network nodes, wherein the one or more workflow failures arise due to any or a
15 combination of an issue in fulfilment management service provisioning, an
incorrect request reception, or network node-related issue. For example, the present disclosure encompasses, the identification unit [202] is responsible for identifying workflow failures that may occur at various network nodes. The one or more workflow failures comprises failures arising due to receiving incorrect requests and
20 issues related to at least one of a network node and fulfilment management service
provisioning. These failures can arise from issues related to fulfilment management service provisioning, incorrect request reception (this could be due to errors in the data format, missing required information, wrong parameters, or any other issues that make the request invalid or improperly structured) or problems associated with
25 network nodes. Further, it may be noted that aforementioned examples of workflow
failures and the corresponding issues are only exemplary and not to be construed to limit the scope of the present subject matter. Other examples of workflow failures and the corresponding issues would also lie within the scope of the present subject matter.
30
16

[0056] The system [200] further comprises an analysis unit [204] connected to the
identification unit [202], wherein the analysis unit [204] configured to analyse the
identified one or more workflow failures. The present disclosure encompasses, the
analysis unit [204] may thereafter evaluate the workflow failures that have been
5 identified by the identification Unit [202]. Particularly, the analysis unit [204] is
configured to analyse these identified workflow failures to understand their nature and causes, if any workflow failure occurs in any state of the network node, then the system [200] may automatically analyse.
10 [0057] The system [200] may further include an execution unit [206] connected
to the analysis unit [204]. The execution unit [206] may be configured to rectify
and execute the analysed one or more workflow failures using a trained learning
model. The present disclosure encompasses, the execution unit [206] to implement
solutions to rectify the identified workflow failures. The execution unit [206] uses
15 a trained learning model, may employ artificial intelligence and machine learning
techniques to understand the failures and determine the best course of action for
correction. In one example, the execution unit [206] is configured to rectify, using
the trained model, the one or more workflow failures based on error codes identified
in a request-response schema. The request-response schema includes mandatory
20 and optional string identifiers for customer details and error identifications. The
execution unit [206] is configured to retry and complete the workflow automatically
after rectifying the identified one or more workflow failures. The trained learning
models for instance, artificial intelligence, machine learning models rectify the at
least one or more workflow failures based on error code identified in a request-
25 response schema. The request-response schema includes mandatory identifiers,
which are essential data elements required for processing (e.g., customer ID, order
number), and optional identifiers, which provide additional information that can
help in error resolution (e.g., customer preferences, error descriptions).
30 [0058] Now, any error identified at the request schema, incorrect request but the
disclosure is not limited thereto may result into workflow failure.
17

[0059] Returning to the present example, the system [200] further includes a
storage unit [208] connected to the execution unit [206], wherein the storage unit
[208] configured to store, data associated with the rectified one or more workflow
5 failures. The present disclosure encompasses the stored data comprises one or more
network nodes with corresponding request and response mappings signifying success and failure statuses, wherein if the workflow failure occurs in any state of a network node of the one or more nodes the failure is rectified automatically. The request-response schema includes mandatory and optional string identifiers for
10 customer details and error identifications wherein the data stored by the storage unit
[208] is utilized for continual learning and improvement of the trained model, thereby progressively enhancing accuracy of rectification steps over time. The storage unit [208] maintains a detailed log of all the rectified workflow failures, aiding in efficient diagnostic analysis and further refinement of the rectification
15 processes.
[0060] Referring to FIG. 3, an exemplary method flow diagram [300] for
mitigating workflow failures in a fulfilment management system, in accordance
with exemplary implementations of the present disclosure is shown. In an
20 implementation the method [300] is performed by the system [200]. Further, in an
implementation, the system [200] may be present in a server device to implement the features of the present disclosure. Also, as shown in FIG. 4, the method [300] starts at step [302].
25 [0061] At step 304, the method comprises, identifying, by an identification unit
[202], one or more workflow failures at one or more network nodes, wherein the one or more workflow failures arise due to any or a combination of an issue in fulfilment management service provisioning, an incorrect request reception, or network node-related issue. The present disclosure encompasses the identification
30 unit [202] is responsible for identifying workflow failures that may occur at various
network nodes. These failures can arise from issues related to fulfilment
18

management service provisioning, incorrect request reception (this could be due to
errors in the data format, missing required information, wrong parameters, or any
other issues that make the request invalid or improperly structured) or problems
associated with network nodes. The fulfilment management service provisioning
5 refers to the processes and activities involved in setting up, maintaining, and
delivering fulfilment services. These services encompass the various stages of
processing orders, managing inventory, shipping products, and handling customer
service related to order fulfilment. The one or more workflow failures comprises
failures arising due to receiving incorrect requests and issues related to at least one
10 of a network node and fulfilment management service provisioning. Further, the at
least one or more workflow failures encompasses failures arising due to fulfilment management service (FMS) provisioning, receiving incorrect request, issue related to a network node but the present disclosure is not limited thereto.
15 [0062] At step 306, the method comprises, analysing, by an analysis unit [204],
the identified one or more workflow failures. The present disclosure encompasses, the analysis unit [204] evaluate the workflow failures that have been identified by the identification Unit [202]. Particularly, it is configured to analyse these identified workflow failures to understand their nature and causes, if any workflow failure
20 occurs in any state of the network node, then the system [200] may automatically
analyse.
[0063] At step 308, the method comprises, rectifying and executing, by an executing unit [206], the analysed one or more workflow failures using a trained
25 learning model. The present disclosure encompasses, the execution unit [206] to
implement solutions to rectify the identified workflow failures. The execution unit [206] uses a trained learning model, may employ artificial intelligence and machine learning techniques to understand the failures and determine the best course of action for correction wherein the method comprises rectifying, by the execution unit
30 [206] using the trained model, the one or more workflow failures based on error
codes identified in a request-response schema. The request-response schema
19

includes mandatory and optional string identifiers for customer details and error
identifications, retrying and completing, by the execution unit [206], the workflow
automatically after rectifying the identified one or more workflow failures. The
trained learning models for instance, artificial intelligence, machine learning
5 models rectify the at least one or more workflow failures based on error code
identified in a request-response schema. The request-response schema includes
mandatory identifiers, which are essential data elements required for processing
(e.g., customer ID, order number), and optional identifiers, which provide
additional information that can help in error resolution (e.g., customer preferences,
10 error descriptions)
[0064] Now, any error identified at the request schema, incorrect request but the disclosure is not limited thereto may result into workflow failure.
15 [0065] At step 310, the method comprises, storing, by a storage unit [208], data
associated with the rectified one or more workflow failures. The present disclosure encompasses the stored data comprises one or more network nodes with corresponding request and response mappings signifying success and failure statuses, wherein if the workflow failure occurs in any state of a network node of
20 the one or more nodes the failure is rectified automatically. The request-response
schema includes mandatory and optional string identifiers for customer details and error identifications, wherein the data stored by the storage unit [208] is utilized for continual learning and improvement of the trained model, thereby progressively enhancing accuracy of rectification steps over time. The storage unit [208]
25 maintains a detailed log of all the rectified workflow failures, aiding in efficient
diagnostic analysis and further refinement of the rectification processes.
[0066] Thereafter, the method terminates at step [312].
20

[0067] Referring to FIG. 4, an exemplary flow diagram [400] for mitigating workflow failures in a fulfilment management system, in accordance with exemplary implementations of the present disclosure is shown.
5 [0068] The flow diagram [400] examines workflow failures at network nodes
such as network node 1 (NN1) [401], network node 2 (NN2) [402], network node 3 (NN3) [403] and network node 4 (NN4) [404]. These network nodes are individual points or devices within the network where data processing and communication occur. Each node can have different roles or functions within the
10 network infrastructure. Now, at network node 2, a workflow failure is identified.
The system detects a specific workflow failure occurring at network node 2 (NN2)[402]. This involves recognizing that a problem exists at NN2 [402], which requires intervention. The system uses advanced machine learning models that have been trained on historical data to address and correct the workflow failure. The
15 Trained Learning Models are algorithms that can predict and implement solutions
based on patterns and data they have learned from past experiences. The rectification process of correcting the identified issue at NN2 [402] using the learning models. This could involve rerouting data, adjusting configurations, or applying specific fixes that the models suggest. After rectifying the issue, the
20 system collects and stores various data metrics related to the workflow failure and
its rectification. These data metrics include performance indicators, error rates, recovery times, and other relevant data. Further, a storage system where the collected data metrics are saved and comprising success status and failure status for each network node for future reference. These records indicate whether each
25 network node's operations were successful or if they encountered failures. This data
is stored for future reference to help improve the system's performance and prevent similar issues.
[0069] For example: Consider a telecommunications network with several nodes responsible for routing calls, managing data traffic, and ensuring connectivity. The
30 flow diagram [400] depicts constant monitoring of these nodes. One day, network
node 2 (NN2) encounters a problem where it cannot process data correctly, leading
21

to a workflow failure. The system identifies this issue and uses its machine learning
models, which have been trained on past data, to determine the best way to fix the
problem. The system implements the recommended solution, restoring normal
operation at NN2. After resolving the issue, the system stores detailed metrics about
5 the failure and the rectification process in a data repository. This includes data about
what went wrong and how it was fixed, allowing the network to learn from this incident and improve its future performance.
[0070] The present disclosure further discloses a non-transitory computer readable storage medium storing instructions for mitigating workflow failures in a
10 fulfilment management system, the instructions include executable code which,
when executed by a one or more units of a system, causes: an identification unit [202] of the system to identify one or more workflow failures at one or more network nodes, wherein the one or more workflow failures arise due to any or a combination of an issue in fulfilment management service provisioning, an
15 incorrect request reception, or network node-related issue; an analysis unit [204] of
the system to analyse the identified one or more workflow failures; an execution unit [206] to rectify and execute the analysed one or more workflow failures using a trained learning model; and a storage unit [208] of the system to store, data associated with the rectified one or more workflow failures.
20
[0071] As is evident from the above, the present disclosure provides a technically advanced solution for mitigating workflow failures in a fulfilment management system. The present disclosure nullifies human intervention and automates the process using trained learning models. Also, the present disclosure improves
25 significantly on better optimisation of network resources by addressing the
workflow failure in a timely manner.
[0072] While considerable emphasis has been placed herein on the disclosed
implementations, it will be appreciated that many implementations can be made and
30 that many changes can be made to the implementations without departing from the
principles of the present disclosure. These and other changes in the implementations
22

of the present disclosure will be apparent to those skilled in the art, whereby it is to be understood that the foregoing descriptive matter to be implemented is illustrative and non-limiting.
5 [0073] Further, in accordance with the present disclosure, it is to be acknowledged
that the functionality described for the various the components/units can be
implemented interchangeably. While specific embodiments may disclose a
particular functionality of these units for clarity, it is recognized that various
configurations and combinations thereof are within the scope of the disclosure. The
10 functionality of specific units as disclosed in the disclosure should not be construed
as limiting the scope of the present disclosure. Consequently, alternative arrangements and substitutions of units, provided they achieve the intended functionality described herein, are considered to be encompassed within the scope of the present disclosure.
23

We Claim:
1. A method [300] for mitigating workflow failures in a fulfilment
5 management system, the method comprising:
identifying [304], by an identification unit [202], one or more workflow
failures at one or more network nodes, wherein the one or more workflow failures
arise due to any or a combination of an issue in fulfilment management service
provisioning, an incorrect request reception, or network node-related issue;
10 analysing [306], by an analysis unit [204], the identified one or more
workflow failures;
rectifying and executing [308], by an executing unit [206], the analysed one or more workflow failures using a trained learning model; and
storing [310], by a storage unit [208], data associated with the rectified one
15 or more workflow failures.
2. The method [300] as claimed in claim 1, wherein the method comprises
rectifying, by the execution unit [206] using the trained model, the one or more
workflow failures based on error codes identified in a request-response schema.
20
3. The method [300] as claimed in claim 2, wherein the request-response
schema includes mandatory and optional string identifiers for customer details and
error identifications.
25 4. The method [300] as claimed in claim 1, wherein the method comprises
retrying and completing, by the execution unit [206], the workflow automatically after rectifying the identified one or more workflow failures.
5. The method [300] as claimed in claim 1, wherein the one or more workflow
30 failures comprises failures arising due to receiving incorrect requests and issues
related to at least one of a network node and fulfilment management service provisioning.
24

6. The method [300] as claimed in claim 1, wherein the data stored by the
storage unit [208] is utilized for continual learning and improvement of the trained
model, thereby progressively enhancing accuracy of rectification steps over time.
5
7. A system [200] for mitigating workflow failures in a fulfilment management
system, the system comprises:
an identification unit [202], configured to identify one or more workflow
failures at one or more network nodes, wherein the one or more workflow failures
10 arise due to any or a combination of an issue in fulfilment management service
provisioning, an incorrect request reception, or network node-related issue;
an analysis unit [204] connected to the identification unit [202], wherein the
analysis unit [204] configured to analyse the identified one or more workflow
failures;
15 an execution unit [206] connected to the analysis unit [204], wherein the
execution unit [206] configured to rectify and execute the analysed one or more workflow failures using a trained learning model; and
a storage unit [208] connected to the execution unit [206], wherein the
storage unit [208] configured to store, data associated with the rectified one or more
20 workflow failures.
8. The system [200] as claimed in claim 7, wherein the execution unit [206] is
configured to rectify, using the trained model, the one or more workflow failures
based on error codes identified in a request-response schema.
25
9. The system [200] as claimed in claim 8, wherein the request-response
schema includes mandatory and optional string identifiers for customer details and
error identifications.
30 10. The system [200] as claimed in claim 7, wherein the execution unit [206] is
configured to retry and complete the workflow automatically after rectifying the identified one or more workflow failures.
25

11. The system [200] as claimed in claim 7, wherein the one or more workflow
failures comprises failures arising due to receiving incorrect requests and issues
related to at least one of a network node and fulfilment management service
5 provisioning.
12. The system [200] as claimed in claim 7, wherein the data stored by the
storage unit [208] is utilized for continual learning and improvement of the trained
model, thereby progressively enhancing accuracy of rectification steps over time.
Dated this the 3rd day of July, 2023
(GARIMA SAHNEY)
IN/PA-1826
AGENT OF THE APPLICANT(S)
OF SAIKRISHNA AND ASSOCIATES