1
FORM 2
THE PATENTS ACT, 1970
(39 OF 1970)
&
5 THE PATENT RULES, 2003
COMPLETE SPECIFICATION
(See section 10 and rule 13)
10
“METHOD AND SYSTEM FOR DYNAMIC WORKFLOW
CREATION”
15 We, Jio Platforms Limited, an Indian National, of Office - 101, Saffron, Nr.
Centre Point, Panchwati 5 Rasta, Ambawadi, Ahmedabad - 380006, Gujarat,
India.
20 The following specification particularly describes the invention and the manner in
which it is to be performed.
2
METHOD AND SYSTEM FOR DYNAMIC WORKFLOW CREATION
TECHNICAL FIELD
5 [0001] Embodiments of the present disclosure generally relate to fulfilment
management services (FMS). More particularly, embodiments of the present
disclosure relate to method and system for dynamic workflow creation.
BACKGROUND
10
[0002] The following description of the related art is intended to provide
background information pertaining to the field of the disclosure. This section may
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
15 to enhance the understanding of the reader with respect to the present disclosure,
and not as admissions of the prior art.
[0003] Wireless communication technology has rapidly evolved over the past few
decades, with each generation bringing significant improvements and
20 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
services became possible, and text messaging was introduced. 3G technology
marked the introduction of high-speed internet access, mobile video calling, and
25 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 being
deployed, promising even faster data speeds, low latency, and the ability to connect
multiple devices simultaneously. With each generation, wireless communication
30 technology has become more advanced, sophisticated, and capable of delivering
more services to its users.
3
[0004] Generally, a fulfilment management services in network nodes encompass
the coordination and optimization of tasks within a network environment. These
fulfilment management services aim to streamline and automate the workflow
process by ensuring efficient resource allocation, task scheduling, and intelligent
5 routing. By leveraging software systems and algorithms, network nodes can
dynamically adapt to changing requirements and priorities and enable seamless
execution of complex workflows.
[0005] Traditional FMS workflows are often static and predefined, making them
10 inflexible to changes in the network environment or business requirements.
Designing workflows typically requires manual effort from developers or engineers
to define the sequence of APIs, their parameters, and the associated logic. This
process is time-consuming and prone to human error. Integrating new APIs or
changing existing ones in a workflow can be challenging in traditional systems due
15 to the static nature of the workflows. In conventional systems, retrieving relevant
data metrics for different network nodes can be cumbersome and may require
separate queries or manual data extraction. Managing the states of a workflow, such
as request schema, attributes, endpoints, and mappings, can be complex and errorprone in traditional systems. The claimed invention facilitates automatic state
20 creation and association, enhancing the accuracy and reliability of the workflow
management process.
[0006] Thus, there exists an imperative need in the art to provide an efficient system
and method for cleanup of network resources after handover procedure.
25
OBJECTS OF THE INVENTION
[0007] Some of the objects of the present disclosure, which at least one
embodiment disclosed herein satisfies are listed herein below.
30
[0008] It is an object of the present disclosure to provide a method and system for
dynamic workflow creation in the FMS.
4
[0009] It is another object of the present disclosure to provide a method and system
for dynamic workflow creation in the FMS that automates the generation of
workflows based on triggers such as images or flowcharts, reducing the need for
manual intervention and increasing efficiency.
5
[0010] It is another object of the present disclosure to provide a method and system
for dynamic workflow creation in the FMS that allows for the flexible adaptation
of workflows to different sequences of APIs corresponding to various network
nodes, enhancing the system's responsiveness to changing requirements.
10
[0011] It is another object of the present disclosure to provide a method and system
for dynamic workflow creation in the FMS that simplifies the integration and
modification of APIs in the workflow by extracting the sequence of APIs from a
trigger and dynamically generating the workflow.
15
[0012] It is another object of the present disclosure to provide a method and system
for dynamic workflow creation in the FMS that streamlines the retrieval of data
metrics associated with different network nodes based on the generated dynamic
workflow, improving data management efficiency.
20
[0013] It is another object of the present disclosure to provide a method and system
for dynamic workflow creation in the FMS that facilitates the automatic creation
and association of states in the workflow, such as request schema, attributes,
endpoints, and mappings, enhancing the accuracy and reliability of the workflow
25 management process.
SUMMARY
[0014] This section is provided to introduce certain aspects of the present disclosure
30 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.
5
[0015] An aspect of the present disclosure provides a method for dynamic
workflow creation. The method comprises receiving, by a receiving unit, a trigger,
wherein the trigger corresponds to representing a sequence of application
programming interfaces (APIs). The method further comprises extracting, by an
5 extraction unit, the sequence of APIs that corresponds to one or more network nodes
based on the received trigger. The method further comprises generating, by a
workflow generation unit using one or more trained models, a dynamic workflow
based on the extracted sequence of APIs. Thereafter, the method comprises
retrieving, by a retrieving unit, one or more data metrics from a storage unit based
10 on the generated dynamic workflow, where the one or more data metrics are
associated with the one or more network nodes.
[0016] In an aspect, the one or more data metrics correspond to information
associated with at least one of the one or more network nodes, segregation of the
15 API based on application, schema associated with a request, and one or more
attributes associated with the schema associated with the request.
[0017] In an aspect, the sequence of APIs comprises a series of API signatures,
each signature of the series of API signatures comprises at least one of parameters,
20 data types, and return values that define a specific function associated with an API.
[0018] In an aspect, the one or more trained models are trained based on a dataset
comprising a plurality of interfaces and corresponding API signatures.
25 [0019] In an aspect, the one or more trained models comprises at least one of a
Convolutional Neural Network (CNN) (Bidirectional Encoder Representations
from Transformers) BERT, and a Recurrent Neural Network (RNN) model, and
Long Short-Term Memory (LSTM).
30 [0020] In an aspect, the trigger is received based on receipt of flowchart, image,
voice, video from a user.
6
[0021] In an aspect, the method comprises creating a set of states comprising a
request schema, a set of attributes, a plurality of end points, and a plurality of
mappings.
5 [0022] In an aspect, the method comprises automatically associating the set of
states.
[0023] In an aspect, the method comprises performing, by the workflow generation
unit, at least one of image processing, video processing, and voice processing on
10 the received trigger.
[0024] Another aspect of the present disclosure provides a system for dynamic
workflow creation. The system comprises a receiving unit configured to receive a
trigger, wherein the trigger corresponds to representing a sequence of application
15 programming interfaces (APIs). The system comprises an extraction unit
configured to extract the sequence of APIs that corresponds to one or more network
nodes based on the received trigger. The system comprises a workflow generation
unit configured to generate, using one or more trained models, a dynamic workflow
based on the extracted sequence of APIs. The system comprises a retrieving unit
20 configured to retrieve one or more data metrics from a storage unit based on the
generated dynamic workflow, where the one or more data metrics are associated
with the one or more network nodes.
[0025] An aspect of the present disclosure provides a user equipment comprising a
25 processor. The processor is configured to transmit a trigger for dynamic workflow
creation in a fulfilment management system (FMS), wherein the trigger corresponds
to at least one of a voice or a video representing a sequence of application
programming interfaces (APIs) and wherein for dynamic workflow creation
comprises: extracting the sequence of APIs that corresponds to one or more network
30 nodes based on the received trigger; generating, using one or more trained models,
a dynamic workflow based on the extracted sequence of APIs; and retrieving one
or more data metrics from a storage unit based on the generated dynamic workflow,
7
where the one or more data metrics are associated with the one or more network
nodes.
[0026] Yet another aspect of the present disclosure provides a non-transitory
5 computer-readable storage medium storing instruction for dynamic workflow
creation the storage medium comprising executable code which, when executed by
one or more units of a system, causes: a receiving unit configured to receive a
trigger, wherein the trigger corresponds to representing a sequence of application
programming interfaces (APIs); an extraction unit configured to extract the
10 sequence of APIs that corresponds to one or more network nodes based on the
received trigger; a workflow generation unit configured to generate, using one or
more trained models, a dynamic workflow based on the extracted sequence of APIs;
and a retrieving unit configured to retrieve one or more data metrics from a storage
unit based on the generated dynamic workflow, where the one or more data metrics
15 are associated with the one or more network nodes.
DESCRIPTION OF THE DRAWINGS
[0027] The accompanying drawings, which are incorporated herein, and constitute
20 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 not to be construed as
25 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
8
[0028] FIG. 1A illustrates an exemplary block diagram representation of 5th
generation core (5GC) network architecture, in accordance with exemplary
embodiment of the present disclosure.
5 [0029] FIG. 1B illustrates an exemplary block diagram of a system for dynamic
creation of a workflow in a fulfilment management system (FMS), in accordance
with exemplary embodiments of the present disclosure.
[0030] FIG. 2 illustrates an exemplary method flow diagram indicating for the
10 dynamic creation of a workflow in a fulfilment management system (FMS), in
accordance with exemplary embodiments of the present disclosure.
[0031] Figure 3A and 3B illustrates an exemplary workflow created by a fulfilment
management service (FMS), in accordance with exemplary embodiments of the
15 present disclosure.
[0032] FIG. 4 illustrates an exemplary block diagram of a computing device upon
which an embodiment of the present disclosure may be implemented.
20 [0033] FIG. 5 illustrates an exemplary block diagram of a user equipment (UE) for
dynamic creation of workflow in a fulfilment management system (FMS), in
accordance with exemplary embodiments of the present disclosure.
[0034] The foregoing shall be more apparent from the following more detailed
25 description of the disclosure.
DESCRIPTION
[0035] In the following description, for the purposes of explanation, various
30 specific details are set forth in order to provide a thorough understanding of
embodiments of the present disclosure. It will be apparent, however, that
embodiments of the present disclosure may be practiced without these specific
9
details. Several features described hereafter can 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. Some of the problems discussed above might not be
5 fully addressed by any of the features described herein. Example embodiments of
the present disclosure are described below, as illustrated in various drawings in
which like reference numerals refer to the same parts throughout the different
drawings.
10 [0036] 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.
It should be understood that various changes may be made in the function and
15 arrangement of elements without departing from the spirit and scope of the
disclosure as set forth.
[0037] It should be noted that the terms "mobile device", "user equipment", "user
device", “communication device”, “device” and similar terms are used
20 interchangeably for the purpose of describing the invention. These terms are not
intended to limit the scope of the invention or imply any specific functionality or
limitations on the described embodiments. The use of these terms is solely for
convenience and clarity of description. The invention is not limited to any particular
type of device or equipment, and it should be understood that other equivalent terms
25 or variations thereof may be used interchangeably without departing from the scope
of the invention as defined herein.
[0038] Specific details are given in the following description to provide a thorough
understanding of the embodiments. However, it will be understood by one of
30 ordinary skill in the art that the embodiments may be practiced without these
specific details. For example, circuits, systems, networks, processes, and other
components may be shown as components in block diagram form in order not to
10
obscure the embodiments in unnecessary detail. In other instances, well-known
circuits, processes, algorithms, structures, and techniques may be shown without
unnecessary detail in order to avoid obscuring the embodiments.
5 [0039] 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 can be performed in parallel or
concurrently. In addition, the order of the operations may be re-arranged. A process
10 is terminated when its operations are completed but could have additional steps not
included in a figure.
[0040] The word “exemplary” and/or “demonstrative” is used herein to mean
serving as an example, instance, or illustration. For the avoidance of doubt, the
15 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
known to those of ordinary skill in the art. Furthermore, to the extent that the terms
20 “includes,” “has,” “contains,” and other similar words are used in either the detailed
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.
25 [0041] As used herein, an “electronic device”, or “portable electronic device”, or
“user device” or “communication device” or “user equipment” or “device” refers
to any electrical, electronic, electromechanical, and computing device. The user
device is capable of receiving and/or transmitting one or parameters, performing
function/s, communicating with other user devices, and transmitting data to the
30 other user devices. The user equipment may have a processor, a display, a memory,
a battery, and an input-means such as a hard keypad and/or a soft keypad. The user
equipment may be capable of operating on any radio access technology including
11
but not limited to IP-enabled communication, Zig Bee, Bluetooth, Bluetooth Low
Energy, Near Field Communication, Z-Wave, Wi-Fi, Wi-Fi direct, etc. For
instance, the user equipment may include, but not limited to, a mobile phone,
smartphone, virtual reality (VR) devices, augmented reality (AR) devices, laptop,
5 a general-purpose computer, desktop, personal digital assistant, tablet computer,
mainframe computer, or any other device as may be obvious to a person skilled in
the art for implementation of the features of the present disclosure.
[0042] Further, the user device may also comprise a “processor” or “processing
10 unit” includes processing unit, wherein processor refers to any logic circuitry for
processing instructions. The 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 association with a
DSP core, a controller, a microcontroller, Application Specific Integrated Circuits,
15 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 disclosure. More specifically, the processor is a hardware processor.
20 [0043] As portable electronic devices and wireless technologies continue to
improve and grow in popularity, the advancing wireless technologies for data
transfer are also expected to evolve and replace the older generations of
technologies. In the field of wireless data communications, the dynamic
advancement of various generations of cellular technology are also seen. The
25 development, in this respect, has been incremental in the order of second generation
(2G), third generation (3G), fourth generation (4G), and now fifth generation (5G),
and more such generations are expected to continue in the forthcoming time.
[0044] Radio Access Technology (RAT) refers to the technology used by mobile
30 devices/ user equipment (UE) to connect to a cellular network. It refers to the
specific protocol and standards that govern the way devices communicate with base
stations, which are responsible for providing the wireless connection. Further, each
12
RAT has its own set of protocols and standards for communication, which define
the frequency bands, modulation techniques, and other parameters used for
transmitting and receiving data. Examples of RATs include GSM (Global System
for Mobile Communications), CDMA (Code Division Multiple Access), UMTS
5 (Universal Mobile Telecommunications System), LTE (Long-Term Evolution),
and 5G. The choice of RAT depends on a variety of factors, including the network
infrastructure, the available spectrum, and the mobile device's/device's capabilities.
Mobile devices often support multiple RATs, allowing them to connect to different
types of networks and provide optimal performance based on the available network
10 resources.
[0045] A Fulfilment management system supports translation of any kind of
incoming request from a northbound interface to plurality of outgoing predefined
sequence of calls from application program interface (API) to southbound API. The
15 fulfilment management system can take a single request containing superset of all
attributes (of all the southbound APIs) and then execute the sequence of southbound
API calls (workflow) and provides final response after completion of APIs.
Fulfilment management system processes which are configured to run any process
supports sequential, parallel, conditional and loop workflow pattern.
20
[0046] The 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
workflows. The FMS orchestrates and manages requests and responses between
25 different systems or interfaces. The key functions performed by the FMS may
include:
[0047] Translation of Requests: The FMS accepts any incoming request from a
northbound interface. This request can then be translated into multiple outgoing
30 API calls to one or more southbound interfaces.
13
[0048] Superset of Attributes: The system can manage a single request that
contains a superset of all attributes of all the southbound APIs. This enables it to
understand and manage complex requests that might cover multiple aspects of the
system's functions.
5
[0049] Sequential Execution of API Calls: After translating the incoming request,
the FMS can execute a sequence of API calls to the southbound interfaces. The
sequence and number of these calls (n) can vary based on the requirements of the
incoming request.
10
[0050] Response Generation: The FMS provides milestone or final responses
after the completion of the individual or all API calls. This ensures the northbound
interface is kept informed of the progress and outcomes of its requests.
15 [0051] Workflow Pattern Support: The FMS is designed to support various
workflow patterns. This includes sequential execution (one step after another),
parallel execution (multiple steps at the same time), conditional execution (based
on certain conditions), and loop execution (repeated steps). The choice of pattern
depends on the specific needs of the process or request being managed.
20
[0052] An application programming interface (API) is a set of protocols, rules, and
tools that specifies how software components should interact and communicate
with each other. The 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
25 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. For example, if an application needs to
retrieve some data from a database, it uses a database API to send a query to the
database and receive the results. In the context of the Fulfilment Management
30 System, APIs would be used to send requests between different systems or
interfaces (northbound and southbound interfaces), allowing them to communicate
and share data.
14
[0053] As discussed in the background section, Traditional FMS workflows are
often static and predefined, making them inflexible to changes in the network
environment or business requirements. Designing workflows typically requires
manual effort from developers or engineers to define the sequence of APIs, their
5 parameters, and the associated logic. This process is time-consuming and prone to
human error. Integrating new APIs or changing existing ones in a workflow can be
challenging in traditional systems due to the static nature of the workflows. In
conventional systems, retrieving relevant data metrics for different network nodes
can be cumbersome and may require separate queries or manual data extraction.
10 Managing the states of a workflow, such as request schema, attributes, endpoints,
and mappings, can be complex and error-prone in traditional systems.
[0054] To overcome these and other inherent problems in the art, the present
disclosure proposes a solution of a method and system for dynamic workflow
15 creation in a fulfilment management system (FMS) that leverages a combination of
triggers, such as images or flowcharts, and a trained model to automate and adapt
workflows based on the needs of different network nodes. This approach addresses
the rigidity of traditional workflows by allowing for dynamic adjustments to the
workflow as the inputs change, thereby enhancing the system's flexibility and
20 responsiveness to varying requirements. Furthermore, the proposed solution
automates the process of designing workflows by extracting the sequence of APIs
from the provided triggers and generating the workflow dynamically using the
trained model. This reduces the reliance on manual efforts, thereby decreasing the
likelihood of errors and increasing efficiency. The automation extends to the
25 integration and modification of APIs, as updates to the trigger automatically result
in corresponding changes in the generated dynamic workflow, simplifying the
process of incorporating new APIs or altering existing ones. In addition, the
solution streamlines the retrieval of relevant data metrics for different network
nodes by automatically fetching this information based on the generated dynamic
30 workflow. This eliminates the need for separate queries or manual data extraction,
leading to a more efficient approach to data management. Finally, the proposed
method and system facilitate the automatic creation and association of states within
15
the workflow, such as request schema, attributes, endpoints, and mappings. This
enhances the accuracy and reliability of the workflow management process,
addressing the complexity and error-proneness associated with state management
in traditional systems.
5
[0055] Hereinafter, exemplary embodiments of the present disclosure will be
described with reference to the accompanying drawings.
[0056] FIG. 1A illustrates an exemplary block diagram representation of 5th
10 generation core (5GC) network architecture [100], in accordance with exemplary
embodiment of the present disclosure. As shown in FIG. 1, the 5GC network
architecture [100] includes a user equipment (UE) [102], a radio access network
(RAN) [104], a plurality if network functions or network entities such as, an access
and mobility management function (AMF) [106], a Session Management Function
15 (SMF) unit [108], a Service Communication Proxy (SCP) [110], an Authentication
Server Function (AUSF) [112], a Network Slice Specific Authentication and
Authorization Function (NSSAAF) [114], a Network Slice Selection Function
(NSSF) [116], a Network Exposure Function (NEF) [118], a Network Repository
Function (NRF) [120], a Policy Control Function (PCF) [122], a Unified Data
20 Management (UDM) [124], an application function (AF) [126], a User Plane
Function (UPF) [128], a data network (DN) [130], wherein all the components are
assumed to be connected to each other in a manner as obvious to the person skilled
in the art for implementing features of the present disclosure.
25 [0057] The User Equipment (UE) [102] interfaces with the network via the Radio
Access Network (RAN) [104]; the Access and Mobility Management Function
(AMF) [106] manages connectivity and mobility, while the Session Management
Function (SMF) unit [108] administers session control; the service communication
proxy (SCP) [110] routes and manages communication between network services,
30 enhancing efficiency and security, and the Authentication Server Function (AUSF)
[112] handles user authentication; the NSSAAF [114] for integrating the 5G core
network with existing 4G LTE networks i.e., to enable Non-Standalone (NSA) 5G
16
deployments, the Network Slice Selection Function (NSSF) [116], Network
Exposure Function (NEF) [118], and Network Repository Function (NRF) [120]
enable network customization, secure interfacing with external applications, and
maintain network function registries respectively; the Policy Control Function
5 (PCF) [122] develops operational policies, and the Unified Data Management
(UDM) [124] manages subscriber data; the Application Function (AF) [126]
enables application interaction, the User Plane Function (UPF) [128] processes and
forwards user data, and the Data Network (DN) [130] connects to external internet
resources; collectively, these components are designed to enhance mobile
10 broadband, ensure low-latency communication, and support massive machine-type
communication, solidifying the 5GC as the infrastructure for next-generation
mobile networks.
[0058] Radio Access Network (RAN) [104] is the part of a mobile
15 telecommunications system that connects user equipment (UE) [102] to the core
network (CN) and provides access to different types of networks (e.g., 5G network).
It consists of radio base stations and the radio access technologies that enable
wireless communication.
20 [0059] Access and Mobility Management Function (AMF) [106] (alternatively
referred to as AMF unit [106]) is a 5G core network function responsible for
managing access and mobility aspects, such as UE registration, connection, and
reachability. It also manages mobility management procedures like handovers and
paging.
25
[0060] Session Management Function (SMF) [108] is a 5G core network function
responsible for managing session-related aspects, such as establishing, modifying,
and releasing sessions. It coordinates with the User Plane Function (UPF) for data
forwarding and manages IP address allocation and QoS enforcement.
30
[0061] Service Communication Proxy (SCP) [110] is a network function in the
5G core network that facilitates communication between other network functions
17
by providing a secure and efficient messaging service. It acts as a mediator for
service-based interfaces.
[0062] Authentication Server Function (AUSF) [112] is a network function in
5 the 5G core responsible for authenticating UEs during registration and providing
security services. It generates and verifies authentication vectors and tokens.
[0063] Network Slice Specific Authentication and Authorization Function
(NSSAAF) [114] is a network function that provides authentication and
10 authorization services specific to network slices. It ensures that UEs can access only
the slices for which they are authorized.
[0064] Network Slice Selection Function (NSSF) [116] is a network function
responsible for selecting the appropriate network slice for a UE based on factors
15 such as subscription, requested services, and network policies.
[0065] Network Exposure Function (NEF) [118] is a network function that
exposes capabilities and services of the 5G network to external applications,
enabling integration with third-party services and applications.
20
[0066] Network Repository Function (NRF) [120] is a network function that acts
as a central repository for information about available network functions and
services. It facilitates the discovery and dynamic registration of network functions.
25 [0067] Policy Control Function (PCF) [122] is a network function responsible for
policy control decisions, such as QoS, charging, and access control, based on
subscriber information and network policies.
[0068] Unified Data Management (UDM) [124] is a network function that
30 centralizes the management of subscriber data, including authentication,
authorization, and subscription information.
18
[0069] Application Function (AF) [126] is a network function that represents
external applications interfacing with the 5G core network to access network
capabilities and services.
5 [0070] User Plane Function (UPF) [128] is a network function responsible for
handling user data traffic, including packet routing, forwarding, and QoS
enforcement.
[0071] Data Network (DN) [130] refers to a network that provides data services
10 to user equipment (UE) in a telecommunications system. The data services may
include but are not limited to Internet services, private data network related services.
[0072] FIG. 1B illustrates an exemplary block diagram of a system [101] for
dynamic workflow creation in a fulfilment management system (FMS), in
15 accordance with exemplary embodiments of the present disclosure. As shown in
FIG. 1B, the system [101] includes a receiving unit [103], an extraction unit [105],
a workflow generation unit [107], and a retrieving unit [109], wherein all the
components are assumed to be connected to each other in a manner as obvious to
the person skilled in the art for implementing features of the present disclosure.
20 Also, in FIG. 1 only a few units are shown, however, the system [101] may
comprise multiple such units or the system [101] may comprise any such numbers
of said units, as required to implement the features of the present disclosure.
[0073] The system [101] for dynamic workflow creation in a fulfilment
25 management system (FMS) is shown in FIG. 1B. The dynamic workflow refers to
a flexible and adaptable sequence of tasks or processes that can be automatically
generated and modified in response to specific inputs or conditions. Unlike static
workflows, which are predefined and unchangeable, dynamic workflows adjust and
optimize based on real-time data, user inputs, or changing requirements. For
30 example, in a fulfilment management system, a user might upload a flowchart
depicting a series of steps for processing an order. The system analyses this input
and dynamically generates a workflow that includes tasks such as inventory check,
19
payment processing, packaging, and shipping. Also, API signatures change based
on data metric information retrieved. For instance, if the earlier network node is
busy (obtained from data metrics), then another instance of that network node is
selected, resulting in changes in API signatures. This adaptability ensures that the
5 workflow can reroute tasks to available nodes, maintaining efficiency and
continuity. This dynamic effect allows the workflow to remain efficient and
relevant to the current context, enhancing productivity and accuracy.
[0074] The system [101] comprises the receiving unit [103]. The receiving unit
10 [103] is configured to receive a trigger, wherein the trigger corresponds to at least
one of an image or a flow chart or a voice or a video representing a sequence of
application programming interfaces (APIs). The trigger facilitates in initiating the
dynamic workflow creation process within the system. The trigger can be an image,
such as a diagram or photograph, or a flow chart that visually depicts the sequence
15 of APIs involved in a particular workflow. The ability of the receiving unit [103] to
accept triggers in these formats allows for a more intuitive and user-friendly way
for users to input the desired sequence of APIs. Once the trigger is received, it is
passed on to other components of the system for further processing, including the
extraction of the API sequence, generation of the dynamic workflow, and retrieval
20 of data metrics based on the generated workflow. This process facilitates the
automation and customization of workflows in the fulfilment management system
(FMS), addressing the limitations of traditional static workflows.
[0075] The system [101] comprises the extraction unit [105] communicatively
25 coupled to the receiving unit [103]. The extraction unit [105] is configured to extract
the sequence of APIs that corresponds to one or more network nodes based on the
received trigger. Once the receiving unit [103] receives the trigger, which could be
in the form of an image, a flow chart, a voice command, or a video representing a
sequence of APIs, this information is forwarded to the extraction unit [105].
30
[0076] The extraction unit [105] then processes the trigger to identify and extract
the specific sequence of APIs depicted in the trigger. The extraction involves using
20
image processing or video processing techniques to identify the one or more
network nodes and requests within the trigger input. For example, if a user uploads
a flowchart, the extraction unit [105] analyses the visual elements of the flowchart
to identify nodes such as inventory, PCF [122], and UDM [124], as well as specific
5 requests like "Find free number." Examples of the one or more network nodes
includes, but not limited only to PCF [122], SMF 108, UDM [124], and other
network nodes of the network architecture as disclosed in FIG. 1A.
[0077] The extraction unit [105] processes the trigger to map the identified one or
10 more nodes and requests to the sequence API calls. The sequence of API calls is
then sequenced to create a dynamic workflow. For instance, in a flowchart
illustrating network operations, the extraction unit [105] would recognize each
visual element representing a node or a request, extract the corresponding API calls,
and determine their sequence. For voice commands, NLP techniques transcribe the
15 spoken words into text and analyse the text to identify the sequence of API calls
and corresponding network nodes. Video inputs are processed by breaking down
the video into frames and analysing each frame to identify key elements that
represent API calls and their sequence.
20 [0078] The sequence defines the order and nature of the APIs that need to be
executed for a particular process or task within the fulfilment management system
(FMS). The extraction process facilitates in automating and customizing workflows
in the FMS, allowing for greater flexibility and efficiency in workflow
management. For example, a user uploads a video demonstrating a workflow with
25 various API interactions between network nodes. The extraction unit [105] employs
advanced video processing algorithms to analyse the video content, identifying
frames that represent distinct API calls and the associated nodes. By recognizing
patterns and sequences within the video, the extraction unit [105] determines the
order and parameters of the API calls. The extracted sequence is then used to create
30 a dynamic workflow, ensuring that the correct API calls are made to the appropriate
network nodes in the specified order.
21
[0079] The system [101] comprises the workflow generation unit [107]
communicatively coupled to the extraction unit [105]. The workflow generation
unit [107] is configured to generate, using one or more trained models, a dynamic
workflow based on the extracted sequence of APIs. Once the extraction unit [105]
5 has identified and extracted the sequence of APIs from the received trigger, this
sequence is passed on to the workflow generation unit [107]. Here, a trained model
is employed to transform the extracted sequence of APIs into a dynamic workflow.
[0080] The one or more trained models comprise at least one of a Convolutional
10 Neural Network (CNN) for image processing, a Transformer-based model such as
BERT (Bidirectional Encoder Representations from Transformers) for Natural
Language Processing (NLP), and a Recurrent Neural Network (RNN) model such
as Long Short-Term Memory (LSTM) for video processing. The CNN is trained on
a dataset of flowchart images annotated with corresponding API calls and network
15 nodes, enabling it to identify nodes such as inventory, PCF, and UDM within new
flowchart images. The Transformer-based model is trained on a dataset of voice
commands and textual descriptions of workflows paired with correct API
sequences, allowing it to transcribe and interpret spoken instructions to extract
relevant API calls and their sequence. The RNN model is trained on videos
20 demonstrating various workflows, with each frame annotated with corresponding
API interactions, enabling it to analyse the sequence of frames to identify key
elements and the order of API calls for generating a dynamic workflow from video
inputs. The trained model is trained on a dataset comprising various interfaces and
corresponding API signatures, incorporating techniques from Natural Language
25 Processing, for example, Vector DB, retrieval augmented generation (RAG).
[0081] The signatures correspond to information relates to the API, including
details such as API credentials, parameters, attributes, request schema, response
schema, terms of use, error messages, endpoints, and data models. Each network
30 node is associated with different API documentation or signatures. Consequently,
the model is trained on the comprehensive information to accurately understand and
generate the necessary API sequences for various network nodes. For example, an
22
API for accessing user data might have documentation specifying the required
authentication credentials, input parameters like user ID, attributes such as
username and email, the format of the request and response, usage terms, error
messages, and the endpoint URL. Each network node is associated with different
5 API documentation or signatures. Consequently, the model is trained on this
comprehensive information to accurately understand and generate the necessary
API sequences for various network nodes. For instance, a network node handling
inventory management will have distinct API signatures detailing how to query,
update, and manage inventory data, while another node for user authentication will
10 have APIs focused on login, token generation, and session management.
[0082] The dynamic workflow generated by the workflow generation unit [107] is
configured for specific sequence of APIs and is configured to be adaptable,
allowing for changes in the workflow as the requirements of the FMS evolve. The
15 ability to generate dynamic workflows based on different sequences of APIs is a
key feature of the system, providing flexibility and efficiency in workflow
management that addresses the limitations of traditional static workflows.
[0083] The system [101] comprises the retrieving unit [109] communicatively
20 coupled to the workflow generation unit [107]. The retrieving unit [109] is
configured to retrieve one or more data metrics from a storage unit based on the
generated dynamic workflow, where the one or more data metrics are associated
with the one or more network nodes. After the workflow generation unit [107] has
created the dynamic workflow using the extracted sequence of APIs, the retrieving
25 unit [109] fetches relevant data metrics that are necessary for the execution of the
workflow. The data metrics could include information such as network
performance, resource availability, or other node-specific parameters for the proper
functioning of the workflow. The storage unit, from which the data metrics are
retrieved, serves as a repository for all such data, ensuring that the retrieving unit
30 [109] has access to the most up-to-date information. By associating these data
metrics with the respective network nodes involved in the workflow, the system
[101] ensures that each node can operate optimally, based on its current state and
23
available resources. The capability of retrieving relevant data metrics dynamically
based on the workflow requirements enhances the efficiency and adaptability of the
FMS, addressing some of the key challenges in traditional workflow management
systems.
5
[0084] Referring to FIG. 2 an exemplary method flow diagram [200], for dynamic
workflow creation in accordance with exemplary embodiments of the present
invention is shown. In an implementation the method [200] is performed by the
system [101]. As shown in Figure 2, the method [200] starts at step [202].
10
[0085] At [204], the method flow [200] as disclosed by the present disclosure
comprises receiving, by a receiving unit [103], a trigger, wherein the trigger
corresponds to any or a combination of an image or a flow chart representing a
sequence of application programming interfaces (APIs). The trigger is received as
15 an uploaded image or flow chart created on a page or a whiteboard by the user. In
a preferred implementation, the trigger may be received at a user device, but the
disclosure is not limited thereto. For instance, a user may create the flow chart on a
sheet of paper and upload on the user device. Furthermore, the method comprises
performing, by the receiving unit [103], image processing to the received trigger.
20
[0086] The trigger facilitates in initiating the dynamic workflow creation process
within the system. The trigger can be an image, such as a diagram or photograph,
or a flow chart that visually depicts the sequence of APIs involved in a particular
workflow. The ability of the receiving unit [103] to accept triggers in these formats
25 allows for a more intuitive and user-friendly way for users to input the desired
sequence of APIs. Once the trigger is received, it is passed on to other components
of the system for further processing, including the extraction of the API sequence,
generation of the dynamic workflow, and retrieval of data metrics based on the
generated workflow. This process facilitates the automation and customization of
30 workflows in the fulfilment management system (FMS), addressing the limitations
of traditional static workflows.
24
[0087] Next at step [206], the method comprising extracting, by an extraction unit
[105], the sequence of APIs that corresponds to one or more network nodes based
on the received trigger. The sequence of APIs includes a series of API signatures,
each signature of the series of API signature comprises at least one of parameters,
5 data types, and return values that define a specific function or method associated
with an API.
[0088] The signatures correspond to information relates to the API, including
details such as API credentials, parameters, attributes, request schema, response
10 schema, terms of use, error messages, endpoints, and data models. Each network
node is associated with different API documentation or signatures. Consequently,
the model is trained on the comprehensive information to accurately understand and
generate the necessary API sequences for various network nodes. For example, an
API for accessing user data might have documentation specifying the required
15 authentication credentials, input parameters like user ID, attributes such as
username and email, the format of the request and response, usage terms, error
messages, and the endpoint URL. Each network node is associated with different
API documentation or signatures. Consequently, the model is trained on this
comprehensive information to accurately understand and generate the necessary
20 API sequences for various network nodes. For instance, a network node handling
inventory management will have distinct API signatures detailing how to query,
update, and manage inventory data, while another node for user authentication will
have APIs focused on login, token generation, and session management.
25 [0089] Now, the sequence of APIs may be captured from the received trigger at the
receiving unit [103]. Once the receiving unit [103] receives the trigger, which could
be in the form of an image or a flow chart representing a sequence of APIs, this
information is forwarded to the extraction unit [105]. The extraction unit [105] then
processes this trigger to identify and extract the specific sequence of APIs depicted
30 in the trigger. The sequence defines the order and nature of the APIs that need to be
executed for a particular process or task within the fulfilment management system
(FMS). The extraction process facilitates in automating and customizing workflows
25
in the FMS, allowing for greater flexibility and efficiency in workflow
management.
[0090] Further, at step [208] the method encompasses generating, by a workflow
5 generation unit [107] using a trained model, a dynamic workflow based on the
extracted sequence of APIs. The trained model is trained based on a dataset
comprising a plurality of interfaces and their corresponding API signatures. The
trained model is trained further based on Natural Language Processing and image
processing techniques. Once the extraction unit [105] has identified and extracted
10 the sequence of APIs from the received trigger, this sequence is passed on to the
workflow generation unit [107]. Here, a trained model is employed to transform the
extracted sequence of APIs into a dynamic workflow. The trained model is trained
on a dataset comprising various interfaces and corresponding API signatures,
incorporating techniques from Natural Language Processing and/or image
15 processing. The dynamic workflow generated by the workflow generation unit
[107] is configured for specific sequence of APIs and is configured to be adaptable,
allowing for changes in the workflow as the requirements of the FMS evolve. The
ability to generate dynamic workflows based on different sequences of APIs is a
key feature of the system, providing flexibility and efficiency in workflow
20 management that addresses the limitations of traditional static workflows.
[0091] In an implementation, the dynamic workflow may utilise AI/ML techniques
to train on a plurality of interfaces and also their corresponding API signatures. In
a preferred implementation, generating automatically generate a dynamic workflow
25 based on Natural Language Processing and/or image processing techniques but the
present disclosure is not limited thereto. Continuing with the above example, the
uploaded image on the user device may be processed using image processing
techniques for automatically generating a dynamic workflow.
30 [0092] In an implementation, the method flow comprises creating a set of states
further comprising a request/response schema, a set of attributes, a plurality of end
26
points and a plurality of mappings. Furthermore, automatically stitching the set of
states and subsequently, automatically generating dynamic workflow.
[0093] For example, the method flow involves creating a set of states, each
5 comprising a request/response schema, a set of attributes, a plurality of endpoints,
and a plurality of mappings. For example, consider a fulfilment management system
designed to automate the processing of online orders. The method would create
several states to manage different stages of the order process.
10 [0094] One state might be the "Check Inventory State." In this state, the
request/response schema includes a request with the item ID and quantity, and the
response provides the availability status. The attributes associated with this state
could include the item ID, quantity, and warehouse location. The endpoints would
involve the inventory service URL, and the mappings would connect the request
15 details to the inventory database fields and map the response to an availability
status.
[0095] Another state could be the "Process Payment State." Here, the
request/response schema involves sending payment details in the request and
20 receiving the transaction status in the response. Attributes might include the credit
card number, expiration date, and amount. The endpoints would point to the
payment gateway URL, with mappings that link the payment details in the request
to the payment gateway fields and map the response to the transaction status.
25 [0096] A third state might be the "Package Item State" In this state, the
request/response schema includes order details in the request and packaging status
in the response. Attributes could encompass the order ID, item list, and packaging
instructions. The endpoints would refer to the packaging service URL, with
mappings that connect the order details in the request to the packaging system fields
30 and map the response to a packaging status.
27
[0097] Finally, there could be a "Ship Order State." In this state, the
request/response schema involves shipment details in the request and a tracking
number in the response. Attributes might include the order ID, destination address,
and shipping method. The endpoints would point to the shipping service URL, with
5 mappings that link the shipment details in the request to the shipping service fields
and map the response to the tracking number.
[0098] Furthermore, the fulfilment management system automatically stitches
these states together to form a coherent sequence and subsequently generates a
10 dynamic workflow. For example, the system links the "Check Inventory" state to
the "Process Payment" state, then to the "Package Item" state, and finally to the
"Ship Order" state. It would be appreciated by the person skilled in the art that the
automatic stitching facilitates that the workflow executes in the correct order. If the
"Check Inventory" state indicates an item is out of stock, the dynamic workflow
15 can adjust by including a state to "Notify Customer" or "Reorder Item,"
demonstrating the flexibility and adaptability of the dynamic workflow. Further,
the proposed technique minimizes manual effort, streamlines the integration
process, and ensures that the workflow remains efficient and relevant to the current
context.
20
[0099] Thereafter, the method [200] comprises at step [210] retrieving, by a
retrieving unit [109], one or more data metrics from a storage unit based on the
generated dynamic workflow, where the one or more data metrics are associated
with the one or more network nodes. After the workflow generation unit [107] has
25 created a dynamic workflow using the extracted sequence of APIs, the retrieving
unit [109] fetches relevant data metrics that are necessary for the execution of the
workflow. The data metrics could include information such as network
performance, resource availability, or other node-specific parameters for the proper
functioning of the workflow. The storage unit, from which the data metrics are
30 retrieved, serves as a repository for all such data, ensuring that the retrieving unit
[109] has access to the most up-to-date information. By associating these data
metrics with the respective network nodes involved in the workflow, the system
28
[101] ensures that each node can operate optimally, based on its current state and
available resources. The capability of retrieving relevant data metrics dynamically
based on the workflow requirements enhances the efficiency and adaptability of the
FMS, addressing some of the key challenges in traditional workflow management
5 systems. The data metrics corresponds to information associated with the one or
more network nodes, segregation of an API based on application, schema associated
with request or response, and one or more attributes associated with the schema
associated with request or response. The one or more data metrics comprises a
repository of data associated with the one or more network nodes. Further, the data
10 may be associated with API segregation that may comprise a request/response
schema encompassing a set of attribute details.
[00100] Segregation of the API is based on application refers to organizing and
categorizing APIs according to their specific functions and use cases within
15 different applications. For example, when the system receives a trigger in the form
of a flowchart or image, it employs AI/ML techniques to interpret the sequence of
APIs. These APIs are then segregated based on their application. For instance, APIs
related to number management might include functions for querying available
numbers and updating their status. APIs for network provisioning might include
20 configuring the Policy Control Function (PCF) and the Unified Data Manager
(UDM). When generating a dynamic workflow, the system ensures that the APIs
are grouped and utilized according to their specific applications, such as number
management or network provisioning, thereby optimizing the workflow for
efficiency and accuracy. This segregation allows the system to automatically
25 associate the appropriate set of states for each application, ensuring that the
generated workflow aligns with the distinct functional requirements of each API
category.
[00101] The method terminates at step [212].
30
29
[00102] FIG. 3A illustrates an exemplary workflow [300a] created by fulfilment
management service (FMS), in accordance with exemplary embodiments of the
present disclosure.
5 [00103] At [302], an image, video, or voice corresponding to a sequence of
application programming interfaces (APIs) is uploaded. This uploaded media acts
as a trigger for the workflow generation process within the Fulfilment Management
System (FMS).
10 [00104] At [304], the sequence of APIs, corresponding to one or more network
nodes, is extracted based on the received trigger. AI/ML-based techniques are then
employed to interpret the trigger and generate a dynamic workflow based on the
extracted sequence of APIs. These techniques involve analysing the uploaded
media to understand the structure and interactions of the APIs visually or audibly
15 represented.
[00105] At [306], one or more data metrics are retrieved from a storage unit based
on the generated dynamic workflow. The one or more data metrics provide essential
information associated with the one or more network nodes (include Network Node
20 1, Network Node 2, Network Node 3, and Network Node 4) disclosed at [308].
[00106] FIG. 3B illustrates an exemplary workflow created by a fulfilment
management service (FMS), in accordance with exemplary embodiments of the
present disclosure.
25
[00107] The process starts when the user provides workflow [300b] as a trigger in
the form of a flowchart or image. The trigger is received by the system's receiving
unit, which can interpret inputs such as voice, video, or images that represent a
sequence of APIs.
30
[00108] Upon receiving the trigger, the extraction unit processes the flowchart to
extract the sequence of APIs associated with different network nodes. Each step in
30
the flowchart corresponds to specific API calls. For example, "Step 1: Find free
number inventory" involves an API call to query the database for available
numbers, while "Step 2: Update number status inventory" involves another API call
to change the status of the selected number to reserved.
5
[00109] The workflow generation unit then utilizes one or more trained models,
which have been trained on datasets comprising various interfaces and their
corresponding API signatures. These models are further refined using Natural
Language Processing (NLP) and image processing techniques to accurately
10 interpret the details of the flowchart. Based on the extracted sequence of APIs, the
workflow generation unit creates a dynamic workflow that automates the outlined
steps.
[00110] Once the dynamic workflow is generated, the retrieving unit accesses
15 relevant data metrics from a storage unit. These metrics are associated with the
network nodes involved in the workflow. For example, during "Step 3: Create
provisioning PCF" and "Step 4: Create provisioning UDM," the system retrieves
necessary configuration data for the Policy Control Function (PCF) and User Data
Management (UDM) systems, respectively. This ensures that all provisioning steps
20 are correctly executed based on the predefined API signatures.
[00111] Each step in the flowchart comprises specific API signatures that define
parameters, data types, and return values. These signatures ensure that each API
call performs its intended function, such as querying databases, updating statuses,
25 or provisioning network functions. The system also creates a set of states that
include request schemas, attributes, endpoints, and mappings, which are
automatically associated by the workflow generation unit to form a coherent and
executable workflow.
30 [00112] FIG. 4 illustrates an exemplary block diagram of a computing device [400]
(also referred to herein as a computer system [400]) upon which an embodiment of
the present disclosure may be implemented. In an implementation, the computing
31
device implements the method for dynamic workflow creation in the FMS using
the system [101]. In another implementation, the computing device itself
implements the method for dynamic workflow creation in the FMS by using one or
more units configured within the computing device, wherein said one or more units
5 are capable of implementing the features as disclosed in the present disclosure.
[00113] The computing device [400] encompasses a wide range of electronic
devices capable of processing data and performing computations. Examples of
computing device [400] include, but are not limited only to, personal computers,
10 laptops, tablets, smartphones, servers, and embedded systems. The devices may
operate independently or as part of a network and can perform a variety of tasks
such as data storage, retrieval, and analysis. Additionally, computing device [400]
may include peripheral devices, such as monitors, keyboards, and printers, as well
as integrated components within larger electronic systems, highlighting their
15 versatility in various technological applications.
[00114] The computing device [400] may include a bus [402] or other
communication mechanism for communicating information, and a processor [404]
coupled with bus [402] for processing information. The processor [404] may be, for
20 example, a general-purpose microprocessor. The computing device [400] may also
include a main memory [406], such as a random-access memory (RAM), or other
dynamic storage device, coupled to the bus [402] for storing information and
instructions to be executed by the processor [404]. The main memory [406] also
may be used for storing temporary variables or other intermediate information
25 during execution of the instructions to be executed by the processor [404]. Such
instructions, when stored in non-transitory storage media accessible to the processor
[404], render the computing device [400] into a special-purpose machine that is
customized to perform the operations specified in the instructions. The computing
device [400] further includes a read only memory (ROM) [408] or other static
30 storage device coupled to the bus [402] for storing static information and
instructions for the processor [404].
32
[00115] A storage device [410], such as a magnetic disk, optical disk, or solid-state
drive is provided and coupled to the bus [402] for storing information and
instructions. The computing device [400] may be coupled via the bus [402] to a
display [412], such as a cathode ray tube (CRT), for displaying information to a
5 computer user. An input device [414], including alphanumeric and other keys, may
be coupled to the bus [402] for communicating information and command
selections to the processor [404]. Another type of user input device may be a cursor
controller [416], such as a mouse, a trackball, or cursor direction keys, for
communicating direction information and command selections to the processor
10 [404], and for controlling cursor movement on the display [412]. 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.
[00116] The computing device [400] may implement the techniques described
15 herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware,
and/or program logic which in combination with the computing device [400] causes
or programs the computing device [400] to be a special-purpose machine.
According to one embodiment, the techniques herein are performed by the
computing device [400] in response to the processor [404] executing one or more
20 sequences of one or more instructions contained in the main memory [406]. Such
instructions may be read into the main memory [406] from another storage medium,
such as the storage device [410]. Execution of the sequences of instructions
contained in the main memory [406] causes the processor [404] to perform the
process steps described herein. In alternative embodiments, hard-wired circuitry
25 may be used in place of or in combination with software instructions.
[00117] The computing device [400] also may include a communication interface
[418] coupled to the bus [402]. The communication interface [418] provides a twoway data communication coupling to a network link [420] that is connected to a
30 local network [422]. For example, the communication interface [418] 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
33
telephone line. As another example, the communication interface [418] may be a
local area network (LAN) card to provide a data communication connection to a
compatible LAN. Wireless links may also be implemented. In any such
implementation, the communication interface [418] sends and receives electrical,
5 electromagnetic, or optical signals that carry digital data streams representing
various types of information.
[00118] The computing device [400] can send messages and receive data, including
program code, through the network(s), the network link [420] and the
10 communication interface [418]. In the Internet example, a server [430] might
transmit a requested code for an application program through the Internet [428], the
Internet Service Provider (ISP) [426], the host [424] the local network [422] and
the communication interface [418]. The received code may be executed by the
processor [404] as it is received, and/or stored in the storage device [410], or other
15 non-volatile storage for later execution.
[00119] FIG. 5 illustrates an exemplary block diagram of a user equipment (UE)
[102] for dynamic creation of workflow in a fulfilment management system (FMS),
in accordance with exemplary embodiments of the present disclosure. In an
20 embodiment, the UE [102] comprises a processor [102A] and a memory [102B].
[00120] As illustrated, the processor [102A] is configured to transmit a trigger. The
trigger is sent for dynamic workflow creation in a fulfilment management system
(FMS). The trigger corresponds to at least one of an image or a voice or a video
25 representing a sequence of application programming interfaces (APIs). Further, for
dynamic workflow creation comprises: extracting the sequence of APIs that
corresponds to one or more network nodes based on the trigger; generating, using
one or more trained models, a dynamic workflow based on the extracted sequence
of APIs; and retrieving one or more data metrics from a storage unit based on the
30 generated dynamic workflow, where the one or more data metrics are associated
with the one or more network nodes.
34
[00121] An aspect of the present disclosure provides a user equipment comprising
a processor. The processor is configured to transmit a trigger for dynamic workflow
creation in a fulfilment management system (FMS), wherein the trigger corresponds
to at least one of a voice or a video representing a sequence of application
5 programming interfaces (APIs) and wherein for dynamic workflow creation
comprises: extracting the sequence of APIs that corresponds to one or more network
nodes based on the trigger; generating, using one or more trained models, a dynamic
workflow based on the extracted sequence of APIs; and retrieving one or more data
metrics from a storage unit based on the generated dynamic workflow, where the
10 one or more data metrics are associated with the one or more network nodes.
[00122] Yet another aspect of the present disclosure provides a non-transitory
computer-readable storage medium storing instruction for dynamic workflow
creation in a fulfilment management system (FMS), the storage medium comprising
15 executable code which, when executed by one or more units of a system, causes: a
receiving unit configured to receive a trigger, wherein the trigger corresponds to at
least one of a voice or a video representing a sequence of application programming
interfaces (APIs); an extraction unit configured to extract the sequence of APIs that
corresponds to one or more network nodes based on the received trigger; a
20 workflow generation unit configured to generate, using one or more trained models,
a dynamic workflow based on the extracted sequence of APIs; and a retrieving unit
configured to retrieve one or more data metrics from a storage unit based on the
generated dynamic workflow, where the one or more data metrics are associated
with the one or more network nodes.
25
[00123] Further, in accordance with the present disclosure, it is to be acknowledged
that the functionality described for the various components/units can be
implemented interchangeably. While specific embodiments may disclose a
particular functionality of these units for clarity, it is recognized that various
30 configurations and combinations thereof are within the scope of the disclosure. The
functionality of specific units, as disclosed in the disclosure, should not be
construed as limiting the scope of the present disclosure. Consequently, alternative
35
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.
5 [00124] As is evident from the above, the present disclosure provides a technically
advanced solution for dynamic workflow creation in a fulfilment management
system (FMS). The invention enables the generation of workflows based on userprovided flowcharts or images, utilizing AI and ML based trained model on existing
interfaces and their API signatures. The method involves receiving a trigger in the
10 form of a flowchart, extracting the sequence of APIs, generating a dynamic
workflow, and retrieving data metrics associated with the workflow. Consequently,
the need for extensive manual workflow design is minimized, and a generic
framework is created to facilitate the seamless integration of new functionalities
into existing systems by creating new services within existing flows. This approach
15 significantly enhances efficiency and reduces the time required for system
integration and workflow execution.
[00125] While considerable emphasis has been placed herein on the disclosed
embodiments, it will be appreciated that many embodiments can be made and that
20 many changes can be made to the embodiments without departing from the
principles of the present disclosure. These and other changes in the embodiments
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.
25
36
We Claim:
1. A method for dynamic workflow creation, said method comprising:
receiving, by a receiving unit [103], a trigger, wherein the trigger
corresponds to representing a sequence of application programming
5 interfaces (APIs);
extracting, by an extraction unit [105], the sequence of APIs that
corresponds to one or more network nodes based on the received trigger;
generating, by a workflow generation unit [107] using one or more
trained models, a dynamic workflow based on the extracted sequence of APIs;
10 and
retrieving, by a retrieving unit [109], one or more data metrics from a
storage unit based on the generated dynamic workflow, where the one or more
data metrics are associated with the one or more network nodes.
15 2. The method as claimed in claim 1, wherein the one or more data metrics
corresponds to information associated with at least one of the one or more
network nodes, segregation of an API based on application, schema
associated with a request, and one or more attributes associated with the
schema associated with the request.
20
3. The method as claimed in claim 1, wherein the sequence of APIs comprises
a series of API signatures, each signature of the series of API signature
comprises at least one of parameters, data types, and return values that define
a specific function associated with an API.
25
4. The method as claimed in claim 1, wherein the one or more trained models
are trained based on a dataset comprising a plurality of interfaces and
corresponding API signatures.
30 5. The method as claimed in claim 4, wherein the one or more trained models
comprises at least one of a Convolutional Neural Network (CNN)
(Bidirectional Encoder Representations from Transformers) BERT, and a
37
Recurrent Neural Network (RNN) model, and Long Short-Term Memory
(LSTM).
6. The method as claimed in claim 1, wherein the trigger is received based on
5 receipt of flowchart, image, voice, video from a user.
7. The method as claimed in claim 1, wherein the method comprises creating a
set of states comprising a request schema, a set of attributes, a plurality of end
points, and a plurality of mappings.
10
8. The method as claimed in claim 7, wherein the method comprises
automatically associating, by the workflow generation unit [107], the set of
states.
15 9. The method as claimed in claim 1, wherein the method comprises performing,
by the workflow generation unit [107], at least one of image processing, video
processing, voice processing on the received trigger.
10. A system for dynamic workflow creation, the system comprises:
20 a receiving unit [103] configured to receive a trigger, wherein the
trigger corresponds to representing a sequence of application programming
interfaces (APIs);
an extraction unit [105] configured to extract the sequence of APIs that
corresponds to one or more network nodes based on the received trigger;
25 a workflow generation unit [107] configured to generate, using one or
more trained models, a dynamic workflow based on the extracted sequence
of APIs; and
a retrieving unit [109] configured to retrieve one or more data metrics
from a storage unit based on the generated dynamic workflow, where the one
30 or more data metrics are associated with the one or more network nodes.
38
11. The system as claimed in claim 10, wherein the one or more data metrics
corresponds to information associated with the at least one of one or more
network nodes, segregation of an API based on application, schema
associated with a request, and one or more attributes associated with the
5 schema associated with the request.
12. The system as claimed in claim 10, wherein the sequence of APIs comprises
a series of API signatures, each signature of the series of API signatures
comprises at least one of parameters, data types, and return values that define
10 a specific function associated with an API.
13. The system as claimed in claim 10, wherein the one or more trained models
are trained based on a dataset comprising a plurality of interfaces and
corresponding API signatures.
15
14. The system as claimed in claim 13, wherein the one or more trained models
comprises at least one of at least one of a Convolutional Neural Network
(CNN) (Bidirectional Encoder Representations from Transformers) BERT,
and a Recurrent Neural Network (RNN) model, and Long Short-Term
20 Memory (LSTM).
15. The system as claimed in claim 10, wherein the trigger is received based on
receipt of flowchart, image, voice, video from a user.
25 16. The system as claimed in claim 10, wherein the workflow generation unit is
further configured to create a set of states comprising a request schema, a set
of attributes, a plurality of end points, and a plurality of mappings.
17. The system as claimed in claim 16, wherein the workflow generation unit
30 [107] is further configured to automatically associate the set of states.
39
18. The system as claimed in claim 10, wherein the workflow generation unit
[107] is further configured to perform at least one of image processing, video
processing, and voice processing on the received trigger.
5 19. A user equipment [102] comprising:
a processor [102A] configured to:
transmit a trigger for dynamic workflow creation, wherein the
trigger corresponds to representing a sequence of application
programming interfaces (APIs) and wherein for dynamic workflow
10 creation comprises:
extracting the sequence of APIs that corresponds to one or
more network nodes based on the trigger;
generating, using one or more trained models, a dynamic
workflow based on the extracted sequence of APIs; and
15 retrieving one or more data metrics from a storage unit
based on the generated dynamic workflow, where the one or more
data metrics are associated with the one or more network nodes.