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 AUTOMATICALLY ASSIGNING A TARGET
TRAINING COURSE”
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 AUTOMATICALLY ASSIGNING A TARGET
TRAINING COURSE
TECHNICAL FIELD
[0001] Embodiments of the present disclosure generally relate to automated training
systems. More particularly, embodiments of the present disclosure relate to automatically assigning a target training course.
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 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.
[0003] In today's fast-paced and constantly evolving work environment, it is essential
for businesses to have a well-trained and knowledgeable workforce. Training employees can be a time-consuming and expensive process, but it is necessary to ensure that they have the skills and knowledge needed to perform their jobs effectively.
[0004] One area where businesses have struggled is in the assignment of training to
employees. Traditionally, training allocation was done manually, which involved collecting preferences, checking prerequisites, and manually matching skill with respect to available courses. This process was time-consuming and often resulted in errors and inefficiencies. Instructors and administrative staff had to spend a significant amount of time on course allocation, which could have been spent on other critical tasks.
[0005] One of the most significant challenges in training allocation and management is
the time and effort required to identify suitable training courses for employees. This process involves analyzing the employee's job role, skill set, and career goals to determine the most appropriate training program and ensure that the training is aligned with the job

requirements. This can be a time-consuming and complex process, particularly in organizations with a large workforce.
[0006] Another issue that exists in training management is the lack of a standardized
approach to training. Many organizations rely on ad-hoc training programs, which may not be tailored to the specific needs of their employees. This can lead to employees attending training courses that are not relevant to their job roles, resulting in a waste of time and resources. That said, there is a significant challenge faced in designing effective training programs that cater to different learning styles and preferences of employees. This requires a thorough understanding of the various training methods and tools available.
[0007] Furthermore, tracking employee progress and evaluating the effectiveness of
training programs can be challenging. Many organizations struggle to measure the impact of their training programs on employee performance and business outcomes. This can make it difficult to justify the investment in training and to identify areas for improvement. It is important to evaluate whether the training has helped employees acquire new skills and knowledge, and whether they are able to apply it in their job role. This requires setting clear learning objectives and using appropriate evaluation methods such as surveys, assessments, and feedback from supervisors.
[0008] Finally, keeping up with the latest trends and developments in training and
development can be an additional challenge. It is important to stay updated on new technologies, tools, and methods that can enhance the effectiveness of training programs. This requires continuous learning and development of skills for those responsible for managing training programs. Accordingly, it may be noted that managing training for employees is a complex task that requires careful planning, coordination, and evaluation.
[0009] Thus, there exists an imperative need in the art for a system that may address
the various challenges involved in allocation as well as management of various training programs leading to improved productivity and performance in employee’s training, which the present disclosure aims to address.
SUMMARY

[0010] This section is provided to introduce certain aspects of the present 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.
[0011] An aspect of the present disclosure may relate to a method for automatically
assigning a target training course. The method includes detecting, by a detection unit, a course assignment trigger. Further, the method encompasses identifying, by an identification unit via a data aggregator, a course auto-assignment request associated with a user based on the course assignment trigger. The method further includes determining, by an analysis unit via a workflow engine, one or more user parameters associated with the user based on the course auto-assignment request. Furthermore, the method encompasses identifying, by a course selector unit via the workflow engine from a course database, at least one training course based on the one or more user parameters. The method further includes identifying, by the course selector unit via the workflow engine from a course database, at least one assignment associated with the at least one training course. Furthermore, the method includes automatically assigning, by a course assignor unit to the user, the target training course based on predefined course assignment rules, wherein the target training course comprises the at least one training course and the at least one assignment associated with said at least one training course.
[0012] In an exemplary aspect of the present disclosure, the course assignment trigger
is detected by the detection unit, at least in an event of an onboarding action associated with the user is detected.
[0013] In an exemplary aspect of the present disclosure, the one or more user
parameters associated with the user is at least one of a skill parameter associated with the user, an experience parameter associated with the user, and a job role parameter associated with the user.
[0014] In an exemplary aspect of the present disclosure, the method further comprises
triggering, by a notification unit to the user, one or more notifications associated with the target training course via a predefined communication medium.

[0015] In an exemplary aspect of the present disclosure, the predefined course
assignment rules correspond to one or more rules created for assigning the target training course based on skill set and role of one or more users.
[0016] Another aspect of the present disclosure may relate to a system for automatically
assigning a target training course. The system includes a detection unit configured to detect, a course assignment trigger. The system further includes an identification unit connected to at least the detection unit, the identification unit configured to identify, via a data aggregator, a course auto-assignment request associated with a user based on the course assignment trigger. Further, the system includes an analysis unit connected to at least the identification unit, the analysis unit configured to determine, via a workflow engine, one or more user parameters associated with the user based on the course auto-assignment request. The system further includes a course selector unit connected to at least the analysis unit. The course selector unit is configured to identify, via the workflow engine from a course database, at least one training course based on the one or more user parameters. The course selector unit is further configured to identify, via the workflow engine from a course database, at least one assignment associated with the at least one training course. The system further includes a course assignor unit connected to at least the course selector unit, the course assignor unit configured to automatically assign, to the user, the target training course based on predefined course assignment rules, wherein the target training course comprises the at least one training course and the at least one assignment associated with said at least one training course.
[0017] Yet another aspect of the present disclosure may relate to a non-transitory
computer readable storage medium storing instructions for automatically assigning a target training course. The instructions include executable code which, when executed by one or more units of a system, causes detection unit to detect a course assignment trigger. The instructions when executed further causes an identification unit to identify, via a data aggregator, a course auto-assignment request associated with a user based on the course assignment trigger. The instructions when executed further causes an analysis unit to determine, via a workflow engine, one or more user parameters associated with the user based on the course auto-assignment request. The instructions when executed further causes a course selector unit to identify, via the workflow engine from a course database, at least one training course based on the one or more user parameters. The instructions when

executed further causes the course selector unit to identify, via the workflow engine from a course database, at least one assignment associated with the at least one training course. The instructions when executed further causes a course assignor unit to automatically assign, to the user, the target training course based on predefined course assignment rules, wherein the target training course comprises the at least one training course and the at least one assignment associated with said at least one training course.
[0018] Yet another aspect of the present disclosure relates to a user equipment (UE) for
automatically assigning a target training course comprising a system. The system further comprising a detection unit configured to detect, a course assignment trigger. The system further comprising an identification unit connected to at least the detection unit, the identification unit configured to identify, via a data aggregator, a course auto-assignment request associated with a user based on the course assignment trigger. The system further comprising an analysis unit connected to at least the identification unit, the analysis unit configured to determine, via a workflow engine, one or more user parameters associated with the user based on the course auto-assignment request. The system further includes a course selector unit connected to at least the analysis unit. The course selector unit is configured to identify, via the workflow engine from a course database, at least one training course based on the one or more user parameters. The course selector unit is configured to identify, via the workflow engine from a course database, at least one assignment associated with the at least one training course. The course assignor unit is connected to at least the course selector unit. The course assignor unit is configured to automatically assign, to the user, the target training course based on predefined course assignment rules. The target training course comprises the at least one training course and the at least one assignment associated with said at least one training course.
OBJECTS OF THE INVENTION
[0019] Some of the objects of the present disclosure, which at least one embodiment
disclosed herein satisfies are listed herein below.
[0020] It is an object of the present disclosure to provide a system and a method to
automate assignment of training courses to individuals based on their skills, job role, experience, domain or field, department, past training records, and other relevant factors.

[0021] It is another object of the present disclosure to provide a system to eliminate the
need for manual matching of skill and processing of course assignments, thereby saving valuable time for both employees and administrative staff.
[0022] It is yet another object of the present disclosure to optimize resource utilization
during training allocation by ensuring that training resources are allocated based on organizational priorities and employee needs.
DESCRIPTION OF THE DRAWINGS
[0023] The accompanying drawings, which are incorporated herein, and constitute a
part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Also, the embodiments shown in the figures are 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.
[0024] FIG. 1 illustrates an exemplary Service Management Platform (SMP) [100]
architecture, in accordance with the exemplary embodiments of the present disclosure.
[0025] FIG. 2 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.
[0026] FIG. 3 illustrates an exemplary block diagram of a system for automatically
assigning a target training course, in accordance with exemplary implementations of the present disclosure.

[0027] FIG. 4 illustrates a method flow diagram for automatically assigning a target
training course, in accordance with exemplary implementations of the present disclosure.
[0028] FIG. 5 illustrates an exemplary implementation of a method flow for
automatically assigning a target training course, in accordance with exemplary implementations of the present disclosure.
[0029] FIG. 6 illustrates another exemplary implementation of a method for
automatically assigning a target training course, in accordance with exemplary implementations of the present disclosure.
[0030] The foregoing shall be more apparent from the following more detailed
description of the disclosure.
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 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.
[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. 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 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.
[0034] Also, it is noted that individual embodiments may be described as a process
5 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 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.
10
[0035] The word “exemplary” and/or “demonstrative” is used herein to mean 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
15 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 “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
20 any additional or other elements.
[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
25 processor, a conventional processor, a digital signal processor, a plurality of
microprocessors, one or more microprocessors in 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
30 any other functionality that enables the working of the system according to the present
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 device”, “a wireless
9

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
5 computer, desktop, personal digital assistant, tablet computer, wearable device or any other
computing device which is capable of implementing at least some of 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
10 present disclosure.
[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
15 includes read-only memory (“ROM”), random access memory (“RAM”), 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.
20 [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 called.
25
[0040] 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 microprocessors in association with a
30 DSP core, a controller, a microcontroller, Application Specific Integrated Circuits (ASIC),
Field Programmable Gate Array circuits (FPGA), any other type of integrated circuits, etc.
[0041] As used herein the transceiver unit include at least one receiver and at least one
transmitter configured respectively for receiving and transmitting data, signals, information
10

or a combination thereof between units/components within the system and/or connected with the system.
[0042] As discussed in the background section, the current known solutions for training
5 management have several shortcomings. The present disclosure aims to overcome the
above-mentioned and other existing problems in this field of technology by providing method and system of automatically assigning a target training course.
[0043] Referring to FIG. 1, an exemplary Service Management Platform (SMP) [100]
10 is shown, in accordance with the exemplary embodiments of the present disclosure. As
shown in FIG. 1, the SMP [100] includes a SMP manager (SMPM) [101], a user interface
(UI) [102], a workflow design service (WFD) [104], a workflow assign service (WFA)
[106], a workflow execution engine (WFEE) [108], an application programming interface
(API) Gateway [110], a lightweight directory access protocol (LDAP) unit [112], a customer
15 assurance unit (CA) [114], a resource assurance unit (RA) [116], a service assurance unit
(SA) [118], a data ingestion service (DI) [120], a workflow manager (WFM) [122], an
Identity Access Manager (IAM) [126], an asynchronous streaming engine (ASYNC) [128],
a persistent data store engine [130], a SMP cache store [132], a work order manager [WOM]
[134]. The components of the SMP [100] may perform the steps of automatically assigning
20 a target training course to a user.
[0044] The SMP [100] has a microservices based architecture which improves
scalability and resiliency. The services of SMP [100] work in tandem to provide workflow management, customer assurance, resource assurance and service assurance functions. The
25 SMP [100] integrates with operations support systems (OSS)/ business support systems
(BSS) platforms like customer relationship management (CRM) [140], network management system (NMS) [142], cognitive platform (CP) [144] and integrated performance management (IPM) [146]. The integration with the CRM [140] may provide customer assurance to manage all customer related trouble tickets, service requests, and the
30 like. The integration with the CRM [140] may further manage the assignment of target
training course to the customer. The integration with the NMS [142], the CP [144] and the IPM [146] provides resource, service assurance respectively.
11

[0045] The SMP Manager (SMPM) [101] is responsible to manage the operations in
the SMP [100] efficiently.
[0046] The UI [102] lets the user to explore available options to deal with trouble
5 tickets, work orders etc. The UI [102] has all the options which are available on the SMP
[100].
[0047] The WFD [104] is responsible for providing a mechanism to design and create
workflow from the UI [102] for the generated trouble ticket and service request related to
10 the service assurance, customer assurance and resource assurance. The WFD [104] shares
the designed workflow with the workflow manager (WFM) [122] for provisioning.
[0048] The WFA [106] service provides a mechanism to assign operation context
(OCs) based on circle and job role provided by the LDAP unit [112]. The job role may be
15 provided by the LDAP unit [112]. The WFA [106] also comprises a mechanism for
managing service level agreements and escalation matrix. The operation context (may also be referred to as an assignment group) is an entity that contains members or sub-entities such as the managers, coordinator, approvers, among other such sub-entities.
20 [0049] The WFEE [108] shall provide a mechanism to manage execution of generated
workflow. The WFEE [108] is responsible for archiving all completed workflow. The WFEE [108] shall provide mechanism to manage workflow in case of SLA breach.
[0050] The API Gateway [110] provides a runtime and a backend component (an API
25 proxy) for API calls. The API Gateway secures, protects, manages, and scales API calls by
intercepting API requests and applying policies, such as throttling and security, using handlers and managing API statistics.
[0051] The LDAP unit [112] is an open and cross platform protocol used for directory
30 services authentication. The directory services store the users, passwords, and computer
accounts, and share that information with other entities on the network.
[0052] The CA unit [114] provides a mechanism to manage all trouble tickets and
service request related to the CRM.
12

[0053] The RA unit [116] provides a mechanism to manage all trouble tickets and
service request related to the NMS.
5 [0054] The service assurance (SA) unit [118] provides a mechanism to manage all
trouble tickets and service request related to the CP [144] and the IPM [146].
[0055] The data ingestion service (DI) [120] provides mechanism to ingest data into
the ASYNC streaming engine [128] related to trouble ticket management and workflow
10 management.
[0056] The workflow manager (WFM) [122] provides mechanism to manage, and
provide workflow created against the trouble ticket or service request for service assurance, customer assurance, and resource assurance. 15
[0057] The ELB [124] fulfils the load balancing needs of the SMP [100]. The load
balancing algorithm is round-robin for all of its components.
[0058] The IAM [126] act as Authorization and Authentication Application Tool for
20 the SMP [100]. When a user logs in from the UI, the IAM [126] provides a token to be used
in the subsequent Requests. The token contains information about the user, user-agent, internet protocol (IP), last access time and policy. The information helps to separate authorized requests from unauthorized requests.
25 [0059] The ASYNC [128] streaming engine is focused on streaming data.
[0060] The persistent data store [130] is a search engine based on the lucene library.
The persistent data store [130] provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents. 30
[0061] The SMP cache store [132] is an in-memory data structure store, used as cache
service in the SMP [100].
[0062] The WOM [134] is responsible for the maintaining the work order management
35 process efficiently.
13

[0063] FIG. 2 illustrates an exemplary block diagram of a computing device [200]
upon which the features of the present disclosure may be implemented in accordance with
exemplary implementation of the present disclosure. In an implementation, the computing
5 device [200] may also implement a method for automatically assigning a target training
course, utilising the system. In another implementation, the computing device [200] itself implements the method automatically assigning a target training course, using one or more units configured within the computing device [200], wherein said one or more units are capable of implementing the features as disclosed in the present disclosure.
10
[0064] The computing device [200] may include a bus [202] or other communication
mechanism for communicating information, and a hardware processor [204] coupled with bus [202] for processing information. The hardware processor [204] may be, for example, a general purpose microprocessor. The computing device [200] may also include a main
15 memory [206], such as a random access memory (RAM), or other dynamic storage device,
coupled to the bus [202] for storing information and instructions to be executed by the processor [204]. The main memory [206] also may be used for storing temporary variables or other intermediate information during execution of the instructions to be executed by the processor [204]. Such instructions, when stored in non-transitory storage media accessible
20 to the processor [204], render the computing device [200] into a special-purpose machine
that is customized to perform the operations specified in the instructions. The computing device [200] further includes a read only memory (ROM) [208] or other static storage device coupled to the bus [202] for storing static information and instructions for the processor [204].
25
[0065] A storage device [210], such as a magnetic disk, optical disk, or solid-state drive
is provided and coupled to the bus [202] for storing information and instructions. The computing device [200] may be coupled via the bus [202] to a display [212], such as a cathode ray tube (CRT), Liquid crystal Display (LCD), Light Emitting Diode (LED) display,
30 Organic LED (OLED) display, etc. for displaying information to a computer user. An input
device [214], including alphanumeric and other keys, touch screen input means, etc. may be coupled to the bus [202] for communicating information and command selections to the processor [204]. Another type of user input device may be a cursor controller [216], such as a mouse, a trackball, or cursor direction keys, for communicating direction information and
14

command selections to the processor [204], and for controlling cursor movement on the display [212]. 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.
5 [0066] The computing device [200] 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 computing device [200] causes or programs the computing device [200] to be a special-purpose machine. According to one implementation, the techniques herein are performed by the computing device [200] in response to the
10 processor [204] executing one or more sequences of one or more instructions contained in
the main memory [206]. Such instructions may be read into the main memory [206] from another storage medium, such as the storage device [210]. Execution of the sequences of instructions contained in the main memory [206] causes the processor [204] to perform the process steps described herein. In alternative implementations of the present disclosure,
15 hard-wired circuitry may be used in place of or in combination with software instructions.
[0067] The computing device [200] also may include a communication interface [218]
coupled to the bus [202]. The communication interface [218] provides a two-way data communication coupling to a network link [220] that is connected to a local network [222].
20 For example, the communication interface [218] 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 [218] may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links may also be
25 implemented. In any such implementation, the communication interface [218] sends and
receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.
[0068] The computing device [200] can send messages and receive data, including
30 program code, through the network(s), the network link [220] and the communication
interface [218]. In the Internet example, a server [230] might transmit a requested code for an application program through the Internet [228], the ISP [226], the local network [222], the host [224], and the communication interface [218]. The received code may be executed
15

by the processor [204] as it is received, and/or stored in the storage device [210], or other non-volatile storage for later execution.
[0069] The present disclosure is implemented by a system [300] (as shown in FIG. 3).
5 In an implementation, the system [300] may include the computing device [200] (as shown
in FIG. 2). It is further noted that the computing device [200] is able to perform the steps of a method [400] (as shown in FIG. 4).
[0070] Referring to FIG. 3, an exemplary block diagram of a system [300] for
10 automatically assigning a target training course, is shown, in accordance with the exemplary
implementations of the present disclosure. The system [300] comprises at least one detection unit [302], at least one identification unit [304], at least one analysis unit [306], at least one course selector unit [308], at least one course assignor unit [310], at least one notification unit [312] and a course database [314]. Also, all of the components/ units of the system
15 [300] are assumed to be connected to each other unless otherwise indicated below. Also, in
FIG. 3 only a few units are shown, however, the system [300] may comprise multiple such units or the system [300] may comprise any such numbers of said units, as required to implement the features of the present disclosure. Further, in an implementation, the system [300] may be present in a user device to implement the features of the present disclosure.
20 The system [300] may be a part of the user device / or may be independent of but in
communication with the user device (may also referred herein as a UE). In another implementation, the system [300] may reside in a server or a network entity. In yet another implementation, the system [300] may reside partly in the server/ network entity and partly in the user device.
25
[0071] 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
30 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 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.
16

[0072] The system [300] is configured for automatically assigning a target training
course, with the help of the interconnection between the components/units of the system [300]. 5
[0073] The system [300] includes a detection unit [302] configured to detect, a course
assignment trigger. The course assignment trigger is detected by the detection unit [302] at least in an event of an onboarding action associated with a user is detected. In an implementation of the present disclosure, the course assignment trigger may be sent to a
10 data aggregation microservice. The data aggregation microservice is a single system that
performs multiple services. For instance, the data aggregation microservice may perform services such as retrieving of data, storing data, processing of data, and the like. In an implementation of the present disclosure, the user may be a student, an employee, a trainee, etc. When the user is to be onboarded in a company, institution, etc., an onboarding event
15 may be generated by an onboarding system. When the onboarding system generates the
onboarding event, the onboarding system may send a trigger to the system [300] to initiate the course assignment trigger. The onboarding system may be associated with the system [300] via any communication network to interact with the system [300].
20 [0074] The system [300] further includes an identification unit [304]. The identification
unit [304] is connected to at least the detection unit [302]. The identification unit [304] is configured to identify, via a data aggregator, a course auto-assignment request associated with a user based on the course assignment trigger.
25 [0075] The system [300] further includes an analysis unit [306]. The analysis unit [306]
is connected to at least the identification unit [304]. The analysis unit [306] configured to determine, via a workflow engine, one or more user parameters associated with the user based on the course auto-assignment request. The one or more user parameters associated with the user is at least one of a skill parameter associated with the user, an experience
30 parameter associated with the user, and a job role parameter associated with the user. The
skill parameter refers to the areas of expertise of the user. The experience parameter refers to time duration for which the user has been working. For instance, the experience parameter
17

may include the user’s experience duration of 5 years in the job, or skill. The job role parameter refers to a domain or area of work associated with the user.
[0076] The system [300] further includes a course selector unit [308]. The course
5 selector unit [308] is connected to at least the analysis unit [306]. The course selector unit
[308] is configured to identify, via the workflow engine from a course database [314], at
least one training course based on the one or more user parameters. The course database
[314] may store one or more training courses. The course selector unit [308] is further
configured to identify, via the workflow engine from the course database [314], at least one
10 assignment associated with the at least one training course.
[0077] In an implementation of the present disclosure, the course selector unit [308]
may identify the at least one training course based on the skill parameter, the experience parameter, the job role parameter associated with the user. For instance, the experience
15 parameter of the user is 5 years, the course selector unit [308] may identify the at least one
training course, from the one or more training courses stored at the course database [314], which may be required for users with 5 years of experience, such as management courses, leadership courses, etc. Further, the course selector unit [308] may identify the at least one assignment associated with the at least one training course. In another embodiment, the user
20 is a new employee of an organization. Based on the one or more parameters, a new employee
may be required to complete a mandatory onboarding course, along with the one or more training courses based on the skill parameter, the new employee may be required to complete the mandatory onboarding course.
25 [0078] The system [300] further includes a course assignor unit [310]. The course
assignor unit [310] is connected to at least the course selector unit [308], the course assignor unit [310] is configured to automatically assign, to the user, the target training course based on predefined course assignment rules. The target training course comprises the at least one training course and the at least one assignment associated with the at least one training
30 course. The predefined course assignment rules correspond to one or more rules created for
assigning the target training course based on skill set and role of one or more users. For instance, the predefined course assignment rules relate to assigning a mandatory ‘Company Policy Course’. The predefined course assignment rules include assigning the Company Policy Course to the user who is onboarded in an organization. When the onboarding system
18

generates the onboarding event, the onboarding system may send a trigger to the system [300] to initiate the course assignment trigger for the Company Policy Course.
5 [0079] The system further comprises a notification unit [312] configured to trigger, to
the user, one or more notifications associated with the target training course via a predefined
communication medium. The target training course comprises the at least one training
course and the at least one assignment associated with said at least one training course. The
predefined communication medium may include an alert, a message, an email, a short
10 message service (SMS), and the like. The user may be provided with a User Interface (UI)
to access the target training course. The UI may include tools, resources, instructions, and the like to complete the target training course.
[0080] Referring to FIG. 4, an exemplary method flow diagram [400] for automatically
15 assigning a target training course, in accordance with exemplary implementations of the
present disclosure is shown. In an implementation the method [400] is performed by the system [300]. Further, in an implementation, the system [300] may be present in a server device to implement the features of the present disclosure. Also, as shown in FIG. 4, the method [400] starts at step [402].
20
[0081] At step [404], the method comprises detecting, by a detection unit [302], a
course assignment trigger. The course assignment trigger is detected by the detection unit [302] at least in an event of an onboarding action associated with the user is detected. In an implementation of the present disclosure, the course assignment trigger may be sent to a
25 data aggregation microservice. The data aggregation microservice is a single system that
performs multiple services. For instance, the data aggregation microservice may perform services such as retrieving of data, storing data, processing of data, and the like. When the user is to be onboarded in a company, institution, etc., an onboarding event may be generated by an onboarding system. When the system [300] receives the onboarding event, the system
30 [300] may initiate the course assignment trigger. The onboarding system may be associated
with the system [300] via any communication network to interact with the system [300].
19

[0082] At step [406], the method comprises identifying, by an identification unit [304]
via a data aggregator, a course auto-assignment request associated with a user based on the
course assignment trigger. In an implementation of the present disclosure, once the course
assignment trigger is detected by the detection unit [302], the identification unit [304] may
5 identify the course auto-assignment request from the course assignment trigger.
[0083] Next at step [408], the method comprises determining, by an analysis unit [306]
via a workflow engine, one or more user parameters associated with the user based on the course auto-assignment request. The one or more user parameters associated with the user
10 is at least one of a skill parameter associated with the user, an experience parameter
associated with the user, and a job role parameter associated with the user. The one or more user parameters associated with the user is at least one of a skill parameter associated with the user, an experience parameter associated with the user, and a job role parameter associated with the user. The skill parameter refers to areas of expertise of the user. The
15 experience parameter refers to the time duration for which the user has been working. For
instance, the experience parameter may include the user’s experience duration of 5 years in the job, or skill. The job role parameter refers to a domain or area of work associated with the user.
20 [0084] Next, at step [410], the method comprises identifying, by a course selector unit
[308] via the workflow engine from a course database [314], at least one training course based on the one or more user parameters. The course database [314] may store one or more training courses. In an implementation of the present disclosure, the course selector unit [308] may identify the at least one training course based on the skill parameter, the
25 experience parameter, the job role parameter associated with the user. For instance, if the
experience parameter of the user is 5 years, the course selector unit [308] may identify the
at least one training course, from the one or more training courses stored at the course
database [314], which may be required for users with 5 years of experience, such as
management courses, leadership courses, etc.
30
[0085] Next, at step [412], the method comprises identifying, by the course selector
unit [308] via the workflow engine from the course database [314], at least one assignment
associated with the at least one training course. In an implementation of the present
20

disclosure, based on the identification of the at least one training course by the course
selector unit [308], the course selector unit [308] may further identify the at least one
assignment associated with the at least one training course. In another embodiment, the user
is a new employee of an organization. Based on the one or more parameters, a new employee
5 may be required to complete a mandatory onboarding course, along with the one or more
training courses based on the skill parameter, the new employee may be required to complete the mandatory onboarding course.
[0086] Next, at step [414] automatically assigning, by a course assignor unit [310] to
10 the user, the target training course based on predefined course assignment rules. The target
training course comprises the at least one training course and the at least one assignment
associated with said at least one training course. The predefined course assignment rules
correspond to one or more rules created for assigning the target training course based on
skill set and role of one or more users. For instance, the predefined course assignment rules
15 relate to assigning a mandatory ‘Company Policy Course’. The predefined course
assignment rules include assigning the Company Policy Course to the user who is onboarded
in an organization. When the onboarding system generates the onboarding event, the
onboarding system may send a trigger to the system [300] to initiate the course assignment
trigger for the Company Policy Course.
20
[0087] The method further comprises triggering, by a notification unit [312] to the user,
one or more notifications associated with the target training course via a predefined
communication medium. The predefined communication medium may include an alert, a
message, an email, a short message service (SMS), and the like. This UI likely includes the
25 necessary tools, resources, and instructions for the user to engage in the training process
effectively.
[0088] The method terminates at step [416].
30 [0089] The method [400] enables the system [300] to automatically allocate and
manage training resources for a large number of employees/users, saving time and effort for administrators while ensuring that employees receive the training they need to perform their jobs effectively.
21

[0090] Referring to FIG. 5, it illustrates an exemplary implementation of a method flow
for automatically assigning a target training course, in accordance with exemplary implementations of the present disclosure.
5 [0091] At step [502], a user data is collected. The user data may include the one or
more parameters associated with the user.
[0092] At step [504], the one or more parameters associated with the user included in
the user data may be fetched from the course auto-assignment request. The one or more
10 parameters includes the skill parameter, the experience parameter, and the department
parameter.
[0093] At step [506], the one or more parameters fetched from the course auto-
assignment request may be sent to a course module.
15 [0094] At step [508], the course module may identify the at least one training course
according to the one or more parameters.
[0095] At step [510], based on the one or more parameters, the course module may
send the identified the at least one training course to the user.
20
[0096] Referring to FIG. 6, it illustrates an exemplary method implementation for
automatically assigning a target training course, in accordance with exemplary
implementations of the present disclosure.
25 [0097] At step 1, the mSMP [602] or the SMP UI [604] may send the course assignment
trigger to a data aggregation microservice. The data aggregation micro service streams the
request to the course selector unit [308], located in the SMP workflow design (WFD) [606].
The SMP WFD [606] may identify the one or more parameters associated with the user from
the course assignment trigger.
30
[0098] At step 2, the SMP WFD [606] / course selector unit [308] may identify the at
least one target training course based on the one or more parameters. The course selector
unit [308] may further identify the at least one assignment associated with the training
course.
35
22

[0099] At step 3, the SMP WFD [606] may notify to the user by providing one or more
notifications of the target training course via the predefined communication medium. The
predefined communication medium includes but may not be limited to an alert, a message,
an email, a short message service (SMS), for the target training course.
5
[0100] At step 4, the user may complete and send the completed assignments as per the
target training course associated with the user to the SMP WFD [606]. The course
assignment trigger is complete.
10 [0101] At step 5, once the course assignment trigger is finished, the SMP WFD [606]
may assign a second assignment and training course based on the one or more parameters of the user, stored in the course database [314] to the user.
[0102] At step 6, the SMP WFD [606] may send the second assignment and the training
15 course to the user via the SMP UI [604].
[0103] At step 6, the user may be notified via an alert, email, SMS for the second
assignment and the training course via the mSMP [602].
20 [0104] The present disclosure further discloses a non-transitory computer readable
storage medium storing instructions for automatically assigning a target training course. The instructions include executable code which, when executed by one or more units of a system, causes a detection unit [302] to detect a course assignment trigger. The instructions when executed by the system further cause an identification unit [304] connected to at least the
25 detection unit [302], the identification unit [304] configured to identify, via a data
aggregator, a course auto-assignment request associated with a user based on the course assignment trigger. The instructions when executed by the system further cause an analysis unit [306] connected to at least the identification unit [304], the analysis unit [306] configured to determine, via a workflow engine, one or more user parameters associated
30 with the user based on the course auto-assignment request. The instructions when executed
by the system further cause a course selector unit [308] to identify, via the workflow engine from a course database [314], at least one training course based on the one or more user parameters. The instructions when executed by the system further cause the course selector unit [308] to identify, via the workflow engine from the course database [314], at least one
35 assignment associated with the at least one training course. The instructions when executed
23

by the system further cause a course assignor unit [310] to automatically assign, to the user, the target training course based on predefined course assignment rules, wherein the target training course comprises the at least one training course and the at least one assignment associated with said at least one training course. 5
[0105] The present disclosure further discloses a user equipment (UE) for
automatically assigning a target training course comprising a system. The system further comprising a detection unit [302] configured to detect, a course assignment trigger. The system further includes an identification unit [304] connected to at least the detection unit
10 [302]. The identification unit [304] is configured to identify, via a data aggregator, a course
auto-assignment request associated with a user based on the course assignment trigger. The system further includes an analysis unit [306] connected to at least the identification unit [304]. The analysis unit [306] is configured to determine, via a workflow engine, one or more user parameters associated with the user based on the course auto-assignment request.
15 The course selector unit [308] connected to at least the analysis unit [306]. The course
selector unit [308] is configured to identify, via the workflow engine from a course database [314], at least one training course based on the one or more user parameters. The course selector unit [308] is configured to identify, via the workflow engine from the course database [314], at least one assignment associated with the at least one training course. The
20 system further includes a course assignor unit [310] connected to at least the course selector
unit [308]. The course assignor unit [310] is configured to automatically assign, to the user, the target training course based on predefined course assignment rules, wherein the target training course comprises the at least one training course and the at least one assignment associated with said at least one training course.
25
[0106] As is evident from the above, the present disclosure provides a technically
advanced solution for automatically assigning the training course. The automated training allocation processes of the current disclosure assignment is a valuable technology that offers several advantages over manual processes. First, it can help businesses ensure that all
30 employees receive the training they need to perform their jobs effectively. This can improve
productivity by reducing errors and mistakes. Second, it can save businesses time and money by automating the training assignment process. It can help businesses stay in compliance with industry regulations and standards by ensuring that all employees receive the necessary training required for a task effectively.
24

[0107] While considerable emphasis has been placed herein on the disclosed
implementations, it will be appreciated that many implementations can be made and 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 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.

We Claim:
1. A method for automatically assigning a target training course, the method comprising:
- detecting, by a detection unit [302], a course assignment trigger;
- identifying, by an identification unit [304] via a data aggregator, a course auto-assignment request associated with a user based on the course assignment trigger;
- determining, by an analysis unit [306] via a workflow engine, one or more user parameters associated with the user based on the course auto-assignment request;
- identifying, by a course selector unit [308] via the workflow engine from a course database [314], at least one training course based on the one or more user parameters;
- identifying, by the course selector unit [308] via the workflow engine from the course database [314], at least one assignment associated with the at least one training course; and
- automatically assigning, by a course assignor unit [310] to the user, the target training course based on predefined course assignment rules, wherein the target training course comprises the at least one training course and the at least one assignment associated with said at least one training course.

2. The method as claimed in claim 1, wherein the course assignment trigger is detected by the detection unit [302] at least in an event of an onboarding action associated with the user is detected.
3. The method as claimed in claim 1, wherein the one or more user parameters associated with the user is at least one of a skill parameter associated with the user, an experience parameter associated with the user, and a job role parameter associated with the user.
4. The method as claimed in claim 1, the method further comprises triggering, by a notification unit [312] to the user, one or more notifications associated with the target training course via a predefined communication medium.
5. The method as claimed in claim 1, wherein the predefined course assignment rules correspond to one or more rules created for assigning the target training course based on skill set and role of one or more users.

6. A system [300] for automatically assigning a target training course, the system [300]
comprises:
- a detection unit [302] configured to detect, a course assignment trigger;
- an identification unit [304] connected to at least the detection unit [302], the identification unit [304] configured to identify, via a data aggregator, a course auto-assignment request associated with a user based on the course assignment trigger;
- an analysis unit [306] connected to at least the identification unit [304], the analysis unit [306] configured to determine, via a workflow engine, one or more user parameters associated with the user based on the course auto-assignment request;
- a course selector unit [308] connected to at least the analysis unit [306], the course selector unit [308] configured to:
o identify, via the workflow engine from a course database [314], at least one training course based on the one or more user parameters, and
o identify, via the workflow engine from the course database [314], at least one assignment associated with the at least one training course; and
- a course assignor unit [310] connected to at least the course selector unit, the course
assignor unit configured to automatically assign, to the user, the target training course
based on predefined course assignment rules, wherein the target training course
comprises the at least one training course and the at least one assignment associated
with said at least one training course.
7. The system [300] as claimed in claim 6, wherein the course assignment trigger is detected by the detection unit [306] at least in an event of an onboarding action associated with the user is detected.
8. The system [300] as claimed in claim 6, wherein the one or more user parameters associated with the user is at least one of a skill parameter associated with the user, an experience parameter associated with the user, and a job role parameter associated with the user.

9. The system [300] as claimed in claim 6, the system further comprises a notification unit [312] configured to trigger, to the user, one or more notifications associated with the target training course via a predefined communication medium.
10. The system [300] as claimed in claim 6, wherein the predefined course assignment rules correspond to one or more rules created for assigning the target training course based on skill set and role of one or more users.
11. A user equipment (UE) for automatically assigning a target training course comprising a system, the system further comprising:

- a detection unit [302] configured to detect, a course assignment trigger;
- an identification unit [304] connected to at least the detection unit [302], the identification unit [304] configured to identify, via a data aggregator, a course auto-assignment request associated with a user based on the course assignment trigger;
- an analysis unit [306] connected to at least the identification unit [304], the analysis unit [306] configured to determine, via a workflow engine, one or more user parameters associated with the user based on the course auto-assignment request;
- a course selector unit [308] connected to at least the analysis unit [306], the course selector unit [308] configured to:
o identify, via the workflow engine from a course database [314], at least one training course based on the one or more user parameters, and
- identify, via the workflow engine from the course database [314], at least one assignment associated with the at least one training course; and
- a course assignor unit [310] connected to at least the course selector unit [308], the course assignor unit [310] configured to automatically assign, to the user, the target training course based on predefined course assignment rules, wherein the target training course comprises the at least one training course and the at least one assignment associated with said at least one training course.