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 MONITORING A
NETWORK”
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 MONITORING A
NETWORK
FIELD OF THE DISCLOSURE
[0001] The present disclosure relates generally to the field of wireless
communication systems. More particularly, the present disclosure relates to methods and systems for automatic network monitoring and network structure probing i.e., automatically monitoring a network.
BACKGROUND
[0002] The following description of 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 be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.
[0003] Wireless communication technology has rapidly evolved over the past
few decades, with each generation bringing significant improvements and advancements. The first generation of wireless communication technology was based on analog technology and offered only voice services. However, with the advent of the second-generation (2G) technology, digital communication and data services became possible, and text messaging was introduced. 3G technology marked the introduction of high-speed internet access, mobile video calling, and location-based services. The fourth generation (4G) technology revolutionized wireless communication with faster data speeds, better network coverage, and improved security. Currently, the fifth generation (5G) technology is being deployed, promising even faster data speeds, low latency, and the ability to connect multiple devices simultaneously. With each generation, wireless communication

technology has become more advanced, sophisticated, and capable of delivering more services to its users.
[0004] In the context of 5G networks, probing plays a crucial role in ensuring
the robustness and efficiency of the network infrastructure. With the advent of 5G technology, which introduces new capabilities such as ultra-low latency, high bandwidth, and massive connectivity, the need for sophisticated probing techniques becomes paramount. Network operators and administrators rely on probing mechanisms to continuously monitor and analyse various aspects of 5G networks, including signal strength, latency, packet loss, and Quality of Service (QoS) metrics. Probing enables them to identify potential bottlenecks, optimize network performance, and troubleshoot issues promptly, thereby ensuring seamless delivery of high-speed, low-latency services to end-users.
[0005] Moreover, in the dynamic and complex environment of 5G networks,
probing also serves as a vital tool for security and risk management. With the proliferation of IoT devices, edge computing, and mission-critical applications in 5G ecosystems, the attack surface and potential vulnerabilities increase significantly. Probing techniques such as vulnerability scanning, intrusion detection, and traffic analysis help detect and mitigate security threats, unauthorized access attempts, and malicious activities in real-time. By proactively probing the network for security risks and anomalies, organizations can enhance their cyber defence posture and safeguard sensitive data.
[0006] Further, traditional network monitoring and network structure probing
methods have long faced challenges due to the limitations imposed by physical taps, aggregators, and packet capturing tools. Physical taps, which involve physically accessing and tapping into network cables, often require significant effort and resources. They can disrupt network connectivity and pose risks of damage or interference to the network infrastructure. Similarly, aggregators, which are used to collect network traffic from multiple sources, face limitations in terms of scalability

and flexibility. They can struggle to handle large volumes of network data, leading to potential data loss or delays in capturing critical information. Additionally, aggregators may not provide granular visibility into specific network segments or devices, limiting their effectiveness in complex network environments. Further, the packet capturing tools, although commonly used for network monitoring, also present challenges. They typically require deep packet inspection and analysis, which can be time-consuming and resource intensive. Furthermore, encrypted traffic poses a significant hurdle for traditional packet capturing tools, as they are unable to decipher encrypted content, limiting their ability to provide comprehensive insights. These challenges have led to the development of alternative approaches and technologies in network monitoring and structure probing. For instance, software-defined networking (SDN) and network functions virtualization (NFV) have emerged as solutions that offer greater flexibility, scalability, and visibility. They enable centralized management and control of network resources, allowing for efficient monitoring and probing without the need for physical access.
[0007] In addition to the benefits they offer, the adoption of software-defined
networking (SDN) and network functions virtualization (NFV) also brings certain drawbacks. One significant challenge is the complexity associated with implementing these technologies. Transitioning from traditional networking infrastructures to SDN and NFV architectures requires substantial expertise and resources, making the migration process intricate and demanding. Moreover, virtualizing network functions may introduce performance concerns, as virtualized components may not always match the performance levels of dedicated hardware solutions. This performance overhead can impact the overall efficiency and reliability of the network. Furthermore, SDN and NFV architectures pose new security risks, including vulnerabilities in virtualized network elements and potential threats associated with centralized control functions.

[0008] Therefore, since the currently available network monitoring and
structure probing methods have faced challenges with physical taps, aggregators, and packet capturing tools, the evolving landscape of network technologies and further the currently available solutions have high capital and operational expenditures (CAPEX and OPEX) even for ingesting a small amount of data. Thus, there exists an imperative need in the art for automatic network monitoring and network structure probing.
OBJECTS OF THE DISCLOSURE
[0009] Some of the objects of the present disclosure, which at least one
embodiment disclosed herein satisfies are listed herein below.
[0010] It is an object of the present disclosure to provide a system and a method
for automatic network monitoring and network structure probing in a 5G network.
[0011] It is another object of the present disclosure to provide a solution to
enable continuous monitoring of a communication network with a combo-core network by collecting Streaming Data Records (SDRs) from network nodes.
[0012] It is another object of the present disclosure to facilitate deep analysis
of network data by streaming and indexing the SDRs.
[0013] It is another object of the present disclosure to ensure communication
network and subscriber assurance by leveraging the collected SDRs and by monitoring network statuses and behaviours, in order to identify potential issues that could impact network performance and security, allowing for timely corrective measures.

[0014] It is another object of the present disclosure to enhance one or more
network troubleshooting capabilities by providing one or more uninterrupted data sets on network statuses and behaviours.
[0015] It is another object of the present disclosure to reduce capital and
operational expenditures (CAPEX and OPEX) associated with a communication network monitoring and analytics.
[0016] It is another object of the present disclosure to utilize the collected data
proactively for network planning and performance improvement.
[0017] It is another object of the present disclosure to ensure a better customer
experience by leveraging the collected data by promptly addressing network issues and optimizing network performance, the disclosure aims to provide reliable and seamless connectivity to end-users.
SUMMARY
[0018] 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.
[0019] An aspect of the present disclosure may relate to a method for
automatically monitoring a network. The method comprises receiving, by a transceiver unit, a Streaming Data Record (SDR) data associated with a network procedure failure, wherein the SDR data comprises at least one of a clear code associated with the network procedure failure, and an information associated with the network procedure failure. The method further comprises fetching, by a validation unit, a set of pre-configured validation policy based on the network procedure failure. The method further comprises performing, by the validation unit,

a validation associated with the SDR data based on the set of pre-configured validation policy. The method further comprises detecting, by the validation unit, a validation status associated with the validation, wherein the validation status is at least one of a validation pass status and a validation fail status. The method further comprises generating, by a analytics engine, an enriched SDR data based on the validation pass status, wherein the enriched data is generated in a predefined format. The method further comprises generating, by the analytics engine, a network analysis report associated with the network function based on the enriched SDR data. The method further comprises automatically monitoring, by a monitoring unit, the network based on at least the network analysis report.
[0020] In an exemplary aspect of the present disclosure, the method also
comprises normalising, by the analytics engine the enriched SDR data, and thereafter, storing, by the analytics engine, the enriched SDR data in a database in a predefined record format, wherein predefined record format is at least one of a raw record format and a computed record format.
[0021] In an exemplary aspect of the present disclosure, the network analysis
report associated with the network function is generated based on at least one of the raw record format and the computed record format.
[0022] In an exemplary aspect of the present disclosure, the validation pass
status is detected based on comparing the SDR data and at least one pre-configured validation policy from the set of pre-configured validation policy.
[0023] In an exemplary aspect of the present disclosure, the method further
comprises displaying via an interface the network analysis report.
[0024] Another aspect of the present disclosure may relate to a system for
automatically monitoring a network. The system comprises a transceiver unit that is configured to receive, a Streaming Data Record (SDR) data associated with a

network procedure failure, wherein the SDR data comprises at least one of a clear code associated with the network procedure failure, and an information associated with the network procedure failure. Further, the system comprises a validation unit that is configured to fetch, a set of pre-configured validation policy based on the network procedure failure. Further, the validation unit is configured to perform, a validation associated with the SDR data based on the set of pre-configured validation policy. Further, the validation unit is configured to detect a validation status associated with the validation, wherein the validation status is at least one of a validation pass status and a validation fail status. Further, the system comprises a analytics engine that is configured to generate, an enriched SDR data based on the validation pass status, wherein the enriched data is generated in a predefined format. Further, the analytics engine is configured to generate, a network analysis report associated with a network function based on the enriched SDR data. Further, the system comprises a monitoring unit that is configured to automatically monitor the network based on at least the network analysis report.
[0025] Yet another aspect of the present disclosure may relate to a User
Equipment (UE). The User Equipment (UE) may include a memory and a processor coupled to the memory. The processor may be configured to transmit, to a system, a set of pre-configured validation policy based on a network procedure failure. The set of pre-configured validation policy, when received by the system, may be used for generating a network analysis report. The processor may thereafter be configured to receive the network analysis report from the system. The network analysis report may be used for monitoring the network. The network analysis report may be generated by the system based on: receiving a Streaming Data Record (SDR) data associated with the network procedure failure, wherein the SDR data comprises at least one of a clear code associated with the network procedure failure, and an information associated with the network procedure failure; on fetching the set of pre-configured validation policy based on the network procedure failure, performing a validation associated with the SDR data based on the set of pre-configured validation policy; detecting a validation status associated with the

validation, wherein the validation status is at least one of a validation pass status and a validation fail status; generating an enriched SDR data based on the validation pass status, wherein the enriched SDR data is generated in a predefined format; and generating a network analysis report associated with the network function based on the enriched SDR data.
[0026] Further, another aspect of the present disclosure relates to a non-
transitory computer readable storage medium storing instructions for service fallback in 5G core (5GC) network, the instructions include executable code which when executed by a processor, cause the processor to: receiving a Streaming Data Record (SDR) data associated with a network procedure failure, wherein the SDR data comprises at least one of a clear code associated with the network procedure failure, and an information associated with the network procedure failure; fetch a set of pre-configured validation policy based on the network procedure failure, perform a validation associated with the SDR data based on the set of pre-configured validation policy, detect a validation status associated with the validation, wherein the validation status is at least one of a validation pass status and a validation fail status; generate an enriched SDR data based on the validation pass status, wherein the enriched SDR data is generated in a predefined format; generate a network analysis report associated with the network function based on the enriched SDR data; and monitor the network based on at least the network analysis report.
BRIEF DESCRIPTION OF DRAWINGS
[0027] The accompanying drawings, which are incorporated herein, and
constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Some drawings may indicate the components

using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings includes disclosure of electrical components, electronic components or circuitry commonly used to implement such components.
[0028] FIG. 1 illustrates an exemplary block diagram [100] representation of
5th generation core (5GC) network architecture.
[0029] FIG. 2 illustrates an exemplary block diagram [200] of a system for
automatically monitoring a network, in accordance with exemplary embodiments of the present disclosure.
[0030] FIG. 3 illustrates an exemplary method flow diagram [300] indicating
the process for automatically monitoring a network, in accordance with exemplary embodiments of the present disclosure.
[0031] FIG. 4 illustrates an exemplary scenario block [400] diagram of a
system for automatic network monitoring, in accordance with exemplary embodiments of the present disclosure.
[0032] FIG. 5 illustrates an exemplary scenario method [500] flow diagram
indicating the process for automatic network monitoring, in accordance with exemplary embodiments of the present disclosure.
[0033] FIG. 6 depicts an exemplary sequence flow diagram [600] for
automatically monitoring a network, in accordance with exemplary embodiments of the present disclosure.
[0034] FIG. 7 illustrates an exemplary block diagram of a computing device
[700] upon which an embodiment of the present disclosure may be implemented.

[0035] The foregoing shall be more apparent from the following more detailed
description of the disclosure.
DETAILED DESCRIPTION
5
[0036] 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
10 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 fully addressed by any of the features described herein. Example embodiments of
15 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.
[0037] The ensuing description provides exemplary embodiments only, and is
20 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
25 of the disclosure as set forth.
[0038] It should be noted that the terms "mobile device", "user equipment",
"user device", “communication device”, “device” and similar terms are used
interchangeably for the purpose of describing the disclosure. These terms are not
30 intended to limit the scope of the disclosure or imply any specific functionality or
limitations on the described embodiments. The use of these terms is solely for
11

convenience and clarity of description. The disclosure is not limited to any particular type of device or equipment, and it should be understood that other equivalent terms or variations thereof may be used interchangeably without departing from the scope of the disclosure as defined herein. 5
[0039] 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, networks, processes, and other
10 components may be shown as components in block diagram form in order not to
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.
15 [0040] 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.
20 A process is terminated when its operations are completed but could have additional
steps not included in a figure.
[0041] The word “exemplary” and/or “demonstrative” is used herein to mean
serving as an example, instance, or illustration. For the avoidance of doubt, the
25 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
30 “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
12

similar to the term “comprising” as an open transition word—without precluding any additional or other elements.
[0042] As used herein, a “processing unit” or “processor” or “operating
5 processor” includes one or more processors, wherein processor refers to any logic
circuitry for processing instructions. A processor may be a general-purpose processor, a special purpose processor, a conventional processor, a digital signal processor, a plurality of microprocessors, one or more microprocessors in association with a (Digital Signal Processing) DSP core, a controller, a
10 microcontroller, Application Specific Integrated Circuits, Field Programmable
Gate Array circuits, any other type of integrated circuits, etc. The processor may perform signal coding data processing, input/output processing, and/or any other functionality that enables the working of the system according to the present disclosure. More specifically, the processor or processing unit is a hardware
15 processor.
[0043] As used herein, “mobile device”, “a user equipment”, “a user device”,
“a smart-user-device”, “a smart-device”, “an electronic device”, “a mobile device”, “a handheld device”, “a wireless communication device”, “a mobile
20 communication device”, “a communication device” may be any electrical,
electronic and/or computing device or equipment, capable of implementing the features of the present disclosure. The user equipment/device may include, but is not limited to, a mobile phone, smart phone, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, wearable device or any other
25 computing device which is capable of implementing the features of the present
disclosure. Also, the user device may contain at least one input means configured to receive an input from at least one of a transceiver unit, a processing unit, a storage unit, and any other such unit(s) which are required to implement the features of the present disclosure. The user equipment may be capable of operating on any radio
30 access technology including but not limited to IP-enabled communication, Zig Bee,
Bluetooth, Bluetooth Low Energy, Near Field Communication, Z-Wave, Wi-Fi,
13

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, a general-purpose computer, desktop, personal digital assistant,
tablet computer, mainframe computer, or any other device as may be obvious to a
5 person skilled in the art for implementation of the features of the present disclosure.
[0044] 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
10 medium 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.
15
[0045] 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
20 each other, which also includes the methods, functions, or procedures that may be
called.
[0046] One or more modules, units, components used herein may be software
modules configured via hardware modules/processors, or hardware processors, the
25 processors being 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, Field Programmable Gate Array circuits, any other type of integrated circuits, etc.
30 [0047] It should be noted that the terms "mobile device", "user equipment",
"user device", “communication device”, “device” and similar terms are used
14

interchangeably for the purpose of describing the disclosure. These terms are not
intended to limit the scope of the disclosure 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 disclosure is not limited to any
5 particular type of device or equipment, and it should be understood that other
equivalent terms or variations thereof may be used interchangeably without departing from the scope of the disclosure as defined herein.
[0048] As used herein, an “electronic device”, or “portable electronic device”,
10 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
other user devices. The user equipment may have a processor, a display, a memory,
15 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
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,
20 smartphone, virtual reality (VR) devices, augmented reality (AR) devices, laptop,
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.
25 [0049] Further, the user device may also comprise a “processor”
or “processing 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
30 association with a DSP core, a controller, a microcontroller, Application Specific
Integrated Circuits, Field Programmable Gate Array circuits, any other type of
15

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. 5
[0050] 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
10 advancement of various generations of cellular technology are also seen. The
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.
15 [0051] Radio Access Technology (RAT) refers to the technology used by
mobile 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 RAT has its own set of protocols and standards for communication, which
20 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 (Universal Mobile Telecommunications System), LTE (Long-Term Evolution), and 5G. The choice of RAT depends on a variety of factors, including the network
25 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
resources.
[0052] As discussed in the background section, that in 5G network, probing is
30 essential for maintaining network robustness and efficiency. With 5G's advanced
capabilities like ultra-low latency and high bandwidth, sophisticated probing
16

techniques are crucial for continuous monitoring and analysis. Network operators
rely on probing to assess signal strength, latency, packet loss, and Quality of Service
metrics, enabling them to identify bottlenecks, optimize performance, and
troubleshoot issues promptly. Additionally, in the dynamic 5G environment,
5 probing serves as a vital tool for security and risk management. However, the
current known solutions for automatic network monitoring network structure
probing have several shortcomings such as physical taps, aggregators, and packet
capturing tools which involve physically accessing and tapping into network cables,
which often require significant effort and resources for network monitoring network
10 structure probing .
[0053] The present disclosure aims to address the aforementioned and other
existing issues in this field of technology by introducing a novel solution involving the implementation of a probing agent within the combo-core network nodes. This
15 probing agent is responsible for collecting probing data, referred to as Streaming
Data Records (SDRs), whenever an error or network anomaly occurs within the network functions (NFs). The NFs generate these SDRs, including clear codes, at the procedure level. Furthermore, the SDRs, along with the clear code, may be transmitted by the solution proposed in the present disclosure for further analysis
20 by an operator. The collected SDRs are subsequently indexed by the artificial
intelligence/machine learning unit, enabling further analysis that aids in network monitoring, troubleshooting, and root cause analysis.
[0054] Hereinafter, exemplary embodiments of the present disclosure will be
25 described with reference to the accompanying drawings.
[0055] FIG. 1 illustrates an exemplary block diagram representation of 5th
generation core (5GC) network architecture. As shown in FIG. 1, the 5G network
architecture [100] includes a user equipment (UE) [102], a radio access network
30 (RAN) [104], an access and mobility management function (AMF) [106], a Session
Management Function (SMF) [108], a Service Communication Proxy (SCP) [110],
17

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],
5 a Unified Data 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.
10 [0056] Radio Access Network (RAN) [104] is the part of a mobile
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.
15
[0057] Access and Mobility Management Function (AMF) [106] is a 5G core
network function responsible for managing access and mobility aspects, such as UE registration, connection, and reachability. It also handles mobility management procedures like handovers and paging.
20
[0058] 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 handles IP address allocation and QoS enforcement.
25
[0059] Service Communication Proxy (SCP) [110] is a network function in the
5G core network that facilitates communication between other network functions by providing a secure and efficient messaging service. It acts as a mediator for service-based interfaces.
30
18

[0060] Authentication Server Function (AUSF) [112] is a network function in
the 5G core responsible for authenticating UEs during registration and providing security services. It generates and verifies authentication vectors and tokens.
5 [0061] Network Slice Specific Authentication and Authorization Function
(NSSAAF) [114] is a network function that provides authentication and authorization services specific to network slices. It ensures that UEs can access only the slices for which they are authorized.
10 [0062] Network Slice Selection Function (NSSF) [116] is a network function
responsible for selecting the appropriate network slice for the UE based on factors such as subscription, requested services, and network policies.
[0063] Network Exposure Function (NEF) [118] is a network function that
15 exposes capabilities and services of the 5G network to external applications,
enabling integration with third-party services and applications.
[0064] Network Repository Function (NRF) [120] is a network function that
acts as a central repository for information about available network functions and
20 services. It facilitates the discovery and dynamic registration of network functions.
[0065] 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. 25
[0066] Unified Data Management (UDM) [124] is a network function that
centralizes the management of subscriber data, including authentication, authorization, and subscription information.
19

[0067] 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 [0068] User Plane Function (UPF) [128] is a network function responsible for
handling user data traffic, including packet routing, forwarding, and QoS enforcement.
[0069] 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.
[0070] Referring to FIG. 2, an exemplary block diagram of a system [200] for
automatically monitoring a network is shown, in accordance with the exemplary
15 embodiments of the present disclosure. The system [200] comprises at least one
transceiver unit [202], at least one validation unit [204], at least one analytics engine [206] and at least one monitoring unit [208]. All of the components as mentioned in the block diagram lies within the system [200] and shall be considered to be interconnected with each other. Also, in FIG. 2 only a few units are shown,
20 however, the system [200] may comprise multiple such units or the system [200]
may comprise any such numbers of said units, as required to implement the features of the present disclosure. Further, the system [200] may be present in a user device to implement the features of the present disclosure. The system [200] may also be a part of the user device / or may be independent of but in communication with the
25 user device (may also referred herein as a UE). The present disclosure further
discloses that the system [200] may reside in a server or a network entity and the
system [200] may also reside partly in the server/ network entity and partly in the
user device.
[0071] The system [200] is configured for automatically monitoring a network,
30 with the help of the interconnection between the components/units of the system
[200].
20

[0072] In order for automatically monitoring a network, the transceiver unit
[202] is configured to receive, a Streaming Data Record data (SDR data) associated
with a network procedure failure, wherein the SDR data comprises at least one of a
5 clear code associated with the network procedure failure, and an information
associated with the network procedure failure. The SDR data may be a session
details record (SDR) associated with a data for a transaction or procedure such as
during registration, voice call or handover related to communication networks or a
call flow in the network. The SDR data may be transmitted continuously or
10 periodically or may also be time-stamped. The SDR may also be a call data records
(CDR) in network nodes or a debugging record and logs.
[0073] Additionally, the network procedure failure occurs when a task or
operation within a network encounters an issue or fails to complete successfully,
15 such as call setup failures or handover failures in telecommunications networks,
where connections between devices or between different network elements cannot be established or maintained properly. Additionally, the network procedure failures may include packet loss, data corruption, or errors in data transmission, affecting the reliability and performance of the network. The network procedure failures may
20 disrupt communication services, degrade network quality, and impact user
experience. Further, the Network Function (NF) refers to a specialized software/ hardware component or application that performs a specific task or provides a particular service within a network. The network functions in the 5G network architecture have been discussed with reference to FIG. 1.
25
[0074] The present invention encompasses that the SDR data refers to
continuously flowing data generated by various sources within a network, typically in real-time. The SDR may be the record of the entire call session in a network. This data may be dynamic and include information such as sensor readings, event logs,
30 transaction records, or any other data that is continuously produced and updated.
21

[0075] The Clear Code captures the internal cause codes or codes that a
Network Function (NF) will dump for a network procedure associated with a
network. In an implementation of the present disclosure, the clear code may be
captured as a part of at least one of a session detail record (SDR) and call data
5 records (CDR) generated by the NF. Further, in an implementation of the present
disclosure, the clear code may comprise at least one of a unique predefined structure
and a unique predefined format, wherein the unique predefined structure and the
unique predefined format comprises at least 31 digits. Further, each digit from the
31 digits or group of digits from the 31 digits may signify one or more aspects of
10 the network parameters like result of the call flow (success or failure), type of
procedure/call flow experienced, service operation, interface on which transaction happened, system error conditions and response codes, status of the of procedure/call flow (procedure/call flow failure or procedure/call flow success).
15 [0076] For instance, in a telecommunications network, if a network procedure
fails, the SDR data received by the transceiver unit [202] may include a clear code indicating the specific type of failure (such as a call drop or packet loss) and additional information like timestamps, signal strength, or affected devices. This data helps the analyse the failure, identify its root cause, and initiate appropriate
20 one or more actions for resolution or mitigation.
[0077] Further, upon receiving the SDR data, the validation unit [204] is
configured to fetch, a set of pre-configured validation policy based on the network
procedure failure. Also, the validation unit [204] is connected to at least the
25 transceiver unit [202].
[0078] For instance, the validation unit [204] fetches the set of predefined
validation policies tailored to the specific type of failure detected. The validation
policies serve as guidelines to assess the nature and severity of the failure, allowing
30 for systematic analysis and troubleshooting within the network infrastructure.
22

[0079] The set of pre-configured validation policies refers to a predefined
collection of rules or guidelines established to assess and validate specific
conditions or events within a system. These policies are configured in advance
based on known requirements, best practices, or regulatory standards. When an
5 event occurs that requires validation, such as a network procedure failure, the
validation unit [204] may refer to these pre-defined policies to determine the appropriate actions or responses. The set of pre-configured validation policies help ensure consistency, reliability, and compliance within the system's operations. An example of the set of pre-configured validation policies involves that the SDR data
10 received can be different for network functions, procedures and their versions.
Further, several policies are configured for each case to validate the SDR data, and produce error-free, uncorrupted, zero-loss SDR data and forward the valid SDR data to at least one 5G Real Time Conductor [406]. The 5G Real Time Conductors [406] performs primary operations on the incoming data, such as sorting the data,
15 filtering certain configured attributes and fields which are not required for analysis,
and splitting the data, etc.
[0080] Further, the validation unit [204] of the system [200] is configured to
perform, a validation associated with the SDR data based on the set of pre-
20 configured validation policy. For instance, the validation unit [204] is configured
to execute a validation process according to the set of pre-configured validation
policies which serve as guidelines to evaluate the integrity, accuracy, and
compliance of the SDR data with predetermined criteria or standards.
25 [0081] Further, the validation unit [204] of the system [200] is configured to
detect a validation status associated with the validation, wherein the validation status is at least one of a validation pass status and a validation fail status. The present disclosure encompasses that the validation pass status is detected based on comparing the SDR data and least one pre-configured validation policy from the set
30 of pre-configured validation policy. In other words, the SDR data is analysed based
on the pre-configured validation policy and if the SDR data passes the rules or
23

conditions set in said policy, a validation pass status is detected. If, on the other hand, the SDR data does not pass the rules or conditions set in said policy, a validation fail status is detected.
5 [0082] Further, the analytics engine [206] is connected to at least the validation
unit [204] and upon detection of the validation status, the analytics engine [206] is configured to generate, an enriched SDR data based on the validation pass status, wherein the enriched data is generated in a predefined format. The present disclosure encompasses that the enriched SDR data refers to the collected SDR data
10 that has been enhanced or augmented with an additional information, by creating
dynamic fields from existing fields in the data and performing one or more operations such as split, join, append, and concatenation on the data. Further the enrichment includes but not limited to adding metadata, contextual information, or derived metrics to the SDR data. For example, the analytics engine [206] may
15 enrich the SDR data with static information (e.g. device manufacturer, model,
subscriber group, etc.). In another implementation of the disclosure, Artificial Intelligence /machine learning based techniques may be used for enrichment of the incoming live SDR data.
20 [0083] The present disclosure encompasses that the analytics engine [206] is
further configured to normalise the enriched SDR data and store the enriched SDR data in a database via storage unit in a predefined record format, wherein predefined record format is at least one of a raw record format and a computed record format. The ‘normalization’ is a process of standardizing data to a common format or
25 structure. Normalization involves transforming the enriched SDR data into a
consistent format suitable for storage and analysis. The normalisation involves shrinking the data size nearly by one-sixth of original size by intelligently parsing the data and converting in structured format like parsing static and dynamic content separately and creating one file for static records such as a CSV file or a JSON file
30 and another file for dynamic records such as ASN.1 encoded file.
24

[0084] The present disclosure encompasses that the predefined format refers to
a standard structure or schema in which the enriched SDR data is organized to
ensure consistency and compatibility within the system [200]. Also, as used herein,
the ‘raw record format’ refers to an original, unprocessed format of the collected
5 data which may include raw sensor readings, timestamps, and other basic
information without any additional processing or editing. An exemplary illustration
of the raw record format has been provided below. Further, it should be noted that
the below illustrated format displays the data in rows and columns in an exemplary
manner, and the rows and columns of the illustrated format can also be interchanged
10 without deviating from the scope of the present subject matter. Such transposed
format is also provided by the present disclosure, and would lie within the scope of the present subject matter.

Time Interval 2024-04-19-16:28:15 to 2024-04-24-16:28:15
Report Type Global detail data
SUPERCOR ENAME MU MU
CORRELA TION-ID imsi-405862100332591 imsi-405862100332899
UNIQUEC
ORRELATI
ONID PCF-MU-002-mu96dc103pcfxoc1j-58609eb54796846d2c4c645ca 93b07cc1507deb0055b376d08 722bed851f1fb5-1665108 PCF-MU-002-mu96dc103pcfxoc1j-58609eb54796846d2c4c645ca 93b07cc1507deb0012b376d08 722bed851f1fb5-1665108
SUPI - -
IMPU - -
PEI - -
IMEISV - -
NFNAME PCF PCF
25

CLEARCO DE 2.071E+30 2.071E+30
TIMESTA
MP_OF_RE
QUEST 04-24-2024-16:23:05.069 04-24-2024-16:23:05.066
TIMESTA
MP_OF_RE
SPONSE 04-24-2024-16:23:05.071 04-24-2024-16:23:05.066
RESPONSE CODE 424 -9999
RESPONSE DESCRIPTI ON Error response received for Update Notify Request and response SM update notify request rejected for stale session
CALLFLO WNAME PCFinitiatedSMPolicyAss ociationModification PCFinitiatedSMPolicyAss ociationModification
VERSION 1.0.0 1.0.0
PDR ID - -
GPSI - -
SUPI - -
CAUSE - -
[0085] Continuing further, the ‘computed record format’ refers to a data format
derived from computational analysis of the enriched SDR data. The computed
record format may include aggregated statistics or derived metrics. An exemplary
5 illustration of the computed record format has been provided below. Further, it
should be noted that the below illustrated format displays the data in rows and
columns in an exemplary manner, and the rows and columns of the illustrated
format can also be interchanged without deviating from the scope of the present
subject matter. Such transposed format is also provided by the present disclosure,
10 and would lie within the scope of the present subject matter.
26

Report Name: pcf latest report,
Report Type: NF Procedure Wise Top N ClearCode Contribution,
Time Interval: from 2024-04-10-16:48:08 to 2024-04-24-17:15:57,NF Name:
PCF
circle MU MU
nfName PCF PCF
callFlowN ame PCFinitiatedSMPolicyAss ociationModification PCFinitiatedSMPolicyAss ociationModification
Clearcode 2.07E+30 2.07E+30
ALIAS
CLEARC
ODE - -
count 550 130867
Contributi on(%) 0.273 64.936
[0086] Further, the analytics engine [206] of the system [200] is configured to
generate, a network analysis report associated with the network function based on
the enriched SDR data. The present disclosure encompasses that the network
5 analysis report associated with the network function is generated based on at least
one of the raw record formats and the computed record format.
[0087] Upon generation of the network analysis report, the monitoring unit
[208] that is connected to at least the analytics engine [206], is configured to
10 monitor the network based on at least the network analysis report. The present
invention encompasses that the network analysis report is displayed via an interface.
Further, the present invention encompasses that the monitoring is done via one of
15 one or more monitoring tools, and/ or one or more monitoring protocols.
27

[0088] Referring to FIG. 3, an exemplary method flow diagram [300] for
automatically monitoring a network, in accordance with exemplary embodiments
of the present disclosure is shown. In an implementation the method [300] is
performed by the system [200]. Also, as shown in FIG. 3, the method [300] starts
5 at step [302].
[0089] At step [304], the method [300] as disclosed by the present disclosure
comprises receiving, by a transceiver unit [202], a Streaming Data Record (SDR)
Streaming Data Record (SDR) data associated with the network procedure failure,
10 wherein the SDR data comprises at least one of a clear code associated with the
network procedure failure, and an information associated with the network procedure failure.
[0090] Additionally, the network procedure failure occurs when a task or
15 operation within a network encounters an issue or fails to complete successfully,
such as call setup failures or handover failures in telecommunications networks,
where connections between devices or between different network elements cannot
be established or maintained properly. Additionally, the network procedure failures
may include packet loss, data corruption, or errors in data transmission, affecting
20 the reliability and performance of the network. The network procedure failures may
disrupt communication services, degrade network quality, and impact user
experience. Further, the Network Function (NF) refers to a specialized software/
hardware component or application that performs a specific task or provides a
particular service within a network. The network functions in the 5G network
25 architecture have been discussed with reference to FIG. 1.
[0091] The present invention encompasses that the Streaming Data Record
(SDR) data refers to continuously flowing data generated by various sources within
a network, typically in real-time. The SDR may be the record of the entire call
30 session in a network. This data may be dynamic and include information such as
28

sensor readings, event logs, transaction records, or any other data that is continuously produced and updated.
[0092] The Clear Code captures the internal cause codes or codes that the
5 Network Function (NF) will dump for a network procedure associated with a
network. In an implementation of the present disclosure, the clear code may be captured as a part of at least one of a session detail record (SDR) and call data records (CDR) generated by the NF. Further, in an implementation of the present disclosure, the clear code may comprise at least one of a unique predefined structure
10 and a unique predefined format, wherein the unique predefined structure and the
unique predefined format comprises at least 31 digits. Further, each digit from the 31 digits or group of digits from the 31 digits may signify one or more aspects of the network parameters like result of the call flow (success or failure), type of procedure/call flow experienced, service operation, interface on which transaction
15 happened, system error conditions and response codes, status of the of
procedure/call flow (procedure/call flow failure or procedure/call flow success).
[0093] For instance, in a telecommunications network, if a network procedure
fails, the SDR data received by the transceiver unit [202] may include a clear code
20 indicating the specific type of failure (such as a call drop or packet loss) and
additional information like timestamps, signal strength, or affected devices. This data helps the analyse the failure, identify its root cause, and initiate appropriate one or more actions for resolution or mitigation.
25 [0094] At step [308], the method [300] as disclosed by the present disclosure
comprises fetching, by a validation unit [204], a set of pre-configured validation policy based on the network procedure failure. For instance, the validation unit [204] fetches the set of predefined validation policies tailored to the specific type of failure detected. The validation policies serve as guidelines to assess the nature
30 and severity of the failure, allowing for systematic analysis and troubleshooting
within the network infrastructure.
29

[0095] The set of pre-configured validation policies refers to a predefined
collection of rules or guidelines established to assess and validate specific
conditions or events within a system. These policies are configured in advance
5 based on known requirements, best practices, or regulatory standards. When an
event occurs that requires validation, such as a network procedure failure, the validation unit [204] may refer to these pre-defined policies to determine the appropriate actions or responses. The set of pre-configured validation policies help ensure consistency, reliability, and compliance within the system's operations.
10
[0096] At step [310], the method [300] as disclosed by the present disclosure
comprises performing, by the validation unit [204] , a validation associated with the SDR data based on the set of pre-configured validation policy. For instance, the validation unit [204] is configured to execute a validation process according to the
15 set of pre-configured validation policies which serve as guidelines to evaluate the
integrity, accuracy, and compliance of the SDR data with predetermined criteria or standards.
[0097] At step [312], the method [300] as disclosed by the present disclosure
20 comprises detecting, by the validation unit [204], a validation status associated with
the validation, wherein the validation status is at least one of a validation pass status
and a validation fail status. The present disclosure encompasses that , the validation
pass status is detected based on comparing by the processing the SDR data and least
one of s pre-configured validation policy from the set of pre-configured validation
25 policy. In other words, the SDR data is analysed based on the pre-configured
validation policy and if the SDR data passes the rules or conditions set in said policy, a validation pass status is detected. If, on the other hand, the SDR data does not pass the rules or conditions set in said policy, a validation fail status is detected.
30 [0098] At step [314], the method [300] as disclosed by the present disclosure
comprises generating, by an analytics engine [206], an enriched SDR data based on
30

the validation pass status, wherein the enriched data is generated in a predefined
format. The enriched SDR data refers to the collected SDR data that has been
enhanced or augmented with an additional information. Further the enrichment
includes but not limited to adding metadata, contextual information, or derived
5 metrics to the SDR data. For example, the analytics engine [206] may enrich the
SDR data with static information (e.g. device manufacturer, model, subscriber group, etc.). In another implementation of the disclosure, Artificial Intelligence /machine learning based techniques may be used for enrichment of the incoming live SDR data.
10
[0099] The present disclosure encompasses that the method further comprises
normalising by the analytics engine [206] the enriched SDR data and storing the enriched SDR data in a database via a storage unit in a predefined record format, wherein predefined record format is at least one of a raw record format and a
15 computed record format. The ‘normalization’ is a process of standardizing data to
a common format or structure. Normalization involves transforming the enriched SDR data into a consistent format suitable for storage and analysis.
[0100] The present disclosure encompasses that the predefined format refers to
20 a standard structure or schema in which the enriched SDR data is organized to
ensure consistency and compatibility within the system [200]. Also, as used herein,
the ‘raw record format’ refers to an original, unprocessed format of the collected
data which may include raw sensor readings, timestamps, and other basic
information without any additional processing or editing. The ‘computed record
25 format’ refers to a data format derived from computational analysis of the enriched
SDR data. The computed record format may include aggregated statistics or derived metrics.
[0101] At step [316], the method [300] as disclosed by the present disclosure
30 comprises generating, by the analytics engine [206], a network analysis report
associated with the network function based on the enriched SDR data. The present
31

disclosure encompasses that the network analysis report associated with the network function is generated by the analytics engine [206] based on at least one of the raw record formats and the computed record format.
5 [0102] At step [318], the method [300] as disclosed by the present disclosure
comprises automatically monitoring, by monitoring unit [208] , the network based
on at least the network analysis report. The present disclosure encompasses that the
method [300] further comprises displaying via an interface the network analysis
report. Further, the present disclosure encompasses that the monitoring is done via
10 one of one or more monitoring tools, and/ or one or more monitoring protocols.
Thereafter, the method [300] terminates at step [320].
[0103] Referring to FIG. 4, an exemplary scenario block diagram of a system
[400] for automatically monitoring a network implemented on a 5G architecture is
15 shown, in accordance with the exemplary embodiments of the present disclosure.
The 5G architecture comprise of at least one (Machine Learning) ML probe [404], at least one 5G real time conductor [406], at least one mBus [408], at least one normalization layer [410], at least one distributed data lake [412], at least one compute engine [414], one or more Artificial Intelligence (AI) or ML applications
20 [416], at least one compute cluster [418], at least one 5G workflow unit [420], at
least one user interface [422].
[0104] All of the components as shown in the block diagram are connected to
each other. All of the components as mentioned in the block diagram lies within the
25 system [400] and shall be considered to be interconnected with each other. Also, in
FIG. 4 only a few units are shown, however, the system [400] may comprise multiple such units or the system [400] may comprise any such numbers of said units, as required to implement the features of the present disclosure.
32

[0105] Further, in an implementation, the system [400] may reside in a server
or a network entity. In yet another implementation, the system [400] may reside partly in the server/ network entity and partly in the user device.
5 [0106] In order to facilitate automatic network monitoring network structure
probing, the transceiver unit [202] collects a Streaming Data Records (SDR) data
from the 5G NF [402]. The SDR data include least one of a clear code associated
with the network procedure failure, and an information associated with the network
procedure failure. Thereafter, the validation unit [204] fetches a set of pre-
10 configured validation policy based on the network procedure failure via the
Machine Learning (ML) probe [404] and the 5G real time conductor [406]. The 5G
Real Time Conductors [406] performs primary operations on the incoming data,
such as sorting the data, filtering certain configured attributes and fields which are
not required for analysis, and splitting the data, etc. The present disclosure
15 encompasses that the 5G real time conductor [406] based on a set of pre-defined
rules may process the SDR in order to perform at least one of a validation of the
one or more fields associated with the SDR, a matching of the one or more fields
associated with the SDR, sequence of the fields associated with the SDR, and an
enrichment of the SDR. Further, the SDR data is validated via the validation unit
20 [204] based on the set of pre-configured validation policy.
[0107] Upon validation, the validation unit [204] detects a validation status that
is associated with the validation and the validation status is at least one of a
validation pass status and a validation fail status. Thereafter, an enriched SDR data
25 based on the validation pass status is generated. Further the enriched SDR data is
generated in a predefined format.
[0108] The present invention also encompasses that once the validated data
SDR is received the enrichment of the SDR may be performed by adding one or
30 more additional fields to the validated data SDR based on the set of pre-defined
rules associated with the network to generate an enriched SDR. For instance, each
33

additional field of the one or more additional fields may comprise a set of integers comprising a sequence of numbers associated with the NF of the network.
[0109] The enriched SDR data is further transmitted to a normalization layer
5 [410] via a mBus [408]. The enriched SDR data is thereafter normalized through
the normalization layer [410] to obtain a normalized enriched SDR data. The normalized enriched SDR data is further stored in a distributed data lake [412] of the storage unit .
10 [0110] The present disclosure encompasses that at least the following
enrichment of the SDR is supported by the present disclosure:
• Open Application Model (OAM) Metadata is enriched by adding one or
more additional fields in the OAM broadcast message.
• One or more headers associated with the NF are enriched by adding one or
15 more additional fields in at least one of the network protocol header
messages received from the transceiver unit [202].
• Static content associated with the NF is enriched by adding one or more
additional fields comprising at least one of a user-defined values.
20 [0111] The compute engine [414] is further configured to generate a network
analysis report associated with the network function based on the enriched SDR data via the one or more artificial intelligence (AI) applications or one or more Machine Learning (ML) applications [416] that are also connected with the compute clusters [418]. Also, the compute engine [414] depicted in FIG. 3 is
25 equivalent to the analytics engine [206] as depicted in FIG. 1.
[0112] Additionally, the network based on at least the network analysis report
is monitored by the monitoring unit. The network analysis report is further
displayed via a user interface [422] and the same is forwarded to one or more probed
30 managers [424]. The one or more probed managers [424] is responsible for
managing the set of pre-configured validation policies.
34

[0113] The present disclosure encompasses that the network analysis report
may include one or more user friendly graph and one or more user friendly chart
which are provided by the 5G workflow unit [420]. The 5G workflow unit is
5 configured to perform a sequence of steps that are needed to performed to generate
the network analysis report. The sequence, setup by user, involves generating the
network analysis report, and the 5G workflow unit [420] executes these steps one
by one. For example, the step involves data extraction from specific node,
performing data operations, then computing using compute engine, and then
10 providing the network analysis report via the user interface [422].
[0114] The present disclosure encompasses that the compute engine [414]
transforms data associated with the SDR using set of criteria’s such as a filter criteria and inverse-filter criteria. Once the compute engine [414] forwards the
15 enriched SDR via the message broker, then based on the set of pre-defined rules
associated with the network, the compute engine [414] pulls the enriched SDR from the message broker and apply the filter criteria and stores the transformed data back to the message brokers. The compute engine [414] is connected with the 5G workflow unit [420] for providing the network analysis report by computing the
20 extracted data. The compute engine [414] is configured to perform calculations for
generation of key performance indicators and also performs comparisons based on a threshold.
[0115] The present disclosure encompasses that the 5G real time conductor
25 [406] acts as the SDR producer to produce the SDR via a data queuing and
processing component of the message broker. Further, a data filtration component
streams the data from the message broker and stores the transformed data. Further,
the solution of the present disclosure may compute consumption of the SDR from
the message broker via a normalization layer [410].
30 [0116] Further, the normalization layer [410] is configured to collect normalize
the SDR received from the data queuing and processing component of the message
35

broker. Further, the SDR may be initially fetched from the message Broker and then normalizes the SDR based on the set of pre-defined rules associated with the network. Once the SDR is normalized, then it inserts that SDR into data base.
5 [0117] Further, the 5G real time conductor [406] is configured to fetch the SDR
from data queuing and processing component of the message broker on pre-defined interval of time and correlate the SDR received based on one or more pre-configured inputs of the system to generate a correlated SDR and thereafter may store the correlated SDR in a database (DB). The correlation of the SDR allows to
10 fetch different network function records at different time intervals, and allows to
correlate and aggregate all records for a particular user. The aggregation includes aggregating from multiple data sources parallelly based on varying time frequency. In an exemplary scenario of the present disclosure, the transceiver unit [202] may be connected to an ingestion module to collect the SDR from the data queuing and
15 processing component of the message broker and transfer it to the correlation
engine. Further, in an implementation of the present disclosure the 5G real time conductor may comprise a correlation engine CRE, wherein the CRE correlates to a workflow steps defined by the user based on the SDR received over the message broker, further the CRE may execute the workflow steps and fetch one or more
20 network nodes such as 4G node or 5G node data based on the present solution.
[0118] Further, the present disclosure encompasses fetching the SDR from the
database (DB) and displaying the SDR in a pre-defined format such as a report format and dashboard format. Also, the data related to an actionable item may be
25 fetched from the DB related to an actionable item which triggers a closed loop
actions for automating network processes. Thereafter, the SDR stored is fetched
from DB and may convert the SDR into one or more user friendly graph and one or
more user friendly chart for debugging and analytics of the SDR.
[0119] The disclosure encompasses collecting from user device the SDR data
30 associated with the network such as 5G network. Further, in an exemplary scenario
there may be multiple system which may require this data for correlation and closed
36

loop actions for automating network processes, end to end trouble shooting and 360
network view. The present solution makes the data associated with the SDR
available to different system via one or more exposure functions. Further, said data
associated with the SDR can be pulled by consuming systems via passive mediation
5 or via REST API interface provided by the transceiver unit [202].
[0120] Further, in an exemplary scenario of the present disclosure, one or more
operators associated with the network with massive network deployments spread across vast geographic areas, the system as disclosed by the present disclosure are
10 placed close to the nodes associated with the network as much as possible with
possibility of even placing them on edge as and when required. Under such scenario it is important to have both centralized and decentralized console to analyse the traffic and the network performance for a given geographic region or for complete deployments often with sets of limited access restrictions. Further, such
15 deployments also help in scaling out solutions horizontally more efficiently with
proper traffic segregation. As such the present disclosure provide benefit to central teams and specific area teams to plan this and yet have complete overview of the network.
20 [0121] Referring to FIG. 5 an exemplary flow diagram of method for
automatically monitoring a network in accordance with exemplary embodiments of the present is shown. The method [500] depicted in FIG. 5 is an example of the method [300] which may be performed by the system [200] or system [400].
25 [0122] As shown in FIG. 5 the method [500] starts from [502].
[0123] At step [504], the method comprises collecting streaming data records
(SDR) from a network function (NF) associated with a network, such as 5G, via a
data collection unit. Subsequently, the collected SDR data is validated by a
30 validation unit [204] based on predefined rules associated with the network,
generating a validated SDR. The collection of SDR is triggered by detecting an
37

error with the network function. Each SDR may have a predefined format for recording network procedures or call flow rules.
[0124] At step [506], the method comprises interacting with the data collection
5 unit via one or more network protocol interfaces and receiving the validated data
SDR based on the validation of the fields associated with the SDR. Once the validated data SDR is received, the method continues to perform the enrichment of the SDR by adding one or more additional fields based on predefined rules associated with the network, generating an enriched SDR. Each additional field may
10 comprise a set of integers associated with the NF of the network. The one or more
network protocol interfaces may utilize different protocols for communicating with different units, such as hypertext transfer protocol (HTTP), transmission control protocol (TCP), secure file transfer protocol (SFTP), and may also utilize a software architecture such as representational state transfer (REST).
15
[0125] At step [508], the method comprises transforming the data associated
with the SDR present in a message broker associated with the SDR using an analytics engine [206].
20 [0126] At step [510], the method comprises collecting SDR data from other
components such as data producers and storing the streaming data for a limited time interval as queued data.
[0127] At step [512], the method comprises normalizing the SDR data received
25 from queued data and processing the message broker.
[0128] At step [514], the method comprises fetching the SDR data from queued
data and processing the message broker at predefined intervals to correlate the SDR.
38

[0129] At step [516], the method comprises fetching the SDR data from a
database (DB) and displaying the SDR in predefined formats such as report and dashboard formats.
5 [0130] At step [518], the method comprises collecting SDR data associated
with the network from user devices. This data may be required for various purposes
such as correlation, closed-loop actions for automating network processes,
troubleshooting, and obtaining a comprehensive network view. The data associated
with the SDR is made available to different systems via exposure functions and can
10 be accessed by consuming systems via passive mediation or a REST API interface.
[0131] The method [500] terminates at step [520].
[0132] Referring to FIG. 6 an exemplary sequence flow diagram [600] in other
15 words, for automatically monitoring a network, in accordance with exemplary
embodiments of the present disclosure is shown. In an implementation the method [600] is performed by the system [200] as depicted in FIG. 2 and system [400] as depicted in FIG 4. FIG. 6 is flow diagram of the method for automatically monitoring a network implemented over a 5G architecture. 20
[0133] At step S1, when a 5G network function encounters a network
procedure failure or timeout due to internal or external errors, the process initiates.
Subsequently, at step S2, Streaming Data Record (SDR) data containing clear codes
and information regarding the failed or timed-out procedure is transmitted from the
25 5G network function to a Vprobe unit. The Vprobe unit then validates the SDR data
based on at least one policy configured within it.
[0134] Following successful validation, at step S3, the validated SDR data is
indexed and enriched, resulting in an enhanced dataset. This enriched SDR data is
30 then stored in a database (DB) at step S4, where it is preferably stored in both raw
and computed formats for further analysis. Lastly, at step S5, a dashboard retrieves
39

both the raw and computed data from the database to generate analysis and reports based on the indexed and enriched SDR data. This process enables automated network monitoring and analysis.
5 [0135] The present disclosure also provides a User Equipment (UE). The User
Equipment (UE) may include a memory and a processor coupled to the memory. The processor may be configured to transmit, to a system, a set of pre-configured validation policy based on a network procedure failure. The set of pre-configured validation policy, when received by the system, may be used for generating a
10 network analysis report. The processor may thereafter be configured to receive the
network analysis report from the system. The network analysis report may be used for monitoring the network. The network analysis report may be generated by the system based on: receiving a Streaming Data Record (SDR) data associated with the network procedure failure, wherein the SDR data comprises at least one of a
15 clear code associated with the network procedure failure, and an information
associated with the network procedure failure; on fetching the set of pre-configured validation policy based on the network procedure failure, performing a validation associated with the SDR data based on the set of pre-configured validation policy; detecting a validation status associated with the validation, wherein the validation
20 status is at least one of a validation pass status and a validation fail status; generating
an enriched SDR data based on the validation pass status, wherein the enriched SDR data is generated in a predefined format; and generating a network analysis report associated with the network function based on the enriched SDR data.
25 [0136] FIG. 7 illustrates an exemplary block diagram of a computing device
[700] upon which an embodiment of the present disclosure may be implemented. In an implementation, the computing device [700] implements the method [300] for automatically monitoring a network using the system [200]. In another implementation, the computing device [700] itself implements the method [300] for
30 automatically monitoring a network, using one or more units configured within the
40

computing device [700], wherein said one or more units are capable of implementing the features as disclosed in the present disclosure.
[0137] The computing device [700] may include a bus [702] or other
5 communication mechanism for communicating information, and a hardware
processor [704] coupled with bus [702] for processing information. The hardware
processor [704] may be, for example, a general-purpose microprocessor. The
computing device [700] may also include a main memory [706], such as a random-
access memory (RAM), or other dynamic storage device, coupled to the bus [702]
10 for storing information and instructions to be executed by the processor [704]. The
main memory [706] also may be used for storing temporary variables or other
intermediate information during execution of the instructions to be executed by the
processor [704]. Such instructions, when stored in non-transitory storage media
accessible to the processor [704], render the computing device [700] into a special-
15 purpose machine that is customized to perform the operations specified in the
instructions. The computing device [700] further includes a read only memory
(ROM) [708] or other static storage device coupled to the bus [702] for storing static
information and instructions for the processor [704].
20 [0138] A storage device [710], such as a magnetic disk, optical disk, or solid-
state drive is provided and coupled to the bus [702] for storing information and instructions. The computing device [700] may be coupled via the bus [702] to a display [712], such as a cathode ray tube (CRT), for displaying information to a computer user. An input device [714], including alphanumeric and other keys, may
25 be coupled to the bus [702] for communicating information and command
selections to the processor [704]. Another type of user input device may be a cursor controller [716], such as a mouse, a trackball, or cursor direction keys, for communicating direction information and command selections to the processor [704], and for controlling cursor movement on the display [712]. This input device
30 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.
41

[0139] The computing device [700] 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 [700] causes
5 or programs the computing device [700] to be a special-purpose machine.
According to one embodiment, the techniques herein are performed by the
computing device [700] in response to the processor [704] executing one or more
sequences of one or more instructions contained in the main memory [706]. Such
instructions may be read into the main memory [706] from another storage medium,
10 such as the storage device [710]. Execution of the sequences of instructions
contained in the main memory [706] causes the processor [704] to perform the process steps described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions.
15 [0140] The computing device [700] also may include a communication
interface [718] coupled to the bus [702]. The communication interface [718] provides a two-way data communication coupling to a network link [720] that is connected to a local network [722]. For example, the communication interface [718] may be an integrated services digital network (ISDN) card, cable modem,
20 satellite modem, or a modem to provide a data communication connection to a
corresponding type of telephone line. As another example, the communication interface [718] 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 [718]
25 sends and receives electrical, electromagnetic or optical signals that carry digital
data streams representing various types of information.
[0141] The computing device [700] can send messages and receive data,
including program code, through the network(s), the network link [720] and the
30 communication interface [718]. In the Internet example, a server [730] might
transmit a requested code for an application program through the Internet [728], the
42

ISP [726], the host [724], the local network [722] and the communication interface [718]. The received code may be executed by the processor [704] as it is received, and/or stored in the storage device [710], or other non-volatile storage for later execution. 5
[0142] Further, an aspect of the present disclosure relates to a non-transitory
computer readable storage medium storing instructions for service fallback in 5G core (5GC) network, the instructions include executable code which when executed by a processor, cause the processor to: receive a Streaming Data Record (SDR) data
10 associated with the network procedure failure, wherein the SDR data comprises at
least one of a clear code associated with the network procedure failure, and an information associated with the network procedure failure; fetch a set of pre-configured validation policy based on the network procedure failure, perform a validation associated with the SDR data based on the set of pre-configured
15 validation policy, detect a validation status associated with the validation, wherein
the validation status is at least one of a validation pass status and a validation fail status; generate an enriched SDR data based on the validation pass status, wherein the enriched SDR data is generated in a predefined format; generate a network analysis report associated with the network function based on the enriched SDR
20 data; and monitor the network based on at least the network analysis report.
[0143] As it is evident from the above, that the present disclosure offers a
technologically advanced solution for continuous monitoring and in-depth real-time analysis of networks, resulting in enhanced network and subscriber assurance. By
25 generating uninterrupted data sets on network statuses and behaviours, the solution
offers crucial insights for investigating and troubleshooting potential issues that may affect network performance and security. Additionally, the collected data can be proactively utilized for network planning, performance improvement, and ensuring a better customer experience. Furthermore, the present solution eliminates
30 the need for traditional probing challenges such as physical taps, aggregators, and
packet capturing tools.
43

[0144] This technical advancement offers numerous benefits. Firstly, it enables
seamless integration of probing capabilities into the network infrastructure,
ensuring that the performance of NFs remains unaffected during probing activities.
5 Secondly, it allows for real-time streaming and aggregation of SDRs, streamlining
data analytics and reporting processes. Furthermore, the solution enhances network
planning and troubleshooting by providing comprehensive and reliable data for
analysis. Overall, this technical solution represents an innovative approach to
network monitoring and troubleshooting, offering improved efficiency, reduced
10 costs, and enhanced network performance and security.
[0145] While considerable emphasis has been placed herein on the disclosed
embodiments, it will be appreciated that many embodiments can be made and that
many changes can be made to the embodiments without departing from the
15 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.
20 [0146] Further, in accordance with the present disclosure, it is to be
acknowledged that the functionality described for the various the components/units can be implemented interchangeably. While specific embodiments may disclose a particular functionality of these units for clarity, it is recognized that various configurations and combinations thereof are within the scope of the disclosure. The
25 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.
30
44

We claim:
1. A method [300] for automatically monitoring a network, the method [300]
comprising:
- receiving [304], by a transceiver unit [202], a Streaming Data Record (SDR) data associated with a network procedure failure, wherein the SDR data comprises at least one of a clear code associated with the network procedure failure, and an information associated with the network procedure failure;
- fetching [306], by a validation unit [204], a set of pre-configured validation policy based on the network procedure failure;
- performing [308], by the validation unit [204], a validation associated with the SDR data based on the set of pre-configured validation policy;
- detecting [310], by the validation unit [204], a validation status associated with the validation, wherein the validation status is at least one of a validation pass status and a validation fail status;
- generating [312], by an analytics engine [206], an enriched SDR data based on the validation pass status, wherein the enriched SDR data is generated in a predefined format;
- generating [314], by the analytics engine [206], a network analysis report associated with the network function based on the enriched SDR data; and
- monitoring [316], by a monitoring unit [208], the network based on at least the network analysis report.
2. The method [300] as claimed in claim 1, further comprising:
- normalising, by the analytics engine [206], the enriched SDR data, and
- storing, by the analytics engine [206], the enriched SDR data in a database in a predefined record format, wherein predefined record format is at least one of a raw record format and a computed record format.

3. The method [300] as claimed in claim 2, wherein the network analysis report associated with the network function is generated based on at least one of the raw record format and the computed record format.
4. The method [300] as claimed in claim 1, wherein the validation pass status is detected based on comparing the SDR data and at least one pre-configured validation policy from the set of pre-configured validation policy.
5. The method [300] as claimed in claim 1, further comprising displaying, via an interface, the network analysis report.
6. A system [200] for automatically monitoring a network, the system [200] comprises:

- a transceiver unit [202] configured to receive, a Streaming Data Record (SDR) data associated with a network procedure failure, wherein the SDR data comprises at least one of a clear code associated with the network procedure failure, and an information associated with the network procedure failure;
- a validation unit [204] connected to at least the transceiver unit [202], the validation unit [204] configured to:

• fetch, a set of pre-configured validation policy based on the network procedure failure,
• perform, a validation associated with the SDR data based on the set of pre-configured validation policy, and
• detect a validation status associated with the validation, wherein the validation status is at least one of a validation pass status and a validation fail status;
- an analytics engine [206] connected to at least the validation unit [204],
the analytics engine [206] configured to:

• generate, an enriched SDR data based on the validation pass status, wherein the enriched SDR data is generated in a predefined format, and
• generate, a network analysis report associated with the network function based on the enriched SDR data; and
- a monitoring unit [208] connected to at least the analytics engine [206],
the monitoring unit [208] configured to monitor the network based on at
least the network analysis report.
7. The system [200] as claimed in claim 6, wherein the analytics engine [206] is
further configured to:
- normalise, the enriched SDR data; and
- store the enriched SDR data in a database in a predefined record format, wherein predefined record format is at least one of a raw record format and a computed record format.

8. The system [200] as claimed in claim 7, wherein the network analysis report associated with the network function is generated based on at least one of the raw record format and the computed record format.
9. The system [200] as claimed in claim 6, wherein the validation pass status is detected based on comparing the SDR data and at least one pre-configured validation policy from the set of pre-configured validation policy.
10. The system [200] as claimed in claim 6, the system [200] further comprises displaying via an interface the network analysis report.
11. A User Equipment (UE) comprising:
a memory; and a processor coupled to the memory, wherein the processor is configured to:

transmit, to a system, a set of pre-configured validation policy based on a network procedure failure, wherein the set of pre-configured policy, when received by the system, is used for generating a network analysis report; and
receive, from the system, the network analysis report, wherein the network analysis report is used for monitoring the network, and wherein the network analysis report is generated, by the system, based on:
- receiving a Streaming Data Record (SDR) data associated with the network procedure failure, wherein the SDR data comprises at least one of a clear code associated with the network procedure failure, and an information associated with the network procedure failure;
- on fetching the set of pre-configured validation policy based on the network procedure failure, performing a validation associated with the SDR data based on the set of pre-configured validation policy;
- detecting a validation status associated with the validation, wherein the validation status is at least one of a validation pass status and a validation fail status;
- generating an enriched SDR data based on the validation pass status, wherein the enriched SDR data is generated in a predefined format; and
- generating a network analysis report associated with the network function based on the enriched SDR data.