DESC:
FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003

COMPLETE SPECIFICATION
(See section 10 and rule 13)
1. TITLE OF THE INVENTION
METHOD AND SYSTEM FOR PREDICTING CLEAR CODE FAILURES COUNT IN NETWORK FUNCTIONS (NF)
2. APPLICANT(S)
NAME NATIONALITY ADDRESS
JIO PLATFORMS LIMITED INDIAN OFFICE-101, SAFFRON, NR. CENTRE POINT, PANCHWATI 5 RASTA, AMBAWADI, AHMEDABAD 380006, GUJARAT, INDIA
3.PREAMBLE TO THE DESCRIPTION

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

FIELD OF THE INVENTION
The present invention relates to the field of network monitoring and analysis and, more specifically, more particularly relates to a system and a method for predicting clear code failures count in Network Functions (NFs).
BACKGROUND OF THE INVENTION
[0001] With the increase in number of users, the network service provisions have been implementing to up-gradations to enhance the service quality so as to keep pace with such high demand. With advancement of technology, there is a demand for the telecommunication service to induce up to date features into the scope of provision so as to enhance user experience and implement advanced monitoring mechanisms. There are regular analyses to observe issues beforehand for which many data collection as well as assessment practices are implemented in a network. The network assessment includes monitoring performance of the network functions, network elements etc. The network functions are implemented to execute various network enhancement techniques, and the data related to number of times a certain network function (NF) successfully completed a request or how many time the NF fails to do so, is really essential for estimating the performance of the network functions. The failure of a NF is measured in terms of Clear Code Failures which is a critical metric in telecom networks, indicating the number of times a specific NF failed to clear an operation or transaction. A probing agent is implemented to actively collect probing data, preferably Streaming Data Record (SDR) from Network Functions. The network functions generate the SDR including the clear codes for failed events at procedure level whenever any error scenario occurs or experienced by that network function (NF) or network node. Once these SDRs are generated by NF, then they are streamed towards vProbe, where these records are then finally indexed in a data lake. Then they can be further analyzed which will aid in the overall network monitoring, troubleshooting and root cause analysis.
[0002] However, Network Functions generate vast amounts of data, making it challenging to shift through and isolate Clear Code Failure events for each NF accurately. The data received from NFs was highly variable, with different formats, timestamps, and levels of granularity, making it difficult to standardize for analysis of clear codes failure count. To maintain network quality, it's crucial to estimate Clear Code Failure Counts with a high degree of accuracy. Even a small discrepancy can impact decision-making and network performance.
[0003] Presently, there is no mechanism related to the estimation of NF (Network Function) wise Clear Code Failure Counts. Accurate estimation of these counts is vital for network performance management and quality assurance. However, challenges were faced in obtaining precise NF-wise Clear Code Failure Counts due to data complexities and variations.
[0004] The problem in the current network architecture is that any service disruption scenario can be identified only after graphically visualizing the real time streaming data and then taking appropriate action based on abnormalities in clear code data. This kind of delayed issue detection leads to prolonged service disruption and customer dissatisfaction. There is no available mechanism to predict and identify the possible ahead by means of analyzing real time data.
[0005] There is a requirement of a system and a method to analyze real time data to make predictions and proactively take required action to solve the issue.
SUMMARY OF THE INVENTION
[0006] One or more embodiments of the present disclosure provides a method and system for predicting clear code failures count in Network Functions (NFs).
[0007] In one aspect of the present invention, the system for predicting clear code failures count in the NFs is disclosed. The system includes a retrieving unit configured to retrieve a first set of data pertaining to each of the plurality of NFs from at least one of a database and from each of the plurality of NFs. The system further includes a training unit configured to train a model utilizing the first set of retrieved data to identify patterns related to the clear code failure count for each of the plurality of NFs. The system further includes a predicting unit configured to predict the clear code failure count for each of the plurality of NFs based on the identified patterns.
[0008] In an embodiment, the system further includes a receiving unit, configured to receive a second set of data pertaining to each of the plurality of NFs from each of the plurality of NFs in real time, a comparison unit configured to compare the second set of data with the predicted clear code failure count to identify a deviation there between and a detecting unit configured to detect the one or more anomalies in the at least one of the plurality of NFs on identification of the deviation and an initiating unit, configured to initiate one or more actions in response to detection of the one or more anomalies.
[0009] In an embodiment, the one or more actions correspond to at least one of transmitting an alert to network engineers and allocating one or more resources to address the one or more identified deviations.
[0010] In an embodiment, the detection of the one or more anomalies correspond to the detection of the clear code failure counts in at least one of the NF of the plurality of NFs.
[0011] In an embodiment, the first set of data comprises information associated with the clear code failure events, wherein the information indicates if each of the plurality of NFs is allowed or not allowed to clear an operation.
[0012] In an embodiment, the second set of data comprises information associated with the clear code failure events received in real time.
[0013] In an embodiment, the received data is pre-processed and stored in the database.
[0014] In another aspect of the present invention, a method for predicting clear code failures count in the NFs is disclosed. The method includes the step of retrieving a first set of data pertaining to each of the plurality of NFs from at least one of the database and from each of the plurality of NFs. The method further includes the step of training a model utilizing the first set of retrieved data to identify patterns in the first set of retrieved data. The method further includes the step of predicting the clear code failure count for each of the plurality of NFs based on the identified patterns.
[0015] In another aspect of the invention, a non-transitory computer-readable medium having stored thereon computer-readable instructions is disclosed. The computer-readable instructions are executed by a processor. The processor is configured to retrieve a first set of data pertaining to each of the plurality of NFs from at least one of a database and from each of the plurality of NFs. The processor is configured to train a model utilizing the first set of retrieved data to identify patterns in the first set of retrieved data. The processor is further configured to predict the clear code failure count for each of the plurality of NFs based on the identified patterns.
[0016] Other features and aspects of this invention will be apparent from the following description and the accompanying drawings. The features and advantages described in this summary and in the following detailed description are not all-inclusive, and particularly, many additional features and advantages will be apparent to one of ordinary skill in the relevant art, in view of the drawings, specification, and claims hereof. Moreover, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes and may not have been selected to delineate or circumscribe the inventive subject matter, resort to the claims being necessary to determine such inventive subject matter.
BRIEF DESCRIPTION OF THE DRAWINGS
[0017] 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.
[0018] FIG. 1 is an exemplary block diagram of an environment for predicting clear code failures count in Network Function (NF), according to one or more embodiments of the present invention;
[0019] FIG. 2 is an exemplary block diagram of a system for predicting clear code failures count in the NF, according to one or more embodiments of the present invention;
[0020] FIG. 3 is an exemplary block diagram of an architecture of the system of the FIG. 2, according to one or more embodiments of the present invention;
[0021] FIG. 4 is a flowchart diagram for predicting clear code failures count in the NF, according to one or more embodiments of the present invention; and
[0022] FIG. 5 is a flowchart of a method for predicting clear code failures count in the NF, according to one or more embodiments of the present invention.
[0023] The foregoing shall be more apparent from the following detailed description of the invention.
DETAILED DESCRIPTION OF THE INVENTION
[0024] Some embodiments of the present disclosure, illustrating all its features, will now be discussed in detail. It must also be noted that as used herein and in the appended claims, the singular forms "a", "an" and "the" include plural references unless the context clearly dictates otherwise.
[0025] Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure including the definitions listed here below are not intended to be limited to the embodiments illustrated but is to be accorded the widest scope consistent with the principles and features described herein.
[0026] A person of ordinary skill in the art will readily ascertain that the illustrated steps detailed in the figures and here below are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
[0027] The invention describes the method and system for predicting clear code failures count in the NF. The invention retrieves initial data related to clear code failures, trains a predictive model to identify patterns, and forecasts failure counts for each NF. The system continuously collects real-time data, compares it with predictions to detect anomalies, and triggers actions like alerting network engineers or reallocating resources when deviations are identified. The solution enhances proactive network management and ensures optimal performance by addressing potential issues before they escalate.
[0028] FIG. 1 illustrates an exemplary block diagram of an environment 100 for predicting clear code failures count in Network Function (NF), according to one or more embodiments of the present disclosure. In this regard, the environment 100 includes a User Equipment (UE) 110, a server 115, a network 105 and a system 120 communicably coupled to each other for predicting clear code failures count in the NF. Predicting clear code failure counts in the NFs involves analyzing historical clear code failure data to identify patterns that may forecast future occurrences. The process retrieves relevant data to train the predictive model, which discerns trends and predicts expected failure counts for each NF. By continuously monitoring real-time data and comparing it to the predictions, the system may detect deviations that signal potential anomalies, allowing for proactive actions, such as notifying network engineers or reallocating resources.
[0029] As per the illustrated embodiment and for the purpose of description and illustration, the UE 110 includes, but not limited to, a first UE 110a, a second UE 110b, and a third UE 110c, and should nowhere be construed as limiting the scope of the present disclosure. In alternate embodiments, the UE 110 may include a plurality of UEs as per the requirement. For ease of reference, each of the first UE 110a, the second UE 110b, and the third UE 110c, will hereinafter be collectively and individually referred to as the “User Equipment (UE) 110”.
[0030] In an embodiment, the UE 110 is one of, but not limited to, any electrical, electronic, electro-mechanical or an equipment and a combination of one or more of the above devices such as a 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 computing device.
[0031] The environment 100 includes the server 115 accessible via the network 105. The server 115 may include, by way of example but not limitation, one or more of a standalone server, a server blade, a server rack, a bank of servers, a server farm, hardware supporting a part of a cloud service or system, a home server, hardware running a virtualized server, one or more processors 205 executing code to function as a server, one or more machines performing server-side functionality as described herein, at least a portion of any of the above, some combination thereof. In an embodiment, the entity may include, but is not limited to, a vendor, a network operator, a company, an organization, a university, a lab facility, a business enterprise side, a defense facility side, or any other facility that provides service.
[0032] The network 105 includes, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, or some combination thereof. The network 105 may include, but is not limited to, a Third Generation (3G), a Fourth Generation (4G), a Fifth Generation (5G), a Sixth Generation (6G), a New Radio (NR), a Narrow Band Internet of Things (NB-IoT), an Open Radio Access Network (O-RAN), and the like.
[0033] The network 105 may also include, by way of example but not limitation, at least a portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, etc. one or more messages, packets, signals, waves, voltage or current levels, some combination thereof, or so forth. The network 105 may also include, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, a VOIP or some combination thereof.
[0034] The environment 100 further includes the system 120 communicably coupled to the server 115 and the UE 110 via the network 105. The system 120 is configured to predict the clear code failure count of each of the plurality of NFs in the network 105. As per one or more embodiments, the system 120 is adapted to be embedded within the server 115 or embedded as an individual entity.
[0035] Operational and construction features of the system 120 will be explained in detail with respect to the following figures.
[0036] FIG. 2 is an exemplary block diagram of the system 120 for predicting clear code failures count in the NF, according to one or more embodiments of the present invention.
[0037] As per the illustrated embodiment, the system 120 includes one or more processors 205, a memory 210, a user interface 215, and a database 220. For the purpose of description and explanation, the description will be explained with respect to one processor 205 and should nowhere be construed as limiting the scope of the present disclosure. In alternate embodiments, the system 120 may include more than one processor 205 as per the requirement of the network 105. The one or more processors 205, hereinafter referred to as the processor 205 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, single board computers, and/or any devices that manipulate signals based on operational instructions.
[0038] As per the illustrated embodiment, the processor 205 is configured to fetch and execute computer-readable instructions stored in the memory 210. The memory 210 may be configured to store one or more computer-readable instructions or routines in a non-transitory computer-readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory 210 may include any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as disk memory, EPROMs, FLASH memory, unalterable memory, and the like.
[0039] In an embodiment, the user interface 215 includes a variety of interfaces, for example, interfaces for a graphical user interface, a web user interface, a Command Line Interface (CLI), and the like. The user interface 215 facilitates communication of the system 120. In one embodiment, the user interface 215 provides a communication pathway for one or more components of the system 120. Examples of such components include, but are not limited to, the UE 110 and the database 220.
[0040] The database 220 is one of, but not limited to, a centralized database, a cloud-based database, a commercial database, an open-source database, a distributed database, an end-user database, a graphical database, a No-Structured Query Language (NoSQL) database, an object-oriented database, a personal database, an in-memory database, a document-based database, a time series database, a wide column database, a key value database, a search database, a cache databases, and so forth. The foregoing examples of database 220 types are non-limiting and may not be mutually exclusive e.g., a database can be both commercial and cloud-based, or both relational and open-source, etc.
[0041] In order for the system 120 for predicting clear code failures count in the NF, the processor 205 includes one or more modules. In one embodiment, the one or more modules/units includes, but not limited to, a retrieving unit 225, a training unit 230, a predicting unit 235, a receiving unit 240, a comparison unit 245, a detection unit 250, and an initiating unit 255 communicably coupled to each other for predicting clear code failures count in the NF.
[0042] In one embodiment, the one or more modules includes, but not limited to, the retrieving unit 225, the training unit 230, the predicting unit 235, the comparison unit 240, the detection unit 245, and the initiating unit 250 can be used in combination or interchangeably for predicting the clear code failure count of each of the plurality of NFs in the network 105.
[0043] The retrieving unit 225, the training unit 230, the predicting unit 235, the comparison unit 240, the detection unit 245, and the initiating unit 250, in an embodiment, may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processor 205. In the examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processor 205 may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processor may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the memory 210 may store instructions that, when executed by the processing resource, implement the processor. In such examples, the system 120 may comprise the memory 210 storing the instructions and the processing resource to execute the instructions, or the memory 210 may be separate but accessible to the system 120 and the processing resource. In other examples, the processor 205 may be implemented by electronic circuitry.
[0044] In an embodiment, the retrieving unit 225 is configured to retrieve a first set of data pertaining to each of the plurality of NFs from at least one of the database 220 and from each of the plurality of NFs. The retrieving unit 225 may also directly collect real-time or live data from the individual NFs themselves. The retrieving unit 225 are the actual network components that perform specific functions in the network 105.
[0045] The received data is the pre-processed and stored in the database 220. The received data may include information includes, but not limited to, performance metrics, failure counts, clear code events, and other relevant operational data from the NFs. Before the received data is used for analysis or storage, the data is pre-processed via a data pre-processing unit 315 (as shown in the FIG. 3). The performance metrics refer to measurable data points used to assess the operational health and efficiency of each the NF. The performance metrics are critical for identifying trends, predicting failures, and detecting the one or more anomalies. The performance metrics include, but not limited to, success rate of operations, latency, error rate. The failure counts represent the number of times a specific NFs fails to complete the operation successfully. The failure counts include, but not limited to, login failure count, connection drop count, and message delivery failure count. The clear code event occurs when the NFs fails to meet specific conditions or criteria, meaning it did not perform its intended operation successfully. The clear code event includes, but not limited to, successful task completion, failure to clear the code, and partially cleared code.
[0046] The data pre-processing unit 315 involves cleaning and organizing the raw data to make the data usable. Typical the data pre-processing unit 315 steps might include, but not limited to, data cleaning, normalizing, filtering, and aggregation. The data cleaning involves removing any errors, missing values, or inconsistencies. The normalization involves standardizing the data to a common format or scale, while filtering removes irrelevant or redundant information, and aggregation summarizes data points, such as, but not limited to, calculating averages or totals, to simplify analysis.
[0047] In an embodiment, the first set of data includes information associated with the clear code failure events, information indicates if each of the plurality of NFs is allowed or not allowed to clear an operation. The clear code failure events refer to instances where the NFs either successfully clears or fails to clear an operation, with the clear code indicating the outcome, and the failure event signifying that the operation is not complete successfully. The information associated with clear code failure events includes detailed data such as, but not limited to, the nature of the failure, the time information occurred, the network function involved, and other relevant operational details like error codes and system logs. Further the data indicates whether each of the plurality of the NFs successfully completed the operation without any issues or encountered an error and failed to complete the operation.
[0048] Upon retrieving the first set of data from the retrieving unit 225, the training unit 230 is configured to train a model utilizing the first set of retrieved data to identify patterns related to the clear code failure count for each of the plurality of NFs. The first set of retrieved data refers to the data initially collected from the NFs or the database 220. The pattern refers to repeating trends, behaviors, or correlations within the data that help identify characteristics associated with the clear code failure count for each of the plurality of NFs. The patterns are learned by the model during training to predict or analyze future failures. Examples of patterns include, but not limited to, temporal patterns, performance indicators, traffic patterns, geographic patterns, and failure progression. By understanding the patterns, network operators may take preventive measures, allocate resources more efficiently, and enhance the overall resilience of the network infrastructure.
[0049] In an embodiment, the data may include, but not limited to, performance metrics, operational statistics, or failure event information. The performance metrics are quantitative measures that indicate the effectiveness, efficiency, and quality of the NF's operations. The performance metrics includes, but not limited to, latency, throughput, CPU and memory utilization. The operational statistics are descriptive data points that capture the ongoing operational state or activity levels of the NF. The operational statistics offer insight into how frequently certain operations are performed, how resources are allocated, and the general workload of the NFs. The failure event information details specific instances when the NF fails to complete a task or meet an expected operational outcome. The failure event includes, but not limited to, failed authentication events, connection drop events, packet loss events, error codes from the Application Programming Interface (API) calls.
[0050] During the training process, the model analyzes the data to understand underlying trends, relationships, and patterns. The purpose of training is to help the model detect recurring patterns or correlations in the data. The patterns might relate to failure events, operational inefficiencies, or other key performance indicators that may predict future outcomes or behaviors in the network 105.
[0051] In an embodiment, the predicting unit 235 is configured to predict the clear code failure count for each of the plurality of NFs based on the identified patterns. The prediction is made individually for the NFs within the system 120. Each of the plurality of NFs may have different characteristics or patterns of operation, so the predicting unit 235 generates forecasts specific to each one. Based on the identifies patterns learned during the training phase. The patterns may be based on historical data, operational trends, or previous failure events that help the model predict future failure occurrences.
[0052] Further, the retrieving unit 225 is configured to receive a second set of data pertaining to each of the plurality of NFs from each of the plurality of NFs in real time. The second set of data refers to new or updated data that is different from the previously retrieved first set of data. The second set of data might include updated information such as, but not limited to, real-time operational metrics, failure events, performance statistics, or other key indicators specific to each NF. The receiving unit 225 simultaneously collects unique data from each of the plurality of the NFs and configured to handle input from all the NFs.
[0053] Upon receiving the second set of data from the receiving unit 225, the comparison unit 240 configured to compare the second set of data with the predicted clear code failure count to identify a deviation there between. After the receiving unit 225 collects real-time data, the second set of data from each of the NFs, the second set of data is then passed to the next component for further processing. The second set of data contains up-to-date, real-time information about the NFs, which is essential for accurate analysis. The second set of data includes information associated with the clear code failure events received in real time. The comparison unit 240 takes the real-time data and compares to the predicted values of the clear code failure count generated by the predicting unit 235. The clear code failure count represents the expected number of failure events for the NFs, which the model forecasted based on previous patterns and trends. Further the comparison unit 240 looks for any deviation between the real-time data and the predicted failure count. The deviation indicates the mismatch between the real-time data and the predicted values.
[0054] Upon comparing the second set of data with the predicted clear code failure count to identify the deviation there between, the detecting unit 245 is configured to detect the one or more anomalies in the at least one of the plurality of NFs on identification of the deviation. Once the deviation is identified by the comparison unit 240, the compression unit 240 assesses whether it corresponds to the one or more anomalies in the network 105. The detection of the one or more anomalies corresponding to the detection of the clear code failure counts in at least one of the NF of the plurality of NFs. The one or more anomalies refer to abnormal behaviors or performance issues within the NFs, such as, but not limited to, unexpected failures or operational irregularities. The detecting unit 245 determines whether the one or more anomalies exist based on the detected deviation. The clear code failure count represents the expected number of failure events for each of the plurality of the NFs, which the model forecasted based on previous patterns and trends. Once the deviation between the predicted clear code failure count and the real-time data is detected by the comparison unit 240, the detecting unit evaluates the deviation to see if it signals the genuine the one or more anomalies in the operation of the NFs.
[0055] Thereafter, the initiating unit 250 is configured to initiate one or more actions in response to detection of the one or more anomalies. Once the one or more anomalies or deviations are detected, such as, but not limited to, the mismatch between predicted and real-time failure counts, the initiating unit 250 takes the one or more action to address the issue. The one more action taken are predefined responses to mitigate the one or more anomalies or ensure the network continues to operate smoothly. The one or more actions correspond to at least one of transmitting an alert to network engineers and allocating one or more resources to address the one or more identified deviations. The one or more action transmitting the alert to network engineers, the one or more action involves notifying the relevant personnel (network engineers) about the one or more anomalies. The one or more action may be through automated alerts, notifications, or reports sent via email, text, or other communication channels, so engineers may quickly address the issue.
[0056] FIG. 3 is an exemplary block diagram of an architecture 300 of the system 120 for predicting clear code failures count in the NF, according to one or more embodiments of the present invention.
[0057] The architecture 300 includes a probing unit 305, a data integrator 310, a data pre-processing unit 315, a model training unit 320, a real-time monitoring unit 325, the user interface 215, and the database 220.
[0058] The probing unit 305 is a virtualized probe that collects real-time data from the one or more network elements and functions. The probing unit 305 captures critical network information such as, but not limited to, performance metrics, call parameters, and failure events. The probing unit 305 aligns with the step where data is retrieved from the NFs. The real-time data collected by the probing unit 305 forms the basis for training the predictive model and detecting the one or more anomalies. The probing unit 305 sends the collected data to the data integrator 310.
[0059] The data integrator 310 aggregates data from the probing unit 305 and potentially from other sources such as databases or external services. The data integrator 310 is responsible for unifying all incoming data both real-time and historical data into a consolidated format for further processing. Once the data integrated, the data is sent to the data pre-processing unit 315 for cleaning and transformation.
[0060] The data pre-processing unit 315 is where raw data is transformed into a structured, clean format suitable for analysis. The data pre-processing unit 315 filters out unnecessary data, handles missing values, normalizes data formats, and ensures consistency. The data pre-processing unit 315 prepares both real-time and historical data for the machine learning process. The cleaned and transformed data is passed to the model training unit 320 for pattern recognition and model building. Simultaneously, the pre-processed data may be sent to the database 220 for storage.
[0061] Further, the model training unit 320 uses the pre-processed historical data from the database 220 to train the machine learning model. The machine learning model learns to identify patterns, correlations, and trends in clear code failures or other network performance metrics. Based on the trained model, the model training unit 320 predicts the clear code failure count for each NF. Once the model is trained and predictions are ready, the model is deployed in the real-time monitoring unit 325 for continuous monitoring.
[0062] The database 220 serves as a repository for both pre-processed and raw historical data. the database 220 stores the first set of data that the model uses for training. The database 220 ensures that all historical data records are accessible for future analysis and model refinement. The data stored here is used whenever necessary by the model training unit 320 for pattern identification or retraining the model when required.
[0063] The real-time monitoring unit 325 compares the predicted clear code failure count from the model training unit 320 with the actual real-time data coming from the probing unit 305. The real-time monitoring unit 325 continuously monitors real-time operational data from the network 105. If deviations or the one or more anomalies are identified, the real-time monitoring unit 325 flags the discrepancies. If the one or more anomalies are detected, the real-time monitoring unit 325 triggers alerts and sends updates to the user interface 215. The real-time monitoring unit 325 may also initiate corrective actions, such as, but not limited to, resource allocation or network reconfiguration.
[0064] The user interface 215 provides real-time feedback to network engineers and operators. The user interface 215 displays data visualizations, insights, alerts, and reports related to network function performance and detects the one or more anomalies. The engineers may monitor the network’s health, take preventive or the one or more actions based on the alerts, and make decisions to improve network reliability and performance.
[0065] FIG. 4 is a flowchart diagram for predicting clear code failures count in the NF, according to one or more embodiments of the present invention.
[0066] At step 405, the flow begins with the processing hub probing interface, which gathers real-time data related to the performance and status of the NFs. The processing hub probing interface collects data such as, but not limited to, clear code failure events, operational metrics, and performance data of each NF. The collected data is sent for the data integration.
[0067] At step 410, the data collected from the processing hub probing interface interface and other sources is aggregated into the unified format. The processing hub probing interface includes both real-time and historical data. After aggregation, the data flows into the data pre-processing stage.
[0068] At step 415, the data pre-processing unit 315 is responsible for preparing the raw data for further analysis. The data pre-processing unit 315 includes the data cleaning, data normalization, and data transformation. The data cleaning removes any noise, incomplete or irrelevant data points. The data is standardized, ensuring consistency across various inputs. The data is then transformed into the required format for the model training process. The processed data moves on to the model training unit 320.
[0069] At step 420, the machine learning model is trained using the pre-processed historical data. The model is trained to recognize patterns in clear code failure counts and other relevant metrics for each of the NFs. The machine learning model uses the historical data to learn correlations and predict future failure counts. Once the model is trained, it is deployed for the real-time monitoring unit 325.
[0070] At step 425, the real-time monitoring unit 325 step continuously tracks data from the network and compares with the model predictions. The real-time monitoring unit 325 operational data from the probing unit 305 is monitored and analyzed against the predicted clear code failure counts. If the one or more anomalies are identified, it triggers the one or more action. Based on the monitoring results, the decision is made regarding the model output.
[0071] At step 430, the decision point checks whether the model’s output is optimal, meaning if the real-time data aligns with the predicted results. If the model predictions match the real-time data closely, it is considered optimal. If the model is optimal, the result is sent to the user interface 215, which notifies the network engineers. Thereafter, if the model output is not optimal the flow proceeds to the model retraining to update the model.
[0072] At the step 435, when real-time data deviates from the model’s predicted output, the model is automatically retrained using the latest data, including any newly identified deviations or the one or more anomalies, ensuring continuous learning and improved accuracy. After retraining, the updated model is redeployed into the real-time monitoring process. Simultaneously, the user interface 215 is notified of the retraining outcome, detected the one or more anomalies, and the one or more actions taken, providing network engineers with insights into network health and any corrective measures required. The integrated process ensures the model adapts to new data patterns while keeping engineers informed in real-time.
[0073] FIG. 5 is a flowchart diagram of a method 500 for predicting clear code failures count in the NF, according to one or more embodiments of the present invention. For the purpose of description, the method 500 is described with the embodiments as illustrated in FIG. 2 and should nowhere be construed as limiting the scope of the present disclosure.
[0074] At step 505, the method 500 includes the step of retrieving the first set of data pertaining to each of the plurality of the NFs from at least one of the database 220 and from each of the plurality of NFs. Further the method includes the receiving the second set of data pertaining to each of the plurality of NFs from each of the plurality of NFs in real time. The first set of data includes information associated with the clear code failure events. The information indicates if each of the plurality of NFs is allowed or not allowed to clear the operation. The received data is pre-processed and stored in the database 220.
[0075] At step 510, the method 500 includes the step of training the model utilizing the first set of retrieved data to identify patterns related to the clear code failure count for each of the plurality of NFs.
[0076] At step 515, the method 500 includes the step of predicting the clear code failure count for each of the plurality of NFs based on the identified patterns. Further the method 500 includes the step of receiving the second set of data pertaining to each of the plurality of the NFs from each of the plurality of NFs in real time. The second set of data includes information associated with the clear code failure events received in real time. The received data is pre-processed and stored in the database 220. Thereafter, the method 500 includes the step of comparing the second set of data with the predicted clear code failure count to identify the deviation there between. Subsequently, the method 500 includes the step of detecting the one or more anomalies in the at least one of the plurality of NFs on identification of the deviation. The detection of the one or more anomalies corresponding to the detection of the clear code failure counts in at least one of the NF of the plurality of NFs. Further, the method 500 includes the step of initiating the one or more actions in response to detection of the one or more anomalies. The one or more actions correspond to at least one of transmitting the alert to network engineers and allocating one or more resources to address the one or more identified deviations.
[0077] The present invention further discloses a non-transitory computer-readable medium having stored thereon computer-readable instructions. The computer-readable instructions are executed by the processor 205. The processor 205 is configured to retrieve the first set of data pertaining to each of the plurality of NFs from at least one of the database 220 and from each of the plurality of NFs. The processor 205 is configured to train the model utilizing the first set of retrieved data to identify patterns in the first set of retrieved data. The processor 205 is further configured to predict the clear code failure count for each of the plurality of NFs based on the identified patterns.
[0078] A person of ordinary skill in the art will readily ascertain that the illustrated embodiments and steps in description and drawings (FIG.1-5) are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
[0079] The present disclosure includes technical advancement offers that the interface with probing agent enhances network performance management by standardizing diverse data from Network Functions (NFs) for better accuracy and analysis. The invention utilizes advanced preprocessing techniques and machine learning algorithms for reliable failure predictions. Real-time monitoring allows for quick detection of performance discrepancies, while the user interface providing estimated clear code failure counts supports proactive management and timely interventions, improving overall network reliability.
[0080] The present invention offers multiple advantages over the prior art and the above listed are a few examples to emphasize on some of the advantageous features. The listed advantages are to be read in a non-limiting manner.

REFERENCE NUMERALS
[0081] Environment- 100
[0082] User Equipment (UE)- 110
[0083] Server- 115
[0084] Network- 105
[0085] System -120
[0086] Processor- 205
[0087] Memory- 210
[0088] User interface- 215
[0089] Database - 220
[0090] Retrieving unit - 225
[0091] Training unit - 230
[0092] Predicting unit – 235
[0093] Receiving unit - 240
[0094] Comparison unit - 245
[0095] Detection unit - 250
[0096] Initiating unit - 255
[0097] Probing unit - 305
[0098] Data integrator - 310
[0099] Data pre-processing unit - 315
[00100] Model training unit - 320
[00101] Real-time monitoring unit - 325
,CLAIMS:CLAIMS:
We Claim
1. A method (500) of predicting a clear code failure count of each of a plurality of Network Functions (NFs) in a network (105), comprises the step of,
retrieving, by one or more processors (205), a first set of data pertaining to each of the plurality of NFs from at least one of a database (220) and from each of the plurality of NFs;
training, by the one or more processors (205), a model utilizing the first set of retrieved data to identify patterns related to the clear code failure count for each of the plurality of NFs; and
predicting, by the one or more processors (205), the clear code failure count for each of the plurality of NFs based on the identified patterns.

2. The method (500) as claimed in claim 1, the method (500) comprising:
receiving, by the one or more processors (205), a second set of data pertaining to each of the plurality of NFs from each of the plurality of NFs in real time;
comparing, by the one or more processors (205), the second set of data with the predicted clear code failure count to identify a deviation there between;
detecting, by the one or more processors (205), the one or more anomalies in the at least one of the plurality of NFs on identification of the deviation; and
initiating, by the one or more processors (205), one or more actions in response to detection of the one or more anomalies.

3. The method (500) as claimed in claim 2, wherein the one or more actions correspond to at least one of transmitting an alert to network engineers and allocating one or more resources to address the one or more identified deviations.

4. The method (500) as claimed in claim 1, wherein the detection of the one or more anomalies correspond to the detection of the clear code failure counts in at least one of the NF of the plurality of NFs.

5. The method (500) as claimed in claim 1, wherein the first set of data comprises information associated with the clear code failure events, wherein the information indicates if each of the plurality of NFs is allowed or not allowed to clear an operation.

6. The method (500) as claimed in claim 2, wherein the second set of data comprises information associated with the clear code failure events received in real time.

7. The method (500) as claimed in claim 1, wherein the received data is pre-processed and stored in the database (220).

8. A system (120) for predicting a clear code failure count of each of a plurality of Network Functions (NFs) in a network, comprises:
a retrieving unit (225), configured to, retrieve a first set of data pertaining to each of the plurality of NFs from at least one of a database (220) and from each of the plurality of NFs;
a training unit (230), configured to, train a model utilizing the first set of retrieved data to identify patterns related to the clear code failure count for each of the plurality of NFs; and
a predicting unit (235), configured to, predict the clear code failure count for each of the plurality of NFs based on the identified patterns.

9. The system (120) as claimed in claim 8, comprising:
a receiving unit (225), configured to receive a second set of data pertaining to each of the plurality of NFs from each of the plurality of NFs in real time;
a comparison unit (245), configured to compare the second set of data with the predicted clear code failure count to identify a deviation there between; and
a detecting unit (250), configured to detect the one or more anomalies in the at least one of the plurality of NFs on identification of the deviation; and
an initiating unit (255), configured to initiate one or more actions in response to detection of the one or more anomalies.
10. The system (120) as claimed in claim 8, wherein the one or more actions correspond to at least one of transmitting an alert to network engineers and allocating one or more resources to address the one or more identified deviations.

11. The system (120) as claimed in claim 8, wherein the detection of the one or more anomalies correspond to the detection of the clear code failure counts in at least one of the NF of the plurality of NFs.

12. The system (120) as claimed in claim 8, wherein the first set of data comprises information associated with the clear code failure events, wherein the information indicates if each of the plurality of NFs is allowed or not allowed to clear an operation.

13. The system (120) as claimed in claim 8, wherein the second set of data comprises information associated with the clear code failure events received in real time.

14. The system (120) as claimed in claim 8 wherein the received data is pre-processed and stored in the database (220).