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
SYSTEM AND METHOD FOR CORRELATING MULTIPLE TYPES OF DATA
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
[0001] The present invention relates to the field of wireless communication systems, more particularly relates to a method and a system for correlating multiple types of data.
BACKGROUND OF THE INVENTION
[0002] With increase in number of users, a probing agent is utilized to collect probing data such as but not limited to, Streaming Data Records (SDR) from network nodes. The network functions generate the SDR (including the clear codes) at procedure level whenever any error scenario occurs or experienced by the respective NF. In addition, the probing agent interacts with various applications like Network Management System (NMS), Integrated Protection Module (IPM) & Fiber Management System (FMS) which send network condition data in the form of parameters like alarms, counters, KPIs, etc on a periodic basis. The network condition data in the form of these mentioned parameters are further analysed which will aid in the overall network monitoring, troubleshooting and root cause analysis.
[0003] In traditional practices, a provision is made to analyse each of the network failure events on a use case basis and manually design workflows to handle and resolve these network failure events. However, analysing each of the network failure events and manually designing workflows to resolve them is a time consuming and complex process. Further, even though each network issue is analysed and workflows are designed for resolving the failure event, the same is an exhaustive exercise and there are tendencies that a comprehensive analysis of all the network issues of the network failure event may not be able to be conducted taking all the network issues in combination for each network event. The reason being identifying any correlation between two or more network failure data types may not be possible due to many reasons such, as different teams working on different network data types, coordination issues, not aware of the entire environment of the network, etc.
[0004] There is, therefore, a need for efficient mechanisms for correlating multiple types of network data for managing network failure events.
SUMMARY OF THE INVENTION
[0005] One or more embodiments of the present disclosure provides a method and a system for correlating multiple types of data.
[0006] In one aspect of the present invention, the method for correlating the multiple types of data is disclosed. The method includes the step of receiving, by one or more processors, from one or more sources, multiple types of data associated with one or more network failure events. The method further includes the step of pre-processing, by the one or more processors, the received multiple types of data. The method further includes the step of training, by the one or more processors, an Artificial Intelligence/Machine Learning (AI/ML) model using the pre-processed data. The method further includes the step of correlating, by the one or more processors, utilizing the trained AI/ML model, the multiple types of data to identify patterns related to the one or more network failure events between the multiple types of data. The method further includes the step of notifying, by the one or more processors, one or more workflows pertaining to the identified patterns related to the one or more network failure events based on the correlation of the multiple types of data for managing each of the network failure event.
[0007] In another embodiment, the multiple types of data include at least one of, Streaming Data Records (SDRs), alarms, counters, Key Performance Indicators (KPIs), and clear code counts associated with the one or more network failure events.
[0008] In yet another embodiment, the one or more sources include at least one of, one or more network nodes and a plurality of applications.
[0009] In yet another embodiment, the step of pre-processing, the received multiple types of data further includes the step of extracting, by the one or more processors, one or more features from the received multiple types of data.
[0010] In yet another embodiment, the AI/ML model continuously learns the patterns related to the one or more network failure events over time and evolves based on network conditions.
[0011] In yet another embodiment, the one or more workflows are utilized for managing the network failure events which includes at least one of, providing automated responses, performing fault isolation, performing root cause analysis, and performing error rectification.
[0012] In yet another embodiment, the network failure event includes at least one of, user equipment registration failure, call establishment failure, and bandwidth demand surge.
[0013] In yet another embodiment, the method further includes the step of storing, by the one or more processors, at least one of, the one or more workflows and correlation results in the storage unit.
[0014] In another aspect of the present invention, the system for correlating multiple types of data is disclosed. The system includes a receiving unit, configured to, receive, from one or more sources, multiple types of data associated with one or more network failure events. The system further includes a data preprocessing unit, configured to, pre-process, the received multiple types of data. The system further includes a training unit, configured to, train an Artificial Intelligence/Machine Learning (AI/ML) model using the pre-processed data. The system further includes a correlation unit, configured to, correlate, utilizing the trained AI/ML model, the multiple types of data to identify patterns related to the one or more network failure events between the multiple types of data. The system further includes a notification unit, configured to, notify, one or more workflows pertaining to the identified patterns related to the one or more network failure events based on the correlation of the multiple types of data for managing each of the network failure event.
[0015] In yet another aspect of the present invention, a non-transitory computer-readable medium having stored thereon computer-readable instructions that, when executed by a processor. The processor is configured to receive, from one or more sources, multiple types of data associated with one or more network failure events. The processor is further configured to pre-process, the received multiple types of data. The processor is further configured to train an Artificial Intelligence/Machine Learning (AI/ML) model using the pre-processed data. The processor is further configured to correlate, utilizing the trained AI/ML model, the multiple types of data to identify patterns related to the one or more network failure events between the multiple types of data. The processor is further configured to notify, one or more workflows pertaining to the identified patterns related to the one or more network failure events based on correlation of the multiple types of data for managing each of the network failure event.
[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 correlating multiple types of data, according to one or more embodiments of the present invention;
[0019] FIG. 2 is an exemplary block diagram of a system for correlating the multiple types of data, according to one or more embodiments of the present invention;
[0020] FIG. 3 is an exemplary architecture of the system of FIG. 2, according to one or more embodiments of the present invention;
[0021] FIG. 4 is an exemplary architecture for correlating the multiple types of data, according to one or more embodiments of the present disclosure;
[0022] FIG. 5 is an exemplary signal flow diagram illustrating the flow for correlating the multiple types of data; and
[0023] FIG. 6 is a flow diagram of a method for correlating the multiple types of data, according to one or more embodiments of the present invention.
[0024] The foregoing shall be more apparent from the following detailed description of the invention.
DETAILED DESCRIPTION OF THE INVENTION
[0025] 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.
[0026] 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.
[0027] 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.
[0028] Various embodiments of the present invention provide a system and a method for correlating multiple types of data. The most unique aspect of the invention lies in an ability to interact with one or more sources by means of an interface to receive multiple types of data associated with one or more network failure events. The system is configured to leverage trained Artificial Intelligence/Machine Learning (AI/ML) to correlate the multiple types of data and notify one or more workflows pertaining to the identified patterns related to the one or more network failure events for managing each of the network failure event among the one or more network failure events. Herein one or more workflows refers to a process of correlating the multiple types of data and providing notification to a user.
[0029] Referring to FIG. 1, FIG. 1 illustrates an exemplary block diagram of an environment 100 for correlating multiple types of data according to one or more embodiments of the present invention. The environment 100 includes a User Equipment (UE) 102, a server 104, a network 106, a system 108, and one or more sources 110. Herein, the multiple types of the data are correlated by the system 108 for each of one or more network failure events. The one or more network failure events refer to a condition in the network 106 that results in failures in the network 106 such as at least one of, but not limited to, of service failures, performance degradation, or unavailability of network resources. Furthermore, the one or more network failure events affect the communication between the system 108 and other components over the network 106. Herin, the multiple types of data include at least one of, but not limited to, Streaming Data Records (SDRs), alarms, counters, Key Performance Indicators (KPIs), and clear code counts associated with the one or more network failure events.
[0030] For the purpose of description and explanation, the description will be explained with respect to one or more user equipment’s (UEs) 102, or to be more specific will be explained with respect to a first UE 102a, a second UE 102b, and a third UE 102c, and should nowhere be construed as limiting the scope of the present disclosure. Each of the at least one UE 102 namely the first UE 102a, the second UE 102b, and the third UE 102c is configured to connect to the server 104 via the network 106.
[0031] In an embodiment, each of the first UE 102a, the second UE 102b, and the third UE 102c 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 smartphones, 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.
[0032] The network 106 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 106 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 106 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.
[0034] The environment 100 includes the server 104 accessible via the network 106. The server 104 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, a processor 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.
[0035] The environment 100 further includes the one or more sources 110. In one embodiment, the one or more sources 110 are origins from which multiple types of data is retrieved and utilized for at least one of, but not limited to, analysis, research, and decision-making. In one embodiment, the one or more sources 110 is at least one of, but not limited to, one or more network nodes, a plurality of applications and one or more databases. In one embodiment, the one or more network nodes refer to the various devices and components present in the network 106 that facilitate communication, manage traffic, and ensure connectivity.
[0036] In one embodiment, the one or more network nodes include at least one of, but not limited to, Radio Access Network (RAN), core network, centralized unit, and gNodeB (gNB). In another embodiment, the one or more network nodes include at least one of, but not limited to, servers, routers, switches, and load balancers. In one embodiment, the plurality of applications are applications from which multiple types of data are received in the system 108 related to the one or more network failure events. The plurality of applications typically involves a combination of various monitoring, logging, and diagnostic tools.
[0037] The environment 100 further includes the system 108 communicably coupled to the server 104, the UE 102, and the one or more sources 110 via the network 106. The system 108 is adapted to be embedded within the server 104 or is embedded as the individual entity.
[0038] Operational and construction features of the system 108 will be explained in detail with respect to the following figures.
[0039] FIG. 2 is an exemplary block diagram of the system 108 for correlating the multiple types of data, according to one or more embodiments of the present invention.
[0040] As per the illustrated and preferred embodiment, the system 108 for correlating the multiple types of data, includes one or more processors 202, a memory 204, a storage unit 206 and an Artificial Intelligence/Machine Learning (AI/ML) model 220. The one or more processors 202, hereinafter referred to as the processor 202, 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. However, it is to be noted that the system 108 may include multiple processors as per the requirement and without deviating from the scope of the present disclosure. Among other capabilities, the processor 202 is configured to fetch and execute computer-readable instructions stored in the memory 204.
[0041] As per the illustrated embodiment, the processor 202 is configured to fetch and execute computer-readable instructions stored in the memory 204 as the memory 204 is communicably connected to the processor 202. The memory 204 is 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 for correlating the multiple types of data. The memory 204 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.
[0042] The environment 100 further includes the storage unit 206. As per the illustrated embodiment, the storage unit 206 is configured to store data retrieved from the one or more sources 110. The storage unit 206 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 the storage unit 206 types are non-limiting and may not be mutually exclusive e.g., the database can be both commercial and cloud-based, or both relational and open-source, etc.
[0043] As per the illustrated embodiment, the system 108 includes the AI/ML model 220. In another embodiment, the system 108 includes a plurality of AI/ML models 220. The model 220 facilitates the system 108 in performing tasks such as at least one of, notifying regarding one or more workflows, detecting anomalies, recognizing patterns, making predictions, solving problems, enhancing decision-making, and providing insights across various fields. For example, the AI/ML model 220 facilitates solving real-world problems without extensive manual intervention. In one embodiment, the AI/ML model 220 is trained using the multiple types of data associated with the one or more network failure events. In an alternate embodiment, the AI/ML model 220 is pretrained.
[0044] As per the illustrated embodiment, the system 108 includes the processor 202 for correlating the multiple types of data. The processor 202 includes a receiving unit 208, a data preprocessing unit 210, a training unit 212, a correlation unit 214 and a notification unit 216. The processor 202 is communicably coupled to the one or more components of the system 108 such as the memory 204, the storage unit 206 and the AI/ML model 220. In an embodiment, operations and functionalities of the receiving unit 208, the data preprocessing unit 210, the training unit 212, the correlation unit 214, the notification unit 216 and the one or more components of the system 108 can be used in combination or interchangeably.
[0045] In one embodiment, initially the receiving unit 208 of the processor 202 is configured to receive multiple types of data associated with the one or more network failure events from the one or more sources 110. In one embodiment, the one or more network failure events include at least one of, but not limited to, a UE 102 registration failure, a call establishment failure, and a bandwidth demand surge. For example, if the network 106 is experiencing high traffic or congestion, the network 106 may not be able to process a registration request received from the UE 102. In another example, the call establishment failure refers to a situation where a call is not successfully established between two parties. Herein, the call establishment failure may occur due to at least one of, but not limited to, poor or weak signal between the between the two parties.
[0046] In another embodiment, the one or more network failure events further includes at least one of, but not limited to, a link failure, a routing failure, and a server failure. For example, the link failure occurs when a connection between the one or more nodes is degraded due to one or more issues such as at least one of, but not limited to, one or more fiber cuts. In another example, the routing failure occurs when the network's routing protocol encounters the one or more issues such as at least one of, but not limited to, one or more preset incorrect paths. In another example, the server failure occurs when the server such as a Domain Name System (DNS) server breaks down.
[0047] In one embodiment, the multiple types of data include at least one of, Streaming Data Records (SDRs), alarms, counters, Key Performance Indicators (KPIs), and clear code counts associated with the one or more network failure events. In one embodiment, the SDRs refer to the continuous flow of data associated with one or more network failure events. Herein the SDRs are transmitted by the one or more sources 110 to the receiving unit 208 in real-time or near-real-time. In particular, the SDRs are typically part of streaming data where the multiple types of data is constantly produced. More particularly, the SDRs are often used for at least one of, but not limited to, monitoring, decision-making, predictive analytics, and managing the one or more network failure events.
[0048] In one embodiment, the alarms refer to one or more alerts that indicate the one or more network failure events that require immediate attention. The alarms are typically by at least one of, but not limited to, monitoring systems, sensors, or network components when at least one of, one or more thresholds or predefined conditions are met. In one embodiment, the counters are numerical values that track the occurrence of the one or more network failure events over time. For example, the counters are essential for monitoring at least one of, but not limited to, a health, a performance, and a behavior of the network 106. In one embodiment, the KPIs facilitates the system 108 to track the performance and the health of networks 106. Herein, the KPIs include at least one of, but not limited to, latency, throughput, and packet loss. In one embodiment, the clear code counts refer to a number of occurrences of certain clear codes or error codes recorded within the system 108. The clear code represents the status, such as success, or failure of the network 106. In particular, the clear code represents the one or more network failure events.
[0049] In one embodiment, the receiving unit 208 receives the data from the one or more sources 110 which are present within the network 106 and outside the network 106. In one embodiment, the one or more sources 110 periodically transmits the multiple types of data to the system 108.
[0050] In one embodiment, the receiving unit 208 receives the multiple types of data from the one or more sources 110 via an interface. In one embodiment, the interface includes at least one of, but not limited to, one or more Application Programming Interfaces (APIs) which are used for retrieving the multiple types of data from the one or more sources 110. The one or more APIs are sets of rules and protocols that allow different entities to communicate with each other. The one or more APIs define the methods and data formats that entities can use to request and exchange information, enabling integration and functionality across various platforms. In particular, the APIs are essential for integrating different systems, accessing services, and extending functionality.
[0051] In another embodiment, the receiving unit 208 receives the multiple types of data from the one or more sources 110 via a probing agent. The probing agent in the network 106 refers to at least one of, but not limited to, a software tool, or a device which is used to receive the multiple types of data associated with one or more network failure events within the network 106. In particular, the probing agent actively probes the one or more sources 110 to receive real-time data or historical data associated with the one or more network failure events.
[0052] Upon receiving the multiple types of data from the one or more sources 110, the data preprocessing unit 210 is configured to preprocess the received multiple types of data. In particular, the data preprocessing unit 210 is configured to preprocess the multiple types of data to ensure the data consistency and quality of the data within the system 108. The data preprocessing unit 210 performs at least one of, but not limited to, data normalization, data definition and data cleaning procedures.
[0053] In one embodiment, for preprocessing, the data preprocessing unit 210 extracts one or more features from the received multiple types of data. Herein, the one or more features pertaining to the one or more network failure events include at least one of, but not limited to, a type of the network failure event, timestamps of the one or more network failure events occurred, locations at which the one or more network failure events occurred, severity of the one or more network failure events.
[0054] Upon extracting the one or more features, the data preprocessing unit 210 preprocesses the extracted one or more features. Herein, the data preprocessing unit 210 performs at least one of, but not limited to, reorganizing the multiple types of data, removing the redundant data from the multiple types of data, formatting the multiple types of data, removing null values from the multiple types of data, cleaning the multiple types of data, and handling missing values in the multiple types of data. The main goal of the preprocessing is to achieve a standardized data format across the system 108. While preprocessing, the duplicate data and inconsistencies are eliminated from the retrieved multiple types of data. Subsequent to preprocessing, the retrieved multiple types of data are referred to pre-processed data. The data preprocessing unit 210 is further configured to store the pre-processed data in at least one of, the storage unit 206 for subsequent retrieval and analysis.
[0055] Upon preprocessing the received multiple types of data, the training unit 212 of the processor 202 is configured to train the AI/ML model 220 using the pre-processed data. In order to train the AI/ML model 220, the training unit 212 configures one or more hyperparameters of the AI/ML model 220. In one embodiment, the one or more hyperparameters of AI/ML model 220 includes at least one of, but not limited to, a learning rate, a batch size, and a number of epochs.
[0056] Upon configuring the one or more hyperparameters of the AI/ML model 220, the training unit 212 is further configured to split the pre-processed data into at least one of, but not limited to, training data and testing data for training. For example, the training unit 212 splits the pre-processed data such that 80% of the pre-processed data is considered as the training data and 20% of the pre-processed data is considered as the testing data. Thereafter, the training unit 212 feeds the training data to the AI/ML model 220 based on which the AI/ML model 220 is trained by the training unit 212.
[0057] In one embodiment, the training unit 212 trains the AI/ML model 220 by applying one or more logics. In one embodiment, the one or more logics may include at least one of, but not limited to, a k-means clustering, a hierarchical clustering, a Principal Component Analysis (PCA), an Independent Component Analysis (ICA), a deep learning logics such as Artificial Neural Networks (ANNs), a Convolutional Neural Networks (CNNs), a Recurrent Neural Networks (RNNs), a Long Short-Term Memory Networks (LSTMs), a Generative Adversarial Networks (GANs), a Q-Learning, a Deep Q-Networks (DQN), a Reinforcement Learning Logics, etc.
[0058] In one embodiment, while training the AI/ML model 220 with the pre-processed data, the AI/ML model 220 learns, at least one of, but not limited to, trends/patterns associated with the with one or more network failure events. Herein, the trends are general directions and development observed over time in the one or more network failure events. Herein, the patterns refer to at least one of, but not limited to, regularities, repeated sequences, or recurring characteristics related to the one or more network failure events. In another embodiment, the AI/ML model 220 learns at least one of, but not limited to, trends/patterns based on historical data related to the one or more network failure events.
[0059] Upon training the AI/ML model 220, the correlation unit 214 of the processor 202 is configured to correlate the multiple types of data utilizing the trained AI/ML model 220 in order to identify patterns related to the one or more network failure events between the multiple types of data. In one embodiment the trained AI/ML model 220 is fed with the testing data in order to evaluate performance of the trained AI/ML model 220. Thereafter, the correlation unit 214 correlates the multiple types of data. For example, let us consider a first type of data associated with at least one network failure event and a second type of data associated with at least one of, the another network failure event. Herein, the correlation unit 214 compares the first type of the data and the second type of the data and identifies patterns between the multiple types of data. In particular, the correlation unit 214 identifies commonalities between the first type of the data and the second type of the data.
[0060] For example, let us consider based on the correlation the correlation unit 214 identifies that the first type of the data and the second type of the data is associated with the same network failure event. In another example, based on the identified patterns between the multiple types of data, the correlation unit 214 identifies that when the first type of the data such as KPIs are generated and if the KPIs breaches predefined one or more thresholds, then the second type of the data such as the alarms are generated.
[0061] Upon correlating the multiple types of data, the notification unit 216 of the processor 202 is configured to notify regarding one or more workflows pertaining to the identified patterns related to the one or more network failure events based on the correlation of the multiple types of data for managing each of the network failure event. In particular, based on the correlation, the notification unit 216 provides notification regarding the one or more workflows to the UE 102 in order to easily rectify each of the network failure event. In one embodiment, the notification unit 216 notifies a specific workflow for each of the network failure event. For example, in one scenario, let us consider that the first type of the data and the second type of the data is generated due to the common network failure event. Based on the identified correlation such as the first type of the data and the second type of the data is generated due to the common network failure event, the notification unit 216 notifies the user on the UE 102 regarding a common workflow for the generation of the first type of the data and the second type of the data. Based on the notified common workflow related to the common network failure event, the user performs one or more actions for rectifying the common network failure event. In an alternate embodiment, the system 108 performs the one or more actions utilizing the AI/ML model 220 in order to rectify the common network failure event.
[0062] In one embodiment, the one or more workflows are utilized by the system 108 for rectifying the common network failure event by performing the one or more actions such as at least one of, but not limited to, providing automated responses, performing fault isolation, performing root cause analysis, and performing error rectification. In one embodiment, the notification unit 216 provides automated responses related to the one or more network failure events. For example, let us consider that the one or more network failure events is a link failure between the system 108 and the other components in the network 106. In this scenario, the notification unit 216 provides automated responses which acts as alerts to the users. Herein, the alerts includes at least one of, but not limited to, the link failure details, affected components, and traffic impact.
[0063] In another example, let us consider that the one or more network failure events occurs due to excessive traffic in the network 106. In this scenario, the notification unit 216 performs root cause analysis in order to identify origin of the traffic. Based on the root cause analysis, the system 108redirects the excessive traffic. In yet another example, let us consider that the one or more network failure events is the link failure between the system 108 and the other components in the network 106. In this scenario, the notification unit 216 performs error rectification by linking the system 108 and the other components via alternate link in order to rectify the error. Advantageously, the system 108 has the capability to notify regarding the one or more workflows related to the one or more network failure events in order to simplify a rectification process to resolve the one or more network failure events in the network 106.
[0064] In one embodiment, the AI/ML model 220 continuously learns the patterns related to the one or more network failure events over time. Further, the AI/ML model 220 evolves based on network conditions. In another embodiment, the AI/ML model 220 learns at least one of, but not limited to, the correlation and the one or more workflows notified by the notification unit 216 which facilitates the AI/ML model 220 to manage each of the network failure event in the future. Herein, the notification unit 216 provides feedback to the AI/ML model 220 so that the AI/ML model 220 continuously learns regarding the notified one or more workflows. Due to continuously learning ability of the AI/ML model 220, the training unit 212 fine tunes the AI/ML model 220 based on which the AI/ML model 220 manages the one or more network failure events in the future.
[0065] The receiving unit 208, the data preprocessing unit 210, the training unit 212, the correlation unit 214, and the notification unit 216 in an exemplary embodiment, are implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processor 202. 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 202 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 204 may store instructions that, when executed by the processing resource, implement the processor 202. In such examples, the system 108 may comprise the memory 204 storing the instructions and the processing resource to execute the instructions, or the memory 204 may be separate but accessible to the system 108 and the processing resource. In other examples, the processor 202 may be implemented by electronic circuitry.
[0066] FIG. 3 illustrates an exemplary architecture for the system 108, according to one or more embodiments of the present invention. More specifically, FIG. 3 illustrates the system 108 for correlating the multiple types of data. It is to be noted that the embodiment with respect to FIG. 3 will be explained with respect to the UE 102 for the purpose of description and illustration and should nowhere be construed as limited to the scope of the present disclosure.
[0067] FIG. 3 shows communication between the UE 102, the system 108, and the one or more sources 110. For the purpose of description of the exemplary embodiment as illustrated in FIG. 3, the UE 102, uses network protocol connection to communicate with the system 108, and the one or more sources 110. In an embodiment, the network protocol connection is the establishment and management of communication between the UE 102, the system 108, and the one or more sources 110 over the network 106 (as shown in FIG. 1) using a specific protocol or set of protocols. The network protocol connection includes, but not limited to, Session Initiation Protocol (SIP), System Information Block (SIB) protocol, Transmission Control Protocol (TCP), User Datagram Protocol (UDP), File Transfer Protocol (FTP), Hypertext Transfer Protocol (HTTP), Simple Network Management Protocol (SNMP), Internet Control Message Protocol (ICMP), Hypertext Transfer Protocol Secure (HTTPS) and Terminal Network (TELNET).
[0068] In an embodiment, the UE 102 includes a primary processor 302, and a memory 304 and a User Interface (UI) 306. In alternate embodiments, the UE 102 may include more than one primary processor 302 as per the requirement of the network 106. The primary processor 302, 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.
[0069] In an embodiment, the primary processor 302 is configured to fetch and execute computer-readable instructions stored in the memory 304. The memory 304 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 for correlating the multiple types of data. The memory 304 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.
[0070] In an embodiment, the User Interface (UI) 306 includes a variety of interfaces, for example, a graphical user interface, a web user interface, a Command Line Interface (CLI), and the like. The UI 306 of the UE 102 allows the consumer to transmit requests to the one or more processors 202 for correlating the multiple types of data. In one embodiment, the consumer is at least one of, but not limited to, a network operator. Further, the UE 102 receives information regarding the correlations and the notification regarding the one or more workflows from the one or more processors 202 for resolving the one or more network failure events.
[0071] As mentioned earlier in FIG.2, the system 108 includes the processors 202, the memory 204 and the storage unit 206, for correlating the multiple types of data, which are already explained in FIG. 2. For the sake of brevity, a similar description related to the working and operation of the system 108 as illustrated in FIG. 2 has been omitted to avoid repetition.
[0072] Further, as mentioned earlier the processor 202 includes the receiving unit 208, the data preprocessing unit 210, the training unit 212, the correlation unit 214, and the notification unit 216 which are already explained in FIG. 2. Hence, for the sake of brevity, a similar description related to the working and operation of the system 108 as illustrated in FIG. 2 has been omitted to avoid repetition. The limited description provided for the system 108 in FIG. 3, should be read with the description provided for the system 108 in the FIG. 2 above, and should not be construed as limiting the scope of the present disclosure.
[0073] FIG. 4 is an exemplary the system 108 architecture 400 for correlating the multiple types of data, according to one or more embodiments of the present disclosure.
[0074] The architecture 400 includes a probing agent 402, which is in communication with the one or more sources 110. The architecture 400 further includes a data integration unit 404, a data preprocessor 406, a model training unit 408, a data correlation unit 410, a workflow designing unit 412, and a data lake 414, communicably coupled to each other via the network 106.
[0075] In one embodiment, the probing agent 402 is essential tools for network management and troubleshooting. In particular, the probing agent 402 provides real-time monitoring, diagnostics, and performance analysis. Herein, the probing agent 402 receives multiple types of data such as at least one of, but not limited to, alarms, counters, KPIs, SDRs/Clear codes from one or more sources 110.
[0076] In one embodiment, the data integration unit 404 periodically receives the multiple types of data pertaining to the associated with the one or more network failure events from the probing agent 402. The multiple types of data act as the input stream provided by the probing agent 402 which is crucial for training the AI/ML model 220. Herein, data integration unit 404 combines the multiple types of data received from the probing agent 402 and provides a unified view to the user that enables comprehensive analysis. For example, the system 108 provides an integrated view of the multiple types of data received from the probing agent 402 associated with the one or more network failure events.
[0077] In one embodiment, the data preprocessor 406 receives the multiple types of data from the data integration unit 404 and preprocesses the multiple types of data. For example, the multiple types of data undergo preprocessing to ensure data consistency within the system 108. In particular, the preprocessing involves tasks like data cleaning, normalization, removing duplicate records and handling missing values. In yet another example, the raw data related to the multiple types of data is pre-processed to clean, normalize, and convert the raw data into a structured format suitable for analysis.
[0078] In one embodiment, the model training unit 408 trains the AI/ML model 220 using the data which is pre-processed by the data pre-processor 406.
[0079] In one embodiment, the data correlation unit 410 correlates the multiple types of data to identify patterns between the multiple types of data utilizing the trained AI/ML model 220. Further, the workflow designing unit 412 acts as the notification unit 216 which notifies the UE 102 regarding the one or more workflows based on the correlation of the multiple types of data for managing each of the network failure events. For example, subsequent to notifying regarding the one or more workflows, one or more actions are executed by the system 108 in order to rectify each of the network failure event.
[0080] In one embodiment, the data lake 414 acts as the storage unit 206 which includes a structured collection of at least one of, but not limited to, the preprocessed data, the correlations, and the notified one or more workflows, which are managed and organized in a way that allows system 108 for easy access, retrieval, and manipulation. The data lake 414 is used to store, manage, and retrieve large amounts of information efficiently.
[0081] FIG. 5 is a signal flow diagram illustrating the flow for correlating the multiple types of data, according to one or more embodiments of the present disclosure.
[0082] At step 502, the system 108 receives the multiple types of data from the one or more sources 110. For example, the multiple types of data is associated with at least one of, the one or more network failure events. In one embodiment, the system 108 transmits at least one of, but not limited to, a Hyper Text Transfer Protocol (HTTP) request to the one or more sources 110 to retrieve multiple types of data. In one embodiment, a connection is established between the system 108 and the one or more sources 110 before receiving the multiple types of data from the one or more sources 110. Further, the received multiple types of data are integrated and preprocessed by the system 108.
[0083] At step 504, the system 108 trains the AI/ML model 220 with the received multiple types of data. More particularly, the system 108 trains the AI/ML model 220 with the received multiple types of data subsequent to pre-processing. Herein, the pre-processed data is stored in the storage unit 206 for training the AI/ML model 220.
[0084] At step 506, the system 108 correlates multiple types of data utilizing the trained AI/ML model 220. Herein, the system 108 identifies and learns the patterns related to the one or more network failure events between the multiple types of data. Based on the learned patterns, the system 108 identifies that the multiple types of data which are generated from at least one common network failure event.
[0085] At step 508, the system 108 notifies regarding the one or more workflows related to the one or more network failure events based on the correlation for managing each of the network failure event. Herein, the system 108 notifies regarding the one or more workflows which facilitates the system 108 in order to rectify each of the network failure event. Further, the system 108 transmits a report pertaining to the correlations and the one or more workflows by the system 108 by at least one of, but not limited to, the HTTP request. Further, the user can view the report generated by the system 108 in at least one of, graphical format and tabular format on the UI 306 of the UE 102.
[0086] FIG. 6 is a flow diagram of a method 600 for correlating the multiple types of data, according to one or more embodiments of the present invention. For the purpose of description, the method 600 is described with the embodiments as illustrated in FIG. 2 and should nowhere be construed as limiting the scope of the present disclosure.
[0087] At step 602, the method 600 includes the step of receiving the multiple types of data associated with the one or more network failure events from the one or more sources 110. In one embodiment, the receiving unit 208 receives the multiple types of data from the one or more sources 110. In particular, the receiving unit 208 utilizes at least one of, the probing agent and the one or more APIs for receiving the multiple types of data from the one or more sources 110. Further, the multiple types of data received are integrated by the receiving unit 208.
[0088] At step 604, the method 600 includes the step of pre-processing the received multiple types of data. In one embodiment, the data preprocessing unit 210 pre-processes the received multiple types of data to ensure the data consistency and the quality within the system 108.
[0089] At step 606, the method 600 includes the step of training the Artificial Intelligence/Machine Learning (AI/ML) model 220 using the pre-processed data. In one embodiment, the training unit 212 trains the AI/ML model 220 with the received multiple types of data. For example, let us consider that the received multiple types of data act as the dataset which includes data associated with one or more network failure events. Then, the training unit 212 splits the dataset into the training data and the testing data such as 75% of the dataset is considered as the training data and the 25% of the dataset is considered as the testing data. Thereafter, the training data is fed to each of the AI/ML model 220 for training.
[0090] At step 608, the method 600 includes the step of correlating, utilizing the trained AI/ML model 220, the multiple types of data to identify patterns related to the one or more network failure events between the multiple types of data. In one embodiment, the correlation unit 214 correlates the multiple types of data to identify patterns between the multiple types of data. Based on the identified patterns, the correlation unit 214 determines utilizing the trained AI/ML model 220 whether there are at least one of, but not limited to, similarities between the one or more network failure events. In other words, based on identified patterns the correlation unit 214 recognizes whether the multiple types of data are generated from a common network failure event.
[0091] At step 610, the method 600 includes the step of, notifying, the one or more workflows pertaining to the identified patterns related to the one or more network failure events based on the correlation of the multiple types of data for managing each of the network failure event. In one embodiment, the notification unit 216 notifies regarding the one or more workflows pertaining to the identified patterns related to the one or more network failure events based on the correlation for rectifying each of the network failure events. In one embodiment, the notification unit 216 is further configured to provide at least one of, but not limited to, a graphical representation to the user regarding at least one of, but not limited to, the correlations and the one or more workflows on the UE 102.
[0092] In yet another aspect of the present invention, a non-transitory computer-readable medium having stored thereon computer-readable instructions that, when executed by a processor 202. The processor 202 is configured to receive, from one or more sources 110, multiple types of data associated with one or more network failure events. The processor 202 is further configured to pre-process, the received multiple types of data. The processor 202 is further configured to train the Artificial Intelligence /Machine Learning (AI/ML) model using the pre-processed data. The processor 202 is further configured to correlate, utilizing the trained AI/ML model 220, the multiple types of data to identify patterns related to the one or more network failure events between the multiple types of data. The processor 202 is further configured to notify, one or more workflows pertaining to the identified patterns related to the one or more network failure events based on the correlation of the multiple types of data for managing each of the network failure event.
[0093] A person of ordinary skill in the art will readily ascertain that the illustrated embodiments and steps in description and drawings (FIG.1-6) 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.
[0094] The present disclosure provides technical advancements of generating one or more workflows which are automatically executed by the system in order to rectify the one or more network failure events. Advantageously, the system has the capability to comprehensively provide workflows with a holistic approach with the network in order to simplify the rectification process to resolve one or more network failure events in the network.
[0095] 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

[0096] Environment - 100;
[0097] User Equipment (UE) - 102;
[0098] Server - 104;
[0099] Network- 106;
[00100] System -108;
[00101] One or more sources – 110;
[00102] Processor - 202;
[00103] Memory - 204;
[00104] Storage unit – 206;
[00105] Receiving unit – 208;
[00106] Data preprocessing unit – 210;
[00107] Training unit – 212;
[00108] Correlation unit – 214;
[00109] Generation unit – 216;
[00110] AI/ML Model – 220;
[00111] Primary Processor – 302;
[00112] Memory – 304;
[00113] User Interface (UI) – 306;
[00114] Probing agent – 402;
[00115] Data integration unit – 404;
[00116] Data preprocessor - 406;
[00117] Model training unit – 408;
[00118] Data correlation unit – 410;
[00119] Workflow designing unit – 412;
[00120] Data Lake – 414. ,CLAIMS:CLAIMS
We Claim:
1. A method (600) for correlating multiple types of data, the method (600) comprising the steps of:
receiving, by one or more processors (202), from one or more sources (110), multiple types of data associated with one or more network failure events;
pre-processing, by the one or more processors (202), the received multiple types of data;
training, by the one or more processors (202), an Artificial Intelligence/Machine Learning (AI/ML) model (220) using the pre-processed data;
correlating, by the one or more processors (202), utilizing the trained AI/ML model (220), the multiple types of data to identify patterns related to the one or more network failure events between the multiple types of data; and
notifying, by the one or more processors (202), one or more workflows pertaining to the identified patterns related to the one or more network failure events based on correlation of the multiple types of data for managing each of the network failure event.

2. The method (600) as claimed in claim 1, wherein the multiple types of data include at least one of, Streaming Data Records (SDRs), alarms, counters, Key Performance Indicators (KPIs), and clear code counts associated with the one or more network failure events.

3. The method (600) as claimed in claim 1, wherein the one or more sources (110) includes at least one of, one or more network nodes and a plurality of applications.

4. The method (600) as claimed in claim 1, where the step of pre-processing, the received multiple types of data further includes the step of:
extracting, by the one or more processors (202), one or more features from the received multiple types of data.

5. The method (600) as claimed in claim 1, wherein the AI/ML model (220) continuously learns the patterns related to the one or more network failure events over time and evolves based on network conditions.

6. The method (600) as claimed in claim 1, wherein the one or more workflows are utilized for managing the network failure event which includes at least one of, providing automated responses, performing fault isolation, performing root cause analysis, and performing error rectification.

7. The method (600) as claimed in claim 1, wherein the network failure event includes at least one of, user equipment registration failure, call establishment failure, and bandwidth demand surge.

8. The method (600) as claimed in claim 1, wherein the method (600) further includes the step of:
storing, by the one or more processors (202), at least one of, the one or more workflows and correlation results in the storage unit (206).
9. A system (108) for correlating multiple types of data, the system (108) comprising:
a receiving unit (208), configured to, receive, from one or more sources (110), multiple types of data associated with one or more network failure events;
a data preprocessing unit (210), configured to, pre-process, the received multiple types of data;
a training unit (212), configured to, train an Artificial Intelligence/Machine Learning (AI/ML) model (220) using the pre-processed data;
a correlation unit (214), configured to, correlate, utilizing the trained AI/ML model (220), the multiple types of data to identify patterns related to the one or more network failure events between the multiple types of data; and
a notification unit (216), configured to, notify, one or more workflows pertaining to the identified patterns related to the one or more network failure events based on the correlation of the multiple types of data for managing each of the network failure event.

10. The system (108) as claimed in claim 9 wherein the multiple types of data include at least one of, Streaming Data Records (SDRs), alarms, counters, Key Performance Indicators (KPIs), and clear code counts related to the one or more network failure events.

11. The system (108) as claimed in claim 9, wherein the one or more sources (110) includes at least one of, one or more network nodes and a plurality of applications.

12. The system (108) as claimed in claim 9, where the data preprocessing unit (210), is further configured to:
extract, one or more features from the received multiple types of data.

13. The system (108) as claimed in claim 9, wherein the AI/ML model (220) continuously learns the patterns related to the one or more network failure events over time and evolves based on network conditions.

14. The system (108) as claimed in claim 9, wherein the one or more workflows are utilized for managing the network failure event which includes at least one of, providing automated responses, performing fault isolation, performing root cause analysis, and performing error rectification.

15. The system (108) as claimed in claim 9, wherein the network failure event includes at least one of, user equipment registration failure, call establishment failure, and bandwidth demand surge.

16. The system (108) as claimed in claim 9, wherein the storage unit (206) is further configured to:
store, at least one of, the one or more workflows and correlation results.