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 CUSTOMIZING INDEXING OF DATA IN A NETWORK
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 generally relates to wireless communication networks, and more particularly relates to a method and system for customizing indexing of data in the wireless communication network.
BACKGROUND OF THE INVENTION
[0002] Data communication in today's digital world involves a variety of different protocols and message formats. Specifically, in telecommunications and network systems, various types of payload messages, such as Next Generation Application Protocol (NGAP), Radio Resource Control (RRC), and XN Application Protocol (XNAP), are frequently used for communication. These messages often contain complex structures and data types that are vital for the functioning of the network.
[0003] Traditionally, to decode these payload messages and to interpret the encapsulated information, the system needed a thorough understanding of each message's structure and format. However, this manual approach had several drawbacks:
[0004] Time-Consuming: The manual identification of the structure and type of each payload message was a tedious and time-consuming process. It required a detailed study of the structure and data type of each message.
[0005] Resource Intensive: Each time a new type of message was introduced or an existing one updated, the code had to be manually updated to handle the new or updated message format. This involved development, testing, and deployment phases, which consumed significant time and resources.
[0006] System Downtime: Every time the system underwent a change, it required downtime, leading to interruptions in service. This downtime was needed to implement the changes, perform testing, and deploy the updated system.
[0007] Error Prone: The manual process was prone to errors. A single mistake in understanding the structure or format could lead to incorrect decoding, leading to potential loss or misinterpretation of information.
[0008] These challenges underscored the need for an automated solution that could intelligently identify the structure and type of payload messages and decode them efficiently, without requiring manual intervention or system downtime. The current invention addresses this gap, providing a method for autonomous decoding and indexing of payload messages using artificial intelligence (AI) and machine learning (ML) algorithms.
BRIEF SUMMARY OF THE INVENTION
[0009] One or more embodiments of the present disclosure provide a method and system for customizing indexing of data in a network.
[0010] In one aspect of the present invention, the method for customizing indexing of data in the network is disclosed. The method includes the step of segregating, by one or more processors, data based on a version of the data, the data pertaining to Radio Access Network (RAN) data. The method includes the step of deciphering, by the one or more processors, the segregated data to identify a structure and type of the segregated data. The method includes the step of decoding, by the one or more processors, the deciphered data based on the type of the data. The method includes the step of indexing, by the one or more processors, the decoded data based on an application of one or more rules and metadata to the decoded message.
[0011] In one embodiment, the data is received from one or more Next-Generation Node B (gNodeBs).
[0012] In another embodiment, the type of the segregated data is one of, a Next Generation Application Protocol (NGAP), Radio Resource Control Protocol (RRCP), and XN Application Protocol (XNAP).
[0013] In yet another embodiment, the deciphered data is converted to at least a string data upon the decoding of the deciphered data.
[0014] In yet another embodiment, the one or more rules and metadata are provisioned via a user interface.
[0015] In yet another embodiment, the indexing of the decoded data is performed by identifying the fields in the RAN data.
[0016] In yet another embodiment, upon indexing the decoded data, the method includes the step storing, by the one or more processors, the indexed data in a storage unit for processing as per the one or more rules and metadata.
[0017] In yet another embodiment, the method further comprises the steps of notifying, by the one or more processors, at least one of a service provider and a network operator of an error during one of decoding and indexing the deciphered data and the decoded data, respectively.
[0018] In another aspect of the present invention, the system for customizing indexing of data in the network is disclosed. The system includes a probe unit configured to segregate data based on a version of the data, the data pertaining to Radio Access Network (RAN) data. The system includes a conductor unit configured to decipher the segregated data to identify a structure and type of the segregated data and to decode the deciphered data based on the type of the data. The system includes an indexer unit configured to index the decoded data based on an application of one or more rules and metadata to the decoded message.
[0019] In another aspect of the present invention, a non-transitory computer-readable medium having stored thereon computer-readable instructions that when executed by a processor is disclosed. The processor is configured to segregate data based on a version of the data, the data pertaining to Radio Access Network (RAN) data. The processor is configured to decipher the segregated data to identify a structure and type of the segregated data. The processor is configured to decode the deciphered data based on the type of the data. The processor is configured to index the decoded data based on an application of one or more rules and metadata to the decoded message.
[0020] 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
[0021] 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.
[0022] FIG. 1 is an exemplary block diagram of an environment for customizing indexing of data in a network, according to one or more embodiments of the present disclosure;
[0023] FIG. 2 is an exemplary block diagram of a system for customizing indexing of data in the network, according to the one or more embodiments of the present disclosure;
[0024] FIG. 3 is an exemplary block diagram of an architecture can be implemented in the system of FIG.2, according to one or more embodiments of the present disclosure;
[0025] FIG. 4 is a signal flow diagram illustrating a customizing indexing of data in the network, according to the one or more embodiments of the present disclosure; and
[0026] FIG. 5 is a flow diagram illustrating a method for customizing indexing of data in the network, according to the one or more embodiments of the present disclosure.
[0027] The foregoing shall be more apparent from the following detailed description of the invention.
DETAILED DESCRIPTION OF THE INVENTION
[0028] 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.
[0029] 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.
[0030] 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.
[0031] Referring to FIG. 1, FIG. 1 illustrates an exemplary block diagram of an environment 100 for customizing indexing of data in a network 105, according to one or more embodiments of the present invention. The environment 100 includes the network 105, a UE 110, a server 115, and a system 120. The UE 110 aids a user to interact with the system 120 for customizing indexing of data in the network 105. The customizing indexing of data involves the indexing process to meet specific needs and requirements of the network 105 or application such as types of data, access patterns, performance requirements, and storage constraints. The indexing process is validated by running various scenarios to ensure it meets performance and accuracy requirements. In an embodiment, the user includes, but not limited to, a service provider and a network operator.
[0032] For the purpose of description and explanation, the description will be explained with respect to one or more UEs 110, or to be more specific will be explained with respect 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 one embodiment, each of the first UE 110a, the second UE 110b, and the third UE 110c is one of, but are not limited to, any electrical, electronic, electro-mechanical or an equipment and a combination of one or more of the above devices such as 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.
[0033] Each of the first UE 110a, the second UE 110b, and the third UE 110c is further configured to host one or more applications thereon. Each of the one or more applications is adapted to include one or more applications stacks to aid in performing certain predefined activities of each of the one or more applications. The predefined activities include, but not limited to, accessing the server 115, and transmitting the data modelling request to the one or more applications via the communication network 105.
[0034] A person skilled in the art will appreciate that the UE 110 may include more than one processor and communication ports. The communication port(s) may be any of an RS-232 port for use with a modem-based dialup connection, a 10/100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. The communication port(s) may be chosen depending on a network, such as, but not limited to, a Local Area Network (LAN), a Wide Area Network (WAN), or any of the network to which the computer system connects.
[0035] 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.
[0036] 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 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, a defence facility, or any other facility that provides content.
[0037] The environment 100 further includes the system 120 communicably coupled to the server 115 and each of the first UE 110a, the second UE 110b, and the third UE 110c via the network 105. The system 120 is adapted to be embedded within the server 115 or is embedded as the individual entity, as per multiple embodiments of the present invention. However, for the purpose of description, the system 120 is described as an integral part of the server 115, without deviating from the scope and limiting the scope of the present disclosure.
[0038] Operational and construction features of the system 120 will be explained in detail with respect to the following figures.
[0039] Referring to FIG. 2, FIG. 2 illustrates an exemplary block diagram of the system 120 for customizing indexing of data in the network 105, according to the one or more embodiments of the present disclosure. The system 120 includes a processor 205, a memory 210, a user interface 215, and a database 245. For the purpose of description and explanation, the description will be explained with respect to one or more processors 205, or to be more specific will be explained with respect to the processor 205 and should nowhere be construed as limiting the scope of the present disclosure. The one or more processor 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.
[0040] The information related to indexing of data in the network 105 is provided or stored in the memory 210. 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 EPROM, flash memory, and the like.
[0041] The user interface 215 includes a variety of interfaces, for example, interfaces for a Graphical User Interface (GUI), 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 the one or more components include, but are not limited to, the UE 110, and the database 245.
[0042] The database 245 is configured to store the indexing of the data in the network 105. The database 245 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 245 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.
[0043] Further, the processor 205, 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 processor 205 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 205. 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 order for the system 120 to customize the indexing of data in the network 105. The processor 205 includes a probe unit 220, a conductor unit 225, an indexer unit 230, a storage unit 235, and a notification unit 240 communicably coupled to each other. In an embodiment, operations and functionalities of the probe unit 220, the conductor unit 225, the indexer unit 230, the storage unit 235, and the notification unit 240 can be used in combination or interchangeably.
[0045] The probe unit 220 is configured to segregate data based on a version of the data. In an embodiment, the data pertains to Radio Access Network (RAN) data. The RAN data includes various types of information related to the operation, performance, and management of the radio access network, which connects the UE 110 to the network 105. The RAN data is received from one or more Next Generation Node B (gNBs) 305 (shown in FIG. 3). The gNBs 305 are the base stations in 5G networks that facilitate wireless communication between the UE 110 and the network 105. The gNodeBs 305 handle tasks such as data transmission, mobility management, and resource allocation. In an embodiment, the version of the data signifies the different versions of RAN data. Each message has its own version, and the version varies based on their releases (for example., 15.3, 15.9 etc.,). The probe unit 220 is configured to ensure that the data from the gNBs 305 is accurately segregated and processed based on the version of the data, facilitating efficient network monitoring and management.
[0046] The probe unit 220 is configured to collect the data from the gNBs 305. Each data packet is initially parsed to identify relevant metadata, including the version of the data. In an embodiment, the metadata includes additional information that describes the context or characteristics of the data, which involves fields like data type, protocol version, or network segment. The probe unit 220 is configured to extract the metadata from the data packets, which includes identifying the version of the data, which presents in headers, and specific fields within a data payload. The probe unit 220 uses the extracted version of the data to classify and segregate the data into different groups. The different groups include, but not limited to, rule-based segregation to classify data packets by using one or more rules. For example, if the version of the data is indicated by a specific field in the header, the probe unit 220 checks the field and segregates the data accordingly.
[0047] Upon segregating the data, the conductor unit 225 is configured to decipher the segregated data to identify a structure and type of the segregated data by using an Artificial Intelligence/ Machine Learning (AI/ML) model. The AI/ML model within the conductor unit 225 is a framework designed to process and analyze the data. The AI/ML model leverages ML techniques to identify patterns, classify data types, and infer the structures from the segregated data. The AI/ML model 330 utilizes a variety of ML techniques, such as a supervised learning model which learns from labeled training data, an unsupervised learning model identifies patterns and structures in unlabeled data, and a semi-supervised learning model which is combination of both the supervised and unsupervised learning models. The supervised learning model includes decision trees, Support Vector Machines (SVM), and Neural networks. The AI/ML model is trained on a diverse dataset to recognize various data formats and types, enabling it to accurately decipher and categorize incoming data. The AI/ML model identifies the fields, or characteristics that are most indicative of the structure and type of the data. Based on the type and its structure, the segregated data is deciphered.
[0048] As per one embodiment, deciphering involves interpreting the data to extract the information. The conductor unit 225 is configured to identify the structure and type of the segregated data, which includes various types such as control messages, user data, or signaling information. The conductor unit 225 utilizes decision trees to classify the data based on features such as format, content, and source. For instance, classify data packets into categories like "text data," or "image data,". The decision tree model analyzes data packet fields (e.g., header information, payload type) and splits it into different types of fields to determine the structure and type of the segregated data. The structure of the segregated data determines how the data is organized, such as identifying headers, payloads, and any encapsulated information. The type of the segregated data involves classifying the data based on its purpose and content, such as distinguishing between different protocol messages.
[0049] In an embodiment, the type of the segregated data is one of, a Next Generation Application Protocol (NGAP), Radio Resource Control Protocol (RRCP), and Xn Application Protocol (XnAP). The NGAP facilitates the communication between the gNB 305 and a 5G Core Network (5GC), specifically an Access and Mobility Management Function (AMF). The RRCP is a key component in mobile communication networks, particularly within the RAN. The RRCP is configured to operate at a control plane and is responsible for establishing, maintaining, and releasing connections between the UE 110 and the network 105. The XnAP is designed to support various functions necessary for the efficient operation and coordination of the gNB 305 in the 5G network. Upon identification of the structure and type of the data, the deciphered data is decoded by using the type of each data field (e.g., integers, strings, etc.). The deciphered data is received in a byte array format.
[0050] Upon deciphering the segregated data, the conductor unit 225 is configured to decode the deciphered data based on the type of the data. The decoding involves translating the data from its encoded form into a format that can be easily processed and analyzed. Once the data is decoded, the conductor unit 225 converts the deciphered data to at least a string data format. The conversion involves transforming the deciphered data into a sequence of characters (string), which can be easily handled by various data processing and analysis tools. The at least string data is a versatile format that allows for straight forward manipulation, storage, and transmission.
[0051] Upon decoding the deciphered data based on the type of the data, the indexer unit 230 is configured to index the decoded data based on an application of one or more rules and metadata to the decoded message. In an embodiment, the indexed decoded data contains information about subscribers, gNB id, call start time, call end time, Subscriber Permanent Identifier (SUPI) number, cell id and the like. The one or more rules can be predefined criteria that determine how the data is categorized and indexed. For example, the one or more rules might include organizing data by timestamp, source, destination, or specific protocol type. The metadata includes additional information that describes the context or characteristics of the data, which involves fields like data type, protocol version, or network segment. In an embodiment, the one or more rules and the metadata are provisioned via the user interface 215. The indexing of the decoded data is performed by identifying the fields in the RAN data.
[0052] Upon indexing the decoded data based on the application of one or more rules and metadata to the decoded message, the storage unit 235 is configured to store the indexed data therein for processing as per the one or more rules and metadata upon indexing the decoded data by the indexer unit 230. The stored data is readily available for further analysis and processing based on the one or more rules and metadata applied during the indexing phase.
[0053] Upon storing the indexed data, the notification unit 240 is configured to monitor the processes of decoding and indexing for any errors. The notification unit 240 is configured to notify at least one of the service provider and the network operator of the error during one of decoding and indexing the deciphered data and the decoded data, respectively. By doing so, the system 120 efficiently decoding various types of RAN data and indexes the decoded data in the database 245, without any downtime. The system 120 also indexes the decoded messages in the database for future use. This approach eliminates the need for code-level changes for every different type of payload message and eliminates the phases of development, testing, and deployment, making the process more efficient.
[0054] FIG. 3 illustrates an exemplary block diagram of an architecture 300 that can be implemented in the system of FIG.2, according to one or more embodiments of the present invention. More specifically, FIG. 3 illustrates the system 120 configured for customizing indexing of the data in the network 105. The architecture 300 consists of multiple integral components including the gNBs 305, the probe unit 220, the conductor unit 225, the message broker 310, the user interface 215, the indexer unit 230, and the database 245.
[0055] In the embodiment of the present invention, the gNB 305 manages the initial transmission of RAN data (payload messages) to the probe unit 220. The probe unit 220 is configured to segregate data based on the version of the data. In an embodiment, the data pertaining to Radio Access Network (RAN) data. The RAN data includes various types of information related to the operation, performance, and management of the radio access network, which connects the UE 110 to the network 105. The RAN data is received from one or more Next Generation Node B (gNodeBs) 305. The gNodeBs 305 are the base stations in 5G networks that facilitate wireless communication between the UE 110 and the network 105. The gNodeBs 305 handle tasks such as data transmission, mobility management, and resource allocation.
[0056] The probe unit 220 refers to the module in the data pipeline where the payload messages are received from gNBs 305 and based on the version of the data. The probe unit 220 manages and pushes the RAN data to the message broker 310. The message broker 310 is an intermediary module that receives the RAN data from the probe unit 220 and for the process of de-codification passes the RAN data to the conductor unit 225 and also receives the decoded messages in the at least string data from the conductor unit 225 and passes the data to the indexer unit 230 for the process of indexing.
[0057] According to an embodiment of the present invention, the conductor unit 225, functioning as the core of the system, is the AI/ML model-based decoder with a dataset that uses machine learning techniques to decipher the structure and type of the data and decodifies it as the string data. Upon identifying the type of the payload messages based on the dataset, the decoding AI/ML model within the conductor unit 225 decodes the string data and sends it back to the message broker 310.
[0058] According to an embodiment of the present invention, the indexer unit 230 is configured to index the data by the one or more rules and the metadata provisioned in the user interface 215. The user interface 215 allows the provision of one or more rules and metadata to index the decoded data to the database 245 and for further processing. The indexer unit 230 is configured to collaborate with the database 245 to store the decoded data, effectively indexed according to the one or more rules provisioned by the user interface 215. This concurrent process allows the system 120 to handle diverse the payload messages efficiently, ensuring that the decoded data is indexed suitably for subsequent retrieval.
[0059] In the embodiment of the present invention, the user interface 215 determines the one or more rules and the metadata are provisioned and customized. The information guides the indexer unit 230 to appropriately index the decoded data and for further processing and the indexer unit 230 after receiving the decoded data from the message broker 310. The database 245 is configured for storing the indexed decoded data. The indexer unit 230 is configured to use the one or more rules and metadata provisioned from the user interface 215 to index the decoded data and store in the database 245.
[0060] FIG. 4 is a signal flow diagram illustrating a customizing indexing of data in the network, according to the one or more embodiments of the present disclosure.
[0061] At S402, the probe unit 220 is configured to segregate data based on a version of the data. In an embodiment, the data pertaining to Radio Access Network (RAN) data. The RAN data includes various types of information related to the operation, performance, and management of the radio access network, which connects the UE 110 to the network 105. The RAN data is received from one or more Next Generation Node B (gNodeBs) 305. The gNodeBs 305 are the base stations in 5G networks that facilitate wireless communication between the UE 110 and the network 105. The gNodeBs 305 handle tasks such as data transmission, mobility management, and resource allocation.
[0062] The probe unit 220 is configured to ensure that the data from the gNodeBs 305 are accurately segregated and processed, facilitating efficient network monitoring and management. In an embodiment, the type of the segregated data is one of, the Next Generation Application Protocol (NGAP), the Radio Resource Control Protocol (RRCP), and the Xn Application Protocol (XnAP). The NGAP facilitates the communication between the gNB 305 and the 5G Core Network (5GC), specifically an Access and Mobility Management Function (AMF). The RRCP is a key component in mobile communication networks, particularly within the RAN. The RRCP is configured to operate at a control plane and is responsible for establishing, maintaining, and releasing connections between the UE 110 and the network 105. The XnAP is designed to support various functions necessary for the efficient operation and coordination of the gNB 305 in the 5G network.
[0063] At S404, the conductor unit 225 is configured to decipher the segregated data to identify the structure and type of the segregated data by using the AI/ML model. The AI/ML model within the conductor unit 225 is a framework designed to process and analyze the data. The AI/ML model leverages machine learning techniques to identify patterns, classify data types, and infer the structures from the segregated data. The AI/ML model is trained on a diverse dataset to recognize various data formats and types, enabling it to accurately decipher and categorize incoming data.
[0064] As per one embodiment, deciphering involves interpreting the data to extract the information. The conductor unit 225 is configured to identify the structure and type of the segregated data, which includes various types such as control messages, user data, or signaling information. The structure of the segregated data determines how the data is organized, such as identifying headers, payloads, and any encapsulated information. The type of the segregated data involves classifying the data based on its purpose and content, such as distinguishing between different protocol messages.
[0065] Upon deciphering the segregated data, the conductor unit 225 is configured to decode the deciphered data based on the type of the data. The decoding involves translating the data from its encoded form into a format that can be easily processed and analyzed. Once the data is decoded, the conductor unit 225 converts the deciphered data to at least string data format.
[0066] At S406, the indexer unit 230 is configured to index the decoded data based on an application of one or more rules and metadata to the decoded message. The one or more rules can be predefined criteria that determine how the data is categorized and indexed. For example, the one or more rules might include organizing data by timestamp, source, destination, or specific protocol type. The metadata includes additional information that describes the context or characteristics of the data, which involves fields like data type, protocol version, or network segment. In an embodiment, the one or more rules and the metadata are provisioned via the user interface 215.
[0067] At S408, the storage unit 235 is configured to store the indexed data therein for processing as per the one or more rules and metadata upon indexing the decoded data by the indexer unit 230. The stored data is readily available for further analysis and processing based on the one or more rules and metadata applied during the indexing phase.
[0068] At S410, the notification unit 240 is configured to monitor the processes of decoding and indexing for any errors based on storing the indexed data. The notification unit 240 is configured to notify at least one of the service provider and the network operator of the error during one of decoding and indexing the deciphered data and the decoded data, respectively.
[0069] FIG. 5 is a flow diagram illustrating a method for customizing indexing of data in the network, according to the one or more embodiments of the present disclosure.
[0070] At step 505, the method 500 includes the step of segregating data based on the version of the data by the probe unit 220. In an embodiment, the data pertaining to Radio Access Network (RAN) data. The RAN data includes various types of information related to the operation, performance, and management of the radio access network, which connects the UE 110 to the network 105. The RAN data is received from one or more Next Generation Node B (gNodeBs) 305 (shown in FIG. 3). The gNodeBs 305 are the base stations in 5G networks that facilitate wireless communication between the UE 110 and the network 105. The gNodeBs 305 handle tasks such as data transmission, mobility management, and resource allocation.
[0071] The probe unit 220 is configured to ensure that the data from the gNodeBs 305 are accurately segregated and processed, facilitating efficient network monitoring and management. In an embodiment, the type of the segregated data is one of, a Next Generation Application Protocol (NGAP), Radio Resource Control Protocol (RRCP), and Xn Application Protocol (XnAP).
[0072] At step 510, the method 500 includes the step of deciphering the segregated data to identify the structure and type of the segregated data by using an Artificial Intelligence/ Machine Learning (AI/ML) model by the conductor unit 225. The AI/ML model within the conductor unit 225 is the framework designed to process and analyze the data. The AI/ML model leverages machine learning techniques to identify patterns, classify data types, and infer the structures from the segregated data. The AI/ML model 330 utilizes a variety of ML techniques, such as a supervised learning model which learns from labeled training data, an unsupervised learning model identifies patterns and structures in unlabeled data, and a semi-supervised learning model which is combination of both the supervised and unsupervised learning models. The supervised learning model includes decision trees, Support Vector Machines (SVM), and Neural networks. The AI/ML model is trained on the diverse dataset to recognize various data formats and types, enabling it to accurately decipher and categorize incoming data.
[0073] As per one embodiment, deciphering involves interpreting the data to extract the information. The conductor unit 225 is configured to identify the structure and type of the segregated data, which includes various types such as control messages, user data, or signaling information. The structure of the segregated data determines how the data is organized, such as identifying headers, payloads, and any encapsulated information. The type of the segregated data involves classifying the data based on its purpose and content, such as distinguishing between different protocol messages.
[0074] At step 515, the method 500 includes the step of decoding the deciphered data based on the type of the data by the conductor unit 225. The decoding involves translating the data from its encoded form into a format that can be easily processed and analyzed. Once the data is decoded, the conductor unit 225 converts the deciphered data to at least a string data format. The conversion involves transforming the deciphered data into a sequence of characters (string), which can be easily handled by various data processing and analysis tools. The at least string data is a versatile format that allows for straight forward manipulation, storage, and transmission.
[0075] At step 520, the method 500 includes the step of indexing the decoded data based on an application of one or more rules and metadata to the decoded message the by the indexer unit 230. In an embodiment, the indexed decoded data contains information about subscribers, gNB id, call start time, call end time, Subscriber Permanent Identifier (SUPI) number, cell id and the like. The one or more rules can be predefined criteria that determine how the data is categorized and indexed. For example, the one or more rules might include organizing data by timestamp, source, destination, or specific protocol type. The metadata includes additional information that describes the context or characteristics of the data, which involves fields like data type, protocol version, or network segment. In an embodiment, the one or more rules and the metadata are provisioned via the user interface 215.
[0076] Upon indexing the decoded data based on the application of one or more rules and metadata to the decoded message, the storage unit 235 is configured to store the indexed data therein for processing as per the one or more rules and metadata upon indexing the decoded data by the indexer unit 230. The stored data is readily available for further analysis and processing based on the one or more rules and metadata applied during the indexing phase.
[0077] Upon storing the indexed data, the notification unit 240 is configured to monitor the processes of decoding and indexing for any errors. The notification unit 240 is configured to notify at least one of the service provider and the network operator of the error during one of decoding and indexing the deciphered data and the decoded data, respectively.
[0078] The present invention further discloses a non-transitory computer-readable medium having stored thereon computer-readable instructions that when executed by a processor 205 is disclosed. The processor 205 is configured to segregate data based on a version of the data, the data pertaining to Radio Access Network (RAN) data. The processor 205 is configured to decipher the segregated data to identify a structure and type of the segregated data. The processor 205 is configured to decode the deciphered data based on the type of the data. The processor 205 is configured to index the decoded data based on an application of one or more rules and metadata to the decoded message.
[0079] 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.
[0080] The present disclosure incorporates technical advancement of efficiently decoding various types of RAN data (such as payload messages) and indexes the decoded data in the database. According to an embodiment of the present invention, after the messages are decoded, the present disclosure indexes the data in the database. In case of error scenarios while decoding the messages, if any error arises while decoding/indexing the payload messages, it notifies the end user.
[0081] The present disclosure incorporates the advantage of the present disclosure to auto-detect the type of incoming data and to take the appropriate decoding actions in real time, without any system downtime. The solution also indexes the decoded messages in the database for future use. This approach eliminates the need for code-level changes for every different type of payload message and eliminates the phases of development, testing, and deployment, making the process more efficient.
[0082] 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
[0083] Environment - 100;
[0084] Network - 105;
[0085] User Equipment – 110;
[0086] Server – 115;
[0087] System -120;
[0088] Processor -205;
[0089] Memory – 210;
[0090] User Interface – 215;
[0091] Probe unit- 220;
[0092] Conductor unit- 225;
[0093] Indexer unit- 230;
[0094] Storage unit-235;
[0095] Notification unit-240;
[0096] Database - 245;
[0097] gNB- 305;
[0098] Message broker- 310.

,CLAIMS:CLAIMS
We Claim:
1. A method (500) of customizing indexing of data in a network (105), the method (500) comprising the steps of:
segregating (505), by one or more processors (205), data based on a version of the data, the data pertaining to Radio Access Network (RAN) data;
deciphering (510), by the one or more processors (205), the segregated data to identify a structure and type of the segregated data;
decoding (515), by the one or more processors (205), the deciphered data based on the type of the data; and
indexing (520), by the one or more processors (205), the decoded data based on an application of one or more rules and metadata to the decoded message.

2. The method (500) as claimed in claim 1, wherein the data is received from one or more gNodeB (305).

3. The method (500) as claimed in claim 1, wherein the type of the segregated data is one of, a Next Generation Application Protocol (NGAP), Radio Resource Control Protocol (RRCP), and Xn Application Protocol (XnAP).

4. The method (500) as claimed in claim 1, wherein the deciphered data is converted to at least a string data upon the decoding of the deciphered data.

5. The method (500) as claimed in claim 1, wherein the one or more rules and metadata are provisioned via a user interface (215).

6. The method (500) as claimed in claim 1, wherein the indexing of the decoded data is performed by identifying the fields in the RAN data.

7. The method (500) as claimed in claim 1, wherein upon indexing the decoded data, the method (500) comprises the steps of storing, by the one or more processors (205), the indexed data in a storage unit (235) for processing as per the one or more rules and metadata.

8. The method (500) as claimed in claim 1, wherein the method (500) further comprises the steps of:
notifying, by the one or more processors (205), at least one of a service provider and a network operator of an error during one of decoding and indexing the deciphered data and the decoded data, respectively.

9. A system (120) for customizing indexing of data in a network (105), the system (120) comprising:
a probe unit (220) configured to segregate data based on a version of the data, the data pertaining to Radio Access Network (RAN) data;
a conductor unit (225) configured to decipher the segregated data to identify a structure and type of the segregated data and to decode the deciphered data based on the type of the data; and
an indexer unit (230) configured to index the decoded data based on an application of one or more rules and metadata to the decoded message.

10. The system (120) as claimed in claim 8, wherein the data is received from one or more Next Generation Node B (gNodeB) (305).

11. The system (120) as claimed in claim 8, wherein the type of the segregated data is one of, a Next Generation Application Protocol (NGAP), Radio Resource Control Protocol (RRCP), and Xn Application Protocol (XnAP).

12. The system (120) as claimed in claim 8, wherein the deciphered data is converted to at least a string data upon the decoding of the deciphered data.

13. The system (120) as claimed in claim 8, wherein the one or more rules and metadata are provisioned via a user interface (215).

14. The system (120) as claimed in claim 1, wherein the indexing of the decoded data is performed by identifying the fields in the RAN data.

15. The system (120) as claimed in claim 8, comprising a storage unit (235) configured to store, the indexed data therein for processing as per the one or more rules and metadata upon indexing the decoded data by the indexer unit (230).

16. The system (120) as claimed in claim 8, comprising a notification unit (240) configured to notify, at least one of a service provider and a network operator of an error during one of decoding and indexing the deciphered data and the decoded data, respectively.