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 MANAGING NON-DISCOVERABLE ASSET 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 generally relates to computer networks and server management, and more particularly relates to a system and a method for managing non-discoverable asset data.
BACKGROUND OF THE INVENTION
[0002] Telecommunications systems play a vital role in facilitating efficient communication and the delivery of various services. These systems typically rely on a multitude of interconnected servers and network devices to operate seamlessly. Effective management of these assets is essential for ensuring reliable network performance, resource allocation, and maintenance.
[0003] Traditionally, telecom networks have employed server management systems that utilize various discovery mechanisms to identify and track servers within the network. These mechanisms rely on network protocols, IP addresses, domain name resolution, or similar techniques to locate and catalog servers for management purposes.
[0004] However, certain server configurations or network conditions may render specific servers non-discoverable using traditional methods. These non-discoverable servers may be intentionally configured to remain hidden for security reasons or may be located in network segments with limited accessibility. Existing asset management systems face significant challenges in accurately monitoring and managing assets associated with such non-discoverable servers in a telecom environment.
[0005] The inability to effectively manage assets linked to non-discoverable servers can lead to operational inefficiencies, security vulnerabilities, and increased costs. Critical tasks like inventory management, software updates, security patching, and performance monitoring become arduous and error-prone without reliable asset management systems for non-discoverable servers.
[0006] The existing system fail to retrieve details of internet protocol assets/servers that are not discoverable and have static information which is required by the operations team in case of any issues. This information includes application lead info, a platform lead, a device type, a Network Equipment Identifier (NEID), a location and operation specific point of contact.
[0007] Therefore, there is a need for an innovative solution that overcomes the limitations of existing asset management systems and provides efficient management of assets associated with non-discoverable servers in a telecom system.
SUMMARY OF THE INVENTION
[0008] One or more embodiments of the present disclosure provide a system and method for managing non-discoverable asset data.
[0009] In one aspect of the present invention, a system for managing non-discoverable asset data is disclosed. The system includes a retrieving unit configured to retrieve discoverable asset data pertaining to each of a plurality of assets in a network. The retrieving unit is also configured to execute one or more customized scripts to retrieve the discoverable asset data. The system includes a determining unit configured to determine non-discoverable asset data pertaining to each of the plurality of assets in the network from the discoverable asset data utilizing a trained model. The system includes a merging unit configured to merge the discoverable asset data with the non-discoverable asset data. In one embodiment, the system includes a storage unit configured to store, the merged data in one of a database and a cloud.
[0010] In one embodiment, the non-discoverable asset data includes information of at least one of, applications running on the plurality of assets, a platform lead, one or more devices related to the plurality of assets, a Network Element Identifier (NEID), a location of the plurality of assets and a managing point of contact related to each of the plurality of assets.
[0011] In one embodiment, the model is at least one of an Artificial Intelligence/Machine Learning (AI/ML) model.
[0012] In one embodiment, the system includes a determining unit configured to determine the non-discoverable asset data pertaining to each of the plurality of assets by utilizing the trained model.
[0013] In one embodiment, the determining unit is configured to provide the retrieved discoverable asset data pertaining to each of the plurality of assets to the model and identify a relationship between the retrieved discoverable asset data and the historical data related to the non-discoverable asset data for each of the plurality of assets.
[0014] In one embodiment, the determination unit is configured to generate the required non-discoverable data pertaining to each of the plurality of assets based on a combination of the identified relationship between the retrieved discoverable asset data and the historical data related to the non-discoverable asset data.
[0015] In one embodiment, the system further includes a training unit configured to train, the model based on the retrieved asset data and historical data related to the plurality of assets to determine the non-discoverable asset data. The retrieved asset data include at least one of, discovery data pertaining to the plurality of assets.
[0016] In one embodiment, the model is trained to capture non-discoverable asset data using the retrieved discoverable asset data and the historical data. The historical data include at least one of, an inventory data received from a Unified Inventory Management (UIM), documents of all applications running on the plurality of assets, data related to one or more nodes in the network, High Level Design (HLD) and Low Level Design (LLD) documents associated with each of the plurality of assets.
[0017] In one embodiment, the system further includes a tagging unit configured to tag each of the plurality of assets discovered with the non-discoverable asset data.
[0018] In another aspect of the present invention, a method for managing non-discoverable asset data is disclosed. The method includes the steps of retrieving, by one or more processors, discoverable asset data pertaining to each of a plurality of assets in a network. The method includes the step of determining, by the one or more processors, utilizing a trained model, non-discoverable asset data pertaining to each of the plurality of assets in the network from the discoverable asset data. The method includes the steps of merging, by the one or more processors, the discoverable asset data with the non-discoverable asset data.
[0019] In another aspect of the invention, a non-transitory computer-readable medium having stored thereon computer-readable instructions is disclosed. The computer-readable instructions are executed by a processor. The processor is configured to retrieve, discoverable asset data pertaining to each of a plurality of assets in a network. The processor is further configured to determine utilizing a trained model, non-discoverable asset data pertaining to each of the plurality of assets in the network from the discoverable asset data. The processor is further configured to merge the discoverable asset data with the non-discoverable asset data.
[0020] In another aspect of invention, User Equipment (UE) is disclosed. The UE includes one or more primary processors communicatively coupled to one or more processors, the one or more primary processors coupled with a memory. The processor is configured to transmit the customized scripts to the one or more processors in order to retrieve the non-discoverable asset data of each of the plurality of assets in the network.
[0021] 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
[0022] 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.
[0023] FIG. 1 is an exemplary block diagram of an environment for managing non-discoverable asset data, according to various embodiments of the present invention;
[0024] FIG. 2 is an exemplary architecture of the system for managing non-discoverable asset data, according to various embodiments of the present invention;
[0025] FIG. 3 is a block diagram of the system for managing non-discoverable asset data, according to various embodiments of the present invention;
[0026] FIG. 4 is schematic representation of a workflow of the system of FIG. 3, according to various embodiments of the present invention;
[0027] FIG. 5 is a signal flow diagram for managing non-discoverable asset data, according to various embodiments of the present invention; and
[0028] FIG. 6 shows a flow diagram of a method for managing non-discoverable asset data, according to various embodiments of the present invention.
[0029] The foregoing shall be more apparent from the following detailed description of the invention.
DETAILED DESCRIPTION OF THE INVENTION
[0030] 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.
[0031] 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.
[0032] 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.
[0033] As per various embodiments depicted, the present invention discloses the system and method for managing non-discoverable asset data. The present invention enables comprehensive visibility and control over assets associated with non-discoverable servers in the telecom system. The invention facilitates streamlined asset tracking, inventory management, performance monitoring, and security management, empowering telecom network administrators to efficiently manage their network resources.
[0034] The system of the present invention is configured to execute customized scripts and collect discovery data for all the remote servers/Internet Protocol (IP) assets. Further, the management system retrieves the server details. Herein, the server details pertain to the discoverable asset data. Furthermore, the management system feeds the retrieved server details to an Artificial Intelligence/Machine Learning (AI/ML) model which is continuously being trained on documents of all the network side applications along with inventory data from Unified Inventory Management (UIM). Further based on training dataset, the AI/ML model generates some static or non-discoverable fields related to the server like application lead information, a platform lead, a device type, a Network Element Identifier (NEID), a location and operation specific point of contact. These static fields termed as asset metadata cannot be discovered because it is nowhere present on the server but can be very useful for the operations team as they use this information at the time of any issue.
[0035] The present invention enables comprehensive visibility and control over assets associated with non-discoverable servers in the telecom system. The invention facilitates streamlined asset tracking, inventory management, performance monitoring, and security management, empowering telecom network administrators to efficiently manage their network resources.
[0036] Referring to FIG. 1, FIG. 1 illustrates an exemplary block diagram of an environment 100 for managing a non-discoverable asset data, according to various embodiments of the present invention. The environment 100 includes at least one User Equipment (UE) 101 configured to at least transmit a request from the at least first UE 101a for managing the non-discoverable asset data. In one embodiment, at least one UE 101 is at least one of the first UE 101a, a second UE 101b, and a third UE 101c. In one embodiment, each of the at least first UE 101a, the second UE 101b, and the third UE 101c are configured to at least transmit the request from at least one UE 101 to retrieve asset data in a network 102. In one embodiment, the asset data includes, at least one of discovery data pertaining to the plurality of assets in the network 102.
[0037] In one embodiment, at least the first UE 101a from the at least first UE 101a, the second UE 101b and the third UE 101c are communicatively connected to the system 104 via the network 102. The first UE 101a, the second UE 101b and the third UE 101c will henceforth collectively and individually be referred to as “the UE 101” without limiting the scope and deviating from the scope of the present disclosure.
[0038] More information regarding the same will be provided with reference to the following figures.
[0039] In one embodiment, the UE 101 includes, but are not limited to, a handheld wireless communication device (e.g., a mobile phone, a smart phone, a tablet device, and so on), a wearable computer device (e.g., a head-mounted display computer device, a head-mounted camera device, a wristwatch computer device, and so on), a Global Positioning System (GPS) device, a laptop computer, a tablet computer, or another type of portable computer, a media playing device, a portable gaming system, and/or any other type of computer device with wireless communication capabilities, and the like.
[0040] The environment 100 further includes the server 103 communicably coupled to the UE 101 via the network 102. The server 103 includes 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.
[0041] The network 102 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 102 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.
[0042] Further, the network 102 also includes, by the way of example but not limitation, one or more wireless interfaces/protocols such as, for example, 802.11 (Wi-Fi), 802.15 (including Bluetooth™), 802.16 (Wi-Max), 802.22, Cellular standards such as CDMA, CDMA2000, WCDMA, Radio Frequency (e.g., RFID), Infrared, laser, Near Field Magnetics, etc.
[0043] The environment 100 further includes the system 104 communicably coupled to the server 103 and the UE 101 via the network 102. The system 104 is configured to manage non-discoverable asset data. Further, the system 104 is adapted to be embedded within the server 103 or is embedded as the individual entity independent of the server 103. However, for the purpose of description, the system 104 is described as an integral part of the server 103, without deviating from the scope of the present disclosure.
[0044] Operational and construction features of the system 104 will be explained in detail with respect to the following figures.
[0045] Referring to FIG. 2, FIG. 2 is an exemplary architecture of the system 104 for managing non-discoverable asset data, according to various embodiments of the present invention. The architecture of the system 104 includes processors 302 (explained in the FIG. 3) of the system 104, an Artificial Intelligence/Machine Learning (AI/ML) model 202, a plurality of remote servers 103 and a data lake 206. The processors 302 (explained in the FIG. 3) of the system 104 manages the non-discoverable asset data. The processors 302 (explained in the FIG. 3) of the system 104 executes one or more customized scripts and collect a discovery data from the plurality of remote servers 103. In one embodiment, the discovery data is associated with the plurality of remote servers 103. The discovery data includes a static metadata associated with each of the plurality of remote servers 103, for example at least one of, but not limited to, a location of the server 103 .
[0046] In one embodiment, the AI/ML model 202 executes one or more algorithms and generates a training dataset. One or more algorithm includes but not limited to, a k-means clustering, a hierarchical clustering, a Principal Component Analysis (PCA), an Independent Component Analysis (ICA), a deep learning algorithms 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 Algorithms etc. The AI/ML model 202 is continuously being trained based on the documents of all the network applications along with inventory data from a Unified Inventory Management (UIM) collected from the plurality of remote servers 103
[0047] Further, based on the training dataset, the AI/ML model 202 generates some static asset metadata related to the plurality of remote server 103. Herein, the static asset metadata is the non discoverable asset data. The static metadata includes, but not limited to, application lead information, a platform lead, a device type, and a Network Element Identifier (NEID). In one embodiment, the NEID is used to uniquely identify the relevant network element carrying out Lawful Interceptions (LI) operations, such as LI activation, Intercepted Related Information (IRI) record sending, etc. The NEID may be an Internet Protocol (IP) address or other identifier. The static asset metadata are present on the plurality of remote servers 103 and are useful for the operations team to utilize the static asset metadata at the time of any issue. In one embodiment, the AI/ML model 202 enables the discovery of the static asset metadata and capture the same. In an alternate embodiment, the discovery of the static asset metadata or the non-discoverable asset data is referred to as detection. In particular, the AI/ML model 202 enables the detection of the static asset metadata or the non-discoverable asset data.
[0048] In one embodiment, the AI/ML model 202 is configured to use High Level Design (HLD) and Low Level Design (LLD) documents associated with each of the plurality of servers 103 for training and to capture the static asset metadata associated with each of the plurality of servers 103. The data captured by the AI/ML model 202 is stored in the data lake 206.
[0049] In one embodiment, the plurality of servers 103 is configured to further capture and retrieve real-time discoverable asset data and discover static asset metadata. Further, the processor 302 of the system 104 merge the static asset metadata, and the discoverable asset data of the plurality of server 103, and store the same in the data lake 206. In one embodiment, the data lake 206 is configured with the database 322 (Explained in FIG. 3).
[0050] Referring to FIG. 3, FIG. 3 illustrates a block diagram of the system 104 for managing non-discoverable asset data, according to various embodiments of the present invention. The system 104 includes the processor 302, a memory 304, a user interface 306, a display unit 308, an input device 310 and a database 322. The one or more processors 302, hereinafter referred to as the 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. As per the illustrated embodiment, the system 104 includes one processor 302. However, it is to be noted that the system 104 include multiple processors as per the requirement and without deviating from the scope of the present disclosure. Among other capabilities, the processor 302 is configured to fetch and execute computer-readable instructions stored in the memory 304.
[0051] The memory 304 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 to create or share data packets over a network service. The memory 304 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. In an embodiment, the user interface 306 includes a variety of interfaces, for example, interfaces for data input and output devices, referred to as input/output devices, storage devices, and the like. The user interface 306 facilitates communication of the system 104. In one embodiment, the user interface 306 provides a communication pathway for one or more components of the system 104.
[0052] The user interface 306 may include functionality similar to at least a portion of functionality implemented by one or more computer system interfaces such as those described herein and/or generally known to one having ordinary skill in the art. The user interface 306 may be rendered on the display unit 308, implemented using LCD display technology, OLED display technology, and/or other types of conventional display technology. The display unit 308 is integrated within the system 104 or connected externally. Further the request may be configured to receive requests, queries, or information from the user by using the input device 310. The input device 310 may include, but not limited to, keyboard, buttons, scroll wheels, cursors, touchscreen sensors, audio command interfaces, magnetic strip reader, optical scanner, etc.
[0053] The system 104, may further comprise the database 322. The database 322 may be communicably connected to the processor 302, and the memory 304. The database 322 is configured to store and retrieve the data of the UE 101.
[0054] Further, the processor 302, 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 302. 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 302 may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for processor 302 may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the memory 304 may store instructions that, when executed by the processing resource, implement the processor 302. In such examples, the system 104 may comprise the memory 304 storing the instructions and the processing resource to execute the instructions, or the memory 304 may be separate but accessible to the system 104 and the processing resource. In other examples, the processor 302 may be implemented by electronic circuitry.
[0055] In order for the system 104 to manage non-discoverable asset data in the network 102. The processor 302 includes a retrieving unit 312, a training unit 314, a determining unit 316, a tagging unit 318 and a merging unit 320 communicably coupled to each other. In an embodiment, the retrieving unit 312, the training unit 314 the determining unit 316, the tagging unit 318 and the merging unit 320 are enabled by the processor 302 to manage non-discoverable asset data in the network 102.
[0056] The retrieving unit 312 of the processor 302 is communicably connected to the UE 101 via the network 102. Accordingly, the retrieving unit 312 is configured to retrieve the discoverable asset data pertaining to each of the plurality of assets in the network 102. In one embodiment, the retrieving unit 312 is configured to execute one or more customized scripts to retrieve the discoverable asset data. In other words, the processors 302 is configured to execute the one or more customized scripts and collect the discoverable asset data from the plurality of assets. The one or more customized scripts are a sequence of commands coded in the script based on the type of assets and perform specific tasks or collect relevant information from the plurality of assets. The sequence of commands coded within the scripts can perform a variety of functions, such as configuring device settings, retrieving system logs or status information, performing diagnostics or health checks, and collecting performance metrics or usage statistics, which saves users time to separately login to the identified plurality of assets to check the health status and other details.
[0057] In an embodiment, the retrieving unit 312 is configured to transmit the retrieved discoverable asset data to the training unit 314. On receipt of the retrieved discoverable asset data, the training unit 314 is configured to train the AI/ML model 202 (explained in FIG. 2) based on the retrieved discoverable asset data. The AI/ML model 202 executes one or more algorithms and generates a training dataset. One or more algorithm includes but not limited to the k-means clustering, the hierarchical clustering, the Principal Component Analysis (PCA), an Independent Component Analysis (ICA), the deep learning algorithms such as Artificial Neural Networks (ANNs), the Convolutional Neural Networks (CNNs), the Recurrent Neural Networks (RNNs), the Long Short-Term Memory Networks (LSTMs), the Generative Adversarial Networks (GANs), the Q-Learning, the Deep Q-Networks (DQN), and the reinforcement learning algorithms etc.
[0058] In one embodiment, the training unit 314 is configured to train the AI/ML model 202 utilizing the discovery data pertaining to the plurality of assets and a historical data related to the plurality of remote server 103. In one embodiment, the historical data includes but not limited at least one of, an inventory data received from a Unified Inventory Management (UIM), documents of all applications running on the plurality of remote servers 103, data related to one or more nodes in the network 102, the High Level Design (HLD) and the Low Level Design (LLD) documents associated with each of the plurality of assets.
[0059] In one embodiment, the non-discoverable asset data includes but not limited to the information of at least one of, applications running on the plurality of assets, a platform lead, one or more devices related to the plurality of assets, a Network Element Identifier (NEID), a location of the plurality of assets and a managing point of contact related to each of the plurality of assets. Further, the training unit 314 transmit the discovery data pertaining to the plurality of assets and the historical data related to the plurality of server 103 to the determining unit 316.
[0060] In one embodiment, the determining unit 316 receives the retrieved discoverable asset data pertaining to each of the plurality of assets from the retrieving unit 312. Further, the retrieved discoverable asset data is transmitted to the AI/ML model 202. The AI/ML model 202 identifies a relationship between the retrieved discoverable asset data and the historical data related to the non-discoverable asset data for each of the plurality of assets. In one embodiment, the AI/ML model 202 retrieves the historical data related to the non-discoverable asset data from the database 322. After identifying the relationship between the retrieved discoverable asset data and the historical data related to the non-discoverable asset data, the required non-discoverable data pertaining to each of the plurality of assets is generated based on a combination of the identified relationship between the retrieved discoverable asset data and the historical data related to the non-discoverable asset data. Further, the determining unit 316 determines the non-discoverable asset data pertaining to each of the plurality of assets by utilizing the trained model 202. The determining unit 318 transmits the determined non-discoverable asset data to the tagging unit 318. The tagging unit 318 is configured to tag each of the plurality of assets discovered with the non-discoverable asset data and further transmit the tagged asset to the merging unit 320.
[0061] The merging unit 320 is configured to merge the discoverable asset data with the non-discoverable asset data. In one embodiment, the merged data facilitates a network operator to identify the non-discoverable asset data of each of the plurality of assets in the network 102 in order to resolve one or more issues in the network 102. Further, the merged data is stored in the storage unit which includes at least one of, but not limited to, a database 322 and a cloud. In one embodiment, the database 322 includes for example, e-commerce platforms, banking systems, content management systems, Customer Relationship Management (CRM) systems, and more. In one embodiment, the cloud includes for example a multimedia content, backups, archives, and large datasets used for analytics or machine learning.
[0062] Referring to FIG. 4, FIG. 4 illustrates an exemplary embodiment for managing non-discoverable asset data of system 104 of FIG. 3, according to various embodiments of the present invention. It is to be noted that the embodiment with respect to FIG. 4 will be explained with respect to the first UE 101a for the purpose of description and illustration and should nowhere be construed as limited to the scope of the present disclosure.
[0063] As mentioned earlier in FIG. 1, each of the first UE 101a, the second UE 101b, and the third UE 101c may include an external storage device, a bus, a main memory, a read-only memory, a mass storage device, communication port(s), and a processor. The exemplary embodiment as illustrated in the FIG. 4 will be explained with respect to the first UE 101a. The first UE 101a includes one or more primary processors 402 communicably coupled to the one or more processors 302 of the system 104. The one or more primary processors 402 are coupled with a memory 404 storing instructions which are executed by the one or more primary processors 402. Execution of the stored instructions by the one or more primary processors 402 enables the first UE 101a to transmit the customized scripts to the processor 302. The execution of the stored instructions by the one or more primary processors 402 further enables the first UE 101a to transmit the customized scripts to the one or more processors 302 to retrieve the non-discoverable asset data of each of the plurality of assets in the network 102.
[0064] In the preferred embodiment, the retrieving unit 312 of the processor 302 is communicably connected to one or more primary processors 402 of the first UE 101a. The retrieving unit 312 is configured to retrieve the discoverable asset data pertaining to each of the plurality of assets in the network 102.
[0065] As per the illustrated embodiment, the system 104 includes the one or more processors 302, the memory 304, the user interface 306, the display unit 308, the input device 310 and the database 322. The operations and functions of the one or more processors 302, the memory 304, the user interface 306, the display unit 308, the input device 310 and the database 322, are already explained in FIG. 3. For the sake of brevity, a similar description related to the working and operation of the system 104 as illustrated in FIG. 4 has been omitted to avoid repetition.
[0066] Further, the processor 302 includes the retrieving unit 312, the training unit 314, the determining unit 316, the tagging unit 318, and the merging unit 320. The operations and functions of the retrieving unit 312, the training unit 314, the determining unit 316, the tagging unit 318, and the merging unit 320 are already explained in FIG. 3. Hence, for the sake of brevity, it is to be noted that a similar description related to the working and operation of the system 104 as illustrated in FIG. 4 has been omitted to avoid repetition. The limited description provided for the system 104 in FIG. 4, should be read with the description as provided for the system 104 in the FIG. 3 above, and should not be construed as limiting the scope of the present disclosure.
[0067] Referring to FIG. 5, FIG. 5 is an exemplary signal flow diagram for managing non discoverable asset data, according to one or more embodiments of the present invention; For the purpose of description, the signal flow diagram is described with the embodiments as illustrated in FIG. 3 and should nowhere be construed as limiting the scope of the present disclosure.
[0068] At step 502, the user transmits the one or more customized scripts the processor 302 of the system 104 in order to retrieve the asset data pertaining to each of the plurality of assets 103 in the network 102. The asset data includes at least one of the discoverable asset data pertaining to the plurality of assets 103.
[0069] At step 504, the processor 302 of the system 104 retrieves the discoverable asset data from the plurality of assets 103 in the network 102 based on executing one or more customized scripts.
[0070] At step 506, upon retrieving the discoverable asset data from the plurality of assets 103, the processor 302 of the system 104 transmits the training dataset to the AI/ML model 202. In particular, subsequent to retrieving the discoverable asset data, the training unit 314 of the processor 302 generates the training dataset based on at least one of, retrieved discoverable asset data, historical data related to the plurality of assets 103, the High Level Design (HLD) and Low Level Design (LLD) documents associated with each of the plurality of assets 103
[0071] At step 508, the processor 302 of the system 104 determines the non-discoverable asset data pertaining to each of the plurality of assets 103 by utilizing the AI/ML model 202. The AI/ML model 202 captures the non-discoverable asset data using the retrieved discoverable asset data and the historical data. The historical data include at least one of, an inventory data received from the Unified Inventory Management (UIM), documents of all applications running on the plurality of assets, data related to one or more nodes in the network, High Level Design (HLD) and Low Level Design (LLD) documents associated with each of the plurality of assets 103.
[0072] The processor 302 of the system 104 determines the non-discoverable asset data pertaining to each of the plurality of assets 103 by identifying the relationship between the retrieved discoverable asset data and the historical data related to non-discoverable asset data for each of the plurality of assets 103. Accordingly, generating the required non-discoverable data pertaining to each of the plurality of assets 103 based on the combination of the identified relationship between the retrieved discoverable asset data and the historical data related to non-discoverable asset data. In one embodiment, the non-discoverable asset data includes information of at least one of, applications running on the plurality of assets 103, platform lead, one or more devices related to the plurality of assets, Network Element Identifier (NEID), and managing point of contact related to each of the plurality of assets 103.
[0073] Furthermore, the generated non-discoverable asset data are tagged by the tagging unit 318. Thereafter, at step 510, the processor 302 of the system 104 merges the discoverable asset data with the generated non-discoverable asset data by the merging unit 320 of the processor 302, upon merging the processor 302 of the system 104 stores the merged data in at least one of the database 322 and the cloud.
[0074] Referring to FIG. 6, FIG. 6 illustrates a flow diagram of the method 600 for managing non-discoverable asset data, according to various embodiments of the present invention. The method 600 is adapted for managing non-discoverable asset data. For the purpose of description, the method 600 is described with the embodiments as illustrated in FIG. 3 and should nowhere be construed as limiting the scope of the present disclosure.
[0075] At step 602, the method 600 includes the step of retrieving the asset data by the retrieving unit 312. In one embodiment, the retrieved asset data include at least one of the discovery data pertaining to the plurality of assets. The discoverable asset data is retrieved by executing one or more customized scripts requested by the user.
[0076] At step 604, the method 600 includes the step of providing the retrieved discoverable asset data pertaining to each of the plurality of assets to the training model by the determining unit 316. The training model is at least one of an Artificial Intelligence/Machine Learning (AI/ML) model 202. The training model is trained based on the retrieved asset data and the historical data related to the plurality of assets to determine the non-discoverable asset data. The training model captures the non-discoverable asset data using the retrieved discoverable asset data and the historical data. The historical data include at least one of, an inventory data received from a Unified Inventory Management (UIM), documents of all applications running on the plurality of assets, data related to one or more nodes in the network, High Level Design (HLD) and Low Level Design (LLD) documents associated with each of the plurality of assets.
[0077] In one embodiment, the AI/ML model 202 generates trained dataset, some static asset metadata related to the plurality of server 103, the static asset metadata includes but not limited to application lead information, platform lead, device type, a Network Element Identifier (NEID). In one embodiment, the NEID is uniquely identified the relevant network element carrying out. The NEID may be an Internet Protocol (IP) address or other identifier. The static asset metadata are present on the server 103 are useful for the operations team to utilize the static asset metadata at the time of any issue.
[0078] At step 606, the method 600 includes the step of identifying the relationship between the retrieved discoverable asset data and the historical data related to the non-discoverable asset data for each of the plurality of assets. Accordingly, generating the required non-discoverable data pertaining to each of the plurality of assets based on the combination of the identified relationship between the retrieved discoverable asset data and the historical data related to the non-discoverable asset data. The non-discoverable asset data pertaining to each of the plurality of assets is determined by utilizing the trained model. In one embodiment, the non-discoverable asset data includes information of at least one of, applications running on the plurality of assets, platform lead, one or more devices related to the plurality of assets, Network Element Identifier (NEID), and managing point of contact related to each of the plurality of assets.
[0079] Furthermore, each of the plurality of assets discovered with the non-discoverable asset data are tagged by the tagging unit 318.
[0080] At step 608, the method 600 includes the step of merging the discoverable asset data with the non-discoverable asset data by the merging unit 320, upon merging the discoverable asset data with the non-discoverable asset data, the merged data is stored in one of the database 322 and the cloud.
[0081] The present invention further discloses a non-transitory computer-readable medium having stored thereon computer-readable instructions. The computer-readable instructions are executed by a processor 302. The processor 302 is configured to retrieve the discoverable asset data pertaining to each of the plurality of assets in the network 102. The processor 302 is configured to determine the non-discoverable asset data pertaining to each of the plurality of assets in the network 102 from the discoverable asset data by utilizing the trained model. The processor 302 is further configured to merge the discoverable asset data with the non-discoverable asset data.
[0082] 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.
[0083] The present disclosure incorporates technical advancement for managing non-discoverable asset data improves the overall operational efficiency of telecom environments by overcoming the limitations of existing server discovery mechanisms. It ensures accurate asset identification and management, reduces security risks, minimizes operational costs and enhances the performance and reliability of the telecom network.
[0084] 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
[0085] Environment - 100
[0086] User Equipment - 101
[0087] Network - 102
[0088] Server - 103
[0089] System - 104
[0090] Artificial Intelligence/Machine Learning (AI/ML) model - 202
[0091] Data lake - 206
[0092] Processor - 302
[0093] Memory - 304
[0094] User interface - 306
[0095] Display unit - 308
[0096] Input device - 310
[0097] Retrieving unit - 312
[0098] Training unit - 314
[0099] Determination unit - 316
[00100] Tagging unit - 318
[00101] Merging unit - 320
[00102] Database - 322
[00103] Primary processor - 402
[00104] Memory unit - 404

,CLAIMS:CLAIMS
We Claim:
1. A method (600) for managing non-discoverable asset data, the method (600) comprising the steps of:
retrieving, by one or more processors (302), discoverable asset data pertaining to each of a plurality of assets in a network (102);
determining, by the one or more processors (302), utilizing a trained model, non-discoverable asset data pertaining to each of the plurality of assets in the network (102) from the discoverable asset data; and
merging, by the one or more processors (302), the discoverable asset data with the non-discoverable asset data.

2. The method (600) as claimed in claim 1, wherein upon merging the discoverable asset data with the non-discoverable asset data, the method (600) comprises the step of storing, by the one or more processors (302), the merged data in one of a database (322) and a cloud.

3. The method (600) as claimed in claim 1, wherein the discoverable asset data is retrieved by executing one or more customized scripts.

4. The method (600) as claimed in claim 1, wherein the non-discoverable asset data includes information of at least one of, applications running on the plurality of assets, a platform lead, one or more devices related to the plurality of assets, a Network Element Identifier (NEID), a location of the plurality of assets and a managing point of contact related to each of the plurality of assets.

5. The method (600) as claimed in claim 1, wherein the model is at least one of, an Artificial Intelligence/Machine Learning (AI/ML) model (202).

6. The method (600) as claimed in claim 1, wherein the step of determining, utilizing a trained model, non-discoverable asset data pertaining to each of the plurality of assets, includes the steps of:
providing, by the one or more processors (302), the retrieved discoverable asset data pertaining to each of the plurality of assets to the model;
identifying, by the one or more processors (302), a relationship between the retrieved discoverable asset data and the historical data related to the non-discoverable asset data for each of the plurality of assets; and
generating, by the one or more processors (302), the required non-discoverable asset data pertaining to each of the plurality of assets based on a combination of, the identified relationship between the retrieved discoverable asset data and the historical data related to the non-discoverable asset data.

7. The method (600) as claimed in claim 1, comprising the step of training, by the one or more processors (302), the model based on the retrieved asset data and historical data related to the plurality of assets to determine the non-discoverable asset data, wherein the retrieved asset data include at least one of, discovery data pertaining to the plurality of assets.

8. The method (600) as claimed in claim 1, wherein the model is trained to capture non-discoverable asset data using the retrieved discoverable asset data and the historical data, wherein the historical data include at least one of, an inventory data received from a Unified Inventory Management (UIM), documents of all applications running on the plurality of assets, data related to one or more nodes in the network, High Level Design (HLD) and Low Level Design (LLD) documents associated with each of the plurality of assets.

9. The method (600) as claimed in claim 1, wherein the one or more processors (302) are further configured to tag each of the plurality of assets discovered with the non-discoverable asset data.

10. A system (104) for managing non-discoverable asset data, the system (104) comprising:
a retrieving unit (312) configured to retrieve, discoverable asset data pertaining to each of a plurality of assets in a network (102);
a determining unit (316), configured to, determine, utilizing a trained model, non-discoverable asset data pertaining to each of the plurality of assets in the network from the discoverable asset data; and
a merging unit (320), configured to, merge, the discoverable asset data with the non-discoverable asset data.

11. The system (104) as claimed in claim 10, comprises a storage unit configured to store, the merged data in one of a database (322) and a cloud.

12. The system (104) as claimed in claim 10, wherein the retrieving unit (312) is configured to execute one or more customized scripts to retrieve the discoverable asset data.

13. The system (104) as claimed in claim 10, wherein the non-discoverable asset data includes information of at least one of, applications running on the plurality of assets, a platform lead, one or more devices related to the plurality of assets, a Network Element Identifier (NEID), a location of the plurality of assets and a managing point of contact related to each of the plurality of assets.

14. The system (104) as claimed in claim 10, wherein the model is at least one of, an Artificial Intelligence/Machine Learning (AI/ML) model (202).

15. The system (104) as claimed in claim 10, wherein the determining unit (316) determines, utilizing the trained model, the non-discoverable asset data pertaining to each of the plurality of assets, by:
providing, the retrieved discoverable asset data pertaining to each of the plurality of assets to the model;
identifying, a relationship between the retrieved discoverable asset data and the historical data related to the non-discoverable asset data for each of the plurality of assets;
and
generating, the required non-discoverable asset data pertaining to each of the plurality of assets based on a combination of, the identified relationship between the retrieved discoverable asset data and the historical data related to the non-discoverable asset data.

16. The system (104) as claimed in claim 10, comprising a training unit (318) configured to train, the model based on the retrieved asset data and historical data related to the plurality of assets to determine the non-discoverable asset data, wherein the retrieved asset data include at least one of, discovery data pertaining to the plurality of assets.

17. The system (104) as claimed in claim 10, wherein the model is trained to capture non-discoverable asset data using the retrieved discoverable asset data and the historical data, wherein the historical data include at least one of, an inventory data received from a Unified Inventory Management (UIM), documents of all applications running on the plurality of assets, data related to one or more nodes in the network, High Level Design (HLD) and Low Level Design (LLD) documents associated with each of the plurality of assets.

18. The system (104) as claimed in claim 9, wherein a tagging unit (318) is configured to tag each of the plurality of assets discovered with the non-discoverable asset data.

19. A User Equipment (UE) (101), comprising:
one or more primary processors (402) communicatively coupled to one or more processors (302), the one or more primary processors (402) coupled with a memory (404), wherein said memory (404) stores instructions which when executed by the one or more primary processors (402) causes the UE (101) to:
transmit, the customized scripts to the one or more processors (302) in order to retrieve the non-discoverable asset data of each of the plurality of assets in the network (102); and
wherein the one or more processors (302) is configured to perform the steps as claimed in claim 1.