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 MANAGING MIGRATION OF CUSTOMERS BETWEEN NETWORKS
2. APPLICANT(S)
NAME NATIONALITY ADDRESS
JIO PLATFORMS LIMITED INDIAN OFFICE-101, SAFFRON, NR. CENTRE POINT, PANCHWATI 5 RASTA, AMBAWADI, AHMEDABAD 380006, GUJARAT, INDIA
3.PREAMBLE TO THE DESCRIPTION

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

FIELD OF THE INVENTION
[0001] The present invention relates generally to migration of customers, and in particular the present invention provides a system and a method for managing migration of customers from an existing network to a new network.
BACKGROUND OF THE INVENTION
[0002] In general, multiple customers may use different types of generations of cellular network technology such as fourth generation (4G) or LTE (Long Term Evolution) or a fifth generation (5G). A lot of customers still use the 4G or the LTE as their cellular network technology. The 4G or LTE network compared to the 5G is still inferior with respect to connectivity, network, speed and a lot more advantages that the 5G offers over the 4G or the LTE.
[0003] Further, in order to get better experience of the cellular network, the customers may wish to migrate to the 5G cellular network technology. The 5G migration may be the process of switching or transitioning customers from the existing cellular network technology (e.g., 4G or LTE) to the 5G cellular network technology. While transitioning the customers from the existing cellular network technology (e.g., 4G or LTE) to the 5G cellular network technology, service providers may face some issues or errors. This may lead to the failure in transitioning from the existing cellular network technology (4G or LTE) to the 5G cellular network technology.
[0004] In view of the above, there is a dire need for a system and a method for managing migration of the customers from an existing network to a new network, which ensures smooth transitioning of the customers.
SUMMARY OF THE INVENTION
[0005] One or more embodiments of the present disclosure provide a system and a method of managing migration of a customer between networks.
[0006] In one aspect of the present invention, the method of managing migration of the customer between the networks is provided. The method includes receiving, by one or more processors, data related to at least one of, onboarding of the customer to a new network, deactivation of a profile of the customer from an existing network and service-based data. Further, the method includes pre-processing, by the one or more processors, the received data for training a machine learning model using the pre-processed data. Further, the method includes predicting, by the one or more processors, anomalies during migration of the customer from the existing network to the new network by using the trained machine learning model. Further, the method includes recommending, by the one or more processors, recommend one or more actions to rectify the anomalies in migrating the customer from the existing network to the new network.
[0007] In an embodiment, further, the method includes storing, by the one or more processor, the pre-processed data into a database.
[0008] In an embodiment, further, the method includes displaying, by the one or more processor, the one or more recommended actions on a user interface for viewing by a service provider associated with one or more of the existing network and the new network.
[0009] In one aspect of the present invention, a system for managing migration of a customer between networks is disclosed. The system includes a receiving module, a data pre-processor, a prediction module, a recommendation module and a database. The receiving module is configured to receive data related to at least one of, onboarding of the customer to a new network, deactivation of a profile of the customer from an existing network and service-based data. The data pre-processor is configured to pre-process the received data for training a machine learning model using the pre-processed data. The prediction module is configured to predict anomalies during migration of the customer from the existing network to the new network by using the trained machine learning model. The recommendation module is configured to recommend one or more actions to rectify the anomalies in migrating the customer from the existing network to the new network.
[0010] In one aspect of the present invention, a non-transitory computer-readable medium having stored thereon computer-readable instructions is disclosed. The computer-readable instructions cause the processor to receive data related to at least one of: onboarding of the customer to a new network, deactivation of a profile of the customer from an existing network and service-based data. Further, the processor pre-processes the received data for training a machine learning model using the pre-processed data. Further, the processor predicts anomalies during migration of the customer from the existing network to the new network by using the trained machine learning model. Further, the processor recommends one or more actions to rectify the anomalies in migrating the customer from the existing network to the new network.
[0011] 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
[0012] 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 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.
[0013] FIG. 1 is an exemplary block diagram of an environment for managing migration of a customer between networks, according to various embodiments of the present disclosure.
[0014] FIG. 2 is a block diagram of a system of FIG. 1, according to various embodiments of the present disclosure.
[0015] FIG. 3 is an example schematic representation of the system of FIG. 1 in which various entities operations are explained, according to various embodiments of the present system.
[0016] FIG. 4 illustrates a system architecture for managing migration of the customer between the networks, in accordance with some embodiments.
[0017] FIG. 5 is an exemplary flow diagram illustrating the method for managing migration of the customer between the networks, according to various embodiments of the present disclosure.
[0018] FIG. 6 is an example flow diagram illustrating an internal call flow method for managing migration of the customer between the networks, in accordance with some embodiments.
[0019] Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the present invention. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having benefit of the description herein.
[0020] The foregoing shall be more apparent from the following detailed description of the invention.

DETAILED DESCRIPTION OF THE INVENTION
[0021] 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.
[0022] 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 the 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.
[0023] 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.
[0024] Before discussing example, embodiments in more detail, it is to be noted that the drawings are to be regarded as being schematic representations and elements that are not necessarily shown to scale. Rather, the various elements are represented such that their function and general purpose becomes apparent to a person skilled in the art. Any connection or coupling between functional blocks, devices, components, or other physical or functional units shown in the drawings or described herein may also be implemented by an indirect connection or coupling. A coupling between components may also be established over a wireless connection. Functional blocks may be implemented in hardware, firmware, software or a combination thereof.
[0025] Further, the flowcharts provided herein, describe the operations as sequential processes. Many of the operations may be performed in parallel, concurrently or simultaneously. In addition, the order of operations maybe re-arranged. The processes may be terminated when their operations are completed but may also have additional steps not included in the figured. It should be noted, that in some alternative implementations, the functions/acts/ steps noted may occur out of the order noted in the figured. For example, two figures shown in succession may, in fact, be executed substantially concurrently, or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
[0026] Further, the terms first, second etc… may be used herein to describe various elements, components, regions, layers and/or sections, it should be understood that these elements, components, regions, layers and/or sections should not be limited by these terms. These terms are used only to distinguish one element, component, region, layer or section from another region, layer, or a section. Thus, a first element, component, region layer, or section discussed below could be termed a second element, component, region, layer, or section without departing form the scope of the example embodiments.
[0027] Spatial and functional relationships between elements (for example, between modules) are described using various terms, including “connected,” “engaged,” “interfaced,” and “coupled.” Unless explicitly described as being “direct,” when a relationship between first and second elements is described in the description below, that relationship encompasses a direct relationship where no other intervening elements are present between the first and second elements, and also an indirect relationship where one or more intervening elements are present (either spatially or functionally) between the first and second elements. In contrast, when an element is referred to as being "directly” connected, engaged, interfaced, or coupled to another element, there are no intervening elements present. Other words used to describe the relationship between elements should be interpreted in a like fashion (e.g., "between," versus "directly between," "adjacent," versus "directly adjacent," etc.).
[0028] The terminology used herein is for the purpose of describing particular example embodiments only and is not intended to be limiting. Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
[0029] As used herein, the singular forms “a,” “an,” and “the,” are intended to include the plural forms as well, unless the context clearly indicates otherwise. As used herein, the terms “and/or” and “at least one of” include any and all combinations of one or more of the associated listed items. It will be further understood that the terms “comprises,” “comprising,” “includes,” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
[0030] Unless specifically stated otherwise, or as is apparent from the description, terms such as “processing” or “computing” or “calculating” or “determining” of “displaying” or the like, refer to the action and processes of a computer system, or similar electronic computing device/hardware, that manipulates and transforms data represented as physical, electronic quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.
[0031] Various embodiments of the present invention provide a system and a method for managing migration of customers. The disclosed system and method aim at enhancing the experience of the customer by providing 5G services to the customers. In particular, the present invention provides a unique approach of predicting potential issues and errors (e.g., software error, hardware error, compatibility issue or the like) that might occur during migration of customers from the existing cellular network technology for example (4G or LTE) to a new cellular network technology for example 5G and providing suggestions to the service providers in order to rectify the errors and issues, thereby ensure smooth transition of customers during the migration process.
[0032] In a preferred embodiment, the present invention provides a system and method for managing migration of customers from existing network such as 4G to a new network such as 5G, and any other newly developed network technologies in the future. In an alternative embodiment, the present invention provides a system and method for managing migration of customers from existing network such as 5G to a new network such as 4G. The proposed system and method can be used for 5G-6G or future generation migration.
[0033] FIG. 1 illustrates an exemplary block diagram of an environment (100) for managing migration of a customer between networks (106), according to various embodiments of the present disclosure. The environment (100) comprises a plurality of user equipment’s (UEs) (102-1, 102-2, ……,102-n). The at least one UE (102-n) from the plurality of the UEs (102-1, 102-2, ……102-n) is configured to connect to a system (108) via a communication network (106). Hereafter, label for the plurality of UEs or one or more UEs is 102.
[0034] In accordance with yet another aspect of the exemplary embodiment, the plurality of UEs (102) may be a wireless device or a communication device that may be a part of the system (108). The wireless device or the UE (102) may include, but are not limited to, a handheld wireless communication device (e.g., a mobile phone, a smart phone, a phablet device, and so on), a wearable computer device (e.g., a head-mounted display computer device, a head-mounted camera device, a wristwatch, a computer device, and so on), 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 or Voice Over Internet Protocol (VoIP) capabilities. In an embodiment, the UEs (102) may include, but are not limited to, any electrical, electronic, electro-mechanical or an equipment or 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, where the computing device may include one or more in-built or externally coupled accessories including, but not limited to, a visual aid device such as camera, audio aid, a microphone, a keyboard, input devices for receiving input from a user such as touch pad, touch enabled screen, electronic pen and the like. It may be appreciated that the UEs (102) may not be restricted to the mentioned devices and various other devices may be used. A person skilled in the art will appreciate that the plurality of UEs (102) may include a fixed landline, and a landline with assigned extension within the communication network (106).
[0035] The communication network (106), may use one or more communication interfaces/protocols such as, for example, Voice Over Internet Protocol (VoIP), 802.11 (Wi-Fi), 802.15 (including Bluetooth™), 802.16 (Wi-Max), 802.22, Cellular standards such as Code Division Multiple Access (CDMA), CDMA2000, Wideband CDMA (WCDMA), Radio Frequency Identification (e.g., RFID), Infrared, laser, Near Field Magnetics, etc.
[0036] The communication network (106) includes, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, or some combination thereof. The communication network (106) may include, but is not limited to, a Third Generation (3G) network, a Fourth Generation (4G) network, a Fifth Generation (5G) network, a Sixth Generation (6G) network, a New Radio (NR) network, a Narrow Band Internet of Things (NB-IoT) network, an Open Radio Access Network (O-RAN), and the like.
[0037] The communication network (106) may also include, by way of example but not limitation, at least a portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, etc. one or more messages, packets, signals, waves, voltage or current levels, some combination thereof, or so forth. The communication network (106) may also include, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, a VOIP or some combination thereof.
[0038] One or more network elements can be, for example, but not limited to a base station that is located in the fixed or stationary part of the communication network (106). The base station may correspond to a remote radio head, a transmission point, an access point or access node, a macro cell, a small cell, a micro cell, a femto cell, a metro cell. The base station enables transmission of radio signals to the UE (102) or a mobile transceiver. Such a radio signal may comply with radio signals as, for example, standardized by a 3rd Generation Partnership Project (3GPP) or, generally, in line with one or more of the above listed systems. Thus, a base station may correspond to a NodeB, an eNodeB, a Base Transceiver Station (BTS), an access point, a remote radio head, a transmission point, which may be further divided into a remote unit and a central unit. The 3GPP specifications cover cellular telecommunications technologies, including radio access, core network, and service capabilities, which provide a complete system description for mobile telecommunications.
[0039] The system (108) is communicatively coupled to a server (104) via the communication network (106). The server (104) can be, for example, but not limited to a standalone server, a server blade, a server rack, an application server, a bank of servers, a business telephony application server (BTAS), a server farm, a cloud server, an edge server, home server, a virtualized server, one or more processors executing code to function as a server, or the like. In an implementation, the server (104) may operate at various entities or a single entity (include, but is not limited to, a vendor side, a service provider side, a network operator side, a company side, an organization side, a university side, a lab facility side, a business enterprise side, a defence facility side, or any other facility) that provides service.
[0040] The environment (100) further includes the system (108) communicably coupled to the server (e.g., remote server or the like) (104) and each UE of the plurality of UEs (102) via the communication network (106). The remote server (104) is configured to execute the requests in the communication network (106).
[0041] The system (108) is adapted to be embedded within the remote server (104) or is embedded as an individual entity. The system (108) is designed to provide a centralized and unified view of data and facilitate efficient business operations. The system (108) is authorized to access to update/create/delete one or more parameters of their relationship between the requests for the onboarding of the customer to a new network, which gets reflected in real-time independent of the complexity of network.
[0042] In another embodiment, the system (108) may include an enterprise provisioning server (for example), which may connect with the remote server (104). The enterprise provisioning server provides flexibility for enterprises, ecommerce, finance to update/create/delete information related to the requests for the onboarding of the customer to the new network in real time as per their business needs. A user with administrator rights can access and retrieve the requests for the onboarding of the customer to the new network and perform real-time analysis in the system (108).
[0043] The system (108) 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 business telephony application server (BTAS), 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 implementation, system (108) may operate at various entities or single entity (for example include, but is not limited to, a vendor side, service provider side, a network operator side, a company side, an organization side, a university side, a lab facility side, a business enterprise side, ecommerce side, finance side, a defence facility side, or any other facility) that provides service.
[0044] However, for the purpose of description, the system (108) is described as an integral part of the remote server (104), without deviating from the scope of the present disclosure. Operational and construction features of the system (108) will be explained in detail with respect to the following figures.
[0045] FIG. 2 illustrates a block diagram of the system (108) provided for managing migration of the customer between the networks, according to one or more embodiments of the present invention. As per the illustrated embodiment, the system (108) includes the one or more processors (202), the memory (204), an input/output interface unit (206), a display (208), an input device (210), and the database (214). Further the system (108) may comprise one or more processors (202). The one or more processors (202), hereinafter referred to as the processor (202) may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, single board computers, and/or any devices that manipulate signals based on operational instructions. As per the illustrated embodiment, the system (108) includes one processor. However, it is to be noted that the system (108) may include multiple processors as per the requirement and without deviating from the scope of the present disclosure.
[0046] An information related to the request related to the onboarding of the customer to the new network may be provided or stored in the memory (204) of the system (108). Among other capabilities, the processor (202) is configured to fetch and execute computer-readable instructions stored in the memory (204). The memory (204) 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 (204) may include any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as disk memory, EPROMs, FLASH memory, unalterable memory, and the like.
[0047] The memory (204) may comprise any non-transitory storage device including, for example, volatile memory such as Random-Access Memory (RAM), or non-volatile memory such as Electrically Erasable Programmable Read-only Memory (EPROM), flash memory, and the like. In an embodiment, the system (108) may include an interface(s). The interface(s) may comprise a variety of interfaces, for example, interfaces for data input and output devices, referred to as input/output (I/O) devices, storage devices, and the like. The interface(s) may facilitate communication for the system. The interface(s) may also provide a communication pathway for one or more components of the system. Examples of such components include, but are not limited to, processing unit/engine(s) and the database (214). The processing unit/engine(s) may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s).
[0048] The information related to the requests for the onboarding of the customer to the new network may further be configured to render on the user interface (206). The user interface (206) 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 (206) may be rendered on the display (208), implemented using Liquid Crystal Display (LCD) display technology, Organic Light-Emitting Diode (OLED) display technology, and/or other types of conventional display technology. The display (208) may be integrated within the system (108) or connected externally. Further the input device(s) (210) may include, but not limited to, keyboard, buttons, scroll wheels, cursors, touchscreen sensors, audio command interfaces, magnetic strip reader, optical scanner, etc.
[0049] The database (214) may be communicably connected to the processor (202) and the memory (204). The database (214) may be configured to store and retrieve the request pertaining to features, or services or workflow of the system (108), access rights, attributes, approved list, and authentication data provided by an administrator. In another embodiment, the database (214) may be outside the system (108) and communicated through a wired medium and a wireless medium.
[0050] Further, the processor (202), 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 (202). In the examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processor (202) may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processor (202) may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the memory (204) may store instructions that, when executed by the processing resource, implement the processor (202). In such examples, the system (108) may comprise the memory (204) storing the instructions and the processing resource to execute the instructions, or the memory (204) may be separate but accessible to the system (108) and the processing resource. In other examples, the processor (202) may be implemented by an electronic circuitry.
[0051] In order for the system (108) to manage the migration of the customer between the networks (106), the processor (202) includes a receiving module (216), a data pre-processor (218), a prediction module (220), and a recommendation module (222). The receiving module (216), the data pre-processor (218), the prediction module (220), and the recommendation module (222) may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processor (202). In the examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processor (202) may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processor (202) may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the memory (204) may store instructions that, when executed by the processing resource, implement the processor. In such examples, the system (108) may comprise the memory (204) storing the instructions and the processing resource to execute the instructions, or the memory (204) may be separate but accessible to the system (108) and the processing resource. In other examples, the processor (202) may be implemented by the electronic circuitry.
[0052] In order for the system (108) to manage migration of the customer between the networks (106), the receiving module (216), the data pre-processor (218), the prediction module (220), the recommendation module (222), and the database (214) are communicably coupled to each other. In an embodiment, the receiving module (216) receives data related to onboarding of the customer to a new network, and deactivation of a profile of the customer from an existing network. The existing network is based on fourth generation (4G) or long term evolution (LTE) cellular network technology. The new network is based on fifth generation (5G) cellular network technology. In an example, the data can be, for example, but not limited to a consumer onboarding, customer deactivation and system-based data. In other words, the data may be the historical data of the customers.
[0053] The consumer onboarding refers to the process of integrating new customers into a service or network. The consumer onboarding involves guiding them through the initial setup, providing necessary information, and ensuring they understand how to use the service effectively. In an example, when a new customer switches from one mobile network to another mobile network, the onboarding process might include verifying their identity, setting up their account, providing a user guide, and explaining how to access customer support. The network might also offer an app walkthrough or tutorials on features like data management or billing.
[0054] The customer deactivation refers to the process of formally terminating a customer’s relationship with a service or the network. This can occur for various reasons, such as switching to a different provider or discontinuing use of a service. In an example, if a customer decides to leave their current internet service provider (ISP), the customer will go through a deactivation process that might include returning equipment, settling any outstanding bills, and receiving confirmation of account closure. The ISP may also offer exit surveys to understand the reason for the customer leaving.
[0055] The system-based data refers to the information collected and managed by a network’s systems regarding customer interactions, preferences, and account status. This data is crucial for analysing customer behaviour and managing transitions between services. In an example, during the migration of the customer from one mobile network to another mobile network, the system-based data might include the customer’s usage patterns, billing history, and preferences. This data can help the new network tailor its onboarding process, ensuring that the customer receives personalized offers or services based on their previous usage.
[0056] The data pre-processor (218) pre-processes the received data for training a machine learning model using the pre-processed data. Further, the database (214) stores the pre-processed data in a normalized format (e.g., CSV format, text format or the like). The machine learning model is trained to predict anomalies using the pre-processed data. In an example, the data pre-processor (218) may normalize the retrieved data. The pre-processed data may be stored in the database (214). The data normalization is the process of reorganizing data within the database (214) so that the users can utilize the retrieved data for further analysis.
[0057] The prediction module (220) predicts anomalies during migration of the customer from the existing network to the new network by using the trained machine learning model or AI model (e.g., linear regression, logistic regression, decision trees, and random forests).
[0058] In an example, the prediction module (220) may utilize the historical data of the customers to predict potential issues or errors that may occur while transitioning of customers from the existing cellular network technology (e.g., 4G or LTE) to the 5G cellular network technology. Further, depending on the predicted potential issues or errors, the prediction module (220) may also suggest the preferable or better recommendations in order to rectify the potential issues or errors. For example, while transitioning the customer from the existing cellular network technology (e.g., 4G or LTE) to the 5G cellular network technology, the prediction module (220) may detect the potential issues or errors that may be the one of, but not limited to, hardware or software that may not support 5G. In this regard, the prediction module (220) may suggest or recommend the change in the hardware or software in order to perform smooth transitioning of customers from existing cellular network technology (4G or LTE) to 5G cellular network technology.
[0059] The recommendation module (222) recommend one or more actions (e.g., modifications in network elements such as router, switch, base station or the like) to rectify the anomalies in migrating the customer from the existing network to the new network. Further, the recommendation module (222) displays the one or more suggestions and recommendations on a user interface for viewing by a service provider associated with one or more of the existing network and the new network. The service provider rectifies the anomalies based on the one or more suggestions and recommendations to facilitate the migration. a visual representation of the suggestions may be present in the form of a graph which may indicate various solutions or actions for smooth transition of customers from existing cellular network technology (4G or LTE) to 5G cellular network technology. Further, the suggestions provided by the recommendation module (222) in order to rectify potential issues are displayed in the form of graph via the user interface to the service providers. The visual representation of the suggestions makes easier for service providers to rectify potential issues or errors and ensure smooth transition of customers from existing cellular network technology (4G or LTE) to the 5G cellular network technology. The example for managing migration of a customer between networks is explained in FIG. 4 to FIG. 6.
[0060] FIG. 3 is an example schematic representation of the system (300) of FIG. 1 in which various entities operations are explained, according to various embodiments of the present system. It is to be noted that the embodiment with respect to FIG. 3 will be explained with respect to the first UE (102-1) and the system (108) for the purpose of description and illustration and should nowhere be construed as limited to the scope of the present disclosure.
[0061] As mentioned earlier, the first UE (102-1) includes one or more primary processors (305) communicably coupled to the one or more processors (202) of the system (108). The one or more primary processors (305) are coupled with a memory (310) storing instructions which are executed by the one or more primary processors (305). Execution of the stored instructions by the one or more primary processors (305) enables the UE (102-1). The execution of the stored instructions by the one or more primary processors (305) further enables the UE (102-1) to execute the requests in the communication network (106).
[0062] As mentioned earlier, the one or more processors (202) is configured to transmit a response content related to an onboarding request to the UE (102-1). More specifically, the one or more processors (202) of the system (108) is configured to transmit the response content to at least one of the UE (102-1). A kernel (315) is a core component serving as the primary interface between hardware components of the UE (102-1) and the system (108). The kernel (315) is configured to provide the plurality of response contents hosted on the system (108) to access resources available in the communication network (106). The resources include one of a Central Processing Unit (CPU), memory components such as Random Access Memory (RAM) and Read Only Memory (ROM).
[0063] As per the illustrated embodiment, the system (108) includes the one or more processors (202), the memory (204), the input/output interface unit (206), the display (208), and the input device (210). The operations and functions of the one or more processors (202), the memory (204), the input/output interface unit (206), the display (208), and the input device (210) are already explained in FIG. 2. For the sake of brevity, we are not explaining the same operations (or repeated information) in the patent disclosure. Further, the processor (202) includes the receiving module (216), the data pre-processor (218), the prediction module (220), and the recommendation module (222). The operations and functions of the receiving module (216), the data pre-processor (218), the prediction module (220), and the recommendation module (222) are already explained in FIG. 2. For the sake of brevity, we are not explaining the same operations (or repeated information) in the patent disclosure.
[0064] FIG. 4 illustrates a system architecture (400) for managing migration of the customer between the networks, in accordance with some embodiments. The system architecture (400) includes a Fault management system (FMS) (402) communicating with the processor (202), the AI/ML model (404) and the database (214). In the FMS (402), multiple types of data are fed. The data can be, for example, but not limited to the customer onboarding data, the customer deactivation and the service-based data. The retrieved data from the FMS (402) is processed by the processor (202). The processor (202) may normalize the retrieved data from the FMS (402). The data normalization is the process of reorganizing data within the database (214) so that the users can utilize the data for further queries and analysis.
[0065] The system architecture (400) further includes an AI/ML model (404). The AI/ML model (404) is trained to predict, anomaly detection and LLM generative AI outputs. The AI/ML model (404) extracts the network data and the operation data to perform an AI/ML analysis. The system architecture (400) further includes the database (214) that is a distributed data-lake used to store the processed data and algorithm outputs.
[0066] In an embodiment, the AI/ML model (404) may predict the potential errors or issues that may occur during the process of migration of customers from the existing cellular network technology (e.g., 4G or LTE) to the 5G cellular network technology by using the historical data analysis. The AI/ML model (404) also provides suggestions in order to rectify the potential errors or issues. Advantageously, the proposed method saves time and resources by automatically detecting potential errors or issues and providing suggestions.
[0067] FIG. 5 is an exemplary flow diagram (500) illustrating the method for managing migration of the customer between the networks, according to various embodiments of the present disclosure.
[0068] At 502, the method includes receiving the data related to at least one of: onboarding of the customer to the new network, deactivation of the profile of the customer from the existing network and the service-based data. In an embodiment, the method allows the receiving module (216) to receive the data related to at least one of: onboarding of the customer to the new network, deactivation of the profile of the customer from the existing network and the service-based data.
[0069] At 504, the method includes pre-processing the received data for training the machine learning model using the pre-processed data. In an embodiment, the method allows the data pre-processor (218) to pre-process the received data for training the machine learning model using the pre-processed data.
[0070] At 506, the method includes predicting anomalies during migration of the customer from the existing network to the new network by using the trained machine learning model (404). In an embodiment, the method allows the prediction module (220) to predict the anomalies during migration of the customer from the existing network to the new network by using the trained machine learning model (404).
[0071] At 508, the method includes recommending the one or more actions (e.g., modifications in the network elements or the like) to rectify the anomalies in migrating the customer from the existing network to the new network. In an embodiment, the method allows the recommendation module (222) to recommend the one or more actions (e.g., modifications in network elements or the like) to rectify the anomalies in migrating the customer from the existing network to the new network.
[0072] FIG. 6 is an example flow diagram illustrating an internal call flow method (600) for managing migration of the customer between the networks, in accordance with some embodiments.
[0073] At 602, the method includes retrieving multiple types of data of consumers from the external sources and feed the data to the FMS (402). The data can be, for example, but not limited to the consumer onboarding, the customer deactivation and the service-based data. In other words, the data may be the historical data of the customers.
[0074] At 604, the method includes preprocessing the retrieved data for training the machine learning model using the pre-processed data. The retrieved data from the FMS (402) is processed by the processor (202). The processor (202) may normalize the retrieved data from the FMS (402). The preprocessed data may be stored in the database (214). The data normalization is the process of reorganizing data within the database (214) so that the users can utilize the data for further queries and analysis.
[0075] At 606, the method includes analyzing the historical data for detecting the potential issues or errors and providing the suggestions for the issues or errors. In an embodiment, the brain is a processing/analyzing engine including the AI/ML model (404) that may be designed to analyze multiple algorithms, predictions, anomaly detection and provide generative the AI outputs. The AI/ML model (404) may utilize the historical data of customers in order to predict potential issues or errors that may occur while transitioning customers from the existing cellular network technology (4G or LTE) to the 5G cellular network technology. Further, depending on the predicted potential issues or errors, the AI/ML model (404) may also suggest the preferable or better recommendations in order to rectify the potential issues or errors. For example, while transitioning the customer from existing cellular network technology (4G or LTE) to the 5G cellular network technology, the AI/ML model (404) may detect the potential issues or errors that may be the one of: but not limited to, a hardware or a software that may not support the 5G. In this regard, the AI/ML model (404) may suggest or recommend a change in the hardware or software in order to perform smooth transitioning of customers from the existing cellular network technology (e.g., 4G or LTE) to the 5G cellular network technology.
[0076] At 608, the method includes storing the resulting output such as suggestions to rectify potential issues or errors in the database (214). The suggestions provided by the AI/ML model (404) for the service provider are stored in the database (214).
[0077] At 610, the method includes generating a visual representation of the suggestions provided by the AI/ML model (404) to rectify the potential issues or errors. In an embodiment, the visual representation of the suggestions may be present in the form of a graph which may indicate various solutions or actions for smooth transition of customers from the existing cellular network technology (4G or LTE) to the 5G cellular network technology.
[0078] At 612, the method includes displaying suggestions and all the relevant data and insights derived from the analysis. In an embodiment, the suggestions provided by the AI/ML model (404) in order to rectify potential issues are displayed in the form of graph via the user interface (206) to the service providers. The visual representation of the suggestions makes it easier for service providers to rectify potential issues or errors and ensure smooth transition of customers from the existing cellular network technology (4G or LTE) to the 5G cellular network technology.
[0079] Below is the technical advancement of the present invention:
[0080] The proposed method provides a unique approach of predicting potential issues and errors that might occur during migration of the customers from the existing cellular network technology (e.g., 4G or LTE) to the 5G cellular network technology and providing suggestions to the service providers in order to rectify these errors based on the prediction performed by the AI/ML model utilizing the historical data. This ensures smooth transition of customers from the existing cellular network technology to the 5G.
[0081] The proposed method ensures a seamless transition to the 5G or future generations for the customers by using historical data analysis to predict potential issues and errors. The proposed method suggests the remedial actions to the FMS (402) and improves overall performance. Additionally, the method saves time and resources by automatically detecting problems and proposing solutions. Ultimately, the proposed method enhances customer satisfaction by facilitating a smooth 5G migration process and delivering optimal performance.
[0082] A person of ordinary skill in the art will readily ascertain that the illustrated embodiments and steps in description and drawings (FIGS. 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] Method steps: A person of ordinary skill in the art will readily ascertain that the illustrated steps 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.
[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] UEs– 102, 102-1-102-n
[0087] Server - 104
[0088] Communication network – 106
[0089] System – 108
[0090] Processor – 202
[0091] Memory – 204
[0092] User Interface – 206
[0093] Display – 208
[0094] Input device – 210
[0095] Database – 214
[0096] Receiving module – 216
[0097] Data pre-processor– 218
[0098] Prediction module– 220
[0099] Recommendation module – 222
[00100] System - 300
[00101] Primary processors -305
[00102] Memory– 310
[00103] Kernel– 315
[00104] System architecture - 400
[00105] FMS – 402
[00106] AI/ML model– 404
,CLAIMS:CLAIMS
We Claim:
1. A method of managing migration of a customer between networks, the method comprising the steps of:
receiving, by one or more processors (202), data related to at least one of, onboarding of the customer to a new network, deactivation of a profile of the customer from an existing network and service based data;
pre-processing, by the one or more processors (202), the received data for training a machine learning model using the pre-processed data;
predicting, by the one or more processors (202), anomalies during migration of the customer from the existing network to the new network by using the trained machine learning model (404); and
recommending, by the one or more processors (202), one or more actions to rectify the anomalies in migrating the customer from the existing network to the new network.

2. The method as claimed in claim 1, wherein the method further comprising the step of:
storing, by the one or more processor (202), the pre-processed data into a database (214).

3. The method as claimed in claim 1, wherein the method further comprising the step of:
displaying, by the one or more processor (202), the recommended one or more actions on a user interface (206) for viewing by a service provider associated with one or more of the existing network and the new network.

4. A system (108) for managing migration of a customer between networks, the system (108) comprising:
a receiving module (216) configured to receive data related to at least one of, onboarding of the customer to a new network, deactivation of a profile of the customer from an existing network and service based data;
a data pre-processor (218) configured to pre-process the received data for training a machine learning model using the pre-processed data;
a prediction module (220) configured to predict anomalies during migration of the customer from the existing network to the new network by using the trained machine learning model (404); and
a recommendation module (222) configured to recommend one or more actions to rectify the anomalies in migrating the customer from the existing network to the new network.

5. The system (108) as claimed in claim 4, wherein the system (108) further comprises a database (214) configured to store the pre-processed data.

6. The system (108) as claimed in claim 4, wherein the recommendation module (222) is further configured to:
display the recommended one or more actions on a user interface (206) for viewing by a service provider associated with one or more of the existing network and the new network.