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 IDENTIFYING CUSTOMERS FOR MIGRATING FROM EXISTING NETWORK TO NEW NETWORK

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

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

FIELD OF THE INVENTION
[0001] The present invention relates generally to the migration of customers, and in particular, the present invention provides a system and method for identifying customers eligible for migration from an existing network to a new network.
BACKGROUND OF THE INVENTION
[0002] In general, multiple customers may use different types of generations of broadband cellular network technology such as 4G or LTE (Long Term Evolution) or 5G. A lot of customers still use 4G or LTE as their cellular network technology. 4G or LTE network compared to 5G is still inferior with respect to connectivity, network, speed and a lot more advantages that 5G offers over 4G or LTE.
[0003] In most situations, the customers may not know whether they are actually eligible for using 5G. During the course of marketing the 5G network by the service provider, the service providers may actually manually randomly try to sell the 5G network to the customers and offer attractive plans for the customers to migrate from the existing network to a new network. The migration may be the process of switching or transitioning customers from the existing cellular network technology (4G or LTE) to 5G cellular network technology. Therefore, selling the 5G network may not reach a wider audience since the marketing may be limited to a smaller audience. Further, marketing the 5G network by selling it randomly to customers is a time consuming and cumbersome task without providing a wider reach.
[0004] Currently, this inefficient approach to 5G adoption not only hampers the service provider's ability to maximize their investment in new infrastructure but also leaves many potentially eligible customers unaware of the benefits they could be enjoying. The lack of targeted marketing means that customers who could significantly benefit from 5G capabilities, such as those in areas with strong 5G coverage or those with usage patterns that align well with 5G strengths, may remain on older networks unnecessarily. This situation creates a lose-lose scenario where service providers struggle to achieve optimal returns on their 5G investments, while customers miss out on improved service quality and innovative features.
[0005] The limitations of existing methods are further exacerbated by the dynamic nature of 5G network deployment. As 5G infrastructure continues to expand and evolve, customer eligibility for migration is in a constant state of flux. Current systems lack the capability to adapt to these changes in real-time, leading to missed opportunities for both service providers and customers. Additionally, the failure to leverage vast quantities of available customer data results in suboptimal resource allocation and diminished customer satisfaction.
[0006] The deficiencies in the current state of the art highlight a pressing need for an innovative solution. An automated and dynamic system for identifying customers eligible for 5G migration would not only streamline the transition process but also ensure a better experience for customers. Such a system would need to process complex customer data, including usage patterns, geographical location, device compatibility, and network coverage, to make accurate eligibility determinations.
[0007] Furthermore, an ideal solution would offer scalability to handle large customer bases, precision in identifying truly eligible customers, and the ability to personalize the migration process. By addressing these challenges, a novel system could significantly enhance the efficiency of 5G adoption, optimize resource utilization for service providers, and ultimately contribute to the widespread implementation of this advanced network technology.
[0008] The present invention aims to address these critical gaps in the field, providing a sophisticated, data-driven approach to customer eligibility identification for 5G network migration. By doing so, it seeks to revolutionize the way telecommunications companies manage the transition to next-generation networks, ensuring a more efficient, accurate, and customer-centric migration process.

SUMMARY OF THE INVENTION
[0009] One or more embodiments of the present disclosure provide a system and a method for identifying eligibility of customers for migrating from an existing network to a new network.
[0010] In one aspect of the present invention, the method for identifying the eligibility of customers for migrating from the existing network to the new network is disclosed. The method includes the step of retrieving, by one or more processors, data pertaining to a plurality of customers from one or more sources. The method further includes the step of feeding, by the one or more processors, the retrieved data to a model for training. The method further includes the step of analyzing, by the one or more processors, utilizing the trained model, the retrieved data to identify eligibility of the plurality of customers for migrating from the existing network to the new network.
[0011] In one embodiment, the data pertaining to the plurality of customers includes at least one of, onboarding data, service usage data, deactivation data and historical data.
[0012] In another embodiment, the step of retrieving, by one or more processors, data pertaining to the plurality of customers from one or more sources, further includes the step of pre-processing, by the one or more processors, the retrieved data in order to utilize the pre-processed data for the training of the model and storing, by the one or more processors, the pre-processed data in a storage unit.
[0013] In yet another embodiment, the step of analyzing, by the one or more processors, utilizing the trained model, the retrieved data to identify eligibility of the plurality of customers for migrating from the existing network to the new network includes the steps of performing, by the one or more processors, utilizing the trained model, a trend/pattern analysis related to one or more parameters of each of the plurality of customers, and predicting, by the one or more processors, utilizing the trained model, the plurality of customers eligible for migrating from the existing network to the new network based on the trend/pattern analysis.
[0014] In yet another embodiment, the one or more parameters pertaining to the plurality of customers includes at least one of, customer’s device compatibility, customer’s location information, and customer’s subscription plans.
[0015] In yet another embodiment, the identified data of the plurality of customers eligible for migrating from the existing network to the new network is stored in the storage unit.
[0016] In yet another embodiment, the method further includes the step of generating, by the one or more processors, a visual representation of the identified plurality of customers for migration based on the analysis. The method further includes the step of displaying, by the one or more processors, the generated visual representation of the identified customers for migration to the user.
[0017] In another aspect of the present invention, a system for identifying customers eligibility for migrating from an existing network to a new network is disclosed. The system includes a retrieving unit, configured to, retrieve, data pertaining to a plurality of customers from one or more sources. The system further includes a feeding unit, configured to, feed, the retrieved data to a model for training. The system further includes an analysis unit, configured to analyze, utilizing the trained model, the retrieved data to identify eligibility of the plurality of customers for migrating from the existing network to the new network.
[0018] In another aspect of the present invention, a User Equipment (UE) is disclosed. One or more primary processors communicatively coupled to one or more processors. The one or more primary processors coupled with a memory. The memory stores instructions which when executed by the one or more primary processors causes the UE to transmit a request by the user to the one or more processors. The one or more processors is configured to perform the steps of identifying the plurality of customers eligibility for migrating from the existing network to the new network.
[0019] In yet another aspect of the present invention, a non-transitory computer-readable medium is provided having stored thereon computer-readable instructions that, when executed by a processor. The processor is configured to retrieve data pertaining to a plurality of customers from one or more sources. The processor is configured to feed the retrieved data to a model for training. The processor is configured to analyze, utilizing the trained model, the retrieved data to identify the plurality of customers eligibility for migrating from the existing network to the new network.
[0020] Other features and aspects of this invention will be apparent from the following description and the accompanying drawings. The features and advantages described in this summary and in the following detailed description are not all-inclusive, and particularly, many additional features and advantages will be apparent to one of ordinary skill in the relevant art, in view of the drawings, specification, and claims hereof. Moreover, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes and may not have been selected to delineate or circumscribe the inventive subject matter, resort to the claims being necessary to determine such inventive subject matter.
BRIEF DESCRIPTION OF THE DRAWINGS
[0021] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings includes disclosure of electrical components, electronic components or circuitry commonly used to implement such components.
[0022] FIG. 1 is an exemplary block diagram of an environment for identifying a customer’s eligibility for migrating from an existing network to a new network, according to one or more embodiments of the present invention;
[0023] FIG. 2 is an exemplary block diagram of the system for identifying the customer’s eligibility for migrating from the existing network to the new network, according to one or more embodiments of the present invention;
[0024] FIG. 3 is an exemplary block diagram of the system of FIG. 2, according to one or more embodiments of the present invention;
[0025] FIG. 4 is an exemplary architecture for the system for identifying the customer's eligibility for migrating from the existing network to the new network, according to one or more embodiments of the present disclosure;
[0026] FIG. 5 is a signal flow diagram illustrating the flow of the system for identifying the customer's eligibility for migrating from the existing network to the new network according to one or more embodiments of the present invention; and
[0027] FIG. 6 is a flow diagram of the method for identifying the customer's eligibility for migrating from the existing network to the new network, according to one or more embodiments of the present invention.
[0028] The foregoing shall be more apparent from the following detailed description of the invention.
DETAILED DESCRIPTION OF THE INVENTION
[0029] 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.
[0030] 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.
[0031] 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.
[0032] Various embodiments of the present invention provide a system and a method for identifying customers' eligibility for migration from an existing network to a new network. The disclosed system and method aim to streamline the migration process by automatically retrieving, analyzing, and managing customer data based on an Artificial Intelligence/Machine Learning (AI/ML) model. The customer data is retrieved from one or more sources and fed to the model for training. In particular, the present invention provides a unique approach to analyzing customer eligibility for migration by utilizing the trained model and generating a visual representation of the identified customers for migration, thereby facilitating a seamless transition between networks.
[0033] Referring to FIG. 1, FIG. 1 illustrates an exemplary block diagram of an environment 100 for identifying customers' eligibility for migration from an existing network 106 to a new network 110, according to one or more embodiments of the present invention. The environment 100 includes a User Equipment (UE) 102, a server 104, an existing network 106, a system 108, a new network 110, data sources 112, and a storage unit 114. The UE 102 aids a user to interact with the system 108 by transmitting a request in order to identify customers' eligibility for migration from the existing network 106 to the new network 110.
[0034] The eligibility of customers for migration is determined when the customers no longer optimally utilize the existing network 106 or would benefit from migrating to the new network 110. The customer eligibility may be influenced by various factors such as outdated service plans, inconsistent network usage, or frequent network performance issues that hinder their experience. The eligibility can stem from different data sources, including customer profiles, usage logs, and network performance metrics. Customers with outdated plans, low engagement, or high service disruptions are prime candidates for migration. Additionally, users with inefficient network utilization or plans no longer aligning with their needs may be marked for migration. Failing to identify eligible customers can result in suboptimal network performance, customer dissatisfaction, and missed opportunities for service improvement. Accurately identifying and migrating eligible customers is crucial to enhancing network efficiency, improving customer experience, and ensuring the network provider maintains optimal service delivery.
[0035] For the purpose of description and explanation, the description will be /explained with respect to one or more user equipment’s (UEs) 102, or to be more specific, will be explained with respect to a first UE 102a, a second UE 102b, and a third UE 102c, and should nowhere be construed as limiting the scope of the present disclosure. Each of the at least one UE 102 namely the first UE 102a, the second UE 102b, and the third UE 102c is configured to connect to the server 104 via the communication network 106. Each of at least one UE 102 pertains to the user requesting to identify customers' eligibility for migration from an existing network 106 to a new network 110.
[0036] In an embodiment, each of the first UE 102a, the second UE 102b, and the third UE 102c is one of, but not limited to, any electrical, electronic, electro-mechanical or an equipment and a combination of one or more of the above devices such as 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.
[0037] The existing network 106, from which customers are migrating, may include, but is not limited to, one or more of a Third Generation (3G) network, a Fourth (4G) network, a LTE network, a Legacy Global System for Mobile Communications (GSM) network, or combinations thereof. The new network 110, to which customers are being migrated, primarily refers to Fifth Generation (5G) networks, but may also encompass advanced network technologies such as Sixth Generation (6G) networks, New Radio (NR) networks, or other next-generation cellular technologies. Both the existing and new networks 106, 110 operate over various infrastructure types, including, by way of example but not limited to, terrestrial cellular networks, satellite networks, fixed wireless networks, fiber-optic networks, or hybrid network architectures. The existing and new networks 106, 110 support various communication protocols and standards, such as but not limited to, Narrowband Internet of Things (NB-IoT), massive Machine-Type Communications (mMTC), Ultra-Reliable Low-Latency Communications (URLLC), enhanced Mobile Broadband (eMBB), and Open Radio Access Network (O-RAN) specifications. The migration process facilitated by the present invention aims to transition eligible customers from the existing network infrastructure to the new, more advanced network technologies, taking into account factors such as coverage, capacity, device compatibility, and service requirements.
[0038] Both the existing and new networks 106, 110 comprises of private or public networks, internet-based networks, packet-switched or circuit-switched systems, and may support various protocols, such as VOIP, enabling seamless communication and data transfer. The combination of these networks allows the system 108 to identify eligible customers for migration and ensures smooth transitions between different types of communication infrastructures.
[0039] The environment 100 includes the server 104 accessible via the existing and new networks 106,110. The server 104 may include by way of example but is not limited to one or more of a standalone server, a server blade, a server rack, a bank of servers, a server farm, hardware supporting a part of a cloud service or system, a home server, hardware running a virtualized server, a processor executing code to function as a server, one or more machines performing server-side functionality as described herein, at least a portion of any of the above, some combination thereof. In an embodiment, the entity may include but is not limited to, a vendor, a network operator, a company, an organization, a university, a lab facility, a business enterprise side, a defense facility side, or any other facility that provides service.
[0040] The environment 100 includes the storage unit 114 communicably coupled to the server 104 via the existing and new networks 106,110. The storage unit 114 is an electronic device that is attached to the existing and new networks 106,110, which is capable of creating, receiving, or transmitting information over the existing and new networks 106,110. The storage unit 114 may either be data communication equipment such as a modem, hub, bridge, or switch or data terminal equipment such as a digital telephone handset, a printer, or a host computer. The storage unit 114 facilitates real-time data processing and monitoring within the networked environment. This integration supports automated updates and synchronization across connected devices, providing consistent and up-to-date inventory records.
[0041] The environment 100 features the data sources 112, which serves as a critical component for the migration eligibility determination process. The data sources 112 is an electronic repository designed to store a diverse array of relevant information repositories. The data sources 112 include, but are not limited to: customer relationship management (CRM) systems containing historical customer data, usage patterns, and service subscriptions; billing systems housing payment histories and service plan details; network performance databases storing metrics on existing network quality and capacity; geographical information systems (GIS) providing data on customer locations and new network coverage areas; market research databases containing customer satisfaction surveys and churn predictions; technical compatibility databases detailing device specifications and network requirements; social media APIs offering insights into customer sentiments and complaints; regulatory compliance databases ensuring adherence to telecommunication laws during migration; competitor intelligence sources providing information on alternative service offerings; and internal business intelligence systems aggregating key performance indicators (KPIs) relevant to network migration strategies. The Data Sources 112 would collectively provide the comprehensive dataset necessary for the Server 104 to process and analyze, facilitating accurate assessments of customer eligibility and optimal migration strategies from the existing network 106 to the new network 110.
[0042] The environment 100 further includes the system 108 communicably coupled to the server 104, the storage unit 114 and the UE 102 via the existing and new networks 106, 110. The system 108 is adapted to be embedded within the server 104 or is embedded as the individual entity.
[0043] Operational and construction features of the system 108 will be explained in detail with respect to the following figures.
[0044] FIG. 2 is an exemplary block diagram of the system 108 for identifying the customers' eligibility for migration from the existing network 106 to the new network 110, according to one or more embodiments of the present invention.
[0045] As per the illustrated and preferred embodiment, the system 108 includes one or more processors 202, a memory 204, a user interface 206, and a database 218. The one or more processors 202 includes a retrieving unit 208, a feeding unit 210, an analysis unit 212, a generating unit 214, and a trained model 216.
[0046] In a further embodiment, the data stored in database 218 encompasses various categories essential for the migration eligibility determination process. The categories include customer profile data, such as service plans and usage history, as well as network coverage information and device compatibility details for advanced technologies like 5G. The stored data also comprises historical migration records and success metrics that facilitate performance analysis. Furthermore, the database 218 maintains machine learning model parameters and eligibility criteria defined by the service provider. This diverse array of data elements enables the system 108 to make informed, real-time decisions regarding customer eligibility for migration, ensuring a streamlined and efficient transition between communication networks.
[0047] The one or more processors 202, hereinafter referred to as the processor 202, may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, single board computers, and/or any devices that manipulate signals based on operational instructions. However, it is to be noted that the system 108 may include multiple processors as per the requirement and without deviating from the scope of the present disclosure. Among other capabilities, the processor 202 is configured to fetch and execute computer-readable instructions stored in the memory 204.
[0048] As per the illustrated embodiment, the processor 202 is configured to fetch and execute computer-readable instructions stored in the memory 204 as the memory 204 is communicably connected to the processor 202. The memory 204 is configured to store one or more computer-readable instructions or routines in a non-transitory computer-readable storage medium which may be fetched and executed for identifying customers' eligibility for migration from the existing network 106 to the new network 110. The memory 204 may include any non-transitory storage unit 114 including, for example, volatile memory such as RAM or non-volatile memory such as disk memory, EPROMs, FLASH memory, unalterable memory, and the like.
[0049] As per the illustrated embodiment, the database 218 is configured to store data pertaining to customer profiles, network coverage, device compatibility, and migration eligibility criteria. The database 218 is distinct from the storage unit 114 in its primary function and the nature of data it holds. While the storage unit 114 may contain temporary data and system logs, the database 218 is designed for structured, queryable data essential for the migration eligibility determination process. The database 218 is implemented as one of, but not limited to, a relational database, a cloud-based database, a commercial database, an open-source database, a distributed database, a time series database, a wide column database, a NoSQL database, an object-oriented database, an in-memory database, or a combination thereof. The database 218 is optimized for real-time data retrieval and updates, supporting the dynamic nature of the eligibility determination process. The database 218 employs advanced features such as data partitioning, indexing, and caching to ensure rapid access to frequently queried information. The foregoing examples of the database 218 types are non-limiting and may not be mutually exclusive; for instance, the database 218 can be both distributed and in-memory, or both relational and cloud-based, depending on the specific requirements of the system 108 and the scale of the migration process being managed.
[0050] In an embodiment, the storage unit 114, as described in the present invention, performs several vital functions that complement the database 218. The said functions include high-speed temporary data storage, where the storage unit 114 holds intermediate results such as partial computations and temporary customer lists during the eligibility determination process. The storage unit 114, also maintains detailed system logs and audit trails, recording eligibility checks, data access patterns, and performance metrics essential for auditing and optimization. Additionally, the storage unit 114 serves as a caching layer to enhance system performance by storing frequently accessed data from the database 218, and it stores machine learning model parameters and configuration data critical for real-time processing and eligibility rule enforcement.
[0051] Furthermore, the storage unit 114 plays a crucial role in backup and recovery operations, providing a temporary backup for critical data to ensure swift recovery from system failures or data corruption within the primary database 218. The storage unit 114, is also utilized for data preprocessing tasks such as normalization and feature extraction, which are essential before data is utilized for eligibility determination. For systems with high volumes of eligibility check requests, the storage unit 114 incorporates queue management structures to facilitate orderly processing. Therefore, the storage unit 114 is configured to support the dynamic and computationally intensive requirements of the customer eligibility determination process for network migration, utilizing technologies like solid-state drives (SSDs), RAM-based storage, or distributed in-memory data grids tailored to specific performance needs.
[0052] In an embodiment, the system 108 initiates the eligibility determination process by retrieving relevant customer data from the database 218 upon receiving a request transmitted by the user via the UE 102. In an alternate embodiment, the system 108 autonomously determines the customer’s eligibility for the network migration without requiring a user-initiated request, utilizing real-time data analytics and predefined eligibility criteria stored within the database 218 to streamline the process and enhance operational efficiency.
[0053] In an embodiment, the retrieving unit 208 is configured to retrieve data related to the plurality of customers from one or more sources. The data includes, but are not limited to, customer profile information, service usage history, network coverage details, and device compatibility status. To facilitate the extraction of the data, the retrieving unit 214 interfaces with external databases and APIs, ensuring the availability of up-to-date and relevant information for subsequent analysis.
[0054] In the context of the present invention, identifying customer eligibility for network migration refers to the process of evaluating specific criteria that determine whether the customer can transition from the existing network 106 to the new network 110. The eligibility criteria serve as the foundational guidelines that the analysis unit 212 uses to assess customer data. In an embodiment, the analysis unit 212 employs the trained model 216 to perform this evaluation in real-time, ensuring timely decision-making. In another embodiment, the trained model 216 is developed using a combination of historical data and current customer information sourced from the retrieving unit 214. The identified eligibility criteria include, but are not limited to, factors such as service plan alignment, usage patterns, network availability, and device compatibility status, which collectively inform the migration eligibility determination process.
[0055] In an embodiment, the retrieved data is subsequently pre-processed by the one or more processors 202, which may include cleaning, formatting, and organizing the data for optimal use in subsequent analysis.
[0056] In another embodiment, the pre-processed data is stored in the storage unit 114 for further analysis. The retrieving unit 208 also coordinates with the system's 108 analysis unit 212, ensuring that the pre-processed data is fed to the trained model 216 for trend and pattern analysis. The retrieved and pre-processed data enables the system 108 to make accurate eligibility determinations for network migration, based on factors such as customer device compatibility, location information, and subscription plans. This step ensures that the retrieved data is efficiently utilized throughout the process of determining the customer eligibility for migrating from the existing network 106 to the new network 110.
[0057] For example, in an embodiment of the present invention, the retrieving unit 208 may retrieve onboarding data and service usage data for a large telecommunications provider. The system 108 retrieves customer data from multiple sources, including the provider's internal CRM system and external databases tracking network usage. Once retrieved, the data is pre-processed by the system 108 to remove any incomplete or outdated information and to standardize it for further analysis. The pre-processed data is then stored in the storage unit 114 for further analysis.
[0058] In an embodiment, the feeding unit 210 is configured to feed the pre-processed customer data to the trained model 216 for further analysis. The feeding unit 210 interacts with the retrieving unit 208 to receive the pre-processed data, which includes onboarding data, service usage data, deactivation data, and historical data, among others. The feeding unit 210 ensures that the data is supplied in a structured format compatible with the trained model's 216 input parameters, optimizing the performance of the trained model 216. The feeding process is dynamic, allowing the system 108 to adapt to varying data inputs and ensuring continuous learning and updating of the trained model 216 as new data is retrieved and fed into the system 108.
[0059] Additionally, the feeding unit 210 is responsible for efficiently managing the flow of data into the ML model to prevent data overload or bottlenecks. In one embodiment, the feeding unit 210 operates in real-time, providing data to the trained model 216 as soon as it is pre-processed. This step allows the system 108 to perform eligibility assessments for customer migration on an ongoing basis. The feeding unit 210 also handles the segmentation of data into relevant subsets based on specific criteria, such as customer location, device compatibility, or network usage, enabling the trained model 216 to focus on key factors during its analysis.
[0060] For example, in a scenario where the telecommunications provider wants to identify customers eligible for a new network upgrade, the feeding unit 210 retrieves and feeds relevant data to the trained model 216. The data includes customers' subscription plans, historical usage patterns, and device compatibility with new cellular network technology. By supplying this data in a structured manner, the feeding unit 210 enables the trained model 216 to accurately analyze the information and predict which customers are most likely to benefit from the network upgrade. The trained model 216 then processes the data and outputs a list of eligible customers, which can be used by the provider to target specific users for the migration.
[0061] In an embodiment, the analysis unit 212 is configured to perform advanced data analysis utilizing the trained model 216 to determine the eligibility of customers for migration from the existing network 106 to the new network 110. Upon receiving the data from the feeding unit 210, the analysis unit 212 processes the data to extract relevant patterns, trends, and insights that are essential for assessing customer eligibility. The analysis unit 212 evaluates various factors, such as customer device compatibility, subscription plans, historical usage patterns, and network performance, to generate a precise eligibility profile for each customer. By applying ML algorithms, the analysis unit 212 identifies key correlations between the data and the migration criteria, enhancing the accuracy and reliability of the eligibility determination process.
[0062] In one embodiment, the analysis unit 212 also conducts predictive analysis by leveraging the trained model 216 to forecast customer behavior and network usage trends. This predictive capability enables the system 108 to determine not only current eligibility but also future migration potential, allowing the network provider to make informed decisions regarding network upgrades or expansions. The analysis unit 212 can apply both historical and real-time data, dynamically updating its assessments as new data is fed into the system 108, ensuring that the eligibility determination remains accurate and up to date.
[0063] For example, in a telecommunications scenario, the analysis unit 212 can receive the pre-processed data related to a customer's device compatibility and service usage history. The analysis unit 212 processes this data using the trained model 216 to identify whether the customer's device can support new cellular network technology and whether their usage patterns align with the benefits of a network upgrade. The analysis unit 212 further predicts if the customer will likely experience a significant improvement in network performance upon migration.
[0064] In an embodiment, the generating unit 214 is configured to create the visual representation of the identified customers eligible for migration from the existing network 106 to the new network 110. Upon receiving the results of the analysis from the analysis unit 212, the generating unit 214 processes this information to produce comprehensive visual outputs that effectively communicate the eligibility status of each customer. The visual representation may include graphical elements such as charts, graphs, or dashboards that highlight key metrics, trends, and insights regarding customer eligibility and network migration potential.
[0065] The generating unit 214 also customizes the visual representation based on predefined parameters or user preferences, ensuring that the output is tailored to the needs of the network provider. By incorporating various data visualizations, the generating unit 214 enhances the interpretability of the analysis results, allowing decision-makers to quickly assess the migration landscape and prioritize customers for outreach or migration efforts. This visual output is designed to be intuitive and user-friendly, enabling stakeholders to derive actionable insights efficiently.
[0066] For example, in a telecommunications context, the generating unit 214 might produce a dashboard displaying a list of customers segmented by their eligibility for migration. Each segment could be color-coded to indicate levels of urgency for migration, such as high, medium, or low eligibility. Additionally, the dashboard might include performance indicators, such as average data usage or customer satisfaction scores, providing a holistic view of the customer base. By visualizing this information, the generating unit 214 facilitates strategic planning and targeted marketing efforts by the network provider, allowing them to effectively engage with eligible customers and optimize the migration process.
[0067] The present invention offers several key technical advantages including automated eligibility determination using AI/ML algorithms, which enhances accuracy and reduces manual intervention. The system 108 facilitates real-time data processing from various sources such as onboarding, service usage, and historical data, for timely customer insights. The generating unit 214 produces customizable visual outputs for efficient data interpretation, while the system's 108 scalability supports high volumes of customer data. Additionally, the trend analysis enhances marketing strategies, and improved resource allocation optimizes operational efficiency. The architecture of the system 108 supports continuous model improvement and adaptable parameter configuration to meet evolving business needs.
[0068] FIG. 3 illustrates an exemplary block diagram of the system 108, according to one or more embodiments of the present invention. More specifically, FIG. 3 illustrates the system 108 for identifying the eligibility of the customers for migrating from the existing network 106 to the new network 110. It is to be noted that the embodiment with respect to FIG. 3 will be explained with respect to the UE 102 for the purpose of description and illustration and should nowhere be construed as limited to the scope of the present disclosure.
[0069] FIG. 3 shows communication between the UE 102, the system 108, the storage unit 114. For the purpose of description of the exemplary embodiment as illustrated in FIG. 3, the UE 102, the storage unit 114 uses a network protocol connection to communicate with the system 108. In an embodiment, the network protocol connection is the establishment and management of communication between the UE 102, the system 108, the storage unit 114, over the existing and new network 106,110 (as shown in FIG. 1) using a specific protocol or set of protocols. The network protocol connection includes, but not limited to, Session Initiation Protocol (SIP), System Information Block (SIB) protocol, Transmission Control Protocol (TCP), User Datagram Protocol (UDP), File Transfer Protocol (FTP), Hypertext Transfer Protocol (HTTP), Simple Network Management Protocol (SNMP), Internet Control Message Protocol (ICMP), Hypertext Transfer Protocol Secure (HTTPS) and Terminal Network (TELNET).
[0070] In an embodiment, the UE 102 includes a primary processor 302, a memory 304, and a user interface 306. In alternate embodiments, the UE 102 may include more than one primary processor 302 as per the requirement of the communication network 106. The primary processor 302, may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, single board computers, and/or any devices that manipulate signals based on operational instructions.
[0071] In an embodiment, the primary processor 302 is configured to fetch and execute computer-readable instructions stored in the memory 304. The memory 304 may be configured to store one or more computer-readable instructions or routines in a non-transitory computer-readable storage medium, which may be fetched and executed to transmit request for identifying eligibility of customers for migrating from the existing network 106 to the new network 110. The memory 304 may include any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as disk memory, EPROMs, FLASH memory, unalterable memory, and the like.
[0072] In an embodiment, the user interface 306 of the UE 102 includes a variety of interfaces, for example, a graphical user interface, a web user interface, a Command Line Interface (CLI), and the like. The user interface 306 is configured to allow the customer to transmit requests to identify customer’s eligibility for migration from the existing network 106 to the new network 110. The UE 102 transmits the request to identify customers eligibility for migration from the existing network 106 to the new network 110 through the processor 202 via the user interface 306.
[0073] In one embodiment, the processor 202 identifying the eligibility of the customers for migrating from an existing network 106 to a new network 110.
[0074] As mentioned earlier in FIG. 2, the system 108 includes the processors 202, and the memory 204, for managing the call between the user and the storage unit 114, which are already explained in FIG. 2. For the sake of brevity, a similar description related to the working and operation of the system 108 as illustrated in FIG. 2 has been omitted to avoid repetition.
[0075] Further, as mentioned earlier the processor 202 includes the retrieving unit 208, the feeding unit 210, the analysis unit 212, the generating unit 214, and the trained model 216 which are already explained in FIG. 2. Hence, for the sake of brevity, a similar description related to the working and operation of the system 108 as illustrated in FIG. 2 has been omitted to avoid repetition. The limited description provided for the system 108 in FIG. 3, should be read with the description provided for the system 108 in the FIG. 2 above, and should not be construed as limiting the scope of the present disclosure.
[0076] FIG. 4 is an exemplary architecture of the system 108 identifying eligibility of customers for migrating from an existing network 106 to a new network 110, according to one or more embodiments of the present disclosure.
[0077] The architecture 400 pertains to the system 108 which includes, a User 404, a graphical user interface (GUI) login unit 406, a Fault Management System (FMS) interface 408, a data integration unit 410, and a processing unit 402 which further includes, a data pre-processing module 412, an Algorithm execution module 414, a Data Lake 416, a Workflow Manager 418, a Trend Analysis unit 420 and a User Interface 422.
[0078] In an embodiment, the system 108 includes the user 404 that displays information to service providers. The user 404 is likely the employees of the service provider who need to access the system 108. Given the sensitive nature of customer data and migration analysis, it's reasonable to assume that access to this system would be restricted. The user 404 may include data analysts, network engineers, customer service representatives and managers of the service provider.
[0079] In another embodiment, the GUI login unit 406 is required to serve several purposes including verifying the identity of users trying to access the system 108, determining what level of access or permissions each user has within the system 108, protecting sensitive customer and network data from unauthorized access and potentially customizing the interface or data presented based on the user's 404 role or permissions. The system 108 that is logged in might integrate with the workflow manager 418 to assign tasks or present relevant information to specific users based on their roles and the current state of various workflows.
[0080] In an embodiment, the FMS interface 408 is a sophisticated data management platform designed to streamline operations within telecommunications and service industries. The FMS interface 408 functions by collecting and processing a variety of data types, including historical records, customer onboarding details, deactivation information, and system-generated data. This multifaceted approach ensures that the system 108 maintains a comprehensive view of customer interactions and operational metrics, enabling better decision-making.
[0081] At the core of FMS interface 408 is its ability to interface with an AI/ML model known as the processing unit 402. The processing unit 402 harnesses advanced algorithms for tasks such as trend analysis, prediction, and anomaly detection. By analyzing integrated data, the processing unit 402 identifies patterns that help predict which customers are eligible for migration from existing cellular technologies (like 4G or LTE) to new cellular network technology. The predictive capability not only enhances the customer experience by targeting the right users for upgrades but also optimizes resource allocation, thereby increasing operational efficiency.
[0082] In an embodiment, the data integration unit 410 is a critical component of the system 108, designed to facilitate the seamless amalgamation of diverse datasets originating from multiple sources within the FMS interface 408. The data integration unit 410 process employs a structured methodology to ensure the coherent alignment and unification of disparate data types, such as historical records, customer onboarding information, customer deactivation details, and system-generated data. The integration process encompasses the extraction, transformation, and loading (ETL) of data, wherein data from various origins is retrieved, standardized, and consolidated into a unified format suitable for subsequent analysis.
[0083] The functionality of the data integration unit 410 further encompasses data normalization and validation protocols, ensuring that the integrated datasets are devoid of discrepancies and inconsistencies. This ensures data integrity and enhances the reliability of the analyses performed by the AI/ML model, specifically the processing unit 402. By providing a consolidated view of the operational landscape, the data integration unit 410 enables advanced analytical operations, such as trend analysis and predictive modeling, thereby empowering the processing unit 402 to identify eligible customers for migration efficiently.
[0084] In an embodiment, the processing unit 402 comprises the data preprocessing module 412. The data preprocessing module 412 is responsible for processing data retrieved from the FMS interface 408. This data preprocessing encompasses both data normalization and data cleaning. Data normalization refers to the systematic reorganization of data within a database to enable users to perform subsequent queries and analyses effectively. Data cleaning involves the identification and rectification or removal of erroneous, corrupted, improperly formatted, duplicated, or incomplete entries within a dataset. The data preprocessing module 412 operates to normalize and cleanse the data acquired from the FMS interface 408, thereby ensuring the integrity and utility of the data for further analytical processes.
[0085] The processing unit 402 further comprises the algorithm execution module 414. The algorithm execution module 414 is integral to the operational capabilities of the processing unit 402, utilizes advanced artificial intelligence and machine learning (AI/ML) techniques to analyze the integrated datasets. Through this analytical process, the algorithm execution module 414 is capable of discerning intricate patterns and trends within the data, enabling the identification of customers who are deemed eligible for migration to upgraded service offerings, such as new cellular network technology. The identification process utilizes historical and current data, thus facilitating informed decision-making regarding customer migration strategies.
[0086] Additionally, the processing unit 402 encompasses the data lake 416, which serves as a distributed repository for storing processed data and outputs generated by the algorithms executed within the algorithm execution module 414. This data lake 416 is designed to accommodate vast volumes of data, ensuring scalability and flexibility in data management. The architecture of the data lake 416 allows for efficient storage and retrieval of diverse data types, enhancing the analytical capabilities of the processing unit 402.
[0087] The processing unit 402 further comprises the workflow manager 418, which is responsible for the orchestration and management of workflows within the system 108. The workflow manager 418 oversees the systematic collection of activities that must be executed to achieve specific tasks, thereby ensuring that each step of the process is performed in a coordinated and efficient manner. Additionally, the processing unit 402 incorporates the trend analysis unit 420. The trend analysis unit 420 employs advanced analytical techniques to scrutinize both current and historical data, facilitating the examination and prediction of customers eligible for migration to upgraded service offerings. The system 108 further features the user interface 422, which serves as the visualization layer for presenting data to service providers. Through this user interface 422, the eligible customers for migration, as detected by the processing unit 402, are displayed graphically. This graphical representation enhances the accessibility and comprehensibility of the analytical results, enabling service providers to make informed decisions based on the insights provided.
[0088] In an embodiment, the processing unit 402 may comprise the AI/ML model. The AI/ML model may predict and identify eligible customers for migration from the existing cellular network technology (4G or LTE) to the new cellular network technology by using trend analysis and historical data. Advantageously, the present invention saves time and resources by automatically detecting eligible customers for migration.
[0089] FIG. 5 is the flow diagram illustrating the method for identifying the eligibility of customers for migrating from the existing network 106 to the new network 110, according to one or more embodiments of the present invention. At step 502, the one or more processors 202 retrieve data pertaining to a plurality of customers from one or more sources. At step 504, the retrieved data is fed to a model by the one or more processors 202 for training. At step 506, the processors 202 analyze the retrieved data utilizing the trained model 216 to identify the eligibility of the plurality of customers for migration from the existing network 106 to the new network 110. At step 508, the analysis results in the identification of eligible customers, and at step 510, the method generates a visual representation of these identified customers for migration based on the analysis.
[0090] For example, a telecommunications company is planning to migrate customers from their existing 4G network to a new cellular network technology. Using the described method, the company first retrieves customer data from multiple sources such as customer profiles, usage history, and network performance logs as in Step 502. The data includes information on customers’ data consumption, frequency of usage, device compatibility, and geographic location.
[0091] At Step 504, the processors 202 feed this data into an AI/ML model designed to assess customer readiness for the new cellular network technology. The AI/ML model 216 is trained using historical migration patterns, identifying factors such as high data usage, demand for faster speeds, and compatibility with new cellular network devices.
[0092] In Step 506, the trained model 216 analyses the data to identify customers who meet the criteria for migration. For instance, users with high data consumption, living in urban areas where new infrastructure is available, and using new-compatible devices are deemed eligible for migration.
[0093] At Step 508, the system 108 finalizes its analysis, pinpointing customers who are ideal candidates for the upgrade. Finally, in Step 510, the method generates a visual representation of the identified customers, such as a dashboard showing the list of eligible users by region, data usage, and potential benefits of migration. The company’s marketing team can now use this information to target eligible customers with personalized offers, optimizing the transition to new cellular network technology while saving time and resources.
[0094] FIG. 6 illustrates a flow chart of the method 600 for identifying the eligibility of customers for migrating from the existing network 106 to the new network 110, according to one or more embodiments of the present invention. The method 600 disclosed below is purely exemplary in nature and should not be construed as limiting the scope of the present invention.
[0095] In another embodiment, the method 600 for identifying the customer eligibility for migrating from the existing network 106 to the new network 110 is disclosed. The method 600 involves multiple steps. At step 602, the one or more processors 202 retrieve data pertaining to a plurality of customers from various sources, which may include onboarding data, service usage data, deactivation data, and historical data. At step 604, this data is pre-processed by the processors 202 to ensure its suitability for training the model, and the pre-processed data is stored in a storage unit 114. At step 606, the pre-processed data is fed into an AI/ML model for training. Once the model 216 is trained, the processors 202 analyze the data using trend or pattern analysis related to parameters such as device compatibility, location information, and subscription plans to predict customer eligibility for migration.
[0096] The method 600 further includes automatic execution of actions to generate a visual representation of the identified eligible customers, carried out by the processors 202 utilizing the trained model 216. The visual representation may be in the form of graphs, tables, or dashboards that allow the service provider to easily view the customers ready for migration. Additionally, the customer eligibility analysis logs are stored for monitoring purposes. In one embodiment, these logs may be stored within the system 108 or in separate storage means, providing a record of migration eligibility checks and visualizations. The logs ensure that the system 108 can track customer migration readiness over time and assist in future decision-making for network transitions.
[0097] The present invention further discloses a non-transitory computer-readable medium having stored thereon computer-readable instructions. These instructions are executed by the one or more processors 202 to identify customer eligibility for network migration. In the first step, the one or more processors 202 retrieve customer data from various sources, such as onboarding records, service usage, deactivation data, and historical information, and configure the relevant parameters using a trained AI/ML model 216. Once the parameters are set, the processors 202 feed this data into the trained model 216 to initiate the analysis process. Utilizing the trained model 216, the processors 202 identify eligible customers for migration by analyzing trends and patterns based on predefined criteria, such as device compatibility, location, and subscription plans. This process ensures the accuracy of the analysis, preserving the efficiency and integrity of the migration process by identifying only customers who meet the eligibility criteria.
[0098] Finally, the one or more processors 202 automatically execute actions to identify and generate the visual representation of the eligible customers for migration based on the analysis performed by the trained model 216. This automatic execution is crucial for maintaining efficiency and reducing the need for manual intervention. By automating the identification and visualization process, the system 108 ensures that eligible customers are promptly identified and displayed, enabling service providers to act quickly and efficiently in migrating customers to the new network 110, thereby optimizing network performance and resource allocation.
[0099] 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.
[00100] The present invention provides advantages for utilizing a trained model 216 to automatically identify eligible customers for migration from the existing network 106 to the new network 110, significantly reducing the need for manual oversight. The present invention automates the data retrieval, analysis, and visualization process using preprocessed data, which streamlines the workflow and ensures the timely identification of customers eligible for migration. Further, the present invention provides regular and automated analysis processes that maintain optimal system performance by continuously assessing customer data for migration opportunities. Additionally, the present invention enables automated customer management processes that are inherently more scalable, allowing the system 108 to handle larger volumes of customer data efficiently and accurately.
[00101] 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

[00102] Environment - 100;
[00103] User Equipment (UE) - 102;
[00104] Server - 104;
[00105] Existing Network- 106;
[00106] System -108;
[00107] New Network- 110;
[00108] Data Sources- 112;
[00109] Storage unit – 114;
[00110] Processor - 202;
[00111] Memory - 204;
[00112] User Interface – 206;
[00113] Retrieving unit– 208;
[00114] Feeding unit – 210
[00115] Analysis unit – 212;
[00116] Generating unit - 214;
[00117] Trained Model –216;
[00118] Database –218;
[00119] Primary processor- 302;
[00120] Memory- 304;
[00121] User Interface – 306;
[00122] Processing system –402;
[00123] User – 404;
[00124] GUI Login unit– 406;
[00125] FMS Interface – 408;
[00126] Data Integration unit – 410;
[00127] Data Preprocessing module – 412;
[00128] Algorithm execution module – 414;
[00129] Data Lake– 416;
[00130] Workflow Manager – 418;
[00131] Trend Analysis unit– 420;
[00132] User Interface – 422.

,CLAIMS:

CLAIMS
We Claim:
1. A method of identifying eligibility of customers for migrating from an existing network to a new network, the method comprising the steps of:
retrieving, by one or more processors, data pertaining to a plurality of customers from one or more sources;
feeding, by the one or more processors, the retrieved data to a model for training; and
analysing, by the one or more processors, utilizing the trained model, the retrieved data to identify eligibility of the plurality of customers for migrating from the existing network to the new network.

2. The method as claimed in claim 1, wherein the data pertaining to the plurality of customers includes at least one of, onboarding data, service usage data, deactivation data and historical data.

3. The method as claimed in claim 1, wherein the step of retrieving, by one or more processors, data pertaining to the plurality of customers from one or more sources, further includes the steps of:
pre-processing, by the one or more processors, the retrieved data in order to utilize the pre-processed data for the training of the model; and
storing, by the one or more processors, the pre-processed data in a storage unit.

4. The method as claimed in claim 1, wherein the step of analysing, by the one or more processors, utilizing the trained model, the retrieved data to identify eligibility of the plurality of customers for migrating from the existing network to the new network includes the steps of:
performing, by the one or more processors, utilizing the trained model, a trend/pattern analysis related to one or more parameters of each of the plurality of customers; and
predicting, by the one or more processors, utilizing the trained model, the plurality of customers eligible for migrating from the existing network to the new network based on the trend/pattern analysis.

5. The method as claimed in claim 4, wherein the one or more parameters pertaining to the plurality of customers includes at least one of, customer’s device compatibility, customer’s location information, and customer’s subscription plans.

6. The method as claimed in claim 1, wherein the identified data of the plurality of customers eligible for migrating from the existing network to the new network is stored in the storage unit.

7. The method as claimed in claim 1, wherein the method further comprises the steps of:
generating, by the one or more processors, a visual representation of the identified plurality of customers for migration based on the analysis; and
displaying, by the one or more processors, the generated visual representation of the identified customers for migration to the user.

8. A system for identifying customers eligibility for migrating from an existing network to a new network, the system comprising:
a retrieving unit, configured to, retrieve, data pertaining to a plurality of customers from one or more sources;
a feeding unit, configured to, feed, the retrieved data to a model for training; and
an analysis unit, configured to, analyse, utilizing the trained model, the retrieved data to identify eligibility of the plurality of customers for migrating from the existing network to the new network.

9. The system as claimed in claim 8, wherein the data pertaining to the plurality of customers includes at least one of, onboarding data, service usage data, deactivation data and historical data.

10. The system as claimed in claim 8, wherein upon the retrieving unit retrieving, data pertaining to the plurality of customers from the one or more sources, the system further comprises:
a pre-processing unit, configured to, pre-process, the retrieved data in order to utilize the pre-processed data for the training of the model; and
a storage unit, configured to, store, the pre-processed data.

11. The system as claimed in claim 8, wherein the analysis unit analyses, utilizing the trained model, the retrieved data to identify the plurality of customers eligible for migrating from the existing network to the new network, by:
performing, utilizing the trained model, a trend/pattern analysis related to one or more parameters of each of the plurality of customers; and
predicting, utilizing the trained model, the plurality of customers eligible for migrating from the existing network to the new network based on the trend/pattern analysis.

12. The system as claimed in claim 11, wherein the one or more parameters pertaining to the plurality of customers includes at least one of, customer’s device compatibility, customer’s location information, and customer’s subscription plans.

13. The system as claimed in claim 8, wherein the identified data of the plurality of customers eligible for migrating from the existing network to the new network is stored in the storage unit.

14. The system as claimed in claim 8, wherein a generation unit is configured to:
generate a visual representation of the identified plurality of customers for migration based on the analysis; and
display, the generated visual representation of the identified customers for migration to the user.

15. A User Equipment (UE), comprising:
one or more primary processors communicatively coupled to one or more processors, the one or more primary processors coupled with a memory, wherein said memory stores instructions which when executed by the one or more primary processors causes the UE to:
transmit, a request by a user to the one or more processors for identifying the plurality of customers eligibility for migrating from the existing network to the new network; and
wherein the one or more processors is configured to perform the steps as claimed in claim 1.