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

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

FIELD OF THE INVENTION
[0001] The present invention relates to the field of telecommunications and network management, more particularly relates to management of consumer data in the network.
BACKGROUND OF THE INVENTION
[0002] In general, an onboarding process refers to the subscription set forth by a service provider, which the consumer may review and purchase and thereby get onboarded to the subscription. Onboarding will entitle the consumer to access the offers and activities offered by the service provider.
[0003] For performing an onboarding process, the service provider might face different issues such as competent subscription plans by other service providers. For example, the subscription plan of the current service provider may be costlier compared to other service providers in the market. Due to this issue, the service provider may not be able to onboard or reach a wider audience. Practically, handling of the onboarding process of the consumers is a cumbersome and time-consuming task.
[0004] Apart from onboarding, customer deactivation is another critical concern for service providers. Customers may deactivate their subscriptions due to various reasons, including dissatisfaction with services, better offerings from competitors, pricing concerns, or changes in usage patterns. A high churn rate negatively impacts revenue and long-term customer retention.
[0005] To address customer deactivation, there is a need for a proactive system that identifies potential churn indicators. These indicators may include reduced usage patterns, payment delays, service complaints, or shifting engagement levels. By analyzing such data, the system can generate insights to provide personalized retention strategies, such as tailored offers, loyalty benefits, or targeted engagement activities.
[0006] In view of the above, there is a dire need for a system and method to analyze trends for onboarding consumers/customer deactivation, which ensures that issues related to the onboarding process and deactivation of consumers are substantially reduced.
SUMMARY OF THE INVENTION
[0007] One or more embodiments of the present invention provides a method and a system for managing consumer data in a network.
[0008] In one aspect of the present invention, the method for managing the consumer data in the network is disclosed. The method includes the step of retrieving data pertaining to a consumer in the network from one or more sources. The method includes the step of analysing the retrieved data via an artificial intelligence/machine learning model to identify trends, for anomaly detection and prediction of one or more anomalies. The method includes the step of generating at least one output data in response to the analysis of the retrieved data. Further, the method includes the step of generating a visual representation utilizing the at least one output data.
[0009] In an embodiment, the data corresponds to onboarding data of the consumer, deactivation data of the consumer, and service-based data of the consumer.
[0010] In an embodiment, the retrieved data is pre-processed to convert a raw format of the retrieved data to a standard format.
[0011] In an embodiment, the method includes the step of storing the retrieved data in a database upon pre-processing of the retrieved data.
[0012] In an embodiment, the data is analysed for one or more circles of the network as per an input received from a user.
[0013] In an embodiment, the output data is at least one of but not limited to, trend reports, anomaly detection alerts, predictive insights, performance metrics, and actionable recommendations.
[0014] In one embodiment, the at least one output data is stored in the database, wherein the at least one stored output data is retrieved for further analysis as per requirement of a service provider.
[0015] In an embodiment, the visual representation is displayed on a user interface of the service provider in one of a graphical and chart form.
[0016] In an embodiment, the one or more processors is configured to generate the visual representation on receipt of a request from the service provider via the user interface.
[0017] In an embodiment, the request from the service provider via the user interface is transmitted to a workflow manager to generate the visual representation.
[0018] In another aspect of the present invention, the system for managing the consumer data in the network is disclosed. The system includes a retrieving unit configured to retrieve data pertaining to a consumer in the network from one or more sources. The system further includes an analysing unit configured to analyse, the retrieved data via an artificial intelligence/machine learning model to identify trends, for anomaly detection and prediction of one or more anomalies and at least one output data is generated in response to the analysis of the retrieved data. The system further includes a visualization module configured to generate a visual representation utilizing the at least one output data.
[0019] In yet another aspect of the present invention, a non-transitory computer-readable medium having stored thereon computer-readable instructions that, when executed by a processor, causes the processor to retrieve, data pertaining to a consumer in the network from one or more sources. The processor is further configured to analyse the retrieved data via an artificial intelligence/machine learning model to identify trends, for anomaly detection and prediction of one or more anomalies. The processor is further configured to generate, at least one output data in response to the analysis of the retrieved data and further the processor is configured to generate a visual representation utilizing the at least one output data
[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 a communication system for managing consumer data in a network, according to one or more embodiments of the present disclosure;
[0023] FIG. 2 is an exemplary block diagram of a system for managing the consumer data in the network, according to one or more embodiments of the present disclosure;
[0024] FIG.3 is an exemplary diagram of an architecture of the system of the FIG. 2, according to one or more embodiments of the present disclosure;
[0025] FIG. 4 is a signal flow diagram for managing consumer data in the network, according to one or more embodiments of the present disclosure; and
[0026] FIG. 5 is a flow chart illustrating the method for managing consumer data in the network, according to one or more embodiments of the present disclosure.
[0027] The foregoing shall be more apparent from the following detailed description of the invention.

DETAILED DESCRIPTION OF THE INVENTION
[0028] Some embodiments of the present disclosure, illustrating all its features, will now be discussed in detail. It must also be noted that as used herein and in the appended claims, the singular forms "a", "an" and "the" include plural references unless the context clearly dictates otherwise.
[0029] Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure including the definitions listed here below are not intended to be limited to the embodiments illustrated but is to be accorded the widest scope consistent with the principles and features described herein.
[0030] A person of ordinary skill in the art will readily ascertain that the illustrated steps detailed in the figures and here below are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
[0031] The present disclosure addresses the challenges faced in established technologies, to manage consumer data in a network. The present invention provides improved consumer data processes by efficient handling of the consumer onboarding data. Further the present invention provides enhanced data analysis and insights utilizing Artificial intelligence/Machine Learning (AI/ML) models. The enhanced data analysis and insights includes at least one of but not limited to, trend analysis, anomaly detection, predictive insights
[0032] Referring to FIG. 1, FIG. 1 illustrates an exemplary block diagram of a communication system 100 for managing consumer data in a network 105 according to one or more embodiments of the present disclosure. The consumer data includes at least one of consumer onboarding data. The consumer onboarding refers to the process of integrating and orienting new consumers into a subscription-based or consumer-facing service. The consumer is at least one of, but not limited to, a business entity and an individual.
[0033] The communication system 100 includes the network 105, a server 115, a User Equipment (UE) 110 and a system 120. The UE 110 aids a user to interact with the system 120.
[0034] For the purpose of description and explanation, the description will be explained with respect to the UE 110, or to be more specific will be explained with respect to a first UE 110a, a second UE 110b, and a third UE 110c, and should nowhere be construed as limiting the scope of the present disclosure. Each of the first UE 110a, the second UE 110b, and the third UE 110c is configured to connect to the server 115 via the network 105. In alternate embodiments, the UE 110 may include a plurality of UEs as per the requirement. For ease of reference, each of the first UE 110a, the second UE 110b, and the third UE 110c, will hereinafter be collectively and individually referred to as the “User Equipment (UE) 110.
[0035] In an embodiment, the UE 110 is one of, but not limited to, any electrical, electronic, electro-mechanical or an equipment and a combination of one or more of the above devices such as smartphones, virtual reality (VR) devices, augmented reality (AR) devices, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, mainframe computer, or any other computing device.
[0036] The network 105 may 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 network 105 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, or some combination thereof.
[0037] The network 105 includes, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, or some combination thereof. The network 105 may include, but is not limited to, a Third Generation (3G), a Fourth Generation (4G), a Fifth Generation (5G), a Sixth Generation (6G), a New Radio (NR), a Narrow Band Internet of Things (NB-IoT), an Open Radio Access Network (O-RAN), and the like.
[0038] The network 105 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 network 105 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.
[0039] The communication system 100 includes the server 115 accessible via the network 105. The server 115 may include by way of example but not limitation, one or more of a standalone server, a server blade, a server rack, a bank of servers, a server farm, hardware supporting a part of a cloud service or system, a home server, hardware running a virtualized server, one or more processors executing code to function as a server, one or more machines performing server-side functionality as described herein, at least a portion of any of the above, some combination thereof. In an embodiment, the entity may include, but is not limited to, a vendor, a network operator, a company, an organization, a university, a lab facility, a business enterprise side, a defense facility side, or any other facility that provides service.
[0040] The communication system 100 further includes the system 120 communicably coupled to the server 115 via the network 105. The system 120 is adapted to be embedded within the server 115 or is embedded as the individual entity. However, for the purpose of description, the system 120 is illustrated as remotely coupled with the server 115, without deviating from the scope of the present disclosure.
[0041] Operational and construction features of the system 120 will be explained in detail with respect to the following figures.
[0042] FIG. 2 illustrates an exemplary block diagram of the system 120 for managing consumer data in the network 105, according to one or more embodiments of the present disclosure.
[0043] As per the illustrated embodiment, the system 120 includes one or more processors 205, a memory 210, a User Interface (UI) 215, a processing unit 230, and a database 220. For the purpose of description and explanation, the description will be explained with respect to one processor 205 and should nowhere be construed as limiting the scope of the present disclosure. In alternate embodiments, the system 120 may include more than one processor 205 as per the requirement of the network 105. The one or more processors 205, hereinafter referred to as the processor 205 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, single board computers, and/or any devices that manipulate signals based on operational instructions.
[0044] As per the illustrated embodiment, the processor 205 is configured to fetch and execute computer-readable instructions stored in the memory 210. The memory 210 may be configured to store one or more computer-readable instructions or routines in a non-transitory computer-readable storage medium. The memory 210 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.
[0045] In an embodiment, the UI 215 includes 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 UI 215 facilitates communication of the system 120. In one embodiment, the UI 215 provides a communication pathway for one or more components of the system 120.
[0046] In an embodiment, the database 220 is one of, but not limited to, a Elasticsearch database, a centralized database, a cloud-based database, a commercial database, an open-source database, a distributed database, an end-user database, a graphical database, a No-Structured Query Language (NoSQL) database, an object-oriented database, a personal database, an in-memory database, a document-based database, a time series database, a wide column database, a key value database, a search database, a cache databases, and so forth. The foregoing examples of database 220 types are non-limiting and may not be mutually exclusive e.g., the database can be both commercial and cloud-based, or both relational and open-source, etc.
[0047] In order for the system 120 to manage the consumer data in the network 105, the processor 205 includes one or more modules. In one embodiment, the one or more modules include, but not limited to, a retrieving unit 225, a storage unit 235, analysing unit 240, a visualization module 245 are communicably coupled to each other.
[0048] The retrieving unit 225, the storage unit 235, the analysing unit 240, and the visualization module 245 in an embodiment, may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processor 205. In the examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processor 205 may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for processor 205 may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the memory 210 may store instructions that, when executed by the processing resource, implement the processor 205. In such examples, the system 120 may comprise the memory 210 storing the instructions and the processing resource to execute the instructions, or the memory 210 may be separate but accessible to the system 120 and the processing resource. In other examples, the processor 205 may be implemented by electronic circuitry.
[0049] In one embodiment, the user such as, but not limited to, a network operator, a service provider, and a network administrator utilizes the UI 215 to send a command to retrieve the data pertaining to the consumer in the network 105 from one or more sources.
[0050] In an alternate embodiment, the user such as, but not limited to, a network operator, a service provider, and a network administrator utilizes the UE 110 to send the command to retrieve the data pertaining to the consumer in the network 105 from one or more sources.
[0051] Based on receiving the command to retrieve the data pertaining to a consumer in the network 105 from one or more sources 330 (as shown in the Fig. 3) via the UI 215, the retrieving unit 225 is configured to retrieve data pertaining to the consumer in the network 105 from one or more sources 330. The consumer data corresponds to at least one of but not limited to, an onboarding data of the consumer, a deactivation data of the consumer, and a service-based data of the consumer. The onboarding data of the consumer refers to the information required for integrating a new consumer into the network 105. The onboarding data includes at least one of, but not limited to, personal information of the consumer, account setup details of the consumer, service subscription information of the consumer, verification and authentication data of the consumer. The deactivation data of the consumer refers to information related to the process of terminating a consumer’s account in the network 105. The deactivation data includes at least one of, but not limited to, termination requests, porting requests, account status, deactivation procedures, reason for deactivation. The service-based data of the consumer refers to the information related to the consumer’s interaction and usage of the telecommunication service in the network 105. The service-based data of the consumer includes, at least one of but not limited to, telecommunication service sage statistics, performance metrics of the telecommunication service, service requests and complaints. For example, the service based data includes, at least one of, the total voice call duration over a specific period (e.g., daily, weekly, monthly), number of text messages (SMS/MMS) sent and received, Average download and upload speeds experienced by the consumer, signal strength and coverage quality in different geographic areas, details of service activation or deactivation requests (e.g., adding a data plan or canceling a subscription), requests for changing subscription plans or upgrading/downgrading services, logs of complaints registered by the consumer (e.g., network issues, billing discrepancies, or slow internet) etc.
[0052] The one or more sources 330 is at least one of, but not limited to, Network Management Systems (NMS), identity management systems, data warehouses and analytics platforms, customer service platforms, application servers and Content Delivery Networks (CDNs).
[0053] Further the retrieved data pertaining to the consumer in the network 105 is transmitted to the processing unit 230.
[0054] On the receipt of receiving the retrieved data pertaining to the consumer in the network 105, the retrieved data is configured to pre-process the retrieved data to convert a raw format of the retrieved data to a standard format and to obtain the normalized form of the retrieved data. In an embodiment, the raw format of the data refers to unprocessed state of retrieved data, prior to data processing. The raw format of the retrieved data is characterized by the attributes such as, but not limited to, inconsistent, unstructured, limited usability for analysis. The standard format of the data refers to the processed state of the retrieved data to achieve the characteristics, such as but not limited to, a uniform, structured, and normalized data. The pre-processing of the retrieved data includes one or more steps.
[0055] In an exemplary embodiment, the one or more steps can include, but is not limited to, data cleaning, data reduction, data standardization, data structuring and the like to convert the format of the retrieved data into the standard format and to obtain the normalized form of the retrieved data.
[0056] Upon pre-processing the retrieved data is stored in the database 220 using the storage unit 235. Subsequently, the retrieved data is transmitted to the analysis unit 240.
[0057] On the receipt of receiving pre-processed data, the analysing unit 240 is configured to analyse the pre-processed data via an Artificial Intelligence/Machine Learning (AI/ML) model to identify trends, for anomaly detection and prediction of one or more anomalies.
[0058] In an embodiment, the analysing unit 240 selects the AI/ML model based on the type of analysis required. The AI/ML model include at least one of, but not limited to, time-series forecasting model, classification model, clustering model, and regression model depending on the nature of the pre- processed data and the analysis required
[0059] Further the analysing unit 240 executes the selected AI/ML model on the pre - processed data to perform the analysis. The analysis includes, but is not limited to, identifying trends, detecting anomalies, predicting one or more anomalies of the future and the like.
[0060] In an exemplary, once the particular AI/ML model is selected by the analysing unit 240, the analysing unit 240 executes the selected AI/ML model on the pre-processed data. The initial step of the execution includes, but not limited to, inputting the pre-processed data into the selected AI/ML model. Further, if the selected AI/ML model requires training, thereby the analysing unit 240 trains the selected AI/ML model by utilizing the historical pre-processed data from the at least one of, but not limited to, one or more sources 330 of the historical pre- processed data. The historical pre-processed data refers to the pre-processed data, which accumulated periodically from the past analysis of the system 120, which is stored in the one or more sources 330 of the historical pre- processed data for analysis. The one or more sources 330 of the historical pre- processed data includes but are not limited to, Network Management Systems (NMS), identity management systems, data warehouses, customer service platforms.
[0061] Further, the selected AI/ML model utilizes the historical pre-processed data to generate forecasts to identify trends and patterns. The forecasts include at least one of but are not limited to, predictions about the future trends, values and patterns.
[0062] Further the AI/ML model analyses to identify significant trends periodically to provide periodic trend analysis, cyclical patterns, upward or downward trends and the like.
[0063] For example, the analysing unit 240 selects and utilizes, the time-series forecasting model to analyze pre-processed consumer onboarding data to identify seasonal trends in the consumer behavior periodically. The time-series model includes, at least one of but not limited to, Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), Exponential Smoothing (ETS), prophet. The analysing unit 240 utilizes classification model to categorize pre-processed data into a predefined class. The predefined classes facilitate the user such as but not limited to, service provider in decision-making, risk assessment, pattern recognition, enhancing user experience, improving operational efficiency and the like. The classification model includes, but not limited to, logistic regression, decision trees, random forest, Support Vector Machines (SVM).
[0064] The analysing unit 240 utilizes the cluster models for several purposes related to grouping pre – processed data into clusters. The several purposes include, but not limited to, identifying hidden patterns, exploring data, market analysis, identifying outliers, resource management.
[0065] For anomaly detection, the analysing unit 240 utilizes, but not limited to, machine learning models and deep learning models to identify deviations of pre-processed data points from expected patterns. The machine learning models include, but is not limited to, isolation forest, One-Class Support Vector Machine (SVM), Local Outlier Factor (LOF). The deep learning models can include, but not limited to, autoencoders, Variational Autoencoders (VAE), Generative Adversarial Networks (GANs).
[0066] For the prediction of the one or anomalies, the analysing unit 240 utilizes, but not limited to, regression models, neural networks, hybrid models to forecast future occurrences of anomalies. The regression models include, but are not limited to, linear regression and logistic regression. The neural network includes, but not limited to, Feedforward Neural Networks (FNNs), Recurrent Neural Networks (RNNs). The hybrid models include, but not limited to, combined models.
[0067] In an embodiment, the data is analysed for one or more circles of the network as per an input received from a user. The one or more circles of the network refer to specific geographic areas or service regions that a network operator divides its operations into. For example, telecom operators often divide their coverage areas into regions like "north zone", "south zone" or by state.
[0068] Thereafter, the visualization module 245 is configured to generate at least one output data in response to the analysis of the pre-processed data. The at least one output data refers to the information generated by the system 120 based on the analyses of the pre-processed data. The information includes, at least one of but not limited to, trend reports, anomaly detection alerts, predictive insights, performance metrics, and actionable recommendations. Further, the at least one output data is stored in the database 220. In an embodiment, the at least one stored output data is retrieved for further analysis as per requirement of a service provider.
[0069] Further the visualization module 245 is configured to generate visual representation utilizing the at least one output data on receipt of a request from at least one of, but not limited to, a service provider via the UI 215. In an embodiment, the request from the service provider via the UI 215 is transmitted to a workflow manager 320 to generate the visual representation. The request includes, but is not limited to, type of visual representation required (e.g., graphs, charts, or dashboards), Filters or criteria for the data (e.g., region/circle, time period, anomaly type). The workflow manager acts as an orchestration unit that processes the request from the service provider. Further, upon receiving the request, the workflow manager 320, validates the input parameters from the service provider, fetches the required data or insights (e.g., trend reports, anomaly alerts, predictive insights) stored in the database and generates the visual representation based on the retrieved data.
[0070] In an embodiment, the visual representation is displayed on the UI 215 of the service provider in one of a graphical and chart form. Thereby the system 120 facilitates the service provider to identify and understand the trend analysis to foresee the potential issues. Advantageously the visual representation of the at least one output provides the service provider with a decision-making ability and capability to optimize the overall consumer onboarding experience.
[0071] FIG. 3 is an exemplary architecture 300 of the system 120 for managing the consumer onboarding in the network 105, according to one or more embodiments of the present invention. The exemplary embodiment as illustrated in the FIG. 3 includes a Fault Management System (FMS) 305, a pre-processor 310, an Artificial Intelligence and Machine Learning Module (AI/ML) module 315, the database 220, a workflow manager 320, the visualization module 245, and the UI 215, one or more sources 330
[0072] In one embodiment, the user such as, but not limited to, a network operator, a service provider, and a network administrator utilizes the UI 215 to send the command to retrieve the data pertaining to the consumer in the network 105 from one or more sources.
[0073] Based on the command received to retrieve the data pertaining to the consumer registered in the network 105 from the one or more sources 330 via the UI 215, the FMS 305 retrieves the data pertaining to the consumer registered in the network 105 from the one or more sources 330. The data corresponds to at least one of but not limited to, an onboarding data of the consumer, a deactivation data of the consumer, and a service-based data of the consumer. The one or more sources 330 can include at least one of but not limited to, Network Management Systems (NMS), identity management systems, data warehouses and analytics platforms, customer service platforms, application servers and Content Delivery Networks (CDNs).
[0074] Further the FMS 305 transmits the retrieved data pertaining to the consumer registered in the network 105 to the pre-processor 310. On the receipt of receiving data pertaining to the consumer registered in the network 105, the pre-processor 310 performs pre-processing of the data to convert the raw format of the retrieved data to the standard format and to obtain the normalized form of the data. The pre-processing of the data includes one or more steps such as but not limited to, data cleaning, data reduction, data standardization, data structuring and the like. Upon pre-processing the data, the pre-processed data is transmitted to the AI/ML module 315.
[0075] The AI/ML module 315 is an AI/ML based system designed to perform multiple AI/ML techniques on the pre-processed data to perform trend analysis, to detect anomalies and to generate LLM AI outputs. The AI/ML module 315 analyse the received pre-processed data via an Artificial Intelligence/Machine Learning (AI/ML) model to identify trends, for anomaly detection and prediction of one or more anomalies in the pre-processed data.
[0076] The AI/ML module 315 selects the AI/ML model based on the type of analysis required. The AI/ML model include at least one of, but not limited to, time-series forecasting model, classification model, clustering model, and regression model depending on the nature of the pre- processed data and the analysis required. Further the AI/ML module 315 executes the selected AI/ML model on the pre - processed data to perform the analysis. The analysis includes, but is not limited to, identifying trends, detecting anomalies, predicting one or more anomalies of the future and the like. Further, the AI/ML module 315 generates at least one output data in response to the analysis of the pre-processed data. The at least one output data refers to the information generated by the system 120 based on the analyses of the pre-processed data. The information includes, at least one of but not limited to, trend reports, anomaly detection alerts, predictive insights, performance metrics, and actionable recommendations. Further, the at least one output data is stored in the database 220.
[0077] The workflow manager 320 receives the request from the at least one of, but not limited to, a service provider via the UI 215. The request includes, at least one of but not limited to, visual representation information of the at least one output data. Based on the request received, the workflow manager 320 retrieves at least one output data stored in the database 220 and transmits the retrieved at least one output to the visualization module 325. Further the workflow manager 320 assigns the visual representation task to the trend visualization module 325 corresponding to the request.
[0078] Upon the assignment of the task, the visualization module 325 generate visual representation utilizing the at least one output data received from the workflow manager 320 corresponding to the request received from at least one of, but not limited to, a service provider.
[0079] Further the visual representation is displayed on the UI 215 of the service provider in one of a graphical and chart form. Thereby the system 120 facilitates the service provider to identify and understand the trend analysis to foresee the potential issues. Advantageously the visual representation of the at least one output provides the service provider with a decision-making ability and capability to optimize the overall consumer onboarding experience.
[0080] FIG. 4 is a signal flow diagram for managing the consumer data in the network 105, according to one or more embodiments of the present invention. For the purpose of description, the signal flow diagram is described with the embodiments as illustrated in FIG. 2 and FIG. 3 and should nowhere be construed as limiting the scope of the present disclosure.
[0081] At step 405, the data pertaining to the consumer in the network 105 is retrieved from one or more sources 330.
[0082] At step 410, the retrieved data pertaining to the consumer in the network 105 is transmitted from one or more sources 330 to the FMS 305. The consumer data corresponds to at least one of but not limited to, an onboarding data of the consumer, a deactivation data of the consumer, and a service-based data of the consumer.
[0083] At step 420, the FMS 305 transmits the retrieved data pertaining to the consumer in the network 105 to the pre-processor 310 for pre-processing of the data to the pre-processor 310.
[0084] At step 420, On the receipt of receiving data, the pre-processor 310 performs pre-processing of the data to convert the raw format of the retrieved data to the standard format and to obtain the normalized form of the data. Upon pre-processing the data, the pre-processed data is transmitted to the AI/ML module 315.
[0085] At step 425, the AI/ML module 315 analyse the received pre-processed data via an Artificial Intelligence/Machine Learning (AI/ML) model for trend analysis, anomaly detection and prediction of one or more anomalies in the pre-processed data. Further the AI/ML module 315 generates at least one output data in response to the analysis of the pre-processed data. Further, the at least one output data is stored in the database 220.
[0086] At step 430, the workflow manager 320 receives the request from the at least one of, but not limited to, a service provider via the UI 215. The request includes, at least one of but not limited to, visual representation information of the at least one output data.
[0087] At step 435, based on the request received from the at least one of, but not limited to, a service provider via the UI 215, the workflow manager 320 retrieves at least one output data stored in the database 220.
[0088] At step 440, the workflow manger 320 transmits the retrieved at least one output data to the visualization module 245. Further the workflow manager 320 assigns the visual representation task to the trend visualization module 245 corresponding to the request.
[0089] At step 445, upon the assignment of the task, the visualization module 245 generate visual representation utilizing the at least one output data received from the workflow manager 320 corresponding to the request received from at least one of, but not limited to, a service provider. Further the visual representation is displayed on the UI 215 of the service provider in one of a graphical and chart form.
[0090] FIG. 5 is a flow diagram illustrating a method for managing consumer onboarding in the network 105, according to one or more embodiments of the present disclosure.
[0091] At step 505, the method 500 includes the step of retrieving the data pertaining to the consumer in the network 105 from the one or more sources 330. The data corresponds to at least one of but not limited to, an onboarding data of the consumer, a deactivation data of the consumer, and a service-based data of the consumer. The one or more sources can include at least one of but not limited to, Network Management Systems (NMS), identity management systems, data warehouses and analytics platforms, customer service platforms, application servers and Content Delivery Networks (CDNs).
[0092] Further, the retrieved data is pre-processed to convert a raw format of the retrieved data to a standard format. The pre-processing of the retrieved data includes one or more steps, for example the one or more steps can include, but is not limited to, data cleaning, data reduction, data standardization, data structuring and the like to the format of the retrieved data into the standard format and to normalize the retrieved data. Upon pre-processing the retrieved data, the storage unit 235 is configured to store the pre-processed data in the database 220. In an embodiment, the database 220 is at least one of but not limited to, distributed data lake used to store the processed outputs.
[0093] At step 510, the method 500 includes the step of analysing the pre-processed data via the Artificial Intelligence/Machine Learning (AI/ML) model to identify trends, for anomaly detection and prediction of one or more anomalies. In an embodiment, the analysing unit 240 selects AI/ML model based on the type of analysis required. The AI/ML model can include at least one of but not limited to, time-series forecasting, classification, clustering, and regression depending on the nature of the pre- processed data and the analysis required. Further the analysing unit 240 executes the selected AI/ML model on the pre - processed data for the analyses. The analyses include, but are not limited to, identifying trends, detecting anomalies, predicting one or more anomalies of the future and the like. In an exemplary, once the particular AI/ML model is selected by the analysing unit 240, the analysing unit 240 executes the selected AI/ML model on the pre-processed data. The initial step of the execution includes, but not limited to, inputting the pre-processed data into the selected AI/ML model. Further, if the selected AI/ML model requires training, thereby the analysing unit 240 trains the selected AI/ML model by utilizing the historical pre-processed data from the at least one of, but not limited to, one or more sources 330 of the historical pre- processed data. The historical pre-processed data refers to the pre-processed data, which accumulated periodically from the past analysis of the system 120, which is stored in the one or more sources 330of the historical pre- processed data for analysis. The one or more sources 330 of the historical pre- processed data includes but are not limited to, Network Management Systems (NMS), identity management systems, data warehouses, customer service platforms.
[0094] Further, the selected AI/ML model utilizes the historical pre-processed data to generate forecasts and to identify the trends. The forecasts include at least one of but are not limited to, predictions about the future trends, values and patterns.
[0095] Further the AI/ML model analyses the identify significant trends periodically to provide periodic trend analysis, cyclical patterns, upward or downward trends and the like.
[0096] At step 515, the method 500 includes the step of generating at least one output data in response to the analysis of the pre–processed data. The at least one output data refers to the information generated by the system 120 based on the analyses of the pre-processed data. The information can include at least one of but not limited to, trend reports, anomaly detection alerts, predictive insights, performance metrics, and actionable recommendations. Further, the at least one output data is stored in the database 220. In an embodiment, the at least one stored output data is retrieved for further analysis as per requirement of a service provider.
[0097] At step 520, the method 500 includes the step of generating the visual representation utilizing the at least one output data on receipt of a request from the service provider via the UI 215. In an embodiment, the visual representation is displayed on the UI 215 of the service provider in one of a graphical and chart form. The visual representation facilitates the service provider to identify and understand the trend analysis, anomalies and the like. Advantageously the visual representation of the at least one output provides the service provider with a decision-making ability, improve processes, and capability to optimize the overall consumer onboarding experience.
[0098] A person of ordinary skill in the art will readily ascertain that the illustrated embodiments and steps in description and drawings (FIG.1-5) are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
[0099] The present disclosure incorporates technical advancement that facilitates improved consumer data process by efficient data handling in the network 105. The present invention facilitates streamlined onboarding or deactivation process by efficient data handling and processing various types of consumer data, such as but not limited to, personal information, account setup details, and service subscriptions. By utilizing the Artificial intelligence/ Machine Learning (AI/ML) models advanced analysis, actionable insights, and improved decision-making capabilities. The present invention provides better consumer experience, enhanced network performance, and streamlined operational processes
[00100] The present invention provides various advantages, including optimal resource utilization and reduced execution time. The preset invention provides the enhanced data analysis and insights. The present invention facilitates trend analysis, anomaly detection, and predictive insights utilizing an AI/ML model. The present invention provides performance metrics and actionable recommendations based on data analysis. The present invention generates visual representations of data insights, such as, but not limited to, charts and graphs, which help service providers to understand trends, anomalies, and predictions. Advantageously the visual representations facilitate efficient decision making. The present invention facilitates the service provider to identify and understand the trend analysis to foresee the potential issues.
[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] Communication system – 100
[00103] Network – 105
[00104] Server – 115
[00105] System – 120
[00106] Processor -205
[00107] Memory – 210
[00108] User Interface (UI) – 215
[00109] Database- 220
[00110] Retrieving unit - 225
[00111] Processing unit - 230
[00112] Storage unit –235
[00113] Analysing unit – 240
[00114] Visualization module – 245
[00115] FMS – 305
[00116] Preprocessor – 310
[00117] AI/ML module – 315
[00118] Workflow manager – 320
[00119] One or more sources - 330
,CLAIMS:
CLAIMS:
We Claim
1. A method of managing consumer data in a network, the method comprising the steps of:
retrieving, by one or more processors, data pertaining to a consumer in the network from one or more sources;
analysing, by the one or more processors, the retrieved data via an artificial intelligence/machine learning model to identify trends, for anomaly detection and prediction of one or more anomalies;
generating, by the one or more processors, at least one output data in response to the analysis of the retrieved data; and
generating, by the one or more processors, a visual representation utilizing the at least one output data.

2. The method as claimed in claim 1, wherein the consumer data corresponds to onboarding data of the consumer, deactivation data of the consumer, and service based data of the consumer.

3. The method as claimed in claim 1, wherein, the retrieved data is pre-processed to convert a raw format of the retrieved data to a standard format.

4. The method as claimed in claim 1, wherein the method comprises the step of storing, by the one or more processors, the retrieved data in a database upon pre-processing of the retrieved data.

5. The method as claimed in claim 1, wherein the data is analysed for one or more circles of the network as per an input received from a user.

6. The method as claimed in claim 1, wherein the output data is at least one of but not limited to, trend reports, anomaly detection alerts, predictive insights, performance metrics, and actionable recommendations.

7. The method as claimed in claim 1, wherein the at least one output data is stored in the database, wherein the at least one stored output data is retrieved for further analysis as per requirement of a service provider.

8. The method as claimed in claim 1, wherein the visual representation is displayed on a user interface of the service provider in one of a graphical and chart form.

9. The method as claimed in claim 1, wherein the one or more processors is configured to generate the visual representation on receipt of a request from the service provider via the user interface.

10. The method as claimed in claim 1, wherein the request from the service provider via the user interface is transmitted to a workflow manager to generate the visual representation.

11. A system for managing consumer data in a network, the system comprising:
a retrieving unit configured to retrieve, data pertaining to a consumer in the network from one or more sources;
an analysing unit configured to analyse, the retrieved data via an artificial intelligence/machine learning model to identify trends, for anomaly detection and prediction of one or more anomalies; and
generate, at least one output data in response to the analysis of the retrieved data; and
a visualization module configured to,
generate, a visual representation utilizing the at least one output data.

12. The system as claimed in claim 11, wherein the consumer data corresponds to onboarding data of the consumer, deactivation data of the consumer, and service based data of the consumer.

13. The system as claimed in claim 11, wherein the retrieved data is pre-processed to convert a raw format of the retrieved data to a standard format.

14. The system as claimed in claim 11, comprising a storage unit configured to store, the retrieved data in a database on pre-processing of the retrieved data.

15. The system as claimed in claim 11, wherein the data is analysed for one or more circles of the network as per an input received from a user.

16. The system as claimed in claim 11, wherein the output data is at least one of but not limited to, trend reports, anomaly detection alerts, predictive insights, performance metrics, and actionable recommendations.

17. The system as claimed in claim 11, wherein the at least one output data is stored in the database, wherein the at least one stored output data is retrieved for further analysis as per requirement of a service provider.

18. The system as claimed in claim 11, wherein the visual representation is displayed on a user interface of the service provider in one of a graphical and chart form.

19. The system as claimed in claim 11, wherein the visualization module is configured to generate the visual representation on receipt of a request from the service provider via the user interface.