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 BUILDING A SUBSCRIBER PROFILE USING A TELECOM 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 wireless communication system, more particularly relates to a method and a system for building a subscriber profile using a telecom network.
BACKGROUND OF THE INVENTION
[0002] In order to ascertain telecom subscriber experience, it is very important to retrieve analytics data from analytics engines such as probing units. The probing units include a first probing unit and a second probing unit. The first probing unit and the second probing unit are integral components within a telecommunications network, serving distinct analytical functions. The first probing unit is dedicated to data analytics at the subscriber level, gathering crucial Radio Frequency (RF) data encompassing coverage, usage, and visited areas. The second probing unit operates at the core level, focusing on clear code data which helps us to identify the component at which the problem occurred in case of network failure.
[0003] In the conventional system, telecom operators rely on the insights provided by the first probing unit and the second probing unit to compile comprehensive subscriber profiles. These profiles capture the customer experience from both RF and core perspectives periodically such as for example, daily, weekly, or monthly. Telecom operators use this data to improve overall customer experience, necessitating the merging of the first probing unit and the second probing unit data for a holistic view. However, this process of summarizing and creating subscriber profiles is a cumbersome and time-consuming task since the telecom operators have to time and again refer to data from the first probing unit and the second probing unit individually and in collaboration in order to provide a comprehensive report, which in some situations may lead to confusion and repetition and due to which the telecom operators may miss out on important information, which may be critical to the network performance. Further, while integrating data retrieved from the first probing unit and the second probing unit, there is a tendency of occurrence of errors due to vast amount of data which requires to be examined and thereafter integrated.
[0004] Further, the traditional systems may not provide the report in a timely manner, due to which if any critical issues are identified, the telecom operators may follow a reactive approach to solve the issues, which if not done in a timely manner may affect the performance of the network.
[0005] There is, therefore, a need for efficiently generating a subscriber profile by data retrieved from analytics engines such as probing units.
SUMMARY OF THE INVENTION
[0006] One or more embodiments of the present disclosure provide a method and system for building a subscriber profile using a telecom network.
[0007] In one aspect of the present invention, the system for building the subscriber profile using the telecom network is disclosed. The system includes a collecting unit, configured to collect, analytics data pertaining to the subscriber from one or more sources. The system further includes a training unit, configured to train a model, with the collected analytics data, wherein the model learns at least one of patterns and relationships between different variables of the analytics data. The system further includes a predicting unit, configured to predict, using the trained model, one or more threshold values pertaining to real-time network data. The system further includes a detecting unit, in response to detecting changes in at least one of, the patterns and relationships between the different variables of the analytics data with respect to the real-time network data, an adjusting unit, configured to, dynamically adjust, at least one of, the one or more threshold values and the policies using the trained model. The system further includes a generating unit, configured to, generate, the subscriber profile report using the model based on the learnt patterns and connections between the different variables of the analytics data and adjusted at least one of, the threshold values and the policies.
[0008] In an embodiment, the one or more sources includes one or more probing units.
[0009] In an embodiment, the collecting unit is further configured to pre-process, the collected analytics data.
[0010] In an embodiment, the analytics data includes at least one of, Radio Frequency (RF) level data, network core level data, one or more previous threshold values and one or more pre-defined values of one or more parameters.
[0011] In an embodiment, the generating unit is further configured to generate, an updated subscriber profile report in response to learning new patterns and relationships between different variables with respect to new data.
[0012] In another aspect of the present invention, the method for building the subscriber profile using the telecom network is disclosed. The method includes the step of collecting analytics data pertaining to the subscriber from one or more sources. The method further includes the step of training a model, with the collected analytics data, wherein the model learns at least one of, patterns and relationships between different variables of the analytics data. The method further includes the step of predicting, by the one or more processors, using the trained model, one or more threshold values pertaining to real-time network data. The method further includes the step that in response to detecting, changes in at least one of, the patterns and relationships between the different variables of the analytics data with respect to the real-time network data, dynamically adjusting, by the one or more processors, at least one of, the one or more threshold values and the policies using the trained model. The method further includes the step of generating the subscriber profile report using the model based on the learnt patterns and connections between the different variables of the analytics data and adjusted at least one of, the threshold values and the policies.
[0013] In another aspect of the invention, a non-transitory computer-readable medium having stored thereon computer-readable instructions is disclosed. The computer-readable instructions are executed by a processor. The processor is configured to collect, analytics data pertaining to the subscriber from one or more sources. The processor is configured to train a model, with the collected analytics data, wherein the model learns at least one of, patterns and relationships between different variables of the analytics data. The processor is configured to predict, using the trained model, one or more threshold values pertaining to real-time network data. The processor is configured to detect changes in at least one of, the patterns and relationships between the different variables of the analytics data with respect to the real-time network data, dynamically adjust, at least one of, the one or more threshold values and the policies using the trained model. The processor is configured to generate the subscriber profile report using the model based on the learnt patterns and connections between the different variables of the analytics data and adjusted at least one of, the threshold values and the policies.
[0014] 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
[0015] 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.
[0016] FIG. 1 is an exemplary block diagram of an environment for building a subscriber profile using a telecom network, according to one or more embodiments of the present invention;
[0017] FIG. 2 is an exemplary block diagram of a system for building the subscriber profile using the telecom network, according to one or more embodiments of the present invention;
[0018] FIG. 3 is an exemplary block diagram of an architecture implemented in the system of the FIG. 2, according to one or more embodiments of the present invention;
[0019] FIG. 4 is a flow diagram for building the subscriber profile using the telecom network, according to one or more embodiments of the present invention, according to one or more embodiments of the present invention; and
[0020] FIG. 5 is a schematic representation of a method for building the subscriber profile using the telecom network, according to one or more embodiments of the present invention, according to one or more embodiments of the present invention.
[0021] The foregoing shall be more apparent from the following detailed description of the invention.
DETAILED DESCRIPTION OF THE INVENTION
[0022] 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.
[0023] 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.
[0024] 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.
[0025] The present subject matter provides for a processing hub incorporating an Artificial Intelligence/Machine Learning (AI/ML) model. The processing hub enables telecom operators to take proactive steps in the event of any potential network issues that are detected by the processing hub based on retrieved analytics data from one or more probing units. Further, a policy management system in collaboration with the processing hub predicts optimal threshold values dynamically, considering factors such as, but not limited to, time and geography. Further, the processing hub in collaboration with the policy management system dynamically adjusts thresholds and policies based on factors such as, but not limited to, time and geography. Further, the processing hub generates a (near) real-time summarized report of the complete user profile based on analytics data retrieved from the one or more probing units.
[0026] FIG. 1 illustrates an exemplary block diagram of an environment 100 for building a subscriber profile using a telecom network 106, according to one or more embodiments of the present disclosure. In this regard, the environment 100 includes a User Equipment (UE) 102, a server 104, the telecom network 106 and a system 108 communicably coupled to each other for building the subscriber profile using the telecom network 106.
[0027] In an embodiment, the subscriber profile is a structured representation of information about an individual subscriber. The subscriber profile includes details about the subscriber's usage behavior, preferences, service history, and other relevant characteristics derived from network and service analytics data. The telecom network 106 enables the transmission of data, voice, and video information between users over distances The telecom network 106 includes various interconnected components such as switches, routers, transmission links, and other network elements, allowing communication across different devices and locations.
[0028] As per the illustrated embodiment and for the purpose of description and illustration, the UE 102 includes, but not limited 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. In alternate embodiments, the UE 102 may include a plurality of UEs as per the requirement. For ease of reference, each of the first UE 102a, the second UE 102b, and the third UE 102c, will hereinafter be collectively and individually referred to as the “User Equipment (UE) 102”.
[0029] In an embodiment, the UE 102 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 a smartphone, 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.
[0030] The environment 100 includes the server 104 accessible via the telecom network 106. The server 104 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.
[0031] The telecom network 106 includes, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, or some combination thereof. The network 106 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.
[0032] The telecom network 106 may also include, by way of example but not limitation, at least a portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, etc. one or more messages, packets, signals, waves, voltage or current levels, some combination thereof, or so forth. The telecom network 106 may also include, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, a VOIP or some combination thereof.
[0033] The environment 100 further includes the system 108 communicably coupled to the server 104 and the UE 102 via the network 106. The system 108 is configured to build the subscriber profile using the telecom network 106. As per one or more embodiments, the system 108 is adapted to be embedded within the server 104 or embedded as an individual entity.
[0034] Operational and construction features of the system 108 will be explained in detail with respect to the following figures.
[0035] FIG. 2 is an exemplary block diagram of the system 108 for building the subscriber profile using the telecom network 106, according to one or more embodiments of the present invention.
[0036] As per the illustrated embodiment, the system 108 includes one or more processors 202, a memory 204, a user interface 206, and a database 208. In an embodiment, the system 108 is communicable coupled with one or more probing units 224. For the purpose of description and explanation, the description will be explained with respect to one processor 202 and should nowhere be construed as limiting the scope of the present disclosure. In alternate embodiments, the system 108 may include more than one processor 202 as per the requirement of the telecom network 106. 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.
[0037] As per the illustrated embodiment, the processor 202 is configured to fetch and execute computer-readable instructions stored in the memory 204. The memory 204 may be configured to store one or more computer-readable instructions or routines in a non-transitory computer-readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory 204 may include any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as disk memory, EPROMs, FLASH memory, unalterable memory, and the like.
[0038] In an embodiment, the user interface 206 includes a variety of interfaces, for example, interfaces for a graphical user interface, a web user interface, a Command Line Interface (CLI), and the like. The user interface 206 facilitates communication of the system 108. In one embodiment, the user interface 206 provides a communication pathway for one or more components of the system 108. Examples of such components include, but are not limited to, the UE 102 and the database 208.
[0039] The database 208 is one of, but not limited to, a centralized database, a cloud-based database, a commercial database, an open-source database, a distributed database, an end-user database, a graphical database, a No-Structured Query Language (NoSQL) database, an object-oriented database, a personal database, an in-memory database, a document-based database, a time series database, a wide column database, a key value database, a search database, a cache databases, and so forth. The foregoing examples of database 208 types are non-limiting and may not be mutually exclusive e.g., a database can be both commercial and cloud-based, or both relational and open-source, etc.
[0040] In order for the system 108 for building the subscriber profile using the telecom network 106, the processor 202 includes one or more modules. In one embodiment, the one or more modules includes, but not limited to, a collecting unit 210, a training unit 212, a predicting unit 216, a detecting unit 218, an adjusting unit 220, and a generating unit 222 communicably coupled to each other for building the subscriber profile using the telecom network 106.
[0041] In one embodiment, each of the one or more modules the collecting unit 210, the training unit 212, the predicting unit 216, the detecting unit 218, the adjusting unit 220, and the generating unit 222 can be used in combination or interchangeably for building the subscriber profile using the telecom network 106.
[0042] The collecting unit 210, the training unit 212, the predicting unit 216, the detecting unit 218, the adjusting unit 220, and the generating unit 222 in an embodiment, may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processor 202. In the examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processor 202 may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processor may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the memory 204 may store instructions that, when executed by the processing resource, implement the processor. In such examples, the system 108 may comprise the memory 204 storing the instructions and the processing resource to execute the instructions, or the memory 204 may be separate but accessible to the system 108 and the processing resource. In other examples, the processor 202 may be implemented by electronic circuitry.
[0043] In one embodiment, the collecting unit 210 is configured to collect analytics data pertaining to the subscriber from one or more sources. The one or more sources includes the one or more probing units 224. The analytics data refers to information collected from the one or more sources. In an embodiment, the analytics data is at least one of historical data. The analytics data includes, but is not limited to, subscriber data, network performance metrics, service quality indicators, behavioral data, device data. The subscriber data includes information about individual subscribers such as their service usage, call history, location data and preferences. The network performance metrics includes data regarding network health including bandwidth usage, latency, packet loss and throughput. The service quality indicators include metrics related to the quality of services provided such as dropped calls, data speeds and service availability. The behavioral data includes patterns in how subscribers use different services including time of day, duration and frequency of use. The device data includes information about subscriber devices such as device type, operating system and technical capabilities. In an embodiment, the analytics data includes at least one of, Radio Frequency (RF) level data, network core level data, one or more previous threshold values and one or more pre-defined values of one or more parameters. The RF level data refers to the data collected from the radio access part of the telecom network 106. The RF level data includes metrics related to signal strength, signal quality, interference, noise levels, and other parameters that influence wireless communication performance. The RF level data is essential for understanding how the subscriber's device interacts with the radio network (e.g., cellular towers). The RF level data encompasses coverage, usage, and visited areas. The RF level data includes, but not limited to, Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR), Channel Quality Indicator (CQI), transmission power. The network core level refers to data collected from the core components of the telecom network 106, which handle functions such as routing, switching, and subscriber data management. The network core level data encompasses details about the subscriber's traffic, session management, authentication, policy enforcement, and mobility management. Further, the network core level data focuses on clear code data which helps to identify the component at which the problem occurred in case of network failure. The one or more previous threshold values refers to historical threshold values that were previously set based on the analysis of subscriber behavior or network conditions. The thresholds pertain to aspects like data usage limits, bandwidth allocation, service quality, or network performance metrics. The one or more previous threshold values provide a reference point to compare the current state with historical limits and assess changes or trends over time. The one or more pre-defined values of one or more parameters refers to pre-set values for specific parameters related to the subscriber. The one or more parameters include but are not limited to, quality of service (QoS) metrics, expected data throughput, response times, or other operational factors that are used to monitor and manage network services. The pre-defined values help establish the baseline criteria against which new data can be evaluated.
[0044] In an embodiment, the one or more probing units 224 are the components within the telecom network 106 responsible for monitoring and collecting data by inspecting the network traffic in real time. The subscriber refers to an individual or entity that has signed up for and uses the services provided by a telecommunications operator. The collecting unit 210 is further configured to pre-process the collected analytics data. The pre-processing includes cleaning the received analytics data to remove erroneous, duplicate or anomaly-based data. The removing erroneous data refers to the filtering out data that contains errors or inaccuracies to prevent misleading insights. The removing duplicate data refers to identifying and eliminating redundant data entries to reduce data volume and avoid skewed analysis. The removing anomalous data defers to detecting and removing outliers or anomalies that may not reflect typical subscriber behavior or network conditions.
[0045] Upon collecting and preprocessing the data, the training unit 212 is configured to train a model, with collected analytics data. The model refers to a machine learning model that is trained using collected analytics data to identify patterns and relationships within the data. The model learns at least one of patterns and relationships between different variables of the analytics data. The patterns refer to recurring or consistent trends observed within the analytics data. The trends can manifest as regular behaviors or predictable sequences in subscriber interactions, network conditions, or other measured parameters. The patterns are at least one of, but not limited to, usage patterns, behavioral patterns, network load patterns. The usage patterns refer to recurring times when the subscriber frequently uses data services (for example, heavy internet usage during evenings). The behavioral patterns refer to repeated activities such as making phone calls at specific times or using particular services regularly. The network load patterns refer to seasonal or daily variations in network traffic that occur consistently. The relationships within the data refer to the connections or dependencies between different variables within the analytics data. The relationships indicate how one variable affects or correlates with another, revealing underlying dynamics in the data. For example, the relationships include correlation between data usage and location, network quality and subscriber satisfaction, service use and time of day.
[0046] Upon training the model, the predicting unit 216 is configured to predict one or more threshold values pertaining to real-time network data. The real-time network data refers to the continuous or near-instantaneous data collected from various components of the telecom network 106, providing up-to-date insights about the subscriber's current interactions with the telecom network 106. The threshold values refer to predefined limits or boundaries used to evaluate and manage network performance, service quality, or subscriber behavior. The one or more threshold values pertaining to the real-time network data are predicted by using the trained model. The one or more threshold values include, but are not limited to, data usage threshold, network load threshold, service quality threshold. The data usage threshold is limit on the amount of data the subscriber can use before action is taken such as slowing speeds or sending a usage warning. The network load threshold is a limit on the maximum traffic, a network segment can handle before activating load balancing or prioritizing critical services. The service quality threshold is a minimum acceptable quality level (e.g., latency or signal strength) that, if breached, triggers troubleshooting or resource reallocation.
[0047] Upon predicting the one or more threshold values, the detecting unit 218 is configured to detect the changes in at least one of, the patterns and relationships between the different variables of the analytics data with respect to the real-time network data. The different variables include, but not limited to, data usage, time usage, call duration and frequency, Quality of Service (QoS) metrics, service type preferences, geolocation information, bandwidth consumption, device type, historical usage trends, connection type. The patterns are recurring trends or behaviors identified from historical data, such as subscriber activity patterns (e.g., peak data usage times) or network performance trends (e.g., network congestion during certain hours). For example, the change in a pattern could be, the subscriber usually consumes 2GB of data daily but suddenly starts using 5GB, then the network typically experiences low traffic at midnight, but the real-time network data shows a sudden surge in traffic at that time. The detecting unit 218 identifies that the current behavior no longer matches the established historical pattern, indicating a change. The relationships refer to how different variables in the analytics data influence or are connected to each other, such as how data usage might increase when the subscriber is in a high-speed coverage area or how network congestion affects service quality. For example, the change in the relationship could be, the relationship between network congestion and call quality has shifted, previously, call quality degraded only when congestion hit 80%, but now it's degrading at 60% congestion. Thus, the strong relationship between data usage and a particular location no longer holds, as the subscriber has moved to a new region with different usage patterns. In an embodiment, the detecting unit 218 compares the current analytics data with the patterns and relationships learned by the model. When the real-time network data deviates significantly from these learned norms, the detecting unit 218 identifies changes. For example, the change includes an unusual spike in data consumption, indicating a shift in the subscriber’s behavior, a disruption in the typical correlation between signal strength and data speed, potentially pointing to network issues.
[0048] If the deviations or changes in at least one of the patterns and relationships between the different variables of the analytics data with respect to the real-time network data is detected, the adjusting unit 220 is configured to adjust at least one of the one or more threshold values and the policies using the trained model. For example, if a subscriber’s data usage pattern suddenly increases, the adjusting unit 220 lowers the data cap threshold or adjust QoS policies to manage the change proactively. Similarly, if a relationship between network conditions and service performance changes, the adjusting unit 220 modifies traffic management policies to maintain service quality. The policies refer to a set of rules or guidelines that govern how the system responds to certain conditions in the telecom network 106 or subscriber behavior. The policies include, but are not limited to, traffic management policies, data plan policies, Quality of Service (QoS) policies, security policies. The traffic management policies include rules that determine how to prioritize network traffic based on the subscriber's needs, such as giving priority to emergency services during congestion. The data plan policies include rules that control how much data the subscriber can use before triggering actions like notifications or throttling. The QoS policies include the guidelines to ensure certain levels of performance (e.g., high-priority users get better bandwidth during peak times). The security policies include the rules that ensure network security, such as blocking suspicious activities or unauthorized access based on traffic patterns.
[0049] Based on the learnt patterns and connections between the different variables of the analytics data and adjusted at least one of, the threshold values and the policies, the generating unit 222 is configured to generate the subscriber profile report using the model. The subscriber profile report is a comprehensive document or data output generated by the generating unit 222 that provides detailed insights into the behavior, preferences, and patterns of the telecom subscriber based on the analytics data collected and processed by the telecom network 106. The subscriber profile report includes behavioral insights, QoS metrics, thresholds and policies, historical data comparison, actionable insights. The behavioral insights refer to the subscribes usage patterns such as data usage, service preferences, peak usage times. The QoS metrics include but are not limited to, signal strength, latency and bandwidth, dropped calls or network interruptions. The thresholds and policies include, but are not limited to, data cap thresholds, service prioritization policies, and security policies. The historical data comparison includes, but is not limited to, changes in data usage, new usage trends. The actionable insights include, but are not limited to, service upgrades or downgrades, usage warnings.
[0050] The generating unit 222 is further configured to generate an updated subscriber profile report in response to learning new patterns and relationships between different variables with respect to new data. The generating unit 222 generates updated subscriber profile reports whenever new patterns or relationships are identified in the analytics data. For example, if a subscriber starts using significantly more data for video streaming, the detecting unit 218 detects this pattern change and the generating unit 222 updates the profile report with revised thresholds or policy recommendations to ensure the subscriber’s needs are met efficiently. In an embodiment, the subscriber profile report is a dynamic document that provides a detailed view of a telecom subscriber’s behavior, service quality, and the policies governing their usage. The subscriber profile report is continuously updated based on real-time data and new patterns detected by the trained model, ensuring that both the subscriber and the network provider can respond effectively to changes.
[0051] Therefore, the system 108 provides comprehensive user profile and enhances customer experience. Further, the system 108 automates the process of summarizing and creating subscriber profiles, saving considerable time and effort compared to traditional manual methods. Further, the system 108 helps in identifying potential network issues in real-time by setting dynamic threshold values and promptly generating summarized reports. Further, the system 108 enables proactive responses to prevent major disruptions, contributing to a more stable and reliable network. Further the system 108 automatically adjusts thresholds and policies based on various factors like time and geography.
[0052] FIG. 3 is an exemplary block diagram of an architecture 300 of the system 108 for building the subscriber profile using the telecom network 106, according to one or more embodiments of the present invention.
[0053] The architecture 300 includes the one or more probing units 224, a Distributed File System (DFS) 302, a processing hub 304, a data consumer 1 314a, a data consumer 2 314b, the user interface 206, and the data lake 316. The processing hub 304 includes a data integration unit 306, a data preprocessing unit 308, model training unit 310, and a predicting unit 312.
[0054] In an embodiment, the analytics data pertaining to the subscriber are collected from the one or more probing unit 224 and stored in the DFS 302. The analytics data includes at least one of, one or more previous threshold values and one or more pre-defined values of one or more parameters. The DFS 302 stores the collected analytics data.
[0055] Upon storing the collected analytics data at the DFS 302, the DFS 302 transmits the collected analytics data to the data integration unit 306. The data integration unit 306 integrates the collected analytics data. Upon integrating the collected analytics data, the collected analytics data is transmitted to the data preprocessing unit 308. The data preprocessing unit 308 preprocesses the received analytics data. The data preprocessing unit 308 is responsible for cleaning the received analytics data. The received analytics data is cleaned by removing erroneous, duplicate or anomaly- based data.
[0056] Upon preprocessing the received analytics data, the analytics is transmitted to the model training unit 310. The model training unit 310 trains the model with at least one of the patterns and relationships between the different variables of the analytics data. Further, the model training unit 310 is responsible for predicting the one or more threshold values and policies.
[0057] Upon training the model and predicting the one or more threshold values, the predicting unit 312 is configured to predict the changes in at least one of the patterns and relationships between the different variables of the analytics data. Upon predicting the changes, if required, the one or more threshold values and the policies are adjusted.
[0058] Subsequently, based on the learnt patterns and connections between the different variables of the analytics data and adjusted at least one of, the threshold values and the policies, the subscriber profile report is generated and transmitted to the at least one of the data consumers 1 314a and the data consumer 2 314b. The data consumer 1 314a and data consumer 2 314b is at least one of a telecom operator who monitors network performance data.
[0059] In an embodiment, the data lake 316 acts as a repository for storing large volumes of both raw and processed data. The data lake 316 aligns with the storage of historical data for future analysis, allowing the processing hub 304 to refer back to past analytics to improve predictions and adjustments. The user interface 206 allows the data consumer 1 314a and the data consumer 2 314b to interact with the processing hub 304.
[0060] FIG. 4 is a signal flow diagram for building the subscriber profile using the telecom network 106, according to one or more embodiments of the present invention.
[0061] At step 402, the analytics data are collected from the one or more probing units 224. The analytics data includes at least one of, one or more previous threshold values and one or more pre-defined values of one or more parameters. The collected analytics data are stored in the DFS 302.
[0062] At step 404, upon collecting the analytics data, the collected analytics data is preprocessed. The preprocessing includes cleaning of the received analytics data by removing erroneous, duplicate or anomaly- based data.
[0063] At step 406, upon preprocessing the received analytics data, the model is trained with at least one of the patterns and relationships between the different variables of the analytics data. Upon training the model, the trained model is responsible for predicting the one or more threshold values and policies.
[0064] At step 408, upon training the model and predicting the one or more threshold values, the changes in at least one of the patterns and relationships between the different variables of the analytics data is predicted. Upon predicting the changes, if required, the one or more threshold values and the policies are adjusted.
[0065] At step 410, subsequently, based on the learnt patterns and connections between the different variables of the analytics data and adjusted at least one of, the threshold values and the policies, the subscriber profile report is generated and transmitted to the at least one of the telecom operators.
[0066] At step 412, the current analytics data from the probing unit 224 are collected and retrained the model continuously. Accordingly, the updated subscriber profile report in response to learning new patterns and relationships between different variables with respect to new data is generated and transmitted to the telecom operators.
[0067] FIG. 5 is a flow diagram of a method 500 for building the subscriber profile using the telecom network 106, according to one or more embodiments of the present invention. For the purpose of description, the method 500 is described with the embodiments as illustrated in FIG. 2 and should nowhere be construed as limiting the scope of the present disclosure.
[0068] At step 502, the method 500 includes the step of collecting the analytics data pertaining to the subscriber from one or more sources by the collecting unit 210. The one or more sources includes the one or more probing units 224. The analytics data includes at least one of, RF level data, network core level data, one or more previous threshold values and one or more pre-defined values of one or more parameters. The collecting unit 210 is further configured to pre-process the collected analytics data. The pre-processing comprises, cleaning, by the one or more processors, the received analytics data to remove erroneous, duplicate or anomaly-based data.
[0069] At step 504, the method 500 includes the step of training the model, with the collected analytics data by the training unit 212. The model learns at least one of, patterns and relationships between different variables of the analytics data.
[0070] At step 506, the method 500 includes the step of predicting the one or more threshold values pertaining to the real-time network data by using the trained model by the predicting unit 216.
[0071] At step 508, the method 500 includes the step of detecting the changes in at least one of, the patterns and relationships between the different variables of the analytics data with respect to the real-time network data by the detecting unit 218. In response to detecting, the at least one of, the one or more threshold values and the policies using the trained model are adjusted by the adjusting unit 220.
[0072] At step 512, the method 500 includes the step of generating the subscriber profile report using the model based on the learnt patterns and connections between the different variables of the analytics data and adjusted at least one of, the threshold values and the policies by the generating unit 222. The generating unit 222 is further configured to generate the updated subscriber profile report in response to learning new patterns and relationships between different variables with respect to new data.
[0073] The present invention further discloses a non-transitory computer-readable medium having stored thereon computer-readable instructions. The computer-readable instructions are executed by the processor 202. The processor 202 is configured to collect the analytics data pertaining to the subscriber from one or more sources. The processor 202 is further configured to train the model, with the collected analytics data. The model learns at least one of, patterns and relationships between different variables of the analytics data. The processor 202 is further configured to predict, using the trained model, the one or more threshold values and polices pertaining to the real-time network data. The processor 202 is further configured to detect the changes in at least one of, the patterns and relationships between the different variables of the analytics data with respect to the real-time network data, dynamically adjust, at least one of, the one or more threshold values and the policies using the trained model. The processor 202 is further configured to generate, the subscriber profile report using the model based on the learnt patterns and connections between the different variables of the analytics data and adjusted at least one of, the threshold values and the policies.
[0074] 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.
[0075] The present disclosure incorporates technical advancement of improving the network's resource allocation, reducing the likelihood of congestion, and provides personalized service quality based on real-time data, enhancing both operational efficiency and user experience. The present invention enhances decision-making speed and precision. The present invention reduces delays and minimizes network strain by allowing the network to predict subscriber needs and adjust services accordingly. The present invention automates the process of summarizing and creating subscriber profiles, saving considerable time and effort compared to traditional manual methods. Further, the present invention helps in identifying potential network issues in real-time by setting dynamic threshold values and promptly generating summarized reports. Further, the present invention enables proactive responses to prevent major disruptions, contributing to a more stable and reliable network. Further the present invention automatically adjusts thresholds and policies based on various factors like time and geography.
[0076] 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

[0077] Environment- 100
[0078] User Equipment (UE)- 102
[0079] Server- 104
[0080] Network- 106
[0081] System -108
[0082] Processor- 202
[0083] Memory- 204
[0084] User Interface- 206
[0085] Database- 208
[0086] Collecting Unit- 210
[0087] Training Unit- 212
[0088] Predicting Unit- 216
[0089] Detecting Unit- 218
[0090] Adjusting Unit- 220
[0091] Generating Unit- 222
[0092] One or more probing units- 224
[0093] Distributed File System (DFS)- 302
[0094] Processing hub- 304
[0095] Data integration unit- 306
[0096] Data preprocessing unit – 308
[0097] Model training unit 310
[0098] Predicting unit – 312
[0099] Data consumer 1- 314a
[00100] Data consumer 2- 314b
[00101] Data lake- 316
,CLAIMS:CLAIMS:
We Claim:
1. A method (500) for building a subscriber profile using a telecom network (106), the method (500) comprising the steps of:
collecting, by one or more processors (202), analytics data pertaining to the subscriber from one or more sources;
training, by the one or more processors (202), a model, with the collected analytics data, wherein the model learns at least one of, patterns and relationships between different variables of the analytics data;
predicting, by the one or more processors (202), using the trained model, one or more threshold values pertaining to real-time network data;
in response to detecting, by the one or more processors (202), changes in at least one of, the patterns and relationships between the different variables of the analytics data with respect to the real-time network data, dynamically adjusting, by the one or more processors, at least one of, the one or more threshold values and policies using the trained model; and
generating, by the one or more processors (202), the subscriber profile report using the model based on the learnt patterns and connections between the different variables of the analytics data and adjusted at least one of, the threshold values and the policies.

2. The method (500) as claimed in claim 1, wherein the one or more sources includes one or more probing units (224).

3. The method (500) as claimed in claim 1, wherein the step of, collecting, analytics data from one or more sources, further includes the step of:
pre-processing, by the one or more processors (202), the collected analytics data.

4. The method (500) as claimed in claim 1, wherein the analytics data includes at least one of, Radio Frequency (RF) level data, network core level data, one or more previous threshold values and one or more pre-defined values of one or more parameters.

5. The method (500) as claimed in claim 1, wherein the method further comprises the step of:
generating, by the one or more processor (202), an updated subscriber profile report in response to learning new patterns and relationships between different variables with respect to new data.

6. A system (108) for building a subscriber profile using a telecom network (106), the system (108) comprising:
a collecting unit (210), configured to, collect, analytics data pertaining to the subscriber from one or more sources;
a training unit (212), configured to train a model, with the collected analytics data, wherein the model learns at least one of, patterns and relationships between different variables of the analytics data;
a predicting unit (216), configured to, predict, using the trained model, one or more threshold values pertaining to real-time network data;
in response to detecting, by a detecting unit (218), changes in at least one of, the patterns and relationships between the different variables of the analytics data with respect to the real-time network data, an adjusting unit (220), configured to, dynamically adjust, at least one of, the one or more threshold values and the policies using the trained model; and
a generating unit (222), configured to, generate, the subscriber profile report using the model based on the learnt patterns and connections between the different variables of the analytics data and adjusted at least one of, the threshold values and the policies.

7. The system (108) as claimed in claim 6, wherein the one or more sources includes one or more probing units (224).

8. The system (108) as claimed in claim 6, wherein the collecting unit (210) is further configured to:
pre-process, the collected analytics data.

9. The system (108) as claimed in claim 6, wherein the analytics data includes at least one of, Radio Frequency (RF) level data, network core level data, one or more previous threshold values and one or more pre-defined values of one or more parameters.

10. The system (108) as claimed in claim 6, wherein the generating unit (222) is further configured to:
generate, an updated subscriber profile report in response to learning new patterns and relationships between different variables with respect to new data.