DESC:
FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003

COMPLETE SPECIFICATION
(See section 10 and rule 13)
1. TITLE OF THE INVENTION
SYSTEM AND METHOD FOR MANAGING SUBSCRIPTIONS PERTAINING TO AT LEAST ONE NETWORK SLICE
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 wireless communication systems, more particularly relates to a method and a system for managing subscriptions pertaining to at least one network slice.
BACKGROUND OF THE INVENTION
[0002] With increase in number of users, the network service provisions have to be upgraded to incorporate increased users and to enhance the service quality so as to keep pace with such high demand. There are a lot of factors that need to be cared for when considering quality of a network. To maintain health of a network regular monitoring of various parameters has to be done, like monitoring performance of various network elements and network functions etc. Network functions play a vital role in improving the quality of a network by the way of managing traffic, delegating node allocation, managing performance of routing device etc.
[0003] The network load also plays a vital role in managing policies. A Network Data Analytics Function (NWDAF) is configured for this purpose to offer end users insightful data on slice load analytics and closed-loop reporting, which is a crucial service. However, the current system places the responsibility of managing subscriptions, making policy updates, and terminating subscriptions on the consumer, who must perform these tasks manually, frequently in response to their fluctuating and recurring needs, such as subscribe or unsubscribe from new slice loads, make policy changes for a slice load, etc. Although required, this manual process can be rather time-consuming and tedious, which may cause delays and inefficiencies when obtaining crucial network data. Therefore, there is a need of automated data analysis system.
[0004] Presently there is no mechanism in place for the load analysis, policy up-gradation and managing subscription automatically and it is mostly performed by manual intervention which may involve errors and is time and resource consuming. There is a requirement of a system and a method to perform slice load analysis for various events subscription service operations.
SUMMARY OF THE INVENTION
[0005] One or more embodiments of the present disclosure provides a method and a system for managing subscriptions pertaining to at least one network slice.
[0006] In one aspect of the present invention, the method for managing subscriptions pertaining to at least one network slice is disclosed. The method includes the step of retrieving, by one or more processors, historical data pertaining to a consumer behaviour from a plurality of sources. The method further includes the step of training, by the one or more processors, a model with the retrieved historical data to identify historical trends/patterns related to the consumer behaviour. The method further includes the step of predicting, by the one or more processors, consumer behaviour trends/patterns pertaining to the subscription of at least one network slice utilizing the trained model. The method further includes the step of dynamically generating, by the one or more processors, multiple triggers to initiate one or more actions in response to prediction.
[0007] In another embodiment, the historical data related to the consumer behaviour includes at least one of, information related to usage trends and slice load analytics data pertaining to the at least one network slice, network conditions and policy changes made by the consumers.
[0008] In yet another embodiment, the consumer includes at least one of a network operator.
[0009] In yet another embodiment, the plurality of sources includes at least one of, a database and a Network Data Analytics Function (NWDAF).
[0010] In yet another embodiment, the step of retrieving, the historical data from the plurality of sources, includes the step of preprocessing, by the one or more processors, at least one of the historical data.
[0011] In yet another embodiment, the model is at least one of, an Artificial Intelligence/Machine Learning (AI/ML) model.
[0012] In yet another embodiment, the step of, training, by the one or more processors, a model with the retrieved historical data to identify historical trends/patterns related to the consumer behaviour, includes the steps of determining, by the one or more processors, historical optimal time intervals at which the at least one network slice is utilized by the consumer.
[0013] In yet another embodiment, the step of, predicting, by the one or more processors, consumer behaviour trends/patterns pertaining to the subscription of at least one network slice utilizing the trained model, includes the step of estimating, by the one or more processors, at least one of, a future optimal time or a range of future optimal time intervals at which the consumer consumes the at least one network slice.
[0014] In yet another embodiment, the one or more actions includes, purchasing new subscriptions, updating current subscriptions, unsubscribing current subscriptions, and policy changing of current subscriptions.
[0015] In yet another embodiment, the model is updated based on real time data pertaining to the consumer behaviour received from the plurality of sources
[0016] In yet another embodiment, the one or more processors is further configured to predict a future load matrix pertaining to the at least one network slice based on a historical load matrix pertaining to the same network slice.
[0017] In another aspect of the present invention, the system for managing subscriptions pertaining to at least one network slice is disclosed. The system includes an integration unit, configured to retrieve, historical data pertaining to a consumer behaviour from a plurality of sources. The system further includes a model training unit, configured to train, a model with the retrieved historical data to identify historical trends/patterns related to the consumer behaviour. The system further includes a prediction unit, configured to predict, consumer behaviour trends/patterns pertaining to the subscription of at least one network slice utilizing the trained model. The system further includes a triggering unit configured to dynamically generate, multiple triggers to initiate one or more actions in response to prediction.
[0018] 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. The processor is configured to retrieve, historical data pertaining to a consumer behaviour from a plurality of sources. The processor is further configured to train, a model with the retrieved historical data to identify historical trends/patterns related to the consumer behaviour. The processor is further configured to predict, consumer behaviour trends/patterns pertaining to the subscription of at least network slice utilizing the trained model. The processor is further configured to dynamically generate, multiple triggers to initiate one or more actions in response to prediction.
[0019] 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
[0020] 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.
[0021] FIG. 1 is an exemplary block diagram of an environment for managing subscriptions pertaining to at least one network slice, according to one or more embodiments of the present invention;
[0022] FIG. 2 is an exemplary block diagram of a system for managing subscriptions pertaining to the at least one network slice, according to one or more embodiments of the present invention;
[0023] FIG. 3 is an exemplary architecture of the system of FIG. 2, according to one or more embodiments of the present invention;
[0024] FIG. 4 is an exemplary architecture for managing subscriptions pertaining to the at least one network slice, according to one or more embodiments of the present disclosure;
[0025] FIG. 5 is an exemplary signal flow diagram illustrating the flow for managing subscriptions pertaining to the at least one network slice; and
[0026] FIG. 6 is a flow diagram of a method for managing subscriptions pertaining to the at least one network slice, according to one or more embodiments of the present invention.
[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] Various embodiments of the present invention provide a system and a method for managing subscriptions pertaining to at least one network slice. The most unique aspect of the invention lies in an ability to interact with a Network Data Analytics Function (NWDAF) by means of an interface. The system is configured to leverage trained Artificial Intelligence/Machine Learning (AI/ML) based prediction models with real time load analysis and network performance data to automate subscription management tasks. The system combines data collection, machine learning and real-time response mechanisms to aid in refining network management, monitoring and network policies for betterment of network service quality.
[0032] Referring to FIG. 1, FIG. 1 illustrates an exemplary block diagram of an environment 100 for managing subscriptions pertaining to at least one network slice according to one or more embodiments of the present invention. The environment 100 includes a User Equipment (UE) 102, a server 104, a network 106, a system 108, and a plurality of sources 110. The innovative aspect of the present invention lies in the utilization of advanced predictive analytics, driven by a Machine Learning (ML) methodology, to anticipate optimal moments for consumers to at least one of, subscribe, unsubscribe, or adjust one or more policies for at least one network slice.
[0033] In one embodiment, the network slice is a virtualized, end-to-end, logically isolated network that is tailored to meet the specific requirements of different applications, services, or users within a larger physical network infrastructure. The network slice allows operators to partition the network 106 into multiple distinct virtual networks, each optimized for specific use cases with different performance characteristics such as at least one of, but not limited to, a bandwidth, a latency, reliability, and security.
[0034] In one embodiment, subscribing to at least one network slice refers to a process by which at least one of, the consumer is allocated or granted access to a specific network slice within the network 106. Based on the requirements of the consumer, the consumer transmits at least one request to the system 108 for subscribing at least one network slice.
[0035] For the purpose of description and explanation, the description will be explained with respect to one or more user equipment’s (UEs) 102, or to be more specific will be explained with respect to a first UE 102a, a second UE 102b, and a third UE 102c, and should nowhere be construed as limiting the scope of the present disclosure. Each of the at least one UE 102 namely the first UE 102a, the second UE 102b, and the third UE 102c is configured to connect to the server 104 via the network 106.
[0036] In an embodiment, each of the first UE 102a, the second UE 102b, and the third UE 102c is one of, but not limited to, any electrical, electronic, electro-mechanical or an equipment and a combination of one or more of the above devices such as 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.
[0037] The 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.
[0038] The 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.
[0039] The environment 100 includes the server 104 accessible via the 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, a processor executing code to function as a server, one or more machines performing server-side functionality as described herein, at least a portion of any of the above, some combination thereof. In an embodiment, the entity may include, but is not limited to, a vendor, a network operator, a company, an organization, a university, a lab facility, a business enterprise side, a defense facility side, or any other facility that provides service.
[0040] The environment 100 further includes the plurality of sources 110. In one embodiment, the plurality of sources 110 are origins from which the data is retrieved and utilized for at least one of, but not limited to, analysis, research, and decision-making. In one embodiment, the plurality of sources 110 is at least one of, but not limited to, one or more network functions and one or more databases.
[0041] In one embodiment, the plurality of sources 110 includes at least one of, a Network Data Analytics Function (NWDAF). The NWDAF is a key component within the 5G architecture designed to provide slice load analytics and insights across the network 106. The NWDAF plays a critical role in optimizing network performance, improving user experience, and enabling more intelligent decision-making based on data analytics. The NWDAF leverages big data and machine learning techniques to process and analyze data from one or more Network Functions (NFs) within the network 106 which enables more proactive and dynamic network management, predictive analytics, and enhances the overall efficiency.
[0042] The environment 100 further includes the system 108 communicably coupled to the server 104, the UE 102, and the plurality of sources 110 via the network 106. The system 108 is adapted to be embedded within the server 104 or is embedded as the individual entity.
[0043] Operational and construction features of the system 108 will be explained in detail with respect to the following figures.
[0044] FIG. 2 is an exemplary block diagram of the system 108 for managing subscriptions pertaining to the at least one network slice, according to one or more embodiments of the present invention.
[0045] As per the illustrated and preferred embodiment, the system 108 for managing subscriptions pertaining to the at least one network slice, includes one or more processors 202, a memory 204, a storage unit 206 and a model 220. The one or more processors 202, hereinafter referred to as the processor 202, may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, single board computers, and/or any devices that manipulate signals based on operational instructions. However, it is to be noted that the system 108 may include multiple processors as per the requirement and without deviating from the scope of the present disclosure. Among other capabilities, the processor 202 is configured to fetch and execute computer-readable instructions stored in the memory 204.
[0046] As per the illustrated embodiment, the processor 202 is configured to fetch and execute computer-readable instructions stored in the memory 204 as the memory 204 is communicably connected to the processor 202. The memory 204 is configured to store one or more computer-readable instructions or routines in a non-transitory computer-readable storage medium, which may be fetched and executed for managing subscriptions pertaining to the at least one network slice. The memory 204 may include any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as disk memory, EPROMs, FLASH memory, unalterable memory, and the like.
[0047] The environment 100 further includes the storage unit 206. As per the illustrated embodiment, the storage unit 206 is configured to store data retrieved from the plurality of sources 110. The storage unit 206 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 the storage unit 206 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.
[0048] As per the illustrated embodiment, the system 108 includes the model 220. In another embodiment, the system 108 includes a plurality of models 220. In one embodiment, the model is at least one of, but not limited to, an Artificial Intelligence/Machine Learning (AI/ML) model 220. The model 220 facilitates the system 108 in performing tasks such as at least one of, managing subscriptions pertaining to the at least one network slice, detecting anomalies, recognizing patterns, making predictions, solving problems, enhancing decision-making, and providing insights across various fields. For example, the model 220 facilitates solving real-world problems without extensive manual intervention. In one embodiment, the model 220 is trained using the retrieved historical data pertaining to a consumer behaviour from the plurality of sources 110. In an alternate embodiment, the AI/ML model 220 is pretrained.
[0049] As per the illustrated embodiment, the system 108 includes the processor 202 for managing subscriptions pertaining to at least one network slice. The processor 202 includes an integration unit 208, a model training unit 210, a prediction unit 212, and a triggering unit 214. The processor 202 is communicably coupled to the one or more components of the system 108 such as the memory 204, the storage unit 206 and the model 220. In an embodiment, operations and functionalities of the integration unit 208, the model training unit 210, the prediction unit 212, the triggering unit 214, and the one or more components of the system 108 can be used in combination or interchangeably.
[0050] In one embodiment, initially the integration unit 208 of the processor 202 is configured to retrieve historical data pertaining to the consumer behaviour from the plurality of sources 110. In one embodiment, the historical data pertaining to the consumer behaviour includes at least one of, but not limited to, information related to usage trends and slice load analytics data pertaining to at least one network slice, network conditions, , and one or more policy changes made by the consumers.
[0051] In one embodiment, the slice load analytics data refers to the collection, analysis, and visualization of performance metrics and resource utilization related to at least one network slice. The slice load analytics data facilities the network operators to at least one of, monitor, manage, and optimize the performance of each of the network slices within the network 106. For example, the slice load analytics data includes at least one of, but not limited to, load pertaining to the traffic on the at least one network slice, time duration related to the at least one network slice utilized by the consumer. Herein, the slice load analytics data is collected by the system from the at least one slice by performing the slice load analytics.
[0052] In one embodiment, the slice load analytics data is the retrieved by the integration unit 208 from the plurality of sources 110 such as the NWDAF. In particular, the NWDAF performs the slice load analytics on order to obtain the slice load analytics data pertaining to the at least one network slice. Herein, the slice load analytics data refers to the analysis of the load pertaining to at least one of, but not limited to, traffic, resource utilization, consumer demand across different network slices in the network 106. In other words, an output provided by the NWDAF to the system 108 is considered as the slice load analytics data.
[0053] In one embodiment, the slice load analytics refers to a process of monitoring and analyzing the performance and utilization of one or more network slices in the network 106 environment. The slice load analytics involve collecting data from the plurality of sources 110 related to various metrics, such as at least one of, but not limited to, a bandwidth usage, a latency, a throughput, and a resource allocation, to assess how well each network slice among the one or more network slices is performing.
[0054] In another embodiment, the historical data includes at least one of, but not limited to, consumers past subscription for at least one network slice, information related to the consumers unsubscribed from the at least one network slice. For example, let us consider that the consumer has subscribed to the NWDAF for the one or more services such as forecasting potential failures or the at least one network slice in the network 106, then the data pertaining to the consumer behaviour such as the utilization of the one or more services and the at least one network slice is stored in the NWDAF.
[0055] In one embodiment, the one or more services are provided by at least one of, but not limited to, the plurality of sources 110 such as the NWDAF. The consumers subscribe to the NWDAF in order to avail the one or more services. Herein, the one or more services include at least one of, but not limited to, an anomaly detection, traffic forecasting, video conferencing, forecasting potential failures or degradations in the network performance. In one embodiment, the integration unit 208 retrieves the historical data from the plurality of sources 110 which are present within the network 106 and outside the network 106. In one embodiment, the plurality of sources 110 periodically transmits the data to the system 108.
[0056] In one embodiment, one or more policy is associated with at least one of, but not limited to, adjusting Quality of Service (QoS) parameters, traffic prioritization, policies for mitigating anomalies, and policies for network slicing. For example, the consumer such as a network operator adjusts the QoS parameters based on consumer usage patterns and preferences, if the consumer consumes a high amount of data during certain times of day or requires low-latency applications (like gaming or video streaming), then the NWDAF provide insights to the consumer to prioritize traffic for the user which are consuming high amount of data. In another example, the NWDAF facilities the consumer to understand traffic trends and predict congestion. Based on the data provided by the NWDAF to the consumer, the consumer adjusts traffic prioritization policies, especially for real-time applications (such as Voice over Internet Protocol (VoIP) or video conferencing) during peak periods or in areas with high traffic volume.
[0057] In one embodiment, the integration unit 208 retrieves the historical data from the plurality of sources 110 via an interface. In one embodiment, the interface includes at least one of, but not limited to, one or more Application Programming Interfaces (APIs) which are used for retrieving the historical data from the plurality of sources 110. The one or more APIs are sets of rules and protocols that allow different entities to communicate with each other. The one or more APIs define the methods and data formats that entities can use to request and exchange information, enabling integration and functionality across various platforms. In particular, the APIs are essential for integrating different systems, accessing services, and extending functionality.
[0058] Upon retrieving the historical data from the plurality of sources 110, the integration unit 208 is further configured to preprocess the retrieved historical data. In particular, the integration unit 208 is configured to preprocess the retrieved historical data to ensure the data consistency and quality of the data within the system 108. The integration unit 208 performs at least one of, but not limited to, data normalization, data definition and data cleaning procedures.
[0059] While preprocessing, the integration unit 208 performs at least one of, but not limited to, reorganizing the data, removing the redundant data, formatting the data, removing null values from the data, cleaning the data, and handling missing values. The main goal of the preprocessing is to achieve a standardized data format across the system 108. While preprocessing, the duplicate data and inconsistencies are eliminated from the retrieved historical data. Subsequent to preprocessing, the retrieved historical data is referred to pre-processed data. The integration unit 208 is further configured to store the pre-processed data in at least one of, the storage unit 206 for subsequent retrieval and analysis.
[0060] Upon preprocessing the data, the model training unit 210 of the processor 202 is configured to train the model 220 with the retrieved historical data pertaining to the historical trends/patterns of the consumer behaviour. Herein the trends are general directions and development observed over time in the consumer behaviour. The patterns refer to at least one of, but not limited to, regularities, repeated sequences, or recurring characteristics in the consumer behaviour. In one embodiment, the model training unit 210 trains the model 220 with the pre-processed data. In order to train the model 220, the model training unit 210 configures one or more hyperparameters of the model 220. In one embodiment, the one or more hyperparameters of model 220 includes at least one of, but not limited to, a learning rate, a batch size, and a number of epochs.
[0061] Upon configuring the one or more hyperparameters of the model 220, the model training unit 210 is further configured to split the retrieved historical data into at least one of, but not limited to, training data and testing data for training. For example, let us consider that the retrieved historical data is 1 GB. Further, the model training unit 210 splits the 1 GB data such that 80% of the 1 GB data is considered as the training data and 20% of the 1 GB data is considered as the testing data. Thereafter, the model training unit 210 feeds the training data to the model 220 based on which the model 220 is trained by the model training unit 210.
[0062] In one embodiment, the model training unit 210 trains the model 220 by applying one or more logics. In one embodiment, the one or more logics may include at least one of, but not limited to, a k-means clustering, a hierarchical clustering, a Principal Component Analysis (PCA), an Independent Component Analysis (ICA), a deep learning logics such as Artificial Neural Networks (ANNs), a Convolutional Neural Networks (CNNs), a Recurrent Neural Networks (RNNs), a Long Short-Term Memory Networks (LSTMs), a Generative Adversarial Networks (GANs), a Q-Learning, a Deep Q-Networks (DQN), a Reinforcement Learning Logics, etc.
[0063] In one embodiment, while training the model 220 with the retrieved historical data, the model 220 identifies historical trends/patterns related to the consumer behaviour. In particular, based on the identification of the historical trends/patterns related to the consumer behaviour, the model 220 determines at least one of, but not limited to, historical optimal time intervals during which the consumer has utilized at least one network slice. For example, let us assume that on a particular day of the week, the consumer utilizes the at least one network slice. Based on the historical trends/patterns, the model 220 identifies that on the particular day such as on every Monday the consumer utilizes the at least one network slice.
[0064] In one embodiment, subsequent to identifying at least one of, but not limited to, the historical optimal time intervals during which the consumer utilizes the at least one network slice, the model training unit 210 trains the model 220 with the historical trends/patterns including at least one of, but not limited to, the historical optimal time intervals during which the consumer utilizes the at least one network. For example, based on training, the model 220 understands that in the past at which optimal time or optimal time intervals the consumer had utilized the at least one network slice.
[0065] In another example, the at least one network slice is utilized by the consumer by subscribing and connecting to the at least one network slice tailored to the consumers requirements, such as at least one of, but not limited to, high-speed internet, and low latency. The consumer utilizes the at least one network slice indirectly through the UE 102.
[0066] In one embodiment, the model 220 is trained with the partial datasets. The partial dataset refers to subsets of the total retrieved historical data. In particular, the partial dataset is utilized for training when the complete dataset pertaining to the retrieved historical data is not available in real-time or the complete dataset is too large to process fully at once. For example, training the model 220 with the partial dataset allows the model 220 for faster decision-making, real-time optimization, and efficient use of resources while still maintaining the accuracy and relevance of the analysis.
[0067] Upon training the model 220, the prediction unit 212 of the processor 202 is configured to predict consumer behaviour trends/patterns pertaining to the subscription of at least one network slice utilizing the trained model 220. For example, subsequent to training, the trained model 220 is fed with the testing data in order to evaluate performance of the trained model 220. In one embodiment, the prediction unit 212 estimates utilizing the trained model 220, at least one of, but not limited to, a future optimal time or a range of future optimal time intervals that the consumer will consume the at least one network slice. For example, based on training, trained model 220 estimates that the consumer will consume the network slice load at every Monday. In another example, based on training, trained model 220 estimates that the consumer will consume the at least one network slice at an optimal time interval range such as 10 AM to 12 PM every Monday. Based on the estimation, the triggering unit 214 needs to trigger one or more actions such as at least one of, purchase new subscriptions, update existing subscriptions, and unsubscribe the current subscriptions pertaining to the at least one network slice.
[0068] In one embodiment, the prediction unit 212 is further configured to predict a future load matrix pertaining to the at least one network slice based on a historical load matrix pertaining to the same network slice. Herein, the load matrix is used to represent load on the at least one network slice. In particular, the load pertains to at least one of, but not limited to, traffic, bandwidth consumption, and latency.
[0069] In one embodiment, to predict the load matrix pertaining to the at least one network slice, the integration unit 208 collects load matrix pertaining to the at least one network slice. Further, the integration unit 208 preprocesses the collected load matrix. Thereafter, the model training unit 210 trains the model 220 using at least one of, the historical load matrix and the collected load matrix. Furthermore, utilizing the trained model 220, the prediction unit 212 predicts the future load matrix based on the historical load matrix. For example, using the trained model 220, the prediction unit 212 predicts the future load matrix for the defined network slice. The prediction for the future load matrix is for the load on the defined network slice in at least one of, but not limited to, a next hour, a day, and a week.
[0070] Upon predicting the consumer behaviour, the triggering unit 214 of the processor 202 is configured to dynamically generate multiple triggers to initiate the one or more actions. Herein, the one or more actions includes, at least one of, but not limited to, purchasing new subscriptions, updating current subscriptions, unsubscribing current subscriptions, and policy changing of current subscriptions. For example, based on the predictions of the consumer behaviour, the triggering unit 214 initiates the one or more actions to at least one of, but not limited to, purchasing new subscriptions and updating the current subscriptions which improves the consumers experience. In an embodiment, the triggering unit 214 recommends the consumer for initiating the one or more actions.
[0071] In one embodiment, the predictions regarding the consumer behaviour and the triggered one or more actions are represented to the consumer via the UI 306 in at least one of, but not limited to, a tabular view and a graphical view. Advantageously, due to the representation of the predictions as well as the triggered one or more actions, the consumers experience is enhanced.
[0072] In one embodiment, the model 220 is updated based on real time data pertaining to the consumer behaviour received from at least one of, but not limited to, the plurality of sources 110. In another embodiment, the model 220 periodically updated based on at least one of but not limited to, the change in the network dynamics and changing consumer requirements. Herein, the network dynamics refers to the at least one of, but not limited to, the consumer behaviour, evolution and changes within the network 106 over time. For example, the network dynamics is associated with the load matrix related to the traffic in the network slice. In one embodiment, the changing consumer requirements pertains to at least one of, the change in the optimal time or the range of optimal time intervals that the consumer consumes the at least one network slice.
[0073] In another example, let us consider that a when the model 220 updates itself based on real-time data received from the plurality of sources 110, the several key aspects of the model 220 are changed in order to improve at least one of, but not limited to, the performance and the accuracy in predicting or analyzing future events. Herein, the model 220 used for predicting the consumer behaviour may adjust the one or more hyperparameters like learning rate based on the performance of the previous predictions. Due to the updating, the model 220 refines the identification of the trends/patterns related to the consumer behaviour.
[0074] In one embodiment, the model 220 periodically learns regarding the predicted consumer behaviour and the multiple triggers initiated for performing the one or more actions. In other words, the triggering unit 214 provides feedback to the model 220 so that the model 220 = learns regarding the predicted consumer behaviour and the multiple triggers initiated for performing the one or more actions. Due to periodically learning ability of the model 220, the model training unit 210 fine tunes the model 220 based on which the model 220 provides more accurate predictions in the future.
[0075] The integration unit 208, the model training unit 210, the prediction unit 212, and the triggering unit 214 in an exemplary embodiment, are 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 202. In such examples, the system 108 may comprise the memory 204 storing the instructions and the processing resource to execute the instructions, or the memory 204 may be separate but accessible to the system 108 and the processing resource. In other examples, the processor 202 may be implemented by electronic circuitry.
[0076] FIG. 3 illustrates an exemplary architecture for the system 108, according to one or more embodiments of the present invention. More specifically, FIG. 3 illustrates the system 108 for managing subscriptions pertaining to the at least one network slice. It is to be noted that the embodiment with respect to FIG. 3 will be explained with respect to the UE 102 for the purpose of description and illustration and should nowhere be construed as limited to the scope of the present disclosure.
[0077] FIG. 3 shows communication between the UE 102, the system 108, and the plurality of sources 110. For the purpose of description of the exemplary embodiment as illustrated in FIG. 3, the UE 102, uses network protocol connection to communicate with the system 108, and the plurality of sources 110. In an embodiment, the network protocol connection is the establishment and management of communication between the UE 102, the system 108, and the plurality of sources 110 over the network 106 (as shown in FIG. 1) using a specific protocol or set of protocols. The network protocol connection includes, but not limited to, Session Initiation Protocol (SIP), System Information Block (SIB) protocol, Transmission Control Protocol (TCP), User Datagram Protocol (UDP), File Transfer Protocol (FTP), Hypertext Transfer Protocol (HTTP), Simple Network Management Protocol (SNMP), Internet Control Message Protocol (ICMP), Hypertext Transfer Protocol Secure (HTTPS) and Terminal Network (TELNET).
[0078] In an embodiment, the UE 102 includes a primary processor 302, and a memory 304 and a User Interface (UI) 306. In alternate embodiments, the UE 102 may include more than one primary processor 302 as per the requirement of the network 106. The primary processor 302, may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, single board computers, and/or any devices that manipulate signals based on operational instructions.
[0079] In an embodiment, the primary processor 302 is configured to fetch and execute computer-readable instructions stored in the memory 304. The memory 304 may be configured to store one or more computer-readable instructions or routines in a non-transitory computer-readable storage medium, which may be fetched and executed for managing subscriptions pertaining to the at least one network slice. The memory 304 may include any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as disk memory, EPROMs, FLASH memory, unalterable memory, and the like.
[0080] In an embodiment, the User Interface (UI) 306 includes a variety of interfaces, for example, a graphical user interface, a web user interface, a Command Line Interface (CLI), and the like. The UI 306 of the UE 102 allows the consumer to transmit requests to the one or more processors 202 for managing subscriptions pertaining to the at least one network slice.. Further, the UE 102 receives information regarding the predictions and the multiple triggers based on which the one or more actions are initiated by the one or more processors 202. In one embodiment, the consumer is at least one of, but not limited to, a network operator.
[0081] As mentioned earlier in FIG.2, the system 108 includes the processors 202, the memory 204 and the storage unit 206, for managing subscriptions pertaining to the at least one network slice, which are already explained in FIG. 2. For the sake of brevity, a similar description related to the working and operation of the system 108 as illustrated in FIG. 2 has been omitted to avoid repetition.
[0082] Further, as mentioned earlier the processor 202 includes the integration unit 208, the model training unit 210, the prediction unit 212, and the triggering unit 214 which are already explained in FIG. 2. Hence, for the sake of brevity, a similar description related to the working and operation of the system 108 as illustrated in FIG. 2 has been omitted to avoid repetition. The limited description provided for the system 108 in FIG. 3, should be read with the description provided for the system 108 in the FIG. 2 above, and should not be construed as limiting the scope of the present disclosure.
[0083] FIG. 4 is an exemplary the system 108 architecture 400 for managing subscriptions pertaining to the at least one network slice, according to one or more embodiments of the present disclosure.
[0084] The architecture 400 includes a data consumer 1, a data consumer 2, and the NWDAF 402 which is at least one among the plurality of the sources 110. Herein, the plurality of the sources 110 are in communication with the network components. The architecture 400 further includes a data integrator 404, a data pre-processing unit 406, the model training unit 210, the prediction unit 212, a data lake 408, and the UI 306 communicably coupled to each other via the network 106.
[0085] In one embodiment, the data integrator 404 periodically receives the historical data pertaining to the consumer behaviour from the NWDAF 402. The historical data acts as the input stream provided by the NWDAF 402 which is crucial for training the model 220. Herein, data integrator 404 combines the data retrieved from the NWDAF 402 and provides a unified view to the consumer that enables comprehensive analysis. For example, the system 108 provides an integrated view of the data retrieved from the NWDAF 402 pertaining to the consumer behaviour.
[0086] In one embodiment, the data pre-processing unit 406 receives the historical data from the data integrator 404 and preprocesses the data. For example, the data undergoes preprocessing to ensure data consistency within the system 108. In particular, the preprocessing involves tasks like data cleaning, normalization, removing duplicate records and handling missing values. In yet another example, the raw data is pre-processed to clean, normalize, and convert the raw data into a structured format suitable for analysis. In an embodiment, preprocessing the data includes cleaning the data by removing unwanted columns from a data frame, removing unwanted rows from the data frame that contains invalid column values, such as NaN, None, 0, null, or empty strings.
[0087] In one embodiment, the model training unit 210 trains the model 220 using the data which is pre-processed by the data pre-processing unit 406.
[0088] In one embodiment, the prediction unit 212 estimates, at least one of, the future optimal time or the range of future optimal time intervals at which the consumer will consume the at least one network slice In one embodiment, the prediction unit 212 estimates the future optimal time or the range of future optimal time intervals by using the current trained model 220 or a pre-trained model 220.
[0089] In one embodiment, the data lake 408 acts as the storage unit 206 which includes a structured collection of the preprocessed data, and the predictions made by the prediction unit 212, which are managed and organized in a way that allows system 108 for easy access, retrieval, and manipulation. The data lake is used to store, manage, and retrieve large amounts of information efficiently. In one embodiment, the UI 306 provides a graphical representation of the predictions done by the prediction unit 212 in at least one of, but not limited to, the tabular format and graphical format.
[0090] FIG. 5 is a signal flow diagram illustrating the flow for managing subscriptions pertaining to the at least one network slice, according to one or more embodiments of the present disclosure.
[0091] At step 502, the system 108 retrieves historical data from the plurality of sources 110. For example, the historical data is associated with at least one of, the consumer behaviour. In one embodiment, the system 108 transmits at least one of, but not limited to, a Hyper Text Transfer Protocol (HTTP) request to the plurality of sources 110 to retrieve the historical data. In one embodiment, a connection is established between the system 108 and the plurality of sources 110 before retrieving the historical data from the plurality of sources 110. Further, the retrieved historical data is integrated and preprocessed by the system 108.
[0092] At step 504, the system 108 trains the model 220 with the retrieved historical data. More particularly, the system 108 trains the model 220 with the retrieved historical data subsequent to pre-processing. Herein, the pre-processed data is stored in the plurality of sources 110 for training the model 220.
[0093] At step 506, the system 108 predicts the consumer behaviour utilizing the trained model 220. Herein, the system 108 provides required prediction of the consumer behavior for a particular region and load matrix. Herein, the learned trends/patterns are utilized by the trained model 220 to predict when consumers may likely require the at least one network slice which includes determining whether to purchase new subscriptions related to the at least one network slice, when to update existing subscriptions the at least one network slice, and when to unsubscribe the at least one network slice.
[0094] At step 508, the system 108 generates the multiple triggers to initiate the one or more actions. The multiple triggers are designed to initiate the one or more actions such as at least one of, but not limited to, the subscription updates, the one or more policy changes, or unsubscribe the current subscriptions based on the predictive insights. Further, the system 108 transmits a report pertaining to the predictions and the one or more actions initiated by the system 108 by at least one of, but not limited to, the HTTP request. Further, the consumer can view the report generated by the system 108 in at least one of, graphical format and tabular format on the UI 306 of the UE 102.
[0095] FIG. 6 is a flow diagram of a method 600 for managing subscriptions pertaining to the at least one network slice, according to one or more embodiments of the present invention. For the purpose of description, the method 600 is described with the embodiments as illustrated in FIG. 2 and should nowhere be construed as limiting the scope of the present disclosure.
[0096] At step 602, the method 600 includes the step of retrieving historical data pertaining to the consumer behaviour from the plurality of sources 110. In one embodiment, the integration unit 208 retrieves the data from the plurality of sources 110. In particular, the integration unit 208 utilizes the one or more APIs for retrieving the historical data from the plurality of sources 110. Further, the retrieved historical data is integrated by the integration unit 208. Thereafter, the integrated data is preprocessed by the integration unit 208 to ensure the data consistency and the quality within the system 108.
[0097] At step 604, the method 600 includes the step of training the model 220 with the retrieved historical data to identify the historical trends/patterns related to the consumer behaviour. In one embodiment, the model training unit 210 trains the model 220 with the retrieved historical data. For example, let us consider the retrieved data as the dataset which includes data related to the consumer behaviour. Then, the model training unit 210 splits the dataset into the training data and the testing data such as 75% of the dataset is considered as the training data and the 25% of the dataset is considered as the testing data. Thereafter, the training data is fed to each of the model 220 for training.
[0098] At step 606, the method 600 includes the step of predicting, consumer behaviour trends/patterns pertaining to the subscription of at least one network slice utilizing the trained model 220. In one embodiment, the prediction unit 212 predicts consumer behaviour trends/patterns pertaining to the subscription of at least one network slice. Based on the learned trends/patterns, the prediction unit 212 predicts when consumers may likely require the at least one network slice which includes determining whether to purchase new subscriptions, when to update existing subscriptions, and when it's suitable to unsubscribe.
[0099] At step 608, the method 600 includes the step of, dynamically generating multiple triggers in response to prediction to initiate the one or more actions. In one embodiment, the triggering unit 214 generates multiple triggers based on the predictions. The multiple triggers initiate at least one of, but not limited to, subscription updates, policy changes, and unsubscribe the at least one network slice, thereby automatically managing subscriptions.
[00100] In one embodiment, at least one of, but not limited to, the graphical representation and the tabular representation is provided to the user regarding the at least one of, the generated outputs of the plurality of the trained AI/ML models 220 and the recommendation of at least one trained AI/ML model 220.
[00101] 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 202. The processor 202 is configured to retrieve, historical data pertaining to a consumer behaviour from a plurality of sources 110. The processor 202 is further configured to train the model 220 with the retrieved historical data to identify historical trends/patterns related to the consumer behaviour. The processor 202 is further configured to predict, consumer behaviour trends/patterns pertaining to the subscription of at least one network slice utilizing the trained model 220. The processor 202 is further configured to dynamically generate, multiple triggers to initiate one or more actions.
[00102] A person of ordinary skill in the art will readily ascertain that the illustrated embodiments and steps in description and drawings (FIG.1-6) are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
[00103] The present disclosure provides technical advancements where the system is configured to analyze historical data and real-time network conditions to automate subscription management tasks which eliminate the need for operators to manually perform the subscription management tasks. Due to the automated subscription management tasks, the time is saved, and the risk of errors is reduced. Predictive models of the present system are configured to anticipate when a user is likely to require specific services, such as subscribing to new network slice or updating policies. This ensures timely access to crucial network data, helping users make informed decisions without delays. By accurately predicting when to subscribe or unsubscribe from the at least one network slice and adjust policies, the present system is configured to optimize network resource allocation which means that network resources are allocated efficiently, reducing waste and improving overall network performance. Due to predictive subscription management, the system improves the user experience. The user do not need to manually handle the subscription management tasks which leads to smoother and more user-friendly interaction with the NWDAF services.
[00104] 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

[00105] Environment - 100;
[00106] User Equipment (UE) - 102;
[00107] Server - 104;
[00108] Network- 106;
[00109] System -108;
[00110] Plurality of sources – 110;
[00111] Processor - 202;
[00112] Memory - 204;
[00113] Storage unit – 206;
[00114] Integration unit – 208;
[00115] Model training unit – 210;
[00116] Prediction unit – 212;
[00117] Triggering unit – 214;
[00118] Model – 220;
[00119] Primary Processor – 302;
[00120] Memory – 304;
[00121] User Interface (UI) – 306;
[00122] NWDAF – 402;
[00123] Data integrator – 404;
[00124] Data pre-processing unit - 406;
[00125] Data Lake – 408.
,CLAIMS:CLAIMS
We Claim:
1. A method (600) for managing subscriptions pertaining to at least one network slice, the method (600) comprising the steps of:
retrieving, by one or more processors (202), historical data pertaining to a consumer behaviour from a plurality of sources (110);
training, by the one or more processors (202), a model (220) with the retrieved historical data to identify historical trends/patterns related to the consumer behaviour;
predicting, by the one or more processors (202), consumer behaviour trends/patterns pertaining to the subscription of at least one network slice, utilizing the trained model (220); and
in response to prediction, dynamically generating, by the one or more processors (202), multiple triggers to initiate one or more actions.

2. The method (600) as claimed in claim 1, wherein the historical data related to the consumer behaviour includes at least one of, information related to usage trends and slice load analytics data pertaining to at least one network slice, network conditions, and policy changes made by the consumers.

3. The method (600) as claimed in claim 1, wherein the consumer includes at least one of a network operator.

4. The method (600) as claimed in claim 1, wherein the plurality of sources (110) includes at least one of, a database and a Network Data Analytics Function (NWDAF).

5. The method (600) as claimed in claim 1, wherein the step of retrieving, the historical data from the plurality of sources (110), further includes the step of:
preprocessing, by the one or more processors (202), at least one of the retrieved historical data.

6. The method (600) as claimed in claim 1, wherein the model (220) is at least one of, an Artificial Intelligence/Machine Learning (AI/ML) model.

7. The method (600) as claimed in claim 1, wherein the step of, training, by the one or more processors (202), a model (220) with the retrieved historical data to identify historical trends/patterns related to the consumer behaviour, includes the steps of:
determining, by the one or more processors (202), historical optimal time intervals at which the at least one network slice is utilized by the consumer.

8. The method (600) as claimed in claim 1, wherein the step of, predicting, by the one or more processors (202), consumer behaviour trends/patterns pertaining to the subscription of the at least one network slice, utilizing the trained model (220), includes the step of:
estimating, by the one or more processors (202), at least one of, a future optimal time or a range of future optimal time intervals at which the consumer consumes the at least one network slice.

9. The method (600) as claimed in claim 1, wherein the one or more actions includes, purchasing new subscriptions, updating current subscriptions, unsubscribing current subscriptions, and policy changing of current subscriptions.

10. The method (600) as claimed in claim 1, wherein the model (220) is updated based on real time data pertaining to the consumer behaviour received from the plurality of sources (110).

11. The method (600) as claimed in claim 1, wherein the one or more processors (220) is further configured to predict a future load matrix pertaining to the at least one network slice based on a historical load matrix pertaining to the same network slice.

12. A system (108) for managing subscriptions pertaining to at least one network slice, the system (108) comprising:
an integration unit (208), configured to retrieve, historical data pertaining to a consumer behaviour from a plurality of sources (110);
a model training unit (210), configured to train, a model (220) with the retrieved historical data to identify historical trends/patterns related to the consumer behaviour;
a prediction unit (212), configured to predict, consumer behaviour trends/patterns pertaining to the subscription of at least one network slice utilizing the trained model (220); and
in response to prediction, a triggering unit (214) configured to dynamically generate, multiple triggers to initiate one or more actions.

13. The system (108) as claimed in claim 12, wherein the historical data related to the consumer behaviour includes at least one of, information related to usage trends and slice load analytics data pertaining to at least one network slice, network conditions and policy changes made by the consumers.

14. The system (108) as claimed in claim 12, wherein the consumer includes at least one of a network operator.

15. The system (108) as claimed in claim 12, wherein the plurality of sources (110) includes at least one of, a database and a Network Data Analytics Function (NWDAF).

16. The system (108) as claimed in claim 12, wherein the integration unit (208) is further configured to:
preprocess, the collected historical data by at least one of the retrieved historical data.

17. The system (108) as claimed in claim 12, wherein the model (220) is at least one of, an Artificial Intelligence/Machine Learning (AI/ML) model.

18. The system (108) as claimed in claim 12, wherein the model training unit (210) trains, a model (220) with the retrieved historical data pertaining to identify historical trends/patterns related to the consumer behaviour, by:
determining, historical optimal time intervals at which at least one network slice is utilized by the consumer.

19. The system (108) as claimed in claim 12, wherein the prediction unit (212), predicts, consumer behaviour trends/patterns pertaining to the subscription of the at least one network slice utilizing the trained model (220) by:
estimating, at least one of, a future optimal time or a range of future optimal time intervals at which the consumer consumes the at least one network slice.

20. The system (108) as claimed in claim 12, wherein the one or more actions includes, purchasing new subscriptions, updating current subscriptions, unsubscribing current subscriptions, and policy changing of current subscriptions.

21. The system (108) as claimed in claim 12, wherein the model (220) is updated based on real time data pertaining to the consumer behaviour received from the plurality of sources (110).

22. The system (108) as claimed in claim 12, wherein the prediction unit (212) is further configured to predict a future load matrix pertaining to the at least one network slice based on a historical load matrix pertaining to the same network slice.