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 MONITORING A NETWORK
2. APPLICANT(S)
Name Nationality Address
JIO PLATFORMS LIMITED INDIAN OFFICE-101, SAFFRON, NR. CENTRE POINT, PANCHWATI 5 RASTA, AMBAWADI, AHMEDABAD 380006, GUJARAT, INDIA
3. PREAMBLE TO THE DESCRIPTION

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

FIELD OF THE INVENTION
[0001] The present invention relates generally to a wireless communication system, and in particular, to a method and system for monitoring a network.
BACKGROUND OF THE INVENTION
[0002] In traditional telecommunications networks, communication systems may collect data in order to perform various analysis. The traditional systems often struggle to efficiently integrate and process real-time streaming data. These communication systems lack the ability to provide real-time insights and analytics due to batch processing or delayed data handling. In other words, if the real time data is not integrated properly, the traditional communication systems may fail to perform appropriate real time analysis on the data. Therefore, monitoring network performance, identifying issues, and tracking trends may be cumbersome task and does not provide real-time visibility. Due to this, the system fails to identify real time issues that may degrade the performance of the communication system.
[0003] There is, therefore, a dire need for a method and a system for analysing events using the real time feed that ensures appropriate real time data integration, proactively addressing network issues in real-time and enhancing consumers’ experience.
SUMMARY OF THE INVENTION
[0004] One or more embodiments of the present disclosure provide a system and a method for monitoring a network.
[0005] In one aspect of the present invention, the method for monitoring a network is disclosed. The method includes establishing, by one or more processors, one or more connections with internal and external sources of the network. Further, the method includes receiving, by the one or more processors, data from the internal and the external source in real time on establishment of the one or more connections. The method further includes analysing, by the one or more processors, the received data to detect at least one deviation in the received data. Further, the method further includes generating, by the one or more processors, alerts and resolutions on detection of the at least one deviation.
[0006] In an embodiment, the internal sources provide data pertaining to network performance, subscribers, application usage and subscriber devices.
[0007] In an embodiment, the external sources provide data pertaining to competitors, social media, and customer feedback and surveys.
[0008] In an embodiment, the one or more processors is configured to receive real time data, static data, and batch wise data.
[0009] In an embodiment, converting, by the one or more processors, a format of the received data from a raw format into a standard format; and storing, by the one or more processors, the converted data in a database.
[0010] In an embodiment, the step of analysing the received data to detect the at least one deviation comprises the steps of comparing, by the one or more processors, parameters of the received data with a threshold value of a parameters of the historical data or a trend of the received data with a trend of the historical data. The thresholds are dynamically generated for each of the plurality of time intervals. The plurality of time intervals is defined by the service operator. The step of analysing further includes detecting, by the one or more processors, the at least one deviation based on the comparison.
[0011] In an embodiment, the alerts and resolution are transmitted to a user interface of a service operator, and wherein the alerts and resolution comprise actions to be performed to address the at least one deviation.
[0012] In another aspect of the invention, the system for monitoring a network. The system includes a communication unit configured to establish one or more connections with internal and external sources of the network. The system further includes a receiving unit configured to receive, data from the internal and the external source in real time on establishment of the one or more connections. The system further includes an analysing unit configured to analyse, the received data to detect at least one deviation in the received data. The system further includes a generation unit configured to generate alerts and resolutions on detection of the at least one deviation.
[0013] In another aspect of invention, User Equipment (UE) is disclosed. The UE includes one or more primary processors communicatively coupled to one or more processors, the one or more primary processors coupled with a memory. The processor causes the UE to receive alerts and resolutions with respect to one or more anomalies in the network.
[0014] 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 establish one or more connections with internal and external sources of the network. The processor is further configured to receive data from the internal and the external source in real time on establishment of the one or more connections. The processor is further configured to analyse the received data to detect at least one deviation in the received data. The processor is further configured to generate alerts and resolutions on detection of the at least one deviation.
[0015] 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
[0016] 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.
[0017] FIG. 1 is an exemplary block diagram of an environment for monitoring a network according to one or more embodiments of the present invention;
[0018] FIG. 2 is an exemplary block diagram of a system for monitoring a network according to one or more embodiments of the present invention;
[0019] FIG. 3 is a schematic representation of a workflow of the system of FIG. 1, according to the one or more embodiments of the present invention;
[0020] FIG. 4 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;
[0021] FIG. 5 is a flow diagram for monitoring a network according to one or more embodiments of the present invention; and
[0022] FIG. 6 is a schematic representation of a method for monitoring a network according to one or more embodiments of the present invention.
[0023] The foregoing shall be more apparent from the following detailed description of the invention.
DETAILED DESCRIPTION OF THE INVENTION
[0024] 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.
[0025] 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.
[0026] 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.
[0027] FIG. 1 illustrates an exemplary block diagram of an environment 100 for monitoring a network 105, according to one or more embodiments of the present disclosure. In this regard, the environment 100 includes the network 105, User Equipment (UE) 110, a server 115 and a system 120 communicably coupled to each other for monitoring a network 105. The monitoring of a network 105 refers to real time supervising of the network 105 to observe one or more specific occurrence or set of occurrences or changes within the network 105. The specific occurrences or changes include network performance issues or subscriber behavior changes, security incidents, application related issues, competitor or market related data changes or deviation from historical data.
[0028] As per the illustrated embodiment and for the purpose of description and illustration, the UE 110 includes, but not limited to, a first UE 110a, a second UE 110b, and a third UE 110c, and should nowhere be construed as limiting the scope of the present disclosure. In alternate embodiments, the UE 110 may include a plurality of UEs as per the requirement. For ease of reference, each of the first UE 110a, the second UE 110b, and the third UE 110c, will hereinafter be collectively and individually referred to as the “User Equipment (UE)” 110.
[0029] In an embodiment, the UE 110 is one of, but not limited to, any electrical, electronic, electro-mechanical or an equipment and a combination of one or more of the above devices such as 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 115 accessible via the network 105. The server 115 may include, by way of example but not limitation, one or more of a standalone server, a server blade, a server rack, a bank of servers, a server farm, hardware supporting a part of a cloud service or system, a home server, hardware running a virtualized server, one or more processors executing code to function as a server, one or more machines performing server-side functionality as described herein, at least a portion of any of the above, some combination thereof. In an embodiment, the entity may include, but is not limited to, a vendor, a network operator, a company, an organization, a university, a lab facility, a business enterprise side, a defense facility side, or any other facility that provides service.
[0031] The network 105 includes, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, or some combination thereof. The network 105 may include, but is not limited to, a Third Generation (3G), a Fourth Generation (4G), a Fifth Generation (5G), a Sixth Generation (6G), a New Radio (NR), a Narrow Band Internet of Things (NB-IoT), an Open Radio Access Network (O-RAN), and the like.
[0032] The network 105 may also include, by way of example but not limitation, at least a portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, etc. one or more messages, packets, signals, waves, voltage or current levels, some combination thereof, or so forth. The network 105 may also include, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, a VOIP or some combination thereof.
[0033] The environment 100 further includes the system 120 communicably coupled to the server 115 and the UE 110 via the network 105. The system 120 is configured to monitor the network 105. As per one or more embodiments, the system 120 is adapted to be embedded within the server 115 or embedded as an individual entity.
[0034] Operational and construction features of the system 120 will be explained in detail with respect to the following figures.
[0035] FIG. 2 is an exemplary block diagram of the system 120 for monitoring the network 105, according to one or more embodiments of the present invention.
[0036] As per the illustrated embodiment, the system 120 includes one or more processors 205, a memory 210, a user interface 215, and a storage unit 220. For the purpose of description and explanation, the description will be explained with respect to one processor 205 and should nowhere be construed as limiting the scope of the present disclosure. In alternate embodiments, the system 120 may include more than one processor 205 as per the requirement of the network 105. The one or more processors 205, hereinafter referred to as the processor 205 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, single board computers, and/or any devices that manipulate signals based on operational instructions.
[0037] As per the illustrated embodiment, the processor 205 is configured to fetch and execute computer-readable instructions stored in the memory 210. The memory 210 may be configured to store one or more computer-readable instructions or routines in a non-transitory computer-readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory 210 may include any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as disk memory, EPROMs, FLASH memory, unalterable memory, and the like.
[0038] In an embodiment, the user interface 215 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 215 facilitates communication of the system 120. In one embodiment, the user interface 215 provides a communication pathway for one or more components of the system 120. Examples of such components include, but are not limited to, the UE 110 and the storage unit 220.
[0039] The storage unit 220 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 storage unit 220 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 120 to monitor the network 105 the processor 202 includes one or more modules. In one embodiment, the one or more modules includes, but not limited to, a communication unit 225, a receiving unit 230, a conversion unit 235, an analysing unit 240, and a generation unit 245 communicably coupled to each other to monitor the network 105. In one embodiment, the one or more modules are used in combination or interchangeably for monitoring the network 105.
[0041] The communication unit 225, the receiving unit 230, the conversion unit 235, the analysing unit 240, and the generation unit 245 in an embodiment, may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processor 205. In the examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processor 205 may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processor may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, memory 210 may store instructions that, when executed by the processing resource, implement the processor. In such examples, the system 120 may comprise the memory 210 storing the instructions and the processing resource to execute the instructions, or the memory 210 may be separate but accessible to the system 120 and the processing resource. In other examples, the processor 205 may be implemented by electronic circuitry.
[0042] In one embodiment, the communication unit 225 is configured to establish one or more processors, one or more connections with the data sources 405 (as shown in FIG. 3) in the network. The data sources 405 include internal sources and external sources. The internal sources include, but are not limited to, routers, switches and network monitoring tools providing network performance data. Further, the data from internal sources include the devices connected within the network 105 which are configured to provide data pertaining to subscriber and device. The external sources are sources of data connected external to the network 105. The external sources include at least one of, but not limited to, competitor data, social media platforms, customer feedback and surveys and real time streaming data from databases like Kafka. The data sources 405 provide data including but not limited to, RAN Operation Support System Counter, Call Detail records (CDRs), alarm data, alert data, trouble ticket data comprising customer ticket data and network ticket data, social media data, operation data, key performance data and planning data related to the operations of the wireless network. In an embodiment connection established by the communication unit 225 refers to utilizing one or more tools to connect to the data sources and collect the data from the data sources. For example, the communication unit 225 interfaces with the CRM database through Application Program Interfaces to retrieve information about subscriber accounts, service usage and customer support interactions.
[0043] In an embodiment, the data regarding the network performance include at least data on efficiency of the network 105 based on bandwidth usage, latency, packet loss and uptime. The data on network 105 performance makes the system 120 able to understand the efficiency in functioning of the network 105. The data with respect to subscribers include but is not limited to information on behavior, usage patterns, and geographic locations. The data corresponding to the devices includes at least data on types and statuses of devices. The type and status of the devices include, but is not limited to, smartphones, tablets, laptops or compatibility with the network 105.
[0044] The data related to competitors is the data related to competitor’s networks and services. The data from social media platforms regarding the network’s services real time customer feedback enabling the system to analyse the network conditions. For example, if the communication unit 225 in the system 120 accesses Kafka, which is a real time streaming data platform, then Kafka enables the system 120 to access real time data feed. The customer feedback and survey data are information of at least direct feedback from customers or surveys during customer support interactions.
[0045] Upon establishing connections with the data sources 405 by the communication unit 225, the receiving unit 230 receives the data from the data sources 405. The data received by receiving unit 230 includes at least real time data, static data and batch wise data. The real time data corresponds to the data received from the external or internal sources collected, processed and presented immediately or with minimal delay. The real time data is dynamic data which changes frequently over time and is constantly updated. The static data corresponds to the data received from the external or internal sources that remains relatively constant over time. The batch wise data corresponds to the data received from the external or internal sources collected, processed or transmitted in groups or batches which is often handled at regular intervals.
[0046] In an embodiment, the data received by the receiving unit 230 is raw data. The raw data is the original data received directly by the receiving unit 230 from both internal and external data sources. The conversion unit 235 is configured to convert the raw format of the data into a standard format. The raw data is unstructured or semi-structured and heterogenous formats of data. The raw data after conversion is transformed into standardized data. The conversion or processing of raw data into standard data includes, but is not limited to data cleansing, validation and normalization. The conversion eliminates inconsistencies, fills missing values and ensures data integrity. The standard data is structured and organized, normalized and possesses consistent time stamp formats. In one embodiment, the data which is converted into the standardized format is further stored in a storage unit 220.
[0047] Upon converting the received data into standardized format, the analysing unit 240 analyses the received data. The analysing unit 240 performs the analysis utilizing one or more Machine Learning (ML) based logic models or complex algorithms. The ML is a subset of Artificial Intelligence (AI). The ML utilizes logic models which are trained on datasets and delivers one or more output from unseen data. The output is created based on the insights gained by the logic model during the training. The ML logic model in an embodiment includes, at least, a computational framework that uses machine learning algorithms to enable the analysing unit 240 to perform real time data analysis.
[0048] In an embodiment, the real time data analysis includes analyzing each of the parameters of the network 105. The parameters of network 105 include at least call signal strength in a particular location, number of active user session on the network 105 in real-time, real-time bandwidth usage across different segments of the network 105 and latency in data transmission across the network 105 in real time.
[0049] Further, the analysing unit 240 is configured to compare each of the parameters of the received data with the threshold value of each of the parameters of the historical data or a plurality of trends in the received data with a plurality of trends in the historical data. The threshold value for each of the parameters of the network is the predefined value with respect to each of the network parameters. The threshold value is dynamically generated on a daily basis based on the historical data as well as current data. The dynamic character of the threshold value refers to the changing nature of the values every day for each defined time interval. The time interval is defined by the service operator. For example, a KPI of network traffic for is analysed by the analysing unit 240 for a defined time interval, say 1 minute, and the analysing unit 240 understands the trends of the parameter in the network 105. For another KPI of latency, the time interval for the analysis of one of trends or threshold is different, say 1 hour. The diverse time interval facilitates the dynamic and unique calculation of the threshold values in every interval of time on a daily basis. The uniqueness exists as the watermarks to identify any deviation from the calculated values while comparing the received data with the analysed patterns and trends of each of the parameters in the network 105. The historical data is the data received previously by the system 120. The current data is the data received by the system 120 in real time which possesses immediate and updated values.
[0050] In an embodiment, the unique and dynamic threshold values for each of the parameters of the network 105 are generated utilizing Machine Learning (ML), and the network 105. The ML includes but is not limited to Supervised, Unsupervised, Reinforcement Learning (RL), and Deep learning techniques. The locations of the network used for the generation of threshold values include but are not limited to network nodes, network equipment, utility equipment, device, user device or any other network or UE 110 capable of generating data. The generated data is the one which is accessible by the system 120.
[0051] In an embodiment, the along with the deployed capacity and ML based threshold calculations, a component of geography is also considered. The component of geography further includes varying weightage assigned to each geographical deployment. For example, the weightage for rural geography is lower compared to urban geography in the subscriber related parameters. With change in the threshold values over time, the ratio of contribution in the final threshold also changes. The change in the ratio depends upon the importance of the particular geography on the particular day and time
[0052] The threshold values of the thresholds or trends of the historical data are with respect to at least latency, bandwidth usage, packet loss and other network performance indicators, predefined based on the data utilized to train the computational framework. The received data is analysed and is compared with the predefined threshold values. The comparison identifies whether one or more parameters of the incoming data are making significant deviations from the predefined or historical thresholds. For example, the current data related to real time traffic in the network between 8 am to 9 am is compared with the threshold value of the traffic in the network 105. This comparison is to consider any deviations of the incoming data from the threshold value with respect to each of the performance parameters in the network 105.
[0053] Upon analysing the received data with the threshold values of each of the parameters using the analysing unit 240, the analysing unit 240 is further configured to detect at least one deviation based on the comparison. The analysing unit 240 identifies the one or more deviations from the predefined thresholds of each of the parameters as one or more anomalies or issues. The system 120 continues to perform the comparison if new data is received from one or more data sources. For example, if the latency as learnt by the model from historical data during 8 am to 9 am is 35 ms to 45 ms, whenever the latency for the real time incoming data exceeds the learnt latency, the latency parameter during 8 am to 9 am suffers deviation which is to be addressed by the user. As the comparison continues, the analysing unit 240 proceeds to detect anomalies or issues within the network 105 in real time.
[0054] Upon detecting the deviation within the network 105, the generation unit 245 generates alerts and resolutions on detection of the at least one deviation. The alerts and resolution are transmitted to a user interface 215 or UE 110 of a service operator. The alerts and the resolutions comprise actions to be performed to address the at least one deviation. The one or more alerts and resolutions are transmitted to notify the network operators about the detected anomalies, deviations or potential issues in the network 105. In an embodiment, each of the alerts and resolutions is unique with respect to others.
[0055] In one embodiment, the alerts are designed such that the network operators are given clear and precise information. The precise information includes information on at least type, severity level, exact time, date, location of the anomalies.
[0056] In an embodiment, the resolutions include recommendations to correct the anomalies, issues or deviations, based on historical data and ML models. The resolutions include, but are not limited to, rerouting connections, performing security checks or rebalancing network traffic. In an embodiment the alert further notifies status of the anomalies, which include at least ‘under ‘investigation’, ‘pending’ or ‘resolved’. The notification on status enables the system to keep track of the anomalies in the network 105. In this way, the system establishes connection with data sources, receives data, analyses data to detect the anomalies and finally generates alerts at the user interface 215 regarding the detected anomalies of the network 105. In another embodiment, the alerts are generated by the generating unit and transmitted to the UE 110. For example, if the detected anomaly is related to traffic in the network beyond the threshold value, then the model may suggest that traffic be reduced to say 0.8x of the current traffic. Thereafter, the system 120 triggers at least one of load balancers, network nodes or devices to decide or take action to decrease the load on the connected devices. The action includes reducing the load on connected devices by 0.8x, 0.7x,0.6x until the system becomes stable. The dynamic management of threshold and traffic is based on threshold values calculated utilizing ML, deployed capacity in the network 105 and geography. The repeated set of actions including analysis of real time performance data, detecting deviation from threshold values in the traffic parameter, and automated management of traffic ensures that the system 120 remains stable over time.
[0057] The storage unit 220 is further configured to store information regarding the generated alerts and resolutions. The one or more performance parameters, the anomalies of performance parameters in the network, the actions taken by the system 120 to rectify the anomalies are stored in the storage unit 220. The stored data is utilized to train the model for the future anomalies occurring in the network 105. For example, when the value in the real time increases beyond the threshold value for network congestion due to hardware failure or increase in number of devices, the alerts and notifications transmitted. The notifications include at least one of locations where the anomaly happened or recommendations of actions to be taken to rectify the anomaly. The understanding of which action to be taken is derived from knowing the previous anomalies and the corresponding action taken by the system 120 of which data is stored in the storage unit 220. The stored information is utilized for the alerts and the resolutions required to be generated by the generation unit 245.
[0058] FIG. 3 describes a preferred embodiment of the system 120 of FIG. 2, according to various embodiments of the present invention. It is to be noted that the embodiment with respect to FIG. 3 will be explained with respect to the first UE 110a and the system 120 for the purpose of description and illustration and should nowhere be construed as limited to the scope of the present disclosure.
[0059] As mentioned earlier in FIG. 1, each of the first UE 110a, the second UE 110b, and the third UE 110c may include an external storage device, a bus, a main memory, a read-only memory, a mass storage device, communication port(s), and a processor. The exemplary embodiment as illustrated in FIG. 3 will be explained with respect to the first UE 1 without deviating from the scope of the present disclosure and the limiting the scope of the present disclosure. The first UE 110a includes one or more primary processors 305 communicably coupled to the one or more processors 205 of the system 120.
[0060] The one or more primary processors 305 are coupled with a memory 210 storing instructions which are executed by the one or more primary processors 305. Execution of the stored instructions by the one or more primary processors 305 enables the first UE 110a to receive alerts and resolutions with respect to one or more anomalies in the network.
[0061] As mentioned earlier in FIG. 2, the one or more processors 205 of the system 120 is configured to receive alerts and resolutions with respect to one or more anomalies in the network. As per the illustrated embodiment, the system 120 includes the one or more processors 205, the memory 210, the user interface 215, and the database 220. The operations and functions of the one or more processors 205, the memory 210, the user interface 215, and the database 220 are already explained in FIG. 2. For the sake of brevity, a similar description related to the working and operation of the system 120 as illustrated in FIG. 2 has been omitted to avoid repetition.
[0062] Further, the processor 202 includes the communication unit 225, the receiving unit 230, the conversion unit 235, the analysing unit 240 and the generation unit 245. The operations and functions of the communication unit 225, the receiving unit 230, the conversion unit 235 the analysing unit 240 and the generation unit 245 are already explained in FIG. 2. Hence, for the sake of brevity, a similar description related to the working and operation of the system 120 as illustrated in FIG. 2 has been omitted to avoid repetition. The limited description provided for the system 120 in FIG. 3, should be read with the description provided for the system 120 in the FIG. 2 above, and should not be construed as limiting the scope of the present disclosure.
[0063] FIG. 4 is an exemplary block diagram of an architecture 400, implemented in the system of the FIG.2, according to one or more embodiments of the present invention.
[0064] The architecture 400 includes the data sources 405, a data ingestion layer 410, an Artificial Intelligence/ Machine Learning (AI/ML) system 415, data lake 435 and the user interface 215. The AI/ML system 415 includes a real time analytics engine 420, issue detection unit 425, and alerting and response unit 430.
[0065] In an embodiment, the data ingestion layer 410 is configured to connect and establish one or more connections with the data sources 405. The layer includes, but is not limited to, connectors, data ingestion pipelines and mechanisms for handling real time data streams. In the present invention, the data sources 405 involves the internal data sources located within the network 105 and the external data sources located outside the network 105. The data sources 405 within the network 105 include at least routers, switches, network monitoring tools and other devices connected within the network 105. The data within the network 105 includes but is not limited to, network performance data, subscriber data and device data. The data sources outside the network 105 include at least, but not limited to, competitor data, social media data, customer feedback and surveys.
[0066] Accordingly, the data ingestion layer 410 continuously ingests data from the connected data sources 405.
[0067] Upon ingesting the data from data sources 405 by the data ingestion layer 410, the AI/ML system 415 receives the data from the data ingestion layer 410. The received data is in raw format. The raw data is the original data with heterogenous formats or standards. The raw data is difficult to comprehend for context-based applications. Therefore, the raw data is converted into data with standardized format. The standardized data possesses data with uniform format enabling the processing of the data in the present invention. The standardized data is further stored into the data lake 435. The data lake 435 is a storage unit that holds standardized data and other relevant information for further processes of the present invention.
[0068] Upon conversion of ingested data, the real time analytics engine 420 performs real time data analytics on the standardized data. The real time data analytics involves real time analysing of each of the parameters of the standardized data. The parameters include at least, but are not limited to, call signal strength in a particular location, real time bandwidth usage, latency in data transmission in the network and other network parameters. To perform the data analytics the parameters of the data are compared with the threshold value of each of the parameters of the historical data. The threshold value of each of the parameters is the predefined value of each of the parameters of the network 105. The threshold value is based on historical data or accumulated data collected over a past period. The threshold value refers to a baseline or reference point against which the current or incoming data are compared.
[0069] In an embodiment the data analytics include the computational frameworks based on Machine Learning (ML) logic models. The ML models utilize complex logic models or algorithms to perform different analytical functions. The ML is the subset of Artificial Intelligence (AI) which utilizes data sets to train the models and create human understanding by applying the learning on a new unseen data. The real time analytics engine 420 compares the incoming data with the threshold to examine the deviations from the threshold values.
[0070] Upon analysis of the standardized data by the real time analytics engine 420, the issue detection unit 425 detects one or more deviations of each of the parameters of the network 105. The one or more deviations are departures of each of the parameters from the predefined threshold values. When the one or more deviations of each of the parameters are beyond the threshold values for a predefined specific time interval, the issue detection unit 425 detects the deviation as one or more issues. The specific time interval is defined by the user or service operator. As the incoming data arrives in real time, the one or more issues continues to be detected in real time.
[0071] Upon detection of issues by the issue detection unit 425, the alerting and response unit 430 generates alerts and resolutions on detection of the at least one deviation. The alerts and resolutions comprise actions to be performed to address the at least one deviation. Each of the alerts and resolutions are unique. The alerts notify the network 105 of issues or anomalies.
[0072] In an embodiment, the one or more resolutions are recommendations to correct the issues. The recommendations to correct the issues involve actions, recommendations or steps generated by the alerting and response unit. The resolutions include, but are not limited to, recommendations to redistribute traffic loads across multiple servers to reduce bottlenecks, allocate more resources to the affected application and reroute traffic through a less congested path.
[0073] Upon generating the alerts and resolutions by the alerting and response unit 330 the alerts and resolution are transmitted to the user interface 215 or the UE 110 of a service operator. In an embodiment, the data lake 435 which is the storage unit stores the alerts and resolutions generated by the alerting and response unit 430. The stored alerts and resolutions further constitute the historical data that is held within the data lake 435. The stored alerts and resolutions further enable the AI/ML system 415 to tackle the anomalies or issues detected in the network 105 in real time. This timely receival of data from the data sources 405, analysis of parameters of data, detection of anomalies or issues and generation of alerts and resolutions to the user interface 215 or the UE 110 enables the network 105 to be self-healing in real time.
[0074] FIG. 5 is a flowchart monitoring a network 105, according to one or more embodiments of the present invention.
[0075] At step 505, the data sources 405 are accessed for data source integration. The data source integration is to integrate data from both internal and external sources of the network 105. The data sources 405 include but are not limited to network performance data, subscriber data, device data, competitor data, social media data and customer feedback and surveys.
[0076] At step 510, the data from the data sources 405 is ingested by the process of real time data ingestion from the connected data sources 405. The data including at least, but not limited to network metrics, subscriber information, device details and application usage are being ingested. The ingested raw data is then aggregated and converted to standardized data for further processes of the present invention.
[0077] At step 515, the converted standardized data is stored in the storage unit 220 which serves as a central platform for all incoming data to be stored and accessed for data analysis.
[0078] At step 520, the standardized data is analysed in real time. The analysis involves comparing the parameters of the standardized data on the network 105 with threshold values. The threshold values are predefined values fixed for each of the parameters in a network 105.based on the historical. The step 520 further involves understanding those parameters of the network 105 which deviate from the threshold values.
[0079] At step 525, the comparison of data enables patterns in the incoming data to be recognized and insights to be observed. The patterns recognized in the present invention include identifying similarities or structure within the data to understand recurring patterns automatically. The insights refer to actionable information derived from recognizing the patterns.
[0080] At step 530, based on the patterns recognized, insights perceived, and deviations from threshold values of parameters of the network 105 identified, issues or anomalies are detected in the network 105. The issues are events of deviations from the threshold values that can hamper the network efficiency.
[0081] At step 535, the detection of issues is considered by the system to notify the network 105.
[0082] At step 540, the alerts and responses are transmitted to the user interface 215 to notify the events of anomalies or issues occurring in the network 105. The alerts and responses are unique according to the type of events occurring within the network 105. The responses refer to recommendations to tackle the events and enable reasserting the efficiency in the network 105.
[0083] FIG. 6 is a flow diagram of a method 600 for monitoring a network 105, 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.
[0084] At step 602, the method 600 includes the step of establishing one or more connections with internal and external data sources of the network 105. The internal data sources of the network 105 include sources of data within the network 105. The internal data sources provide data with respect to the network performance, subscribers, devices connected to the network 105. The external data sources of the network 105 include sources of data outside the network 105. The external data sources include but are not limited to competitors’ data, social media data, customer feedback and surveys. The data received includes real time data, static data and batch wise data.
[0085] At step 610, the method of 600 includes the step of receiving data from the internal and the external data sources in real time on establishment of the one or more connections. The received incoming data is in the raw format. The raw format is not appropriate for a system 120 to process for any real time application. Therefore, the raw format of the received data is converted to standard format. The standardized data is then stored in the storage unit 220.
[0086] At step 615, the method of 600, includes the step of analysing the received data to detect at least one deviation in the received data. The data is analysed using Machine Learning based logic models or complex algorithms. The data analysis involves comparing the parameters of the standardized data with predefined threshold value of each of the parameters. The predefined threshold values are the value for each of the parameters in the network 105. The threshold values are predefined based on the historical data on which the ML model is trained. The events when the parameters of the incoming data deviate from the parameters of the historical data crossing the threshold values are considered as deviations, anomalies, or issues. The anomalies are detected by the comparison of incoming data with threshold values.
[0087] At step 620, the method 600 includes the step of generating alerts and resolutions on the detection of the at least one deviation. The alerts and notifications on the anomalies on the issues in the network 105 transmitted to the user interface 215. The alerts enable the network 105 to take action to navigate through the healing of the anomalies. The resolutions are recommendations to correct the one or more anomalies in the network 105. Each of the type of anomalies are to be resolved by unique methods and the resolutions are unique accordingly. The historical data in the storage unit further enables the resolutions to be decided based on the previous events of deviations, alerts, and resolutions.
[0088] 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 205. The processor 205 is configured to receive the data from different data sources 405 across the network 105 and outside the network 105. The processor 205 is further configured to analyse and detect the events of deviation beyond threshold values of parameters in the network 105. The processor 205 is further configured to transmit alerts and resolutions on the events of deviation to the user interface 215.
[0089] 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.
[0090] The present disclosure incorporates technical advancement in solving the problem of managing anomalies or defects in the network. Further, the present invention enhances the efficiency, reliability, and flexibility in the healing of anomalies or issues in the network. The present invention automates the analysing of data received from different sources and detection of anomalies or issues in the network. Further, the present invention also provides alerts and resolutions to handle the anomalies in real time. By automating the anomaly detection and alert and resolution process, the present invention optimizes the use of network resources, data management and defect healing in a network. The automation of data analysis, anomaly detection and alert-resolution transmissions ensure that the network is functioning efficiently and effectively in the most appropriate times and with minimal impact on overall network performance. The present invention further exhibits economic significance in telecommunications industries since it ensures there is less delay in performing a plurality of network operations and optimized self-healing of anomalies or defects in the network.
[0091] 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

[0092] Environment- 100
[0093] Network-105
[0094] User Equipment (UE)- 110
[0095] Server- 115
[0096] System -120
[0097] Processor- 205
[0098] Memory- 210
[0099] User Interface- 215
[00100] Storage unit-220
[00101] Communication unit- 225
[00102] Receiving unit- 230
[00103] Conversion unit- 235
[00104] Analysing unit- 240
[00105] Generation unit- 245
[00106] Primary Processor -305
[00107] Data Sources- 405
[00108] Data Ingestion Layer- 410
[00109] AI/ML System- 415
[00110] Real Time Data Analytics Engine- 420
[00111] Issue Detection Unit- 425
[00112] Alerting and Response Unit- 430
[00113] Data Lake-435

,CLAIMS:

CLAIMS
We Claim:
1. A method (600) of monitoring a network, (105) the method comprising the steps of:
establishing (605), by one or more processors (205), one or more connections with internal and external sources of the network (105);
receiving (610), by the one or more processors (205), data from the internal and the external source in real time on establishment of the one or more connections;
analysing (615), by the one or more processors (205), the received data to detect at least one deviation in the received data; and
generating (620), by the one or more processors (205), alerts and resolutions on detection of the at least one deviation.

2. The method (600) as claimed in claim 1, wherein the internal sources provide data pertaining to network performance, subscribers, application usage and subscriber devices.

3. The method (600) as claimed in claim 1, wherein the external sources provide data pertaining to competitors, social media, and customer feedback and surveys.

4. The method (600) as claimed in claim 1, wherein the one or more processors (205) is configured to receive real time data, static data, and batch wise data.

5. The method (600) as claimed in claim 1, comprising the steps of:
converting, by the one or more processors (205), a format of the received data from a raw format into a standard format; and
storing, by the one or more processors (205), the converted data in a database.

6. The method (500) as claimed in claim 1, wherein the step of analysing the received data to detect the at least one deviation comprises the steps of:
comparing, by the one or more processors (205), parameters of the received data with a threshold value of a parameters of the historical data or a trend of the received data with a trend of the historical data, wherein the thresholds are dynamically generated for each of the plurality of time intervals and wherein the plurality of time intervals are defined by the service operator; and
detecting, by the one or more processors (205), the at least one deviation based on the comparison.

7. The method (500) as claimed in claim 1, wherein the alerts and resolution are transmitted to a user interface (215) of a service operator, and wherein the alerts and resolution comprise actions to be performed to address the at least one deviation.

8. A system (120) for monitoring a network (105), the system comprising:
a communication unit (225) configured to establish, one or more connections with internal and external sources of the network (105);
a receiving unit (230) configured to receive, data from the internal and the external source in real time on establishment of the one or more connections;
an analysing unit (240) configured to analyse, the received data to detect at least one deviation in the received data; and
a generation unit (245) configured to generate, alerts and resolutions on detection of the at least one deviation.

9. The system (120) as claimed in claim 8, wherein the internal sources provide data pertaining to network performance, subscribers, application usage and subscriber devices.

10. The system (120) as claimed in claim 8, wherein the external sources provide data pertaining to competitors, social media, and customer feedback and surveys.

11. The system (120) as claimed in claim 8, wherein the receiving unit (230) is configured to receive real time data, static data, and batch wise data.

12. The system (120) as claimed in claim 8, comprising:
a conversion unit (235) configured to convert, a format of the received data from a raw format into a standard format; and
a storage unit (220) configured to store, the converted data in a database.

13. The system (120) as claimed in claim 8, wherein the analysing unit (235) is configured to:
compare, parameters of the received data with a threshold value of a parameters of the historical data or a trend of the received data with a trend of the historical data, wherein the thresholds are dynamically generated for each of the plurality of time intervals and wherein the plurality of time intervals are defined by the service operator; and
detect, the at least one deviation based on the comparison.

14. The system (120) as claimed in claim 8, wherein the alerts and resolution are transmitted to a user interface (215) of a service operator, and wherein the alerts and resolution comprise actions to be performed to address the at least one deviation.

15. A User Equipment (UE) (102), comprising:
one or more primary processors (305) communicatively coupled to one or more processors (205), the one or more primary processors (305) coupled with a memory (210), wherein said memory (210) stores instructions which when executed by the one or more primary processors (305) causes the UE (110) to:
receive, alerts and resolutions with respect to one or more anomalies in the network (105); and
wherein the one or more processors (202) is configured to perform the steps as claimed in claim 1.