DESC:FORM 2
THE PATENTS ACT, 1970 (39 OF 1970)
&
THE PATENT RULES, 2003
COMPLETE SPECIFICATION
(See section 10 and rule 13)

SYSTEM AND METHOD FOR IDENTIFYING LOCATIONS OF A USER ASSOCIATED WITH A USER EQUIPMENT

Jio Platforms Limited, an Indian company, having registered address at Office -101, Saffron, Nr. Centre Point, Panchwati 5 Rasta, Ambawadi, Ahmedabad - 380006, Gujarat, India

The following specification particularly describes the invention and the manner in which it is to be performed.

TECHNICAL FIELD
[0001] The embodiments of the present disclosure generally relate to the field of wireless communication networks. More particularly, the present disclosure relates to a system and a method for identifying one or more locations of a user associated with a User Equipment (UE).
BACKGROUND OF THE INVENTION
[0002] The subject matter disclosed in the background section should not be assumed or construed to be prior art merely because of its mention in the background section. Similarly, any problem statement mentioned in the background section or its association with the subject matter of the background section should not be assumed or construed to have been previously recognized in the prior art.
[0003] In the digital age, a quality of network connectivity while using online services or applications plays an important role in shaping a user perception on network. With a significant portion of a workforce operating remotely or from home, the user perception is built based on user experience in home location and work location of the user. Therefore, a good Quality of Service (QoS) in terms of the network connectivity is crucial for both the home location and the work location to meet requirements of the user. These requirements include seamless and robust network connectivity for accomplishing tasks, collaborating with colleagues, and engaging in various online activities.
[0004] An evaluation of a network’s performance, quality, and reliability is based on day-to-day experience of the user at each location of the user, whether at the home location or at the work location. Therefore, identifying the user's location among the plurality of locations, be it the home location or the work location is crucial for enhancing the QoS provided by the network thereby improving the user experience and user perception of the network. By identifying the user’s location, network providers can adapt their services to address specific challenges and requirements related to peak usage times, non-peak usage times, underutilization, over utilization of network, or any other requirements associated with network environment.
[0005] Heretofore, conventional methods have not been successful in efficiently classifying locations of the user as one of the user home location or work location owing to various associated limitations. Further, existing technologies such as Internet Protocol (IP) geolocation or manually entered addresses may not always accurately identify the user's exact location. The conventional methods are further affected by proxy servers, or incorrect user input, leading to misinterpretations and potential service disruptions. Additionally, the conventional methods overlook dynamic changes in the user's location, especially in scenarios where individuals move between different workspaces or remote locations throughout the day.
[0006] Thus, to overcome the above-mentioned limitations of the conventional methods, there is a need for a reliable system and a method for identifying one or more locations of a user associated with a User Equipment (UE).
SUMMARY
[0007] The following embodiments present a simplified summary in order to provide a basic understanding of some aspects of the disclosed invention. This summary is not an extensive overview, and it is not intended to identify key/critical elements or to delineate the scope thereof. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
[0008] In an embodiment, a method for identifying one or more locations of a user associated with a User Equipment (UE) is disclosed. The method includes receiving, by a receiving module of a server, trace data associated with the UE. The trace data includes information of a plurality of sessions associated with the UE and location information of the UE. The method further includes identifying, by a cluster identification module of the server, a plurality of location clusters for the UE based on the location information of the UE. Further, the method includes calculating, by a determination module of the server for each location cluster of the plurality of location clusters, an average session end time of sessions among the plurality of sessions within a corresponding location cluster of the plurality of location clusters based on the information of the plurality of sessions associated with the UE. Furthermore, the method includes classifying, by a cluster classification module of the server, the plurality of location clusters in one of work location clusters or home location clusters based on the calculated average session end time of the sessions for each location cluster of the plurality of location clusters. Thereafter, the method includes determining, by the determination module for each location cluster of the plurality of location clusters, an amount of the network usage by the UE at the corresponding location cluster based on the information of the plurality of sessions associated with the UE. Further, the method includes identifying, by a location identification module of the server, at least one of a first cluster among the work location clusters as the work location of the user and a second cluster among the home location clusters as the home location of the user based on the determined amount of the network usage by the UE at the plurality of location clusters.
[0009] In some aspects of the present disclosure, for classifying the plurality of location clusters in one of the work location clusters or the home location clusters, the method includes classifying a location cluster of the plurality of location clusters in the work location clusters if the calculated average session end time of sessions within the location cluster is within a first predefined time period. The method further includes classifying the location cluster of the plurality of clusters in the home location clusters if the calculated average session end time of sessions within the location cluster is within a second predefined time period different from the first predefined time period.
[0010] In some aspects of the present disclosure, for identifying at least one of the first cluster as the work location of the user and the second cluster as the home location of the user, the method includes identifying, as the work location, the first cluster among the work location clusters where the amount of the network usage by the UE is maximum among the work location clusters. The method further includes identifying, as the home location, the second cluster among the home location clusters where the amount of the network usage by the UE is maximum among the home location clusters.
[0011] In some aspects of the present disclosure, the information of the plurality of sessions associated with the UE includes information of session end time of each session of the plurality of sessions, session start time of each session of the plurality of sessions, duration of each session of the plurality of sessions, and location of the UE during each session of the plurality of sessions.
[0012] In some aspects of the present disclosure, for determining the amount of the network usage by the UE at the plurality of location clusters, the method includes determining, for each location cluster of the plurality of location clusters, a sum of duration of the sessions among the plurality of sessions within the corresponding location cluster. The method further includes determining, for each location cluster of the plurality of location clusters, the amount of the network usage at the corresponding location cluster based on the sum of duration of the sessions among the plurality of sessions within the corresponding location cluster.
[0013] In some aspects of the present disclosure, the plurality of location clusters is identified using a machine learning module.
[0014] According to another aspect of the present disclosure, a system for identifying one or more locations of a user associated with a User Equipment (UE) is disclosed. The system includes a receiving module configured to receive trace data associated with the UE. The trace data includes information of a plurality of sessions associated with the UE and location information of the UE. The system further includes a cluster identification module configured to identify a plurality of location clusters for the UE based on the location information of the UE. Further, the system includes a determination module configured to calculate, for each location cluster of the plurality of location clusters, an average session end time of sessions among the plurality of sessions within a corresponding location cluster of the plurality of location clusters based on the information of the plurality of sessions associated with the UE. The determination module is further configured to determine, for each location cluster of the plurality of location clusters, an amount of the network usage by the UE at the corresponding location cluster based on the information of the plurality of sessions associated with the UE. Furthermore, the system includes a cluster classification module configured to classify the plurality of location clusters in one of work location clusters or home location clusters based on the calculated average session end time of the sessions for each location cluster of the plurality of location clusters. Furthermore, the system includes a location identification module configured to identify at least one of a first cluster among the work location clusters as the work location of the user and a second cluster among the home location clusters as the home location of the user based on the determined amount of the network usage by the UE at the plurality of location clusters.
BRIEF DESCRIPTION OF DRAWINGS
[0015] Various embodiments disclosed herein will become better understood from the following detailed description when read with the accompanying drawings. The accompanying drawings constitute a part of the present disclosure and illustrate certain non-limiting embodiments of inventive concepts. Further, components and elements shown in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. For the purpose of consistency and ease of understanding, similar components and elements are annotated by reference numerals in the exemplary drawings.
[0016] FIG. 1 illustrates a diagram depicting an exemplary wireless communication network, in accordance with an embodiment of the present disclosure.
[0017] FIG. 2 illustrates a diagram depicting communication of one or more entities of the wireless communication network with a trace collection entity (TCE) system, in accordance with an embodiment of the present disclosure.
[0018] FIG. 3 illustrates a simplified block diagram of a system for identifying one or more locations of a user associated with a User Equipment (UE), in accordance with an embodiment of the present disclosure.
[0019] FIG. 4 illustrates a block diagram of a Base Station (BS) of the wireless communication network, in accordance with an embodiment of the present disclosure.
[0020] FIG. 5 illustrates a functional block diagram of the system for identifying one or more locations of the user associated with the UE, in accordance with an embodiment of the present disclosure.
[0021] FIG. 6 illustrates a flowchart depicting a method for identifying the one or more locations of the user associated with the UE, in accordance with an exemplary embodiment of the present disclosure.
[0022] FIG. 7 illustrates a schematic block diagram of a computing system for identifying the one or more locations of the user associated with the UE, in accordance with an embodiment of the present disclosure.
DETAILED DESCRIPTION OF THE INVENTION
[0023] Inventive concepts of the present disclosure will now be described more fully hereinafter with reference to the accompanying drawings, in which examples of one or more embodiments of inventive concepts are shown. Inventive concepts may, however, be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Further, the one or more embodiments disclosed herein are provided to describe the inventive concept thoroughly and completely, and to fully convey the scope of each of the present inventive concepts to those skilled in the art. Furthermore, it should be noted that the embodiments disclosed herein are not mutually exclusive concepts. Accordingly, one or more components from one embodiment may be tacitly assumed to be present or used in any other embodiment.
[0024] The following description presents various embodiments of the present disclosure. The embodiments disclosed herein are presented as teaching examples and are not to be construed as limiting the scope of the present disclosure. The present disclosure should in no way be limited to the illustrative implementations, drawings, and techniques illustrated below, including the exemplary design and implementation illustrated and described herein, but may be modified, omitted, or expanded upon without departing from the scope of the present disclosure.
[0025] The following description contains specific information pertaining to embodiments in the present disclosure. The detailed description uses the phrases “in some embodiments” which may each refer to one or more or all of the same or different embodiments. The term “some” as used herein is defined as “one, or more than one, or all.” Accordingly, the terms “one,” “more than one,” “more than one, but not all” or “all” would all fall under the definition of “some.” In view of the same, the terms, for example, “in an embodiment” refers to one embodiment and the term, for example, “in one or more embodiments” refers to “at least one embodiment, or more than one embodiment, or all embodiments.”
[0026] The term “comprising,” when utilized, means “including, but not necessarily limited to;” it specifically indicates open-ended inclusion in the so-described one or more listed features, elements in a combination, unless otherwise stated with limiting language. Furthermore, to the extent that the terms “includes,” “has,” “have,” “contains,” and other similar words are used in either the detailed description, such terms are intended to be inclusive in a manner similar to the term “comprising.”
[0027] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features.
[0028] The description provided herein discloses exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the present disclosure. Rather, the foregoing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing any of the exemplary embodiments. Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it may be understood by one of the ordinary skilled in the art that the embodiments disclosed herein may be practiced without these specific details.
[0029] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein the description, the singular forms "a", "an", and "the" include plural forms unless the context of the invention indicates otherwise.
[0030] The terminology and structure employed herein are for describing, teaching, and illuminating some embodiments and their specific features and elements and do not limit, restrict, or reduce the scope of the present disclosure. Accordingly, unless otherwise defined, all terms, and especially any technical and/or scientific terms, used herein may be taken to have the same meaning as commonly understood by one having ordinary skill in the art.
[0031] An aspect of the present disclosure is to provide a system and a method for identifying home location and work location of users associated with a plurality of User Equipment (UEs) for effective capacity planning for a network and to ensure a reliable network connectivity during peak usage times.
[0032] Another aspect of the present disclosure is to identify the home location and the work location of users for analyzing and optimizing area or nodes in the serving location.
[0033] Another aspect of the present disclosure is to identify the home location and the work location of users to enable network planning teams to effectively improve the user’s experience and perception of the network.
[0034] The term “Trace data” in the entire disclosure may represent log of detailed data of a user device at call level. The trace data is an additional source of information to performance measurements and allows going further in monitoring and optimization operations.
[0035] Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings. FIG. 1 through FIG. 6, discussed below, and the one or more embodiments used to describe the principles of the present disclosure are by way of illustration only and should not be construed in any way to limit the scope of the present disclosure. Those skilled in the art will understand that the principles of the present disclosure may be implemented in any suitably arranged system or device.
[0036] FIG. 1 illustrates a diagram depicting an exemplary wireless communication network 100, in accordance with an embodiment of the present disclosure. The embodiment of the wireless communication network 100 shown in FIG. 1 is for illustration only. Other embodiments of the wireless communication network 100 may be used without departing from the scope of this disclosure.
[0037] As shown in FIG. 1, the wireless communication network 100 includes a plurality of base stations (BSs) 102-2 to 102-N (may also be referred to as “plurality of cells 102-2 to 102-N”). Each base station among the plurality of BSs 102-2 to 102-N may have same or similar configuration and cumulatively may referred to as “BSs 102” or “cells 102”. It is to be noted that the BSs 102 may also be referred to as “cells”, “gNBs”, or “nodes” interchangeably throughout this disclosure without departing from the scope of the invention. Further, the BSs 102 may also be referred to as “access point (AP)”, “evolved NodeB (eNodeB) (eNB)”, “5G node (5th generation node)”, “wireless point”, “transmission/reception point (TRP)”, “Radio Access Network (RAN)” or other terms having equivalent technical meanings.
[0038] The BSs 102 serve a plurality User Equipment (UEs) (only one UE is shown in diagram) in coverage regions 106-2 to 106-N (hereinafter referred to as coverage region 106). As shown in FIG. 1, a UE 104 among the plurality of UEs may connect with one or more BSs among the BSs 102 during different times of a day. Typically, the term “user equipment” or “UE” can refer to any component such as “mobile station”, “subscriber station”, “remote terminal”, “wireless terminal”, “receive point”, “end user device”, or the like.
[0039] The BSs 102 are connected to a network 108 to provide one or more services to the UE 104. The network 108 may include a proprietary Internet Protocol (IP) network, Internet, or other data network. In some embodiments, the BSs 102 may communicate with each other and with the UE 104 using a communication technique, such as a 5th Generation 5G/ New Radio (NR), Long Term Evolution (LTE), Long Term Evolution Advanced (LTE-A), Worldwide Interoperability for Microwave Access (WiMAX), Wireless Fidelity (Wi-Fi), or other wireless communication techniques.
[0040] The network 108 may include suitable logic, circuitry, and interfaces that may be configured to provide several network ports and several communication channels for transmission and reception of data related to operations of various entities of the wireless communication network 100. Each network port may correspond to a virtual address (or a physical machine address) for transmission and reception of the communication data. For example, the virtual address may be an Internet Protocol Version 4 (IPV4) (or an IPV6 address) and the physical address may be a Media Access Control (MAC) address. The network 108 may be associated with an application layer for implementation of communication protocols based on one or more communication requests from the various entities of the wireless communication network 100. The communication data may be transmitted or received via the communication protocols. Examples of the communication protocols may include, but are not limited to, Hypertext Transfer Protocol (HTTP), File Transfer Protocol (FTP), Simple Mail Transfer Protocol (SMTP), Domain Network System (DNS) protocol, Common Management Interface Protocol (CMIP), Transmission Control Protocol and Internet Protocol (TCP/IP), User Datagram Protocol (UDP), Long Term Evolution (LTE) communication protocols, or any combination thereof. In some aspects of the present disclosure, the communication data may be transmitted or received via at least one communication channel of several communication channels in the network 108. The communication channels may include, but are not limited to, a wireless channel, a wired channel, a combination of wireless and wired channel thereof. The wireless or wired channel may be associated with a data standard which may be defined by one of a Local Area Network (LAN), a Personal Area Network (PAN), a Wireless Local Area Network (WLAN), a Wireless Sensor Network (WSN), Wireless Area Network (WAN), Wireless Wide Area Network (WWAN), a metropolitan area network (MAN), a satellite network, the Internet, an optical fiber network, a coaxial cable network, an infrared (IR) network, a radio frequency (RF) network, and a combination thereof. Aspects of the present disclosure are intended to include or otherwise cover any type of communication channel, including known, related art, and/or later developed technologies.
[0041] The BSs 102 also communicates with a server 110 configured to identify one or more locations of the UE 104 in the wireless communication network 100. The server 110 may be a network of computers, a software framework, or a combination thereof, that may provide a generalized approach to create a server implementation. Examples of the server 110 may include, but are not limited to, personal computers, laptops, mini-computers, mainframe computers, any non-transient and tangible machine that can execute a machine-readable code, cloud-based servers, distributed server networks, or a network of computer systems. The server 110 may be realized through various web-based technologies such as, but not limited to, a Java web-framework, a .NET framework, a personal home page (PHP) framework, or any web-application framework.
[0042] Extents of the coverage region 106 are shown as approximately circular or elliptical for the purposes of illustration and explanation only. It should be clearly understood that the coverage region 106 associated with the BSs 102, such as coverage region 106-2, 106-4, may have other shapes, including irregular shapes, depending upon the configuration of the BSs 102, and variations in wireless communication network environment associated with natural and man-made obstructions.
[0043] Although FIG. 1 illustrates one example of the wireless communication network 100, various changes may be made to FIG. 1. For example, the wireless communication network 100 may include any number of BSs in any suitable arrangement. Further, each of the BSs 102 may communicate directly with the server 110. Furthermore, the BSs 102 may provide access to multiple UEs, other or additional external networks, such as external telephone networks or other types of data networks.
[0044] FIG. 2 illustrates a diagram depicting communication of entities of the wireless communication network 100 with a Trace Collection Entity (TCE) system 204, in accordance with an embodiment of the present disclosure. The TCE system 204 is a network entity of the wireless communication network 100 that manages collection and collation of UE measurements data received via the BSs 102. The UE measurement data is associated with the UE 104 and is referred to as “trace data”. The TCE system 204 may be located within the network 108 or the server 110 or may be a separate entity in the wireless communication network 100. The trace data may include information of a plurality of sessions (hereinafter may also be referred to as “sessions”) associated with the UE 104 and location information of the UE 104. The information of the sessions may include timestamps of user’s sessions associated with the UE 104, serving cell information of the UE 104, duration of each session among the sessions, types of network activities, data consumption metrics, or any other user’s session detail.
[0045] The collection of trace data by the TCE system 204 is controlled by a network management system 202 associated with the network 108. The network management system 202 includes an Element Manager (EM) (not shown in FIG. 2) which activates or deactivates collection of the trace data. When the EM activates the collection of the trace data, network elements of the wireless communication network 100 generate the trace data and transfers the trace data to the TCE system 204.
[0046] In one or more embodiments, the EM notifies the BSs 102 of an activation message including configuration information (measurement configuration) measured by the UE 104 and the location information of the UE 104. Each BS among the BSs 102 starts a trace session (Starting Trace Session) for collecting UE measurement information and transmits the configuration information measured by the UE 104. The configuration information includes, for example, a measurement target and a measurement period, or instructions to report location information. The BSs 102 notifies an identifier of the trace session after collecting the UE measurement information. Thereafter, the BSs 102 reports to the TCE system 204, a trace record that records the collected UE measurement information.
[0047] When the BSs 102 executes quality measurement related to a service quality (e.g., QoS), the BSs 102 collect information related to a location of the UE 104 to be a target for the quality measurement. The TCE system 204 may associate the results of the quality measurement with the information related to the location of the UE 104 to estimate the service quality of the BSs 102 for the UE 104 at different locations.
[0048] FIG. 3 illustrates a simplified block diagram of a system 300 for identifying one or more locations of a user associated with the UE 104, in accordance with an embodiment of the present disclosure. The embodiment of the system 300 as shown in FIG. 3 is for illustration only. However, the system 300 may come in a wide variety of configurations, and FIG. 3 does not limit the scope of the present disclosure to any particular implementation of the system 300.
[0049] The system 300 includes the server 110, the TCE system 204, an external database 302, processing modules 304, and other devices 306. The server 110 may be the network of computers, the software framework, or the combination thereof, that may provide the generalized approach to create the server implementation. Examples of the server 110 may include, but are not limited to, cloud-based servers, distributed server networks, or the network of computer systems.
[0050] The TCE system 204 is the network entity of the wireless communication network 100 that manages collection and collation of the UE measurements data referred to as “trace data”. The TCE system 204 may be located within the server 110 or may be a separate entity in the wireless communication network 100 outside to the server 110.
[0051] The external database 302 may store data received from network components of the wireless communication network 100. For example, the external database 302 may store data collected from the TCE system 204. The external database 302 may include one or more relational databases or one or more non-relational databases to store data required by the user associated with the UE 104.
[0052] The processing modules 304 may comprise a central processing unit (CPU) and a graphics processing unit (GPU) for performing computations and handling data processing tasks. The CPU may also be referred to as processor. The processor may include one or more general purpose processors and/or one or more special purpose processors, a microprocessor, a digital signal processor, an application specific integrated circuit, a microcontroller, a state machine, or ay any type of programmable logic array.
[0053] The other devices 114 may include framework servers or other end user devices to receive the output from the system 300. The end user devices may perform network management functions based on identified one or more locations of the user associated with the UE 104.
[0054] FIG. 4 illustrates a block diagram 400 of the BSs 102 of the wireless communication network 100, in accordance with an embodiment of the present disclosure. The embodiment of the BSs 102 as shown in FIG. 4 is for illustration only, and the BSs 102 of FIG. 1 may have same or similar configuration. However, the BSs 102 come in a wide variety of configurations, and FIG. 4 does not limit the scope of the present disclosure to any particular implementation of the BSs 102.
[0055] As shown in FIG. 4, each of the BSs 102 may include components for bi-directional voice and data communications including components for transmitting and receiving communications, a processor 402, a memory 404, a network communication manager 406, a transceiver 408, one or more antennas 410 (hereinafter also referred to as antennas 410”), and a network communication interface 412. The transceiver 408 may include a transmit processing circuitry, and a receive processing circuitry. These components may be in electronic communication via one or more buses (e.g., bus 416).
[0056] The processor 402 may include one or more processors or other processing devices that control the overall operation of the BSs 102. For example, the processor 402 may control reception of forward or downlink channel signals and the transmission of reverse or uplink channel signals by the receive processing circuitry and the transmit processing circuitry of the transceiver 408, in accordance with well-known principles or concept. The processor 402 may support additional functions as well, such as more advanced wireless communication functions.
[0057] The processor 402 is configured to execute programs and other processes stored in the memory 404. The processor 402 is also configured to store data into the memory 404 or fetch data out of the memory 404 as required by an executing process. The processor 402 may also be coupled to the network communication manager 406 that may allow the BSs 102 to communicate with other devices or systems over a network. The network communication manager 406 may support communications over any suitable wired or wireless connection(s) and manage communications with the network 108 (e.g., via one or more wired backhaul links). For example, the network communications manager 406 may manage the transfer of data communications for client devices, such as UEs 104-2 to 104-N.
[0058] The memory 404 is coupled to the processor 402. A part of the memory 404 may include a RAM, and another part of the memory 404 may include a Flash memory or other ROM.
[0059] The transceiver 408 may receive from the antennas 410, incoming Radio Frequency (RF) signals, such as signals transmitted by UEs 104-2 to 104-N in the wireless communication network 100. The transceiver 408 may down-convert the incoming RF signals to generate Intermediate Frequency (IF) or baseband signals. The IF or baseband signals may be sent to the receive processing circuitry, which generates processed baseband signals by filtering, decoding, and/or digitizing the baseband or IF signals. The receiver processing circuitry may transmit the processed baseband signals to the processor 402 for further processing. The transmit processing circuitry may receive analog or digital data from the processor 402 and may encode, multiplex, and/or digitize the outgoing baseband data to generate processed baseband or IF signals. The transceiver 408 may further receive the outgoing processed baseband or IF signals from the transmit processing circuitry and up-converts the baseband or IF signals to RF signals that are transmitted via the antennas 410.
[0060] The network communication interface 412 may be configured to enable the BS 102-2 to communicate with various entities of the wireless communication network 100 (such as UEs, a core network, and the server 110, the TCE system 204, and in some scenarios external user device) via the network 108. Examples of the network communication interface 412 may include, but are not limited to, a modem, a network interface such as an Ethernet card, a communication port, and/or a Personal Computer Memory Card International Association (PCMCIA) slot and card, an antenna, a radio frequency (RF) transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a coder-decoder (CODEC) chipset, a subscriber identity module (SIM) card, and a local buffer circuit. It will be apparent to a person of ordinary skill in the art that the communication interface 412 may include any device and/or apparatus capable of providing wireless or wired communications between the BS 102-2 and various other entities of the wireless communication network 100.
[0061] Although FIG. 4 illustrates one example of a BS among the BSs 102, various changes may be made to FIG. 4. For example, the BSs 102 may include any number of components in addition to the components shown in FIG. 4. Further, various components in FIG. 4 may be combined, further subdivided, or omitted and additional components may be added according to particular needs.
[0062] FIG. 5 illustrates a functional block diagram 500 of the system 300 for identifying one or more locations of the user associated with the UE 104, in accordance with an embodiment of the present disclosure. The embodiment of the functional block diagram 500 as shown in FIG. 5 is for illustration only. However, the functional block diagram 500 of the system 300 may come in a wide variety of configurations, and FIG. 5 does not limit the scope of the present disclosure to any particular implementation the functional block diagram 500 of the system 300.
[0063] As shown in FIG. 5, the functional block diagram 500 of the system 300 includes at least the server 110. The server 110 includes an Input-Output (I/O) interface 502, one or more processors 504 (hereinafter may also be referred to as “processor 504”), a memory 506, a network communication manager 508, a console host 510, a database 512, and one or more processing modules 304 (hereinafter may also be referred to as “processing modules 304”). Components of the server 110 are coupled to each other via a communication bus 514.
[0064] The I/O interface 502 may include suitable logic, circuitry, interfaces, and/or codes that may be configured to receive input(s) and present (or display) output(s) on the server 110. For example, the I/O interface 502 may have an input interface and an output interface. The input interface may be configured to enable a user to provide input(s) to trigger (or configure) the server 110 to perform various operations for identifying one or more locations of a user associated with the UE 104, such as but not limited to, configuring the server 110 to receive the trace data from the TCE system 204. Examples of the input interface may include, but are not limited to, a touch interface, a mouse, a keyboard, a motion recognition unit, a gesture recognition unit, a voice recognition unit, or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of the input interface including known, related art, and/or later developed technologies without deviating from the scope of the present disclosure. The output interface is configured to control an end user device to display an identified home location or work location to the user. Examples of the output interface of the I/O interface 502 may include, but are not limited to, a digital display, an analog display, a touch screen display, an appearance of a desktop, and/or illuminated characters.
[0065] The processor 504 may include various processing circuitry and communicates with the memory 506, the network communication manager 508, the console host 510, and the database 512 via the communication bus 514. The processor 504 is configured to execute instructions 506A (hereinafter also referred to as “a set of instructions 506A”) stored in the memory 506 and to perform various processes. The processor 504 may include one or a plurality of processors, including a general-purpose processor, such as, for example, and without limitation, a central processing unit (CPU), an application processor (AP), a dedicated processor, a graphics-only processing unit such as a graphics processing unit (GPU) or the like, a programmable logic device, or any combination thereof.
[0066] The memory 506 stores the set of instructions 506A required by the processor 504 of the server 110 for controlling its overall operations. The memory 506 may include non-volatile storage elements. Examples of such non-volatile storage elements may include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. In addition, the memory 506 may, in some examples, be considered a non-transitory storage medium. The "non-transitory" storage medium is not embodied in a carrier wave or a propagated signal. However, the term "non-transitory" should not be interpreted as the memory 506 is non-movable. In some examples, the memory 506 may be configured to store larger amounts of information. In certain examples, a non-transitory storage medium may store data that can, over time, change (e.g., in Random Access Memory (RAM) or cache). The memory 506 may be an internal storage unit or an external storage unit of the server 110, cloud storage, or any other type of external storage. In certain examples, the memory 506 configured as the non-transitory storage medium may include hard drives, solid-state drives, flash drives, Compact Disk (CD), Digital Video Disk (DVD), and the like. Further, the memory 506 may include any type of non-transitory storage medium, without deviating from the scope of the present disclosure.
[0067] More specifically, the memory 506 may store computer-readable instructions 506 A including instructions that, when executed by a processor (e.g., the processor 504) cause the server 110 to perform various functions described herein. In some cases, the memory 506 may contain, among other things, a BIOS which may control basic hardware or software operation such as the interaction with peripheral components or devices.
[0068] The network communication manager 508 may manage communications with the BSs 102, the core network, or the UE 104 (e.g., via one or more wired backhaul links). For example, the network communications manager 508 may manage the transfer of data communications for BSs 102 and client devices, such as the base stations 102-2 through 102-N. The network communication manager 508 may include an electronic circuit specific to a standard that enables wired or wireless communication. The network communication manager 508 is configured for communicating with external devices via one or more networks.
[0069] The console host 510 may include suitable logic, circuitry, interfaces, and/or codes that may be configured to enable the I/O interface 502 to receive input(s) and/or render output(s). In some aspects of the present disclosure, the console host 510 may include suitable logic, instructions, and/or codes for executing various operations of one or more computer executable applications to host a console on an external user device, by way of which a user can trigger the server 110 to identify home and work locations of the user. In some other aspects of the present disclosure, the console host 510 may provide a Graphical User Interface (GUI) for the server 110 for user interaction.
[0070] The database 512 is managed by the processor 504 and configured to store the home and work location data of the users. Further, the database may store the information of location clusters associated with the UE 104.
[0071] The processing module(s) 304 may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the server 110. In non-limiting examples, described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing modules(s) 304 may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processor 504 may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing module(s) 304. In such examples, the server 110 may also comprise the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the server 110 and the processing resource. In other examples, the processing module(s) 304 may be implemented using an electronic circuitry.
[0072] In one or more embodiments, the processing module(s) 304 may include a receiving module 516, a cluster identification module 518, a determination module 520, a cluster classification module 522, a location identification module 524, and a clustering engine 526.
[0073] In an aspect, the processor 504, using the receiving module 516, may be configured to receive the trace data associated with the UE 104 from the TCE system 204. The trace data may include at least the information of the sessions associated with the UE 104 the location information of the UE 104 in the wireless communication network. The information of the sessions may include information of end time of each of the sessions associated with the UE 104 at each location associated with the UE 104, start time of each session of the sessions, duration of each session of the sessions, and location of the UE 104 during each session of the sessions. In a non-limiting example, the trace data of past N days (for example 7 days) may be received for each user to start an analysis on the trace data.
[0074] In one or more aspects, the processor 504, using the cluster identification module 518, may be configured to identify a plurality of location clusters (hereinafter may also be referred to as “location clusters”) for the UE 104 based on the location information of the UE 104 included in the trace data. The cluster identification module 518 uses the clustering engine 526 to identify the location clusters for the UE 104. The clustering engine 526 may apply a density-based clustering algorithm on the trace data to identify location clusters for the UE 104. In a non-limiting example, the density-based clustering algorithm may include Density-based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm or any other clustering algorithm.
[0075] For instance, the DBSCAN clustering algorithm is used for clustering data based on density. The DBSCAN clustering algorithm receives the location data associated with the UE 104 and identifies different location clusters of varying shapes and sizes. The location data associated with the UE 104 includes latitude and longitude information of the UE 104 at each session. The DBSCAN clustering algorithm is a rule based unsupervised algorithm.
[0076] In one or more aspects, the DBSCAN clustering algorithm may also be integrate with Machine Learning (ML) algorithms to filter out noise, to detect spatial anomaly, or to get another desired result.
[0077] In one or more aspects, the processor 504, using the determination module 520, may be configured to calculate an average session end time of the sessions at locations within each location cluster of the location clusters based on the information of the sessions at each location within the corresponding location cluster. For instance, the processor 504, using the determination module 520, may calculate, for each location cluster, the average session end time of the sessions available in the trace data geolocated within the corresponding location cluster. In a non-limiting example, if within a specific location cluster, a total 5 sessions are associated with a user device and end time of all 5 sessions are as follows:
[10 AM, 11AM, 12 AM, 3PM, 5PM],
an average session end time may be calculated as:
{(10+11+12+15+17)/5=13 (1PM)}.

[0078] In one or more aspects, the processor 504, using the determination module 520, may also be configured to determine an amount of the network usage by the UE 104 at each location cluster of the location clusters based on the information of the sessions associated with the UE 104 within the corresponding location cluster. For instance, the processor 504, using the determination module 520, may first determine, for each location cluster of the location clusters, a sum of duration of the sessions within the corresponding location cluster. Thereafter, the processor 504, using the determination module 520, may determine, for each location cluster of the location clusters, the amount of the network usage at the corresponding location cluster based on the sum of duration of the sessions within the corresponding location cluster.
[0079] In one or more aspects, the processor 504, using the cluster classification module 522, may classify the location clusters in one of work location clusters or home location clusters based on the calculated average session end time of the sessions for each location cluster of the location clusters. For instance, the processor 504, using the cluster classification module 522, may classify a first location cluster among the location clusters in the work location clusters, if the calculated average session end time of sessions within the first location cluster is within a first predefined time period. In a non-limiting example, the first predefined time period may be from 9AM to 9PM. The first predefined time period may be predefined for the system 300 or may be configurable based on a first user input via an end user device connected with the sever 110.
[0080] Further, the processor 504, using the cluster classification module 522, may classify a second location cluster among the location clusters in the home location clusters, if the calculated average session end time of sessions within the second location cluster is within a second predefined time period different from the first predefined time period. In a non-limiting example, the second predefined time period may be from 9PM to 9AM. The second predefined time period may be predefined for the system 300 or may be configurable based on a second user input via the end user device connected with the sever 110.
[0081] In one or more embodiments, the cluster classification module 522 may also classify the first location cluster among the location clusters in the work location clusters even if the calculated average session end time of sessions within the first location cluster is within the second predefined time period based on a determination that the start time of the sessions within the first location cluster is within the first predefined time period. Therefore, the present disclosure also able to correctly identify the work location of the user associated with the UE 104 in case of extended working hours.
[0082] In one or more embodiments, the cluster classification module 522 may also classify the work location of the user as a work from home location of the user in case the work location of the user is same as the home location of the user. Further, the processor 504, using the location identification module 524,may identify that the user is working from home over a weekend or weekdays based on the determined amount of the network usage by the UE 104, the types of network activities, and data consumption metrics at the home location.
[0083] In one or more aspects, the processor 504, using the location identification module 524,may identify at least one of a first cluster among the work location clusters as the work location of the user based on the determined amount of the network usage by the UE 104 at the work location clusters. For instance, the processor 504, using the location identification module 524, may identify, as the work location, the first cluster among the work location clusters where the amount of the network usage by the UE 104 is maximum among the work location clusters.
[0084] The processor 504, using the location identification module 524, may also identify and a second cluster among the home location clusters as the home location of the user based on the determined amount of the network usage by the UE 104 at the home location clusters. For instance, the processor 504, using the location identification module 524, may identify, as the home location, the second cluster among the home location clusters where the amount of the network usage by the UE 104 is maximum among the home location clusters.
[0085] Although FIG. 5 illustrates one example of the system 300 or server 110, various changes may be made to FIG. 5. Further, the server 110 may include any number of components in addition to those shown in FIG. 5, without deviating from the scope of the present disclosure. Further, various components in FIG. 5 may be combined, further subdivided, or omitted and additional components may be added according to particular needs. For example, in some aspects of the present disclosure, the server 110 may be coupled to an external database that provides data storage space to the server 110.
[0086] FIG. 6 illustrates a flowchart depicting a method 600 for identifying the one or more locations of the user associated with the UE 104, in accordance with an exemplary embodiment of the present disclosure. The method 600 comprises a series of operation steps indicated by blocks 602 through 612 performed by the system 300. The method 600 starts at block 602.
[0087] At block 602, the receiving module 516 may receive the trace data associated with the UE 104. The receiving module 516 may receive the trace data from the TCE system 204. The trace data may include at least the location information of the UE 104 in the wireless communication network and the information of the sessions associated with the UE 104. The information of the sessions may include information of end time of each of the sessions associated with the UE 104 and the duration of each session among the sessions associated with the UE 104. The flow of the method 600 now proceeds to block 604.
[0088] At block 604, the cluster identification module 518 may identify the location clusters for the UE 104 based on the location information of the UE 104 included in the trace data. The cluster identification module 518 identifies the location clusters for the UE 104 using the clustering engine 526. The flow of the method 600 now proceeds to block 606.
[0089] At block 606, the determination module 520 may calculate, for each of the location clusters, the average session end time of sessions among the sessions within the corresponding location cluster of the location clusters based on the information of end time of each of the sessions associated with the UE 104. The flow of the method 600 now proceeds to block 608.
[0090] At block 608, the cluster classification module 522 may classify the location clusters in one of the work location clusters or the home location clusters based on the calculated average session end time of the sessions for each location cluster of the location clusters. For instance, the cluster classification module 522 may classify the first location cluster among the location clusters in the work location clusters, if the calculated average session end time of sessions within the first location cluster is within the first predefined time period. In a non-limiting example, the first predefined time period may be from 9AM to 9PM. Further, the cluster classification module 522 may classify the second location cluster among the location clusters in the home location clusters, if the calculated average session end time of sessions within the second location cluster is within the second predefined time period. In a non-limiting example, the second predefined time period may be from 9PM to 9AM. The flow of the method 600 now proceeds to block 610.
[0091] At block 610, the determination module 520 may determine, for each of the location clusters, the amount of the network usage by the UE 104 at the corresponding location cluster based on the duration of each session among the sessions associated with the UE 104 within the corresponding location cluster. For instance, the determination module 520 may determine the amount of the network usage by the UE 104 at the corresponding location cluster based on the sum of duration of the sessions associated with the UE 104 within the corresponding location cluster. The flow of the method 600 now proceeds to block 612.
[0092] At block 612, the location identification module 524 may identify at least one of the first cluster among the work location clusters as the work location of the user and the second cluster among the home location clusters as the home location of the user based on the determined amount of the network usage by the UE 104 at the location clusters. For instance, the location identification module 524 may identify, as the work location, the first cluster among the work location clusters where the amount of the network usage by the UE 104 is maximum among the work location clusters. Further, the location identification module 524 may identify, as the home location, the second cluster among the home location clusters where the amount of the network usage by the UE 104 is maximum among the home location clusters.
[0093] FIG. 7 illustrates a schematic block diagram of a computing system 700 for identifying the one or more locations of the user associated with the UE 104, in accordance with an embodiment of the present disclosure.
[0094] The computing system 700 includes a network 702, a network interface 704, a processor 706 (similar in functionality to the processor 504 of FIG. 5), an Input/Output (I/O) interface 708 (similar in functionality to the I/O interface 502 of FIG. 5), and a non-transitory computer readable storage medium 710 (hereinafter may also be referred to as the “storage medium 710” or the “storage media 710”). The network interface 704 includes wireless network interfaces such as Bluetooth, Wi-Fi, Worldwide Interoperability for Microwave Access (WiMAX), General Packet Radio Service (GPRS), or Wideband Code Division Multiple Access (WCDMA) or wired network interfaces such as Ethernet, Universal Serial Bus (USB), or Institute of Electrical and Electronics Engineers-864 (IEEE-864).
[0095] The processor 706 may include various processing circuitry/modules and communicate with the storage medium 710 and the I/O interface 708. The processor 706 is configured to execute instructions stored in the storage medium 710 and to perform various processes. The processor 706 may include an intelligent hardware device including a general-purpose processor, such as, for example, and without limitation, the CPU, the AP, the dedicated processor, or the like, the graphics-only processing unit such as the GPU, the microcontroller, the FPGA, the programmable logic device, the discrete hardware component, or any combination thereof. The processor 706 may be configured to execute computer-readable instructions 710-1 stored in the storage medium 710 to cause the system 300 to perform various functions disclosed throughput the disclosure.
[0096] The storage medium 710 stores a set of instructions i.e., computer program instructions 710-1 (hereinafter may also be referred to as instructions 710-1) required by the processor 706 for controlling its overall operations. The storage media 610 may include an electronic storage medium, a magnetic storage medium, an optical storage medium, a quantum storage medium, or the like. For example, the storage media 710 may include, but are not limited to, hard drives, floppy diskettes, optical disks, ROMs, RAMs, EPROMs, EEPROMs, flash memory, magnetic or optical cards, solid-state memory devices, or other types of physical media suitable for storing electronic instructions. In one or more embodiments, the storage media 710 includes a Compact Disk-Read Only Memory (CD-ROM), a Compact Disk-Read/Write (CD-R/W), and/or a Digital Video Disc (DVD). In one or more implementations, the storage medium 710 stores computer program code configured to cause the computing system 700 to perform at least a portion of the processes and/or methods disclosed herein throughput the disclosure.
[0097] Embodiments of the present disclosure have been described above with reference to flowchart illustrations of methods and systems according to embodiments of the disclosure, and/or procedures, algorithms, steps, operations, formulae, or other computational depictions, which may also be implemented as computer program products. In this regard, each block or step of the flowchart, and combinations of blocks (and/or steps) in the flowchart, as well as any procedure, algorithm, step, operation, formula, or computational depiction can be implemented by various means, such as hardware, firmware, and/or software including one or more computer program instructions embodied in computer-readable program code. As will be appreciated, any such computer program instructions may be executed by one or more computer processors, including without limitation a general-purpose computer or special purpose computer, or other programmable processing apparatus to perform a group of operations comprising the operations or blocks described in connection with the disclosed method.
[0098] Further, these computer program instructions, such as embodied in computer-readable program code, may also be stored in one or more computer-readable memory or memory devices (for example, the memory 506 or the storage medium 710) that can direct a computer processor or other programmable processing apparatus to function in a particular manner, such that the instructions 710-1 stored in the computer-readable memory or memory devices produce an article of manufacture including instruction means which implement the function specified in the block(s) of the flowchart(s).
[0099] It will further be appreciated that the term “computer program instructions” as used herein refer to one or more instructions that can be executed by the one or more processors (for example, the processor 504 or the processor 706) to perform one or more functions as described herein. The instructions 710-1 may also be stored remotely such as on a server, or all or a portion of the instructions can be stored locally and remotely.
[00100] Referring to the technical abilities and advantageous effect of the present disclosure, operational advantages that may be provided by above disclosed system and method may include identifying work and home locations of the user thereby enabling network planning teams to implement targeted improvements in optimizing resource allocation and operational process. The network planning teams can anticipate and adjust network capacity to meet the demands of home and work locations, ensuring stable and reliable connectivity during peak usage times.
[00101] Further, the disclosed method helps to facilitate the analysis and optimization of specific geographical area or network nodes within the coverage area. Further, the disclosed method helps to improve the user experience by providing reliable connectivity to users based on their usage requirement at different locations. Further, understanding the clusters of home and work locations enables the operator to optimize the placement and management of network nodes. The identification of user’s location supports the network planning teams in strategically enhancing network performance, thereby further improving user experience and perceived signal quality.
[00102] Those skilled in the art will appreciate that the methodology described herein in the present disclosure may be carried out in other specific ways than those set forth herein in the above disclosed embodiments without departing from essential characteristics and features of the present invention. The above-described embodiments are therefore to be construed in all aspects as illustrative and not restrictive.
[00103] The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. Any combination of the above features and functionalities may be used in accordance with one or more embodiments.
[00104] In the present disclosure, each of the embodiments has been described with reference to numerous specific details which may vary from embodiment to embodiment. The foregoing description of the specific embodiments disclosed herein may reveal the general nature of the embodiments herein that others may, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications are intended to be comprehended within the meaning of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and is not limited in scope.
LIST OF REFERENCE NUMERALS
[00105] The following list is provided for convenience and in support of the drawing figures and as part of the text of the specification, which describe innovations by reference to multiple items. Items not listed here may nonetheless be part of a given embodiment. For better legibility of the text, a given reference number is recited near some, but not all, recitations of the referenced item in the text. The same reference number may be used with reference to different examples or different instances of a given item. The list of reference numerals is:
100 - Wireless communication network
102 - Base Stations (BSs)
102-2 to 102-N - Plurality of BSs
104 - User Equipment (UE)
106 - Coverage region
108 - Network
110 - Server
200 - Communication of network entities with a Trace Collection Entity (TCE) system 204
202 - Network management system
204 - Trace Collection Entity (TCE) system
300 – System for identifying one or more locations of the UE 104
302 – External Database
304 – Processing Modules
306 – Other Devices
400 – Block diagram of a BS 102-2
402 – Processor of the BS 102-2
404 - Memory of the BS 102-2
406 – Network communication manager
408- Transceiver of the BS 102-2
410 – Antenna (s)
412 – Network communication interface
414 - Communication bus
500 – Functional Block diagram of the system
502- Input/Output Interface of the server 110
504 – Processor
506 - Memory
506 A - Set of instructions
508 - Network communication manager
510 - Console host
512 – Database
514 - Communication bus
516 - Receiving module
518 – Cluster identification module
520 - Determination module
522 – Cluster classification module
524 - Location identification module
526 – Clustering Engine
600 - Method for identifying one or more locations of the UE 104
602-612- Operation steps of the method 600
700 – Block diagram of a computing system
702 – Network
704 – Network interface
706 – Processor
708 – Input/Output (I/O) interface
710 – Non-transitory computer readable storage medium
710-1 - Set of instructions
,CLAIMS:We Claim:
1. A method (600) for identifying one or more locations of a user associated with a User Equipment (UE) (104), the method (600) comprising:
receiving (602), by a receiving module (516) of a server (110), trace data associated with the UE (104), wherein the trace data includes information of a plurality of sessions associated with the UE (104) and location information of the UE (104);
identifying (604), by a cluster identification module (518) of the server (110), a plurality of location clusters for the UE (104) based on the location information of the UE (104);
calculating (606), by a determination module (520) of the server (110) for each location cluster of the plurality of location clusters, an average session end time of sessions among the plurality of sessions within a corresponding location cluster of the plurality of location clusters based on the information of the plurality of sessions associated with the UE (104);
classifying (608), by a cluster classification module (522) of the server (110), the plurality of location clusters in one of work location clusters or home location clusters based on the calculated average session end time of the sessions for each location cluster of the plurality of location clusters;
determining (610), by the determination module (520) for each location cluster of the plurality of location clusters, an amount of the network usage by the UE (104) at the corresponding location cluster based on the information of the plurality of sessions associated with the UE (104);
identifying (612), by a location identification module (524) of the server (110), at least one of a first cluster among the work location clusters as the work location of the user and a second cluster among the home location clusters as the home location of the user based on the determined amount of the network usage by the UE (104) at the plurality of location clusters.

2. The method (600) as claimed in claim 1, wherein for classifying the plurality of location clusters in one of the work location clusters or the home location clusters, the method (600) comprises:
classifying a location cluster of the plurality of location clusters in the work location clusters if the calculated average session end time of sessions within the location cluster is within a first predefined time period; and
classifying the location cluster of the plurality of clusters in the home location clusters if the calculated average session end time of sessions within the location cluster is within a second predefined time period different from the first predefined time period.

3. The method (600) as claimed in claim 1, wherein for identifying at least one of the first cluster as the work location of the user and the second cluster as the home location of the user, the method (600) comprises:
identifying, as the work location, the first cluster among the work location clusters where the amount of the network usage by the UE (104) is maximum among the work location clusters; and
identifying, as the home location, the second cluster among the home location clusters where the amount of the network usage by the UE (104) is maximum among the home location clusters.

4. The method (600) as claimed in claim 1, wherein the information of the plurality of sessions associated with the UE (104) includes information of session end time of each session of the plurality of sessions, session start time of each session of the plurality of sessions, duration of each session of the plurality of sessions, and location of the UE (104) during each session of the plurality of sessions.

5. The method (600) as claimed in claim 4, wherein for determining the amount of the network usage by the UE (104) at the plurality of location clusters, the method (600) comprises:
determining, for each location cluster of the plurality of location clusters, a sum of duration of the sessions among the plurality of sessions within the corresponding location cluster;
determining, for each location cluster of the plurality of location clusters, the amount of the network usage at the corresponding location cluster based on the sum of duration of the sessions among the plurality of sessions within the corresponding location cluster.

6. The method (600) as claimed in claim 1, wherein the plurality of location clusters is identified using a clustering engine (526).

7. A system (300) for identifying one or more locations of a user associated with a User Equipment (UE) (104), the system (300) comprising:
a receiving module (516) configured to receive trace data associated with the UE (104), wherein the trace data includes information of a plurality of sessions associated with the UE (104) and location information of the UE (104);
a cluster identification module (518) configured to identify a plurality of location clusters for the UE (104) based on the location information of the UE (104);
a determination module (520) configured to calculate, for each location cluster of the plurality of location clusters, an average session end time of sessions among the plurality of sessions within a corresponding location cluster of the plurality of location clusters based on the information of the plurality of sessions associated with the UE (104);
a cluster classification module (522) configured to classify the plurality of location clusters in one of work location clusters or home location clusters based on the calculated average session end time of the sessions for each location cluster of the plurality of location clusters,
wherein the determination module (520) is further configured to determine, for each location cluster of the plurality of location clusters, an amount of the network usage by the UE (104) at the corresponding location cluster based on the information of the plurality of sessions associated with the UE (104); and
a location identification module (524) configured to identify at least one of a first cluster among the work location clusters as the work location of the user and a second cluster among the home location clusters as the home location of the user based on the determined amount of the network usage by the UE (104) at the plurality of location clusters.

8. The system (300) as claimed in claim 7, wherein, to classify the plurality of location clusters in one of the work location clusters or the home location clusters, the cluster classification module (522) is configured to:
classify a location cluster of the plurality of location clusters in the work location clusters if the calculated average session end time of sessions within the location cluster is within a first predefined time period; and
classify the location cluster of the plurality of clusters in the home location clusters if the calculated average session end time of sessions within the location cluster is within a second predefined time period different from the first predefined time period.

9. The system (300) as claimed in claim 7, wherein, to identify at least one of the first cluster as the work location of the user and the second cluster as the home location of the user, the location identification module (524) is configured to:
identify, as the work location, the first cluster among the work location clusters where the amount of the network usage by the UE (104) is maximum among the work location clusters; and
identify, as the home location, the second cluster among the home location clusters where the amount of the network usage by the UE (104) is maximum among the home location clusters.

10. The system (300) as claimed in claim 7, wherein the information of the plurality of sessions associated with the UE (104) includes information of session end time of each session of the plurality of sessions, session start time of each session of the plurality of sessions, duration of each session of the plurality of session, and location of the UE (104) during each session of the plurality of sessions.

11. The system (300) as claimed in claim 10, wherein to determine the amount of the network usage by the UE (104) at the plurality of location clusters, the determination module (520) is configured to:
determine, for each location cluster of the plurality of location clusters, a sum of duration of the sessions among the plurality of sessions within the corresponding location cluster;
determine, for each location cluster of the plurality of location clusters, the amount of the network usage at the corresponding location cluster based on the sum of duration of the sessions among the plurality of sessions within the corresponding location cluster.

12. The system (300) as claimed in claim 7, wherein the plurality of location clusters is identified using a clustering engine (526).