System for identifying a location of a user and a method thereof

Abstract

The present disclosure may relate to a system (102) for identifying a location of a user in a network. The system includes a memory (204) and one or more processors (202) that execute instructions stored in the memory. Through an input unit (212), the system receives passive data from various data sources (302a, 302b, 302c), which include sensors on user equipment that capture data without requiring active user interaction. The processors process this received data and assign a score to each record from each data source based on predefined conditions. These scores are then aggregated to compute a cumulative score. The identification unit (214) uses this cumulative score to determine whether the user's location is indoors or outdoors by mapping the score to predefined values associated with specific locations.

Description

SYSTEM FOR IDENTIFYING A LOCATION OF A USER AND A METHOD THEREOFRESERVATION OF RIGHTS

[0001] A portion of the disclosure of this patent document contains material, which is subject to intellectual property rights such as, but are not limited to, copyright, design, trademark, Integrated Circuit (IC) layout design, and / or trade dress protection, belonging to JIO PLATFORMS LIMITED or its affiliates (hereinafter referred as owner). The owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all rights whatsoever. All rights to such intellectual property are fully reserved by the owner.FIELD OF THE DISCLOSURE

[0002] The embodiments of the present disclosure generally relate wireless network communication. In particular, the present disclosure relates to a system and a method for determining whether a user equipment is placed indoors or outdoors.DEFINITION

[0003] As used in the present disclosure, the following terms are generally intended to have the meaning as set forth below, except to the extent that the context in which they are used to indicate otherwise.

[0004] GPS (Global Positioning System) refers to a satellite-based navigation system that provides location and time information in all weather conditions, anywhere on or near the Earth, as long as there is an unobstructed line of sight to four or more GPS satellites.

[0005] Wi-Fi (Wireless Fidelity) refers to a wireless networking technology that allows devices to interface with the Internet or communicate with one another wirelessly within a particular area.

[0006] RSRP (Reference Signal Received Power) refers to an average power received from a single reference signal in cellular networks, used as a measure of signal strength.

[0007] Received Signal Strength Indicator (RSSI) is a metric used in wireless communication systems to measure the power level of the received signal from a transmitter. It quantifies the strength of the radio signal received by an antenna.

[0008] SNR (Signal-to-Noise Ratio) refers to a measure that compares the level of a desired signal to the level of background noise, often used to assess the quality of a communication signal.

[0009] Indoor Small Cell refers to a low-power cellular radio access node that operates in licensed and unlicensed spectrum with a range of 10 meters to a few kilometers, typically used to provide network coverage inside buildings.

[0010] LTE (Long-Term Evolution) refers to a standard for wireless broadband communication for mobile devices and data terminals, designed to provide high-speed data for mobile phones and data terminals.

[0011] Passive data refers to information collected from user equipment without requiring active input or engagement from the user.

[0012] KPI (Key Performance Indicator) is a measurable value that demonstrates how effectively a system or process achieves key objectives.

[0013] API (Application Programming Interface) refers to a set of protocols, routines, and tools for building software applications that specify how software components should interact.

[0014] SDK (Software Development Kit) is a collection of software development tools in one installable package, often used for developing applications for a specific platform.BACKGROUND OF THE DISCLOSURE

[0015] The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

[0016] With the rapid advancements in wireless technology, there has been a significant transformation in the operation of user equipments. Network operators can detect and utilize context information related to the user equipments, such as device location and events occurring in specific areas. To enhance user experience, it is crucial for network operators to accurately determine whether a user equipment is located indoors or outdoors. This process, known as indoor / outdoor detection, employs various techniques, including Global Positioning System (GPS), Wireless Fidelity (Wi-Fi), cellular networks, and device sensors. Once the location of the user equipment is determined, the network operators or users can adjust various properties such as range, power, network selection, and cell selection accordingly.

[0017] The emergence of mobile internet has led to remarkable advancements in Location Based Services (LBS), with Seamless Indoor and Outdoor Navigation and Localization (SNAL) gaining significant attention. SNAL enables accurate positioning of mobile users in both indoor and outdoor environments, offering substantial benefits for applications like tracking, navigation, and location-based marketing. However, the complex nature of indoor environments and diverse outdoor scenarios poses significant challenges, as no single positioning technology has been able to meet the varied positioning requirements across these different contexts.

[0018] Furthermore, the data collected from users through various positioning technologies is often not properly segregated or classified based on whether it was captured indoors or outdoors. This lack of distinction makes it difficult to analyze the data effectively and make accurate decisions for planning and optimization purposes. The inability to differentiate between indoor and outdoor data hampers the potential benefits of location-based services and network optimization efforts.

[0019] Existing solutions for indoor / outdoor detection often rely on single data sources or simplistic algorithms, which can lead to inaccurate classifications. GPS-based methods, for instance, may fail in buildings with large windows or outdoor areas with poor satellite visibility. Wi-Fi and cellular signal-based approaches can be unreliable due to signal fluctuations and strong signals in some outdoor environments. Light sensor-based methods may struggle during certain times of the day or in areas with artificial lighting. These limitations highlight the need for a more robust and comprehensive approach to indoor / outdoor detection. Moreover, current systems often require active user interaction or consume significant device resources, leading to poor user experience and reduced battery life. The lack of a passive, energy-efficient solution for continuous indoor / outdoor detection limits the potential applications and adoption of location-based services.

[0020] Conventional systems and methods face difficulty in accurately and efficiently distinguishing between indoor and outdoor environments without significant user interaction or device resource consumption. There is, therefore, a need in the art to provide a method and a system that can overcome the shortcomings of the existing prior arts by offering a comprehensive, real-time, and energy-efficient solution for indoor / outdoor detection, thereby improving the accuracy and usefulness of user data for various applications and network optimizations.SUMMARY OF THE DISCLOSURE

[0021] In an exemplary embodiment, a system for identifying a location of a user in a network is described. The system comprises a memory and one or more processors configured to execute a set of instructions stored in the memory. The processors are configured to receive, by an input unit, passive data from one or more data sources. The data sources comprise a plurality of user equipment sensors capturing passive data, wherein the passive data comprises data collected without active user interaction. The processors process the received data and assign a score value to each record of the passive data corresponding to each data source based on one or more predefined conditions according to a score matrix. The processors aggregate the assigned score values to each record corresponding to each data source to calculate a cumulative score. An identification unit identifies the location of the user as indoor or outdoor by mapping the cumulative score with a set of predefined values corresponding to one or more locations.

[0022] In some embodiments, the one or more data sources comprise at least one of a GPS sensor, a light sensor, an accelerometer, a gyroscope, a magnetometer, and a proximity sensor.

[0023] In some embodiments, the passive data comprises information related to at least one of user activity, user location, number of satellites, light intensity, altitude, coverage area, density of Wi-Fi access points, type of charging source, and type of cell connection.

[0024] In some embodiments, the one or more predefined conditions for assigning the score value comprise at least one of a number of satellites with signal- to-noise ratio (SNR) above a first threshold, an average light intensity value, a user activity, an altitude of the user, a specific frequency band and the reference signal received power (RSRP) is below a second threshold, a number of Wi-Fi access points discovered with received signal strength indicator (RSSI) above a third threshold, and a type of the charging source of the user equipment.

[0025] In some embodiments, the score value is assigned within a range of -30 to +30.

[0026] In some embodiments, the identification unit is configured to classify the received data as an indoor data, or an outdoor data based on the cumulative score.

[0027] In some embodiments, the one or more processors are further configured to store the identified location data in the database and synchronize the stored data with a remote server.

[0028] In some embodiments, the passive data comprises information related to at least one of user activity, user location, number of satellites, light intensity, altitude, coverage area, density of Wi-Fi access points, type of charging source, and type of cell connection.

[0029] In another exemplary embodiment, a method for identifying a location of a user in a network is described. The method comprises receiving, by an input unit, passive data from one or more data sources. The data sources comprise a plurality of user equipment sensors capturing passive data, wherein the passive data comprises data collected without active user interaction. The method further comprises processing, by one or more processors, the received data and assigning a score value to each record of the passive data corresponding to each data source based on one or more predefined conditions according to a score matrix. The method includes aggregating, by the one or more processors, the assigned score values to each record corresponding to each data source to calculate a cumulative score. An identification unit identifies the location of the user as indoor or outdoor by mapping the cumulative score with a set of predefined values corresponding to one or more locations.

[0030] In an aspect, the one or more data sources comprise a plurality of user equipment sensors capturing the passive data.

[0031] In some embodiments, the one or more data sources comprise at least one of a GPS sensor, a light sensor, an accelerometer, a gyroscope, a magnetometer, and a proximity sensor.

[0032] In some embodiments, the passive data comprises information related to at least one of user activity, user location, number of satellites, light intensity, altitude, coverage area, density of Wi-Fi access points, type of charging source, and type of cell connection.

[0033] In some embodiments, the one or more predefined conditions for assigning the score value comprise at least one of a number of satellites with signal- to-noise ratio (SNR) above a first threshold, an average light intensity value, a user activity, an altitude of the user, a specific frequency band and the reference signal received power (RSRP) is below a second threshold, a number of Wi-Fi access points discovered with received signal strength indicator (RSSI) above a third threshold, and a type of the charging source of the user equipment.

[0034] In some embodiments, the score value is assigned within a range of -30 to +30.

[0035] In some embodiments, the method further comprises classifying the received data as an indoor data or an outdoor data based on the cumulative score.

[0036] In some embodiments, the method further comprises storing the identified location data in a database and synchronizing the stored data with a remote server.

[0037] In yet another exemplary embodiment, a non-transitory computer- readable medium storing instructions for identifying a location of a user in a network is described. When executed by one or more processors of a system, the instructions cause the one or more processors to perform operations comprising receiving, by an input unit, passive data from one or more data sources, processing, by one or more processors, the received data and assigning a score value to eachrecord of the passive data corresponding to each data source based on one or more predefined conditions according to a score matrix; aggregating, by the one or more processors, the assigned score values to each record corresponding to each data source to calculate a cumulative score, and identifying, by an identification unit, the location of the user as an indoor location or an outdoor location by mapping the cumulative score with a set of predefined values corresponding to one or more locations.

[0038] In a further exemplary embodiment, a user equipment communicatively coupled to a system for identifying a location of a user in a network is described. The system comprises a memory and one or more processors configured to execute a set of instructions stored in the memory to perform the method for identifying a location of a user. The method comprises receiving, by an input unit, passive data from one or more data sources. The method includes processing, by one or more processors, the received data and assigning a score value to each record of the passive data corresponding to each data source based on one or more predefined conditions according to a score matrix. The method includes aggregating, by the one or more processors, the assigned score values to each record corresponding to each data source to calculate a cumulative score. The method includes identifying, by an identification unit, the location of the user as indoor or outdoor by mapping the cumulative score with a set of predefined values corresponding to one or more locations.

[0039] The foregoing general description of the illustrative embodiments and the following detailed description thereof are merely exemplary aspects of the teachings of this disclosure and are not restrictive.OBJECTIVES OF THE DISCLOSURE

[0040] Some of the objectives of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.

[0041] An objective of the present disclosure is to provide a system and a method that identifies a location of a user.

[0042] An objective of the present disclosure is to identify user data samples, which enhances all planning and optimization projects.

[0043] An objective of the present disclosure is to provide an efficient classification of user data from various data sources to create user profiles.

[0044] An objective of the present disclosure is to tag user data as indoor or outdoor based on user behaviour measurements and location details.

[0045] An objective of the present disclosure is to provide a system and a method that is applicable to 2G, 3G, 4G, 5G, 6G, and beyond all generations of mobile technology with multiple bands and carriers of telecom operators.

[0046] An objective of the present disclosure is to develop a comprehensive and real-time system for differentiating between indoor and outdoor data in an efficient way.

[0047] An objective of the present disclosure is to improve the accuracy and usefulness of user data by providing more detailed and accurate insights.

[0048] An objective of the present disclosure is to utilize passive data collection methods to minimize battery consumption and user interaction.

[0049] An objective of the present disclosure is to implement a scoring system that aggregates data from multiple sensors to enhance the accuracy of indoor-outdoor detection.BRIEF DESCRIPTION OF DRAWINGS

[0050] 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 sameparts 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 the disclosure of electrical components, electronic components or circuitry commonly used to implement such components.

[0051] FIG. 1 illustrates an exemplary network architecture of a system for identifying a location of a user, in accordance with embodiments of the present disclosure.

[0052] FIG. 2 illustrates an exemplary micro service-based architecture of the system, in accordance with embodiments of the present disclosure.

[0053] FIG. 3 illustrates an exemplary block diagram of the system for identifying the location of the user, in accordance with embodiments of the present disclosure.

[0054] FIG. 4 illustrates an exemplary flowchart of a method for identifying the location of the user, in accordance with embodiments of the present disclosure.

[0055] FIG. 5 illustrates an exemplary flow chart illustrating various steps performed by the system during tagging of the data representing the location of the user, in accordance with embodiments of the present disclosure.

[0056] FIG. 6 illustrates another exemplary flow chart illustrating various steps performed by the system during capturing the data from a plurality of sensors, in accordance with embodiments of the present disclosure.

[0057] FIG. 7 illustrates an exemplary computer system in which or with which embodiments of the present disclosure may be implemented.

[0058] The foregoing shall be more apparent from the following more detailed description of the disclosure.LIST OF REFERENCE NUMERALS100 - Network architecture102 - System104 - Network106 - Centralized server108-1, 108-2. . . 108-N - User equipment(s)110-1, 110-2... 110-N - Users202 - One or more processor(s)204 - Memory206 - I / O interface(s)208 - Processing unit(s)210 - Database212 - Input unit214 - Identification unit216 - Other unit (s)302a, 302b, 302c - One Or More Data Sources710 - External Storage Device720 - Bus730 - Main Memory740 - Read Only Memory750 - Mass Storage Device760 - Communication Port770- ProcessorDETAILED DESCRIPTION OF THE DISCLOSURE

[0059] 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, thatembodiments 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. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.

[0060] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

[0061] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.

[0062] Also, it is noted that individual embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to afunction, its termination can correspond to a return of the function to the calling function or the main function.

[0063] The word “exemplary” and / or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and / or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising” as an open transition word without precluding any additional or other elements.

[0064] Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0065] 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 singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and / or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / orgroups thereof. As used herein, the term “and / or” includes any and all combinations of one or more of the items listed in the associated list.

[0066] The widespread adoption of smart mobile devices and ubiquitous internet access has become a driving force behind the development of mobile applications leveraging location-based systems (LBSs). These LBSs play a crucial role across various domains, including but not limited to tracking, navigation, safety-related services, location-sensitive billing, advertising, tourism, healthcare monitoring, and intelligent transportation. Given this importance, there is a pressing need to efficiently tag data as outdoor or indoor, corresponding to the user's location.

[0067] Indoor-outdoor (IO) sensing is broadly categorized into two main approaches: GPS -based techniques and smartphone sensor-based methods. GPS- based techniques typically rely on degrading GPS signals as users transition from outdoor to indoor environments. However, this approach faces limitations, as GPS signals can sometimes penetrate buildings with large windows, potentially leading to ambiguous conclusions about the user's IO state. While the GPS signals may not be a definitive IO state detector, they still provide valuable aid in user positioning. It's worth noting that GPS sensors are among the most power-hungry components of a smartphone, consuming approximately seven times more energy than accelerometer and gyroscope sensors.

[0068] To overcome the limitations of GPS-based methods, sensor-based approaches utilize other built-in sensors of the mobile devices to detect the user's IO state. These sensors may include Wi-Fi, Bluetooth, ambient light sensors, GSM, cellular network sensors, accelerometers, magnetometers, and proximity sensors. Each of these sensors offers unique insights, but they also face their own challenges. For instance, light-based methods may struggle to obtain sufficient light intensity variance under certain conditions, such as at dawn or dusk.

[0069] Cellular signals emanating from cell towers represent another potential data source for IO detection. The principle behind this approach is thesignificant drop in cellular signal strength as users move between outdoor and indoor environments. However, prolonged transitions between these states may necessitate extended data collection periods from cell towers, potentially increasing battery consumption.

[0070] Wi-Fi-based approaches operate on a similar principle for IO state detection, requiring scanning of the Wi-Fi access points (APs). This process demands more time and energy compared to other smartphone sensors. Existing IO sensing methods have limited capabilities and may not always provide real-time data. Addressing these issues, the present system can accurately tag data collected from various sources, thereby effectively indicating the user's location.

[0071] The aspects of the present disclosure are directed to a system and method for identifying the user's indoor or outdoor location using passive data collection techniques. The present disclosure employs a multi-faceted approach that aggregates data from various user equipment sensors without active user interaction, processes this data using a scoring system based on predefined conditions, and calculates a cumulative score to determine the user's location. This disclosed method enhances the accuracy of indoor-outdoor detection while minimizing battery consumption, thereby improving network planning, optimization, and location-based services across multiple generations of mobile technology.

[0072] The various embodiments throughout the disclosure will be explained in more detail with reference to FIGS. 1-7.

[0073] FIG. 1 illustrates an exemplary network architecture (100) of a system (102) for identifying a location of a user, in accordance with embodiments of the present disclosure.

[0074] As illustrated in FIG. 1, one or more user equipment (108-1, 108- 2...108-N) may be connected to the system (102) through a network (104). A person of ordinary skill in the art will understand that the one or more user equipment (108-1, 108-2...108-N) may be collectively referred to as UEs (108) and individually referred to as a UE (108). One or more users may provide passive data to the system (102) through various sensors embedded in the UE (108).

[0075] In an embodiment, the UE (108) may include, but not be limited to, a mobile phone, a laptop, etc. Further, the UE (108) may include one or more inbuilt or externally coupled sensors including, but not limited to, a GPS sensor, a light sensor, an accelerometer, a gyroscope, a magnetometer, and a proximity sensor. Furthermore, the UE (108) may include a smartphone, virtual reality (VR) devices, augmented reality (AR) devices, a general-purpose computer, a desktop, a personal digital assistant, a tablet computer, and a mainframe computer.

[0076] In an embodiment, the network (104) may include, by way of example but not limitation, at least a portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, etc. one or more messages, packets, signals, waves, voltage or current levels, some combination thereof, or so forth. The network (104) 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 2G network, a 3G network, a 4G network, a 5G network, a 6G network, or some combination thereof.

[0077] In an embodiment, the system (102) may continuously collect passive data for a selected UE (108) from one or more data sources. The system (102) may include an input unit, an identification unit, and one or more processors. The input unit may then receive this data. The processors may process the received data and assign score values based on predefined conditions. The identification unit may then identify the location of the user as indoor or outdoor based on the cumulative score. If updates are needed, the system may reconfigure the data collection and provide these results to the UE (108) for storage and synchronization with a remote server.

[0078] Although FIG. 1 shows exemplary components of the network architecture (100), in other embodiments, the network architecture (100) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 1. Additionally, or alternatively, one or more components of the network architecture (100) may perform functions described as being performed by one or more other components of the network architecture (100).

[0079] FIG. 2 illustrates an exemplary micro service-based architecture (200) of the system (102), in accordance with an embodiment of the present disclosure.

[0080] Referring to FIG. 2, in an embodiment, the system (102) includes a memory (204) and one or more processor(s) (202). The memory (204) may store a set of instructions that, when executed by the one or more processors (202), may cause the system (102) to perform various operations for determining whether the user is located indoors or outdoors.

[0081] The one or more processor(s) (202) may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and / or any devices that process data based on operational instructions. Among other capabilities, the one or more processor(s) (202) may be configured to fetch and execute computer-readable instructions stored in the memory (204) of the system (102). In an embodiment, the one or more processor(s) (202) may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the one or more processor(s) (202). In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the one or more processor(s) (202) may be processor-executable instructions stored on a non-transitory machine- readable storage medium and the hardware for the one or more processor(s) (202) may comprise a processing resource (for example, one or more processors), toexecute such instructions. In the present examples, the machine -readable storage medium may store instructions that, when executed by the processing resource, implement the one or more processor(s) (202). In such examples, the system may 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 system and the processing resource. In other examples, the one or more processor(s) (202) may be implemented by electronic circuitry.

[0082] The memory (204) may be configured to store one or more computer-readable instructions or routines in a non-transitory computer readable storage medium, which may be fetched and executed to identify the location of the user and calculate cumulative scores. The memory (204) may comprise any non- transitory storage device including, for example, volatile memory such as randomaccess memory (RAM), or non-volatile memory such as erasable programmable read only memory (EPROM), flash memory, and the like.

[0083] In an embodiment, the system (102) includes an interface(s) (206). The interface(s) (206) may comprise a variety of interfaces, for example, interfaces for data input and output devices (VO), storage devices, and the like. The interface(s) (206) may facilitate communication through the system (102). The interface(s) (206) may also provide a communication pathway for one or more components of the system (102). Examples of such components include, but are not limited to, processing unit(s) (208), and a database (210) for storing location data. Further, the processing unit(s) (208) may include an input unit (212), an identification unit (214), and other unit(s) (216).

[0084] The input unit (212) is configured to receive passive data from one or more data sources. The one or more data sources may comprise various sensors embedded in or connected to a user equipment (UE) device (108). In an example, the system (102) may be installed with the UE (108) such that the UE is able to collect the data passively and tag the data as an indoor data or an outdoor data. TheUE may be configured to the tagged data to the system (102). The UE (108) may be a mobile phone, tablet, laptop, wearable device, or other portable electronic device carried by the user. The sensors may include, but are not limited to, a GPS sensor, a light sensor, an accelerometer, a gyroscope, a magnetometer, and a proximity sensor. One aspect of the system (102) may be its ability to capture passive data from these sensors. Passive data may refer to information collected without requiring any active input or interaction from the user. For example, the accelerometer may continuously measure the device's movement, or the light sensor may detect ambient light levels, all without the user needing to manually initiate these measurements. This passive data collection approach may allow continuous monitoring of the user's environment without draining battery life or interrupting the user's activities.

[0085] The passive data received by the input unit (212) may comprise various types of information related to the user's environment and device status. This may include user activity data, such as whether the user is walking, running, or stationary. It may also include the user's location coordinates as determined by GPS. The number of visible GPS satellites and their signal strength may be another valuable data point. Light intensity measurements from the device's ambient light sensor may indicate whether the user is indoors or outdoors.

[0086] Additional data points may be collected, including the device's altitude, which could help distinguish between ground level and upper floors of buildings. The system (102) may also consider the coverage area of cellular networks and the density of nearby Wi-Fi access points. The type of power source currently charging the device - whether it's connected to AC power or using a battery - may also be factored in. Finally, the type of cellular connection (e.g., 2G, 3G, 4G, 5G, 6G) may provide additional context about the user's environment.

[0087] Once the input unit (212) receives the data, the one or more processors (202) may process the received data. A crucial step in this process may be assigning a score value to each piece of data corresponding to each source. Thisscoring may be based on one or more predefined conditions that have been determined to be indicative of indoor or outdoor environments.

[0088] For example, the one or more predefined conditions may relate to the number of visible GPS satellites with a signal-to-noise ratio (SNR) above a first threshold. In an aspect, the first threshold lies in a range of 10 dB to 30 dB. A high number of visible satellites with strong signals may be indicative of an outdoor environment, while fewer visible satellites or weaker signals may suggest an indoor location. Similarly, the average light intensity value detected by the device's light sensor may be another condition. High light intensity may suggest an outdoor daytime environment, while lower light levels may indicate an indoor setting or nighttime outdoors.

[0089] Another condition that may be considered is whether the device is connected to an indoor small cell. Small cells are low-power cellular radio access nodes that may be used to provide service inside buildings where outdoor signals may not penetrate well. Connection to such a small cell may be a strong indicator of an indoor location.

[0090] The system (102) may also consider the user's activity in conjunction with their geographic coordinates. For instance, if the user is determined to be stationary and their latitude and longitude correspond to a known building location (represented by a building polygon in a geographic database), this may suggest an indoor location. Conversely, rapid movement along roads or paths may indicate an outdoor setting.

[0091] The altitude of the user's device may be another valuable data point. If the altitude is significantly above ground level and corresponds to the height of a known building, this may suggest an indoor location on an upper floor. The system (102) may also consider whether the user is connected to a specific frequency band typically used for indoor coverage, and whether the reference signal received power (RSRP) is below a second threshold, which could indicate signal attenuation due towalls and other indoor obstructions. In an aspect, the second threshold is a configured RSRP threshold, which lies in a range of -50 dBm to -120 dBm.

[0092] The number and signal strength of Wi-Fi access points detected by the device may also be considered. In an aspect, a number of Wi-Fi access points discovered with received signal strength indicator (RSSI) above a third threshold is considered as a predefined condition. In an aspect, the third threshold is a configured RSSI threshold, which lies in a range of -60 dBm to -100 dBm. A high density of strong Wi-Fi signals may suggest an indoor environment like an office or shopping mall, while fewer or weaker Wi-Fi signals might indicate an outdoor setting. Lastly, the system (102) may check whether the device's charging source is AC power, which is more common indoors than outdoors.

[0093] To assign score values based on these conditions, the one or more processors (202) may utilize a score matrix stored in a database (210). This score matrix may define specific score values for different ranges or states of each record. For instance, it may assign a high positive score for strong GPS signals, a slightly lower positive score for moderate GPS signals, a negative score for weak GPS signals, and a high negative score for no GPS signal. The scoring system may employ a weighted approach to calculate the cumulative score, recognizing that some factors may be more indicative of indoor / outdoor status than others. For example: a. GPS signal strength may be given a high weight (e.g., 0.3) due to its strong correlation with outdoor environments. b. Light intensity may receive a moderate weight (e.g., 0.2), as it is a good indicator but can be affected by factors like time of day. c. Wi-Fi access point density might be assigned a weight of 0.15, as it's often indicative of indoor environments but not always definitive.d. Cellular signal characteristics might receive a weight of 0.15, as they can provide useful information about the environment. e. User activity and location (e.g., stationary in a building polygon) could be weighted at 0.1. f. Other factors like altitude and charging source might be given lower weights (e.g., 0.05 each) as they provide supplementary information.

[0094] The cumulative score may then be calculated as a weighted sum of these individual scores: Cumulative Score = S (Factor Score * Factor Weight).

[0095] This weighted approach allows the system to adapt to various scenarios and prioritize the most reliable indoor / outdoor status indicators. The specific weights may be fine-tuned based on empirical data and machine learning algorithms to optimize the classification accuracy across different environments and use cases.

[0096] The score values assigned by the system (102) may typically fall within a range of -30 to +30. This range may allow for nuanced scoring that can capture subtle differences in the certainty of indoor versus outdoor classification. For example, a score of +30 might indicate a very high certainty of an outdoor location, while a score of +5 might suggest a likely but less certain outdoor classification.

[0097] After assigning individual scores to each record, the system (102) may aggregate these scores to calculate a cumulative score. This aggregation may involve a simple summation of all individual scores or employ a more complex weighted average that gives more importance to certain types of data over others. The specific aggregation method may be tailored to optimize the accuracy of the indoor / outdoor classification based on empirical testing and machine learning algorithms.

[0098] Once the cumulative score is calculated, an identification unit (214)within the system (102) may map this score (cumulative score) to a set of predefined values corresponding to different location classifications. For instance, the cumulative score greater than 15 may indicate a high probability of an outdoor location, while the cumulative score less than -15 may suggest a high probability of an indoor location. Scores between -15 and +15 may indicate less certain classifications, possibly representing transitional areas like building entrances or outdoor areas with significant overhead cover. The identification unit (214) is configured to identify the location of the user as indoor or outdoor by mapping the cumulative score with a set of predefined values corresponding to one or more locations. In an example, the one or more locations include an indoor location and an outdoor location.

[0099] The identification unit (214) may thus classify the received data as either indoor data or outdoor data based on this cumulative score. This classification may be used for various purposes, such as optimizing device settings, improving location-based services, or enhancing network planning and optimization.

[0100] The other unit(s) (216) may include modules for processing the received data, assigning score values, and aggregating the assigned score values to calculate a cumulative score.

[0101] An aspect of the system (102) may be its ability to activate the data sources upon detecting a change in activity associated with the user equipment (108). For example, if the accelerometer detects that a previously stationary device has started moving or the GPS detects a significant location change, the system may trigger a new round of data collection and analysis. This adaptive approach may allow real-time indoor / outdoor classification updates as the user moves between different environments.

[0102] Finally, the system (102) may store the identified location data in the database (210) and synchronize this data with a remote server. This feature may allow for long-term analysis of user behavior patterns, improve the accuracy of theclassification algorithm over time, and enable the sharing of anonymized data for broader research or commercial purposes.

[0103] The system (102) for identifying a user's location as indoor or outdoor may represent a significant advancement in location-based services and network optimization. By receiving passive data from multiple sensors, employing a sophisticated scoring system, and providing real-time adaptability, the system offers a more accurate and efficient solution than traditional methods. This approach may lead to improved user experiences in location-based applications, more effective network resource allocation, and new possibilities for context-aware services across various industries.

[0104] Furthermore, the system's capability to store identified location data in a database and synchronize it with a remote server opens up possibilities for longterm analysis and improvement of the classification algorithms.

[0105] Although FIG. 2 shows exemplary components of the system (102), in other embodiments, the system (102) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 2. Additionally, or alternatively, one or more components of the system (102) may perform functions described as being performed by one or more other components of the system (102).

[0106] FIG. 3 illustrates a block diagram of the system (102). The system (102) may be configured to classify the data as outdoor data and indoor data, thereby representing the location of the user. In an aspect, the system (102) may be installed as a data tagging mobile application within the user equipment.

[0107] As shown in FIG. 3, the system (102) may include one or more data sources (302a, 302b, 302c), the input unit (212), the one or more processors (202), the identification unit (214), and the database (210). In an aspect, the input unit (212), the one or more processors (202), the identification unit (214) and the database (210) may be embedded into a single entity.

[0108] The one or more data sources (302a, 302b, 302c) may be configured to generate passive data corresponding to a number of parameters associated with a user. In an example, the number of parameters may include an activity (user behaviour), a location of the user, a number of satellites, a light intensity, an altitude, coverage area, density of wi-fi access points (APs), type of charging source, and a type of cell with which the user equipment is connected. In an aspect, the generated data may include a unique data source ID corresponding to each data source. In an aspect, the one or more data sources (302a, 302b, 302c) may include a plurality of sensors. In an example, the plurality of sensors may be installed (as a plurality of mobile Application Programming Interface (APIs)) within the user equipment (user equipment or mobile device). In some examples, the one or more data sources (302a, 302b, 302c) may be employed as one or more mobile applications designed to track at least one parameter associated with the user. The data tagging mobile application, and the one or more mobile applications may be a software or a mobile application from an application distribution platform. Examples of application distribution platforms include the App Store for iOS provided by Apple, Inc., Play Store for Android OS provided by Google Inc., and such application distribution platforms. For example, the plurality of sensors may include a GPS sensor, a Wi-Fi (Wireless Fidelity), an ambient light sensor, an accelerometer sensor, a magnetometer sensor, a gyroscope sensor, a motion detector sensor, and a proximity sensor. For example, the plurality of sensors may be configured to sense the user's activities, or an environment associated with the user and generate the data accordingly. In an example, each of the one or more data sources (302a, 302b, 302c) may be configured to generate a record corresponding to the number of parameters associated with the user. In an aspect, the one or more data sources (302a, 302b, 302c) may be configured to generate the passive data by aggregating the record generated by each data source. In an example, the one or more data sources (302a, 302b, 302c) may be configured to be commutatively coupled with the input unit (212) over the network (104). In an example, the one or more data sources (302a, 302b, 302c) may be configured to share the generated data with the data tagging mobile application. In another aspect, the data tagging mobileapplication may be configured to process and upload the data collected from the plurality of mobile applications on a remotely placed server.

[0109] In an aspect, the one or more data sources (302a, 302b, 302c) may be configured to transmit the generated passive data in an active mode (in real time). In another aspect, the one or more data sources (302a, 302b, 302c) may be configured to transmit the generated passive data in a passive mode (periodically after a predefined time interval), thereby saving the battery of the user equipment (108). In an aspect, the data tagging mobile application may be configured to detect a change in activity associated with the user equipment. In an aspect, the data tagging mobile application may be further configured to trigger the plurality of mobile applications for capturing the data.

[0110] In an operative aspect, the activity recognition API may be employed for tracking the activity of the user. In an example, the activity recognition API (mobile API) may be configured to operate as a Passive software development kit (SDK) (for example, Activity Recognition API), which is configured to determine what activity the user is performing. The API returns one of the following eight possible activities with certain probabilities (as shown in Table 1).

[0111] The ambient light sensor may be configured to receive at least 20 consecutive samples in lumens. The ambient light sensor may be configured is to detect the lighting conditions around the user and adjust the screen brightness accordingly (known as auto brightness).

[0112] The system (102) may be configured to consider a number of GPS satellites and their corresponding SNR (Signal-to-Noise Ratio) values in dBm.Table 1: various activities of the user equipment

[0113] The input unit (212) may be configured to receive the passive data from the one and more data sources over the network. The one or more data sources may include one or more sensors which are configured to generate information (passive data) based on parameters like user behaviour, location, environmental conditions, and network status. For example, sensors like GPS, accelerometers, ambient light sensors, magnetometers, and motion detectors collect data such as the user's geolocation, movement patterns, light intensity, and proximity to objects. The passive data is continuously generated or sampled at regular intervals, providing insights into the user's activity and surroundings without requiring direct interaction. The passive data generated by these sensors is transmitted to the input unit (212) over the network using various wireless communication protocols. In an aspect, the input unit (212) may include an antenna for receiving and transmitting the data. In some examples, at least one antenna is a near-field antenna, a WiFi antenna, and a radio frequency antenna. The near-field antenna is typically used for short-range communication, which can detect proximity-based signals. The WiFi antenna is used for communication with local Wi-Fi access points (APs), helping gather data on the density of Wi-Fi networks or the user's connectivity status. Additionally, the radio frequency (RF) antenna can receive signals from cellularnetworks or other RF-based systems, facilitating communication over broader distances.

[0114] The one or more processors (202) may be configured to be commutatively coupled with the input unit (212) to receive the passive data. The passive data may need preprocessing to ensure quality and consistency. The one or more processors (202) may be configured to employ preprocessing of the passive data. In an example, the preprocessing may involve cleaning the data, filtering out noise, filling missing values, or normalizing readings to ensure that the data is in a suitable format for analysis. The one or more processors (202) may be configured to process the received passive data by employing at least one processing technique and generating processed data. In an example, the at least one processing technique may include filtration, amplification and up-conversion. In an aspect, the one or more processors (202) may be configured to convert all received data into a database format which is more appropriate for processing a large-scale dataset. In an aspect, the one or more processors (202) may be configured to process and parse the data in a batch process. In an aspect, during the processing, the one or more processors (202) may be configured to extract a set of features and save them in the database (210).

[0115] Further, the one or more processors (202) may be configured to assign a score value to each record of the passive data corresponding to each unique data source ID based on one or more predefined conditions (status). In an aspect, the one or more predefined conditions may include: a. Number of Satellites with SNR > S dBm b. Average Light Intensity value (in lumens) c. Is device latched onto Indoor Small Cell? d. What is the activity of the user, and does the Latitude and longitude of the user lie on a building polygon or outside? e. Altitude of the user, and is it >H meter?f. Is user connected to band 5, and RSRP is less than Z dBm, where Z is a configured RSRP threshold. g. Does the user have N or more Wi-Fi APs discovered with RSRP greater than R dBm? h. Is the charging source of the device “AC”? where:S is the Number of satellite with an SNR threshold,H is an altitude threshold set for indoor sample classification,Z is a reference signal received power (RSRP) threshold set for band 850, for the classification of indoor-outdoor samplesN is a threshold set for the number of Wi-Fi APs in the vicinity of the device for it to be considered an indoor sample,R is an RSSI (received signal strength indicator) threshold set for a Wi-Fi AP to be considered.

[0116] In an aspect, the one or more processors (202) may be configured to assign the score value based on a score matrix fetched from the database (210). The score matrix contains predefined conditions defining how each data type should be scored. For example, if the data is from the GPS sensor, the score matrix may specify a high score for data indicating that the user is within a specific geographic area. Similarly, data from an accelerometer could be evaluated based on movement patterns, such as walking or running, and a score might be assigned based on the activity level. These predefined conditions are the basis for how data records are scored. Once the conditions from the score matrix are applied, the score value is assigned to each data record. This score reflects how well the data aligns with the predefined conditions. For instance, if the GPS data places the user in a target location, the score would be high, signaling the relevance of that data. If the accelerometer data indicates the user is stationary, the score may be lower. In some cases, multiple data records from various sources may be aggregated to form a final score. This aggregation helps combine information from various sensors, such aslocation, activity, and network strength, to comprehensively evaluate. In an aspect, the score matrix may be given as:Table 2: Score matrix

[0117] In an operative aspect, the following score value corresponding to the parameters may be configurable using an admin module. In an aspect, the assigned score for each parameter may be lie between -30 and +30. In an example, the threshold values for certain parameters may be given as (as shown in a threshold matrix): a. Minimum SNR for Satellite Consideration (S) b. Minimum Altitude to consider a sample as indoor (H) c. Maximum RSRP to consider a sample as indoor on Band 5 (Z) d. Minimum RSSI Level for Wi-Fi AP Consideration (R) e. Minimum number of available Wi-Fi APs to consider a sample as indoors (N) f. Light Intensity Thresholds i. LI ii. L2 iii. L3Table 3: Threshold matrix: threshold values for various parameters

[0118] In an aspect, the one or more processors (202) may be configured to aggregate the assigned score value to each record corresponding to each unique data source ID and generate a cumulative score (an aggregated value) corresponding to each parameter. The cumulative score is a single value calculated by combining the individual score values assigned to each sensor or data source based on the one or more predefined conditions. By calculating the cumulative score, the system is configured to evaluate the user’s context, activity, or environment more accurately, considering the data from multiple sensors.

[0119] In an operative aspect, the system receives data from different sensors that track the user’s activity, location, and environment. In this case, the data sources include GPS for location, an accelerometer for movement, and Wi-Fi for proximity to access points. The goal is to assign a score value to each data source based on the one or more predefined conditions and then combine those score values to get the cumulative score.

[0120] At first step, the passive data is received from the various sensors. For example, the GPS data indicates the user is located in New York City, which matches a target location. The accelerometer shows that the user is walking moderately, with an acceleration value of 1.2 m / s2. Additionally, the Wi-Fi sensor detects a signal strength of 85 dBm, indicating proximity to a nearby access point. Once the passive data is received, each record is evaluated against predefined conditions in a score matrix. The matrix specifies how the data should be scored. For instance, the GPS data might assign a score value of 10 if the user is in the target location, the accelerometer data assigns a score value of 7 for moderate walking, and the Wi-Fi data assigns a score value of 5 based on the signal strength being above a threshold of 80 dBm.

[0121] Once the score values are assigned to each data record, the system is configured to aggregate the assigned score values to calculate the cumulative score. The aggregation process typically involves summing the individual scores. In this example, the GPS score is 10, the accelerometer score is 7, and the Wi-Fi score is 5. When summed together, the cumulative score becomes 22. This cumulative score represents the overall context of the user’s activity, considering their location, movement, and proximity to Wi-Fi. The cumulative score of 22 can then be interpreted and used for further analysis or decision-making. For example, if the system has set a threshold (such as a score of 20), this score could indicate that the user is in a targeted area, active, and near an important network, triggering specific actions like sending a notification or logging the activity. Alternatively, if the cumulative score is low, the system may adjust its response or request additional data to refine the analysis. This process allows the system to evaluate multiplesources of data, assign significance to each, and aggregate those evaluations into a single actionable score that informs further decisions or actions.

[0122] By employing the cumulative score, the system may offer several technical advantages listed below:• data aggregation and simplification: The cumulative score consolidates data received from the one or more data sources into a single, unified metric, thereby simplifying the system’s decision-making process and reducing the complexity of handling and interpreting each sensor’s data individually, making the system more efficient and easier to manage.• improved decision-making: By integrating multiple data sources, the cumulative score allows the system to make more accurate and informed decisions. For example, if a user is in a target location (as detected by GPS), but they are not moving (as indicated by the accelerometer), the cumulative score reflects this inactivity, enabling the system to take appropriate actions, such as adjusting settings or triggering alerts.• enhancing contextual awareness by providing a more comprehensive view of the user’s situation. Data from different sensors can sometimes provide conflicting or incomplete insights. For example, GPS might show a user is in an area of interest, but the accelerometer might show no movement. By combining these data points, the cumulative score ensures that the system understands the full context, improving its ability to adapt to dynamic environments and making the system’s response more relevant.• triggering threshold-based actions and responses: When the cumulative score exceeds or falls below a certain threshold, the system may initiate predefined actions, such as sending a notification or adjusting the user interface. This makes it easier to automate responses based on a comprehensive evaluation of the user’s behavior and environment, improving the system’s responsiveness and user experience.

[0123] In an example, the cumulative score also provides a quantifiable measurement that can be used for trend analysis over time. By tracking cumulative scores across different periods, the system can identify user behavior patterns or monitor environment changes. This capability is helpful in detecting anomalies, monitoring performance, and understanding long-term trends, which can be leveraged for optimization or predictive analytics. Additionally, the use of a cumulative score can optimize computational efficiency. The system calculates a single aggregate score instead of processing multiple individual data points from different sensors separately. This reduces computational overhead and accelerates processing, making the system more efficient, especially in real-time applications where timely responses are critical. The cumulative score facilitates easier integration and interoperability with other systems. For example, in loT systems, where data from multiple devices needs to be processed together, the cumulative score simplifies integration, enabling seamless communication between devices and services. In conclusion, the cumulative score offers numerous technical benefits, including simplifying data aggregation, improving decision-making, enhancing contextual awareness, enabling threshold-based actions, ensuring data reliability, allowing for customizable weighting, providing quantifiable metrics for analysis, optimizing computational efficiency, and enhancing system interoperability.

[0124] The identification unit (214) may be configured to classify the data samples (received data) as indoor data (indoor sample) or outdoor data (outdoor sample). In an aspect, the identification unit (214) may be configured to tag the classified data samples as indoor data or outdoor data. The identification unit (214) may be configured to identify the location of the user based on the classified samples. The identification unit (214) may be configured to classify the data samples by mapping the cumulative score (aggregated score value) with a set of predefined values corresponding to one or more locations. The classification process starts with the system calculating by combining data from various sensors such as GPS, accelerometers, and Wi-Fi. These sensors capture different aspects ofthe user’s environment, such as movement, location, and proximity to wireless networks. Once the cumulative score is calculated, the identification unit (214) maps the cumulative score to a predefined range of values (set of predefined values), categorizing the user’s location as either indoor or outdoor and further classifying the location based on the strength or activity level (e.g., high, medium, low).Table 4: Cumulative score for classifying the data samples (received data) as indoor data or outdoor data

[0125] For example, the cumulative score of 16 falls into the "Indoor - High" category according to the predefined mapping. This would indicate that the location of the user is the indoor location, and in a place with a high level of activity or signal strength, such as a crowded building or a well-connected area. On the other hand, if the cumulative score is 12, the classification would fall into the "Indoor - Medium" range, suggesting that the user is indoor, but possibly in a less active or less signal-dense environment (such as a less crowded room or a building with weaker connectivity). In another example, if the cumulative score is -18, it would be mapped to the "Outdoor - High" category, indicating that the user is outdoors in a high-activity area, such as an open park with lots of movement or alocation with a strong signal presence. If the score is -7, the classification would be "Outdoor - Low," suggesting that the user is outdoors but in a quieter or less active area, such as a secluded outdoor space. If the cumulative score falls between -5 to 5, it would be categorized as "Unknown," indicating that the system cannot confidently determine whether the user is indoors or outdoors. This could occur when the sensor data is ambiguous or conflicting, such as when the GPS signal is weak, or the accelerometer does not detect any significant movement. In summary, the identification unit uses the cumulative score to classify the user’s location and environment, whether indoors or outdoors, and provides additional context by categorizing the environment's activity level or signal strength (e.g., high, medium, low). This classification process is crucial for applications that need to adapt to the user’s environment, such as location -based services, environmental monitoring, or activity tracking systems.

[0126] The database (210) may be configured to store program instructions. The database is configured to store the data received from the one or more data sources (302a, 302b, 302c). The program instructions include a program that implements a method to classify the data samples and identify the location of the user based on the classified data samples in accordance with embodiments of the present disclosure and may implement other embodiments described in this specification. The database (210) may be configured to store pre-processed data. In an example, the database (210) may include oracle database, DB2 database, Postgre SQL database, Microsoft SQL Server database, Microsoft Access database or MySQL database.

[0127] FIG. 4 illustrates an exemplary flow chart illustrating a method (400) of identifying the location of the user, in accordance with an embodiment of the present disclosure.

[0128] At step 402, the input unit (212) may be configured to receive the passive data from the one and more data sources. The one or more data sources (302a, 302b, 302c) may be configured to generate the passive data basedcorresponding to a number of parameters associated with the user. In an example, the number of parameters may include an activity (user behavior), a location of the user, a number of satellites, a light intensity, an altitude, coverage area, density of wi-fi access points (APs), type of charging source, and a type of cell with which the user equipment is connected. In an aspect, the generated data may include a unique data source ID corresponding to each data source. In an aspect, the one or more data sources (302a, 302b, 302c) may include a plurality of sensors capturing passive data. The passive data includes data collected without active user interaction. The one or more data sources may include at least one of a GPS sensor, a light sensor, an accelerometer, a gyroscope, a magnetometer, and a proximity sensor. These sensors may capture various types of information related to user activity, user location, number of satellites, light intensity, altitude, coverage area, density of WiFi access points, type of charging source, and type of cell connection.

[0129] At step (404), the method (400) includes processing, by one or more processors (202), the received data and assigning a score value to each record of the passive data corresponding to each data source based on one or more predefined conditions. The predefined conditions for assigning the score value may comprise at least one of a number of satellites with signal-to-noise ratio (SNR) above a first threshold, an average light intensity value, a user activity, an altitude of the user, a specific frequency band and the reference signal received power (RSRP) is below a second threshold, a number of Wi-Fi access points discovered with received signal strength indicator (RSSI) above a third threshold, and a type of the charging source of the user equipment. In an aspect, the third threshold is a configured RSSI threshold, which lies in a range of -60 dBm to -100 dBm. The score value may be assigned based on a score matrix stored in a database (210), typically within a range of -30 to +30.

[0130] At step (406), the method (400) includes aggregating, by the one or more processors (202), the assigned score values to each record corresponding to each data source to calculate a cumulative score. This aggregation process involves complex algorithms that weight different data sources based on their reliability andrelevance in different contexts. At step 406, the one or more processors (202) may aggregate the assigned score value to each record corresponding to each unique data source ID and generate an aggregated value corresponding to each parameter.

[0131] At step (408), the method (400) includes identifying, by an identification unit (214), the location of the user as indoor or outdoor by mapping the cumulative score with a set of predefined values corresponding to one or more locations. This step involves classifying the received data as indoor data or outdoor data based on the cumulative score. For instance, a cumulative score greater than 15 may indicate a high probability of indoor location, while a cumulative score less than -15 may indicate a high probability of outdoor location. The identification unit (214) may be configured to classify the data samples (received data) as indoor data (indoor sample) or outdoor data (outdoor sample) and may identify the location of the user based on the classified samples.

[0132] In some embodiments, the method (400) further includes activating the one or more data sources (302a, 302b, 302c) upon detection of a change in activity associated with a user equipment. This adaptive approach allows for realtime updates to the indoor / outdoor classification as the user moves between different environments.

[0133] The method (400) may also include storing the identified location data in a database (210) and synchronizing the stored data with a remote server. This feature allows for long-term analysis of user behaviour patterns, improvement of the classification algorithm over time, and sharing of anonymized data for broader research or commercial purposes.

[0134] In another exemplary embodiment, a user equipment (108) is described that is configured to perform the method (400) for identifying the location of the user. The user equipment (108) may include various sensors for passive data collection and may be capable of processing this data to determine its indoor / outdoor location.

[0135] FIG. 5 illustrates an exemplary flow chart (500) illustrating various steps performed by the system (102) during tagging of the data representing the location of the user, in accordance with an embodiment of the present disclosure.

[0136] In an aspect, the system (102) may be initialized by a network operator for receiving the data from the user equipment (108) such that the system (102) may be configured to tag the data received from the user equipment (108) and identify the location of the user equipment (108) effectively. In an aspect, the present disclosure may be installed within the user equipment (108) as the data tagging mobile application such that the user equipment (108) may be configured to detect its location accurately and may change the network settings accordingly. During step 502, the user equipment (108) may be requested by the system (102) (or by the data tagging mobile application) to share data. After the initialization of the data tagging mobile application, the data tagging mobile application may be configured to determine the number of conditions associated with the user equipment (108) (step 504). For example, the data tagging mobile application may be configured to determine whether the user equipment (108) is in the coverage area or not. The data tagging mobile application may be configured to determine whether a mobile device activates a mobile application event (configured to share the details of the user equipment with the system (102)). The data tagging mobile application may be further configured to determine whether the screen of the user equipment (108) is ON or OFF. In an aspect, the data tagging mobile application may be configured to determine RSRP threshold associated with the various parameters. At step 506, the data sources (for example, Android API) may be activated to capture a number of measurements. For example, the Android API may be configured to receive data from the plurality of sensors.

[0137] During step 508, the data tagging mobile application may be configured to activate the one or more processors (202) (or a backend API) to assign a final score (aggregated score value or cumulative score) based on the value generated by various sensors.

[0138] During step 510, the data tagging mobile application may be configured to determine the tagging of data (indoor or outdoor) based on the final score.

[0139] During step 512, the data tagging mobile application may be configured to tag data (indoor or outdoor) and store the data in a memory of the user equipment (108). In an aspect, the data tagging mobile application may be configured to sync the stored tagged data with the server and may be configured to store the data in the database (210).

[0140] In an overall aspect, on the basis of any events (on detection of any activity associated with the user equipment (108)), the data tagging mobile application may be configured to capture various values associated with various parameters (key performance indicator) (also known as KPI's values) (such as light meter reading, number of satellites etc.). Once the various KPI's values are captured, the data tagging mobile application is configured to assign the score to each of such value. In an aspect, the KPI's values may have configurable scores which can be handled by the admin using a web application. In an aspect, the mobile SDK (data tagging mobile application) calls the one or more processors (202) to get the final score for the particular parameter, and on the basis of final score, an independent tagging of the data may be defined.

[0141] FIG. 6 illustrates another exemplary flow chart (600) illustrating various steps performed by the system (102) during capturing the data from the plurality of sensors, in accordance with an embodiment of the present disclosure.

[0142] During step 602, the system (102) (or the data tagging mobile application installed within the user equipment) may be configured to determine whether the screen of the user equipment (108) is ON or OFF. If the screen of the user equipment (108) is ON, the system (102) may be configured to consider it as an activity and capture the data (may be using the ambient light sensor). During step 604, the system (102) may be configured to determine whether the user initiated the data tagging mobile application installed in the user equipment (108).If the mobile application has been initiated, the system (102) and the data tagging mobile application may be configured to capture the data.

[0143] During step 606, the system (102) may be configured to determine whether the GPS of the user equipment (108) is ON or OFF. If the GPS is ON, the system (102) may be configured to capture the data.

[0144] During step 608, the system (102) may be configured to determine whether an airplane mode of the user equipment (108) is activated. If the airplane mode is activated, the system (102) may be configured to capture the data.

[0145] During step 610, the system (102) may be configured to determine the RSRP associated with the user equipment (108) and further configured to determine whether the user equipment (108) is receiving a threshold RSRP or not. If the user equipment (108) receives the threshold RSRP, the system (102) may be configured to capture the data.

[0146] During step 612, the system (102) may be configured to determine whether the user equipment (108) is in the coverage area or not. If the user equipment (108) has no coverage area, then the system (102) may be configured to capture the data.

[0147] During step 614, the system (102) may be configured to determine whether the user equipment (108) has been switched from LTE (Long-Term Evolution) / NR (New Radio) to Wi-Fi (wireless fidelity). If any switch from LTE / NR to WiFi occurs, then the system (102) may be configured to capture the data.

[0148] During step 616, the system (102) may be configured to determine whether the user equipment (108) has been switched from Wi-Fi (wireless fidelity) to LTE (Long-Term Evolution) / NR (New Radio). If any switch from WiFi to LTE / NR occurs, then the system (102) may be configured to capture the data.

[0149] In an operational aspect, the data is collected as passive data from the data source on the basis of the events (referring to a change in the activity of the user or location of the user). Along with the events, a number of required KPIs may be captured in a column, and a value may be assigned to each parameter in the column based on the score matrix. This approach allows for a comprehensive and dynamic data collection process, ensuring the system captures relevant information under various conditions and user activities.

[0150] Conventional systems cannot properly classify the data collected from the one or more data sources into distinct categories of 'indoor' or 'outdoor' data. This lack of classification led to challenges in making accurate decisions for tasks such as planning and optimization, as the system could not differentiate between user behavior in different environments. To resolve the problems associated with the conventional systems and make user data more actionable, the present system is configured to classify user activity as 'indoor' or 'outdoor' based on a combination of behavioral patterns and collected data.

[0151] The present system employs an indoor-outdoor classification algorithm to accurately tag the received data (user data or passive data) by determining whether the user is inside or outside a building. For example, the classification may be primarily based on two key types of sensor data: accelerometer and gyroscope readings. These sensors, typically embedded in the UE, provide real-time measurements of movement and orientation. By analyzing the data from these sensors, the system may infer several aspects of user behavior, including whether the user is stationary or moving and what kind of motion they are engaging in. The system is specifically configured to automatically tag user data as either 'indoor' or 'outdoor'. Data tagging helps enhance decision-making accuracy for applications that require understanding the user's environment.

[0152] In another exemplary embodiment, the present disclosure may relate to a non-transitory computer-readable medium storing instructions for identifying the location of a user. When executed by one or more processors (202) of the system(102), the instructions may cause the processors to perform one or more operations. The one or more operations include receiving, by the input unit (212), passive data from one or more data sources comprising user equipment sensors capturing passive data without active user interaction. The one or more operations include processing the received data and assigning score values based on predefined conditions. The one or more operations include aggregating the assigned score values to calculate a cumulative score. The one or more operations include identifying, by an identification unit (214), the user's location as indoor or outdoor by mapping the cumulative score with predefined values. This approach may enable efficient and accurate location identification using passive data collection across multiple sensor types, potentially improving various location-based services and applications.

[0153] The present disclosure provides technical advancement related to indoor / outdoor location detection for mobile devices. This advancement addresses the limitations of existing solutions by implementing a multi-sensor, passive data collection approach combined with a sophisticated scoring system. The disclosure involves a novel method of aggregating and analyzing data from various sensors without active user interaction, which offers significant improvements in accuracy and energy efficiency. By implementing a dynamic scoring matrix and real-time data processing, the disclosed invention enhances location-based services and network optimization, resulting in improved user experience and more efficient resource allocation for network operators.

[0154] FIG. 7 illustrates an example computer system (700) in which or with which the embodiments of the present disclosure may be implemented.

[0155] As shown in FIG. 7, the computer system (700) may include an external storage device (710), a bus (720), a main memory (730), a read-only memory (740), a mass storage device (750), a communication port(s) (760), and a processor (770). A person skilled in the art will appreciate that the computer system (700) may include more than one processor and communication ports. The processor (770) may include various modules associated with embodiments of thepresent disclosure. The communication port(s) (760) may be any of an RS-232 port for use with a modem-based dialup connection, a 10 / 100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fibre, a serial port, a parallel port, or other existing or future ports. The communication ports(s) (760) may be chosen depending on a network, such as a Local Area Network (LAN), Wide Area Network (WAN), or any network to which the computer system (700) connects.

[0156] In an embodiment, the main memory (730) may be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. The read-only memory (740) may be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chip for storing static information e.g., start-up or basic input / output system (BIOS) instructions for the processor (770). The mass storage device (750) may be any current or future mass storage solution, which can be used to store information and / or instructions. Exemplary mass storage solutions include, but are not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and / or Firewire interfaces).

[0157] In an embodiment, the bus (720) may communicatively couple the processor(s) (770) with the other memory, storage, and communication blocks. The bus (720) may be, e.g. a Peripheral Component Interconnect PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), Universal Serial Bus (USB), or the like, for connecting expansion cards, drives, and other subsystems as well as other buses, such a front side bus (FSB), which connects the processor (770) to the computer system (700).

[0158] In another embodiment, operator and administrative interfaces, e.g., a display, keyboard, and cursor control device may also be coupled to the bus (720) to support direct operator interaction with the computer system (700). Other operator and administrative interfaces can be provided through network connections connected through the communication port(s) (760). Componentsdescribed above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system (700) limit the scope of the present disclosure.

[0159] The method and system of the present disclosure may be implemented in a number of ways. For example, the methods and systems of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above unless specifically stated otherwise. Further, in some embodiments, the present disclosure may also be embodied as programs recorded in a recording medium, the programs including machine -readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.

[0160] While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the disclosure and not as limitation.ADVANTAGES OF THE PRESENT DISCLOSURE

[0161] The present disclosure identifies a location of a user with improved accuracy by utilizing a multi-sensor approach and passive data collection, thereby enhancing the reliability of location-based services.

[0162] The present disclosure identifies user data samples, which in turn enhances all planning and optimization projects for network operators, leading to more efficient resource allocation and improved network performance.

[0163] The present disclosure provides efficient classification of data from various data sources to create comprehensive user profiles, enabling more personalized services and targeted marketing opportunities.

[0164] The present disclosure tags user data as indoor or outdoor based on user behaviour measurements and location details, offering valuable insights for applications in fields such as smart home automation, retail analytics, and urban planning.

[0165] The present disclosure is applicable to 2G, 3G, 4G, 5G, 6G and beyond all generations of mobile technology with multiple bands and carriers of telecom operators, ensuring its relevance and adaptability in the evolving telecommunications landscape.

[0166] The present disclosure offers a battery-efficient solution for continuous location monitoring by utilizing passive data collection methods, thus extending device usage time without compromising on location accuracy.

[0167] The present disclosure employs a sophisticated scoring system that can adapt to various environmental conditions and user behaviors, resulting in more robust and reliable indoor / outdoor classification.

[0168] The present disclosure enables real-time updates to location classification upon detecting changes in user activity, providing timely and relevant information for location-based applications and services.

Claims

CLAIMS1. A system (102) for identifying a location of a user in a network, comprising: a memory (204); one or more processors (202) configured to execute a set of instructions stored in the memory (204) to: receive, by an input unit (212), passive data from one or more data sources (302a, 302b, 302c); process the received passive data and assign a score value to each record of the received passive data corresponding to each data source based on one or more predefined conditions according to a score matrix; aggregate the assigned score values to each record of the received passive data corresponding to each data source to calculate a cumulative score; and identify, by an identification unit (214), the location of the user as an indoor location or an outdoor location by mapping the cumulative score with a set of predefined values corresponding to one or more locations.

2. The system (102) as claimed in claim 1, wherein the one or more data sources comprise a plurality of user equipment sensors capturing the passive data.

3. The system (102) as claimed in claim 1, wherein the one or more data sources (302a, 302b, 302c) comprise at least one of a Global Positioning System (GPS) sensor, a light sensor, an accelerometer, a gyroscope, a magnetometer, and a proximity sensor.

4. The system (102) as claimed in claim 1, wherein the passive data comprises information related to at least one of user activity, user location, number ofsatellites, light intensity, altitude, coverage area, density of Wi-Fi access points, type of charging source, and type of cell connection.

5. The system (102) as claimed in claim 1, wherein the one or more predefined conditions for assigning the score value comprise at least one of a number of satellites with signal-to-noise ratio (SNR) above a first threshold, an average light intensity value, a user activity, an altitude of the user, a specific frequency band and the reference signal received power (RSRP) is below a second threshold, a number of Wi-Fi access points discovered with received signal strength indicator (RSSI) above a third threshold, and a type of the charging source of the user equipment.

6. The system (102) as claimed in claim 1, wherein the score value is assigned within a range of -30 to +30.

7. The system (102) as claimed in claim 1, wherein the identification unit (214) is configured to classify the received passive data as an indoor data or an outdoor data based on the cumulative score.

8. The system (102) as claimed in claim 1, wherein the one or more processors (202) are further configured to store the identified location data in the database (210) and synchronize the stored data with a remote server.

9. A method (400) for identifying a location of a user in a network, the method comprising: receiving (402), by an input unit (212), passive data from one or more data sources (302a, 302b, 302c); processing (404), by one or more processors (202), the received passive data and assigning a score value to each record of the received passive data corresponding to each data source based on one or more predefined conditions according to a score matrix;aggregating (406), by the one or more processors (202), the assigned score values to each record corresponding to each data source to calculate a cumulative score; and identifying (408), by an identification unit (214), the location of the user as an indoor location or an outdoor location by mapping the cumulative score with a set of predefined values corresponding to one or more locations.

10. The method (400) as claimed in claim 9, wherein the one or more data sources comprise a plurality of user equipment sensors capturing the passive data.

11. The method (400) as claimed in claim 9, wherein the one or more data sources (302a, 302b, 302c) comprise at least one of a Global Positioning System (GPS) sensor, a light sensor, an accelerometer, a gyroscope, a magnetometer, and a proximity sensor.

12. The method (400) as claimed in claim 9, wherein the passive data comprises information related to at least one of user activity, user location, number of satellites, light intensity, altitude, coverage area, density of Wi-Fi access points, type of charging source, and type of cell connection.

13. The method (400) as claimed in claim 9, wherein the one or more predefined conditions for assigning the score value comprise at least one of a number of satellites with signal-to-noise ratio (SNR) above a first threshold, an average light intensity value, a user activity, an altitude of the user, a specific frequency band and the reference signal received power (RSRP) is below a second threshold, a number of Wi-Fi access points discovered with received signal strength indicator (RSSI) above a third threshold, and a type of the charging source of the user equipment.

14. The method (400) as claimed in claim 9, wherein the score value is assigned within a range of -30 to +30.

15. The method (400) as claimed in claim 9, further comprising classifying the received passive data as an indoor data or an outdoor data based on the cumulative score.

16. A user equipment (108) communicatively coupled to a system (102) for identifying a location of a user in a network, wherein the system (102) comprises: a memory (204); and one or more processors (202) configured to execute a set of instructions stored in the memory (204) to perform the method (400) as claimed in claim 9.

17. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors (202) of a system (102) for identifying a location of a user in a network (104), cause the one or more processors (202) to perform one or more operations comprising: receiving (402), by an input unit (212), passive data from one or more data sources (302a, 302b, 302c); processing (404), by one or more processors (202), the received passive data and assigning a score value to each record of the received passive data corresponding to each data source based on one or more predefined conditions according to a score matrix; aggregating (406), by the one or more processors (202), the assigned score values to each record corresponding to each data source to calculate a cumulative score; and identifying (408), by an identification unit (214), the location of the user as an indoor location or an outdoor location by mapping the cumulative score with a set of predefined values corresponding to one or more locations.

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