FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
THE PATENTS RULES, 2003
COMPLETE
SPECIFICATION
(See section 10; rule 13)
TITLE OF THE INVENTION
SYSTEM FOR IDENTIFYING A LOCATION OF A USER AND A METHOD THEREOF
APPLICANT
JIO PLATFORMS LIMITED
of Office-101, Saffron, Nr. Centre Point, Panchwati 5 Rasta, Ambawadi, Ahmedabad -
380006, Gujarat, India; Nationality : India
The following specification particularly describes
the invention and the manner in which
it is to be performed
2
RESERVATION 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 5 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
10 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.
15 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
20 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
25 wirelessly within a particular area.
3
[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 syst 5 ems 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
10 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
15 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
20 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
25 development tools in one installable package, often used for developing
applications for a specific platform.
4
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 5 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
10 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
15 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
20 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
25 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
5
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 5 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
10 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
15 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
20 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.
25 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
6
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 5 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.
10 [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
15 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 signalto-
noise ratio (SNR) above a first threshold, an average light intensity value, a user
20 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
25 -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.
7
[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 5 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
10 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
15 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
20 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,
25 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
8
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 signalto-
noise ratio (SNR) above a first threshold, an average 5 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.
10 [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
15 identified location data in a database and synchronizing the stored data with a
remote server.
[0037] 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
20 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
25 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
9
outdoor by mapping the cumulative score with a set of predefined values
corresponding to one or more locations.
[0038] The foregoing general description of the illustrative embodiments
and the following detailed description thereof are merely exemplary aspects of the
5 teachings of this disclosure and are not restrictive.
OBJECTIVES OF THE DISCLOSURE
[0039] Some of the objectives of the present disclosure, which at least one
embodiment herein satisfies are as listed herein below.
[0040] An objective of the present disclosure is to provide a system and a
10 method that identifies a location of a user.
[0041] An objective of the present disclosure is to identify user data
samples, which enhances all planning and optimization projects.
[0042] An objective of the present disclosure is to provide an efficient
classification of user data from various data sources to create user profiles.
15 [0043] An objective of the present disclosure is to tag user data as indoor or
outdoor based on user behaviour measurements and location details.
[0044] 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.
20 [0045] 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.
[0046] An objective of the present disclosure is to improve the accuracy and
usefulness of user data by providing more detailed and accurate insights.
10
[0047] An objective of the present disclosure is to utilize passive data
collection methods to minimize battery consumption and user interaction.
[0048] An objective of the present disclosure is to implement a scoring
system that aggregates data from multiple sensors to enhance the accuracy of
5 indoor-outdoor detection.
BRIEF DESCRIPTION OF DRAWINGS
[0049] The accompanying drawings, which are incorporated herein, and
constitute a part of this disclosure, illustrate exemplary embodiments of the
disclosed methods and systems in which like reference numerals refer to the same
10 parts throughout the different drawings. Components in the drawings are not
necessarily to scale, emphasis instead being placed upon clearly illustrating the
principles of the present disclosure. Some drawings may indicate the components
using block diagrams and may not represent the internal circuitry of each
component. It will be appreciated by those skilled in the art that disclosure of such
15 drawings includes the disclosure of electrical components, electronic components
or circuitry commonly used to implement such components.
[0050] 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.
20 [0051] FIG. 2 illustrates an exemplary micro service-based architecture of
the system, in accordance with embodiments of the present disclosure.
[0052] 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.
25 [0053] FIG. 4 illustrates an exemplary flowchart of a method for identifying
the location of the user, in accordance with embodiments of the present disclosure.
11
[0054] 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.
[0055] FIG. 6 illustrates another exemplary flow chart illustrating various
steps performed by the system 5 during capturing the data from a plurality of sensors,
in accordance with embodiments of the present disclosure.
[0056] FIG. 7 illustrates an exemplary computer system in which or with
which embodiments of the present disclosure may be implemented.
[0057] The foregoing shall be more apparent from the following more
10 detailed description of the disclosure.
LIST OF REFERENCE NUMERALS
100 – Network architecture
102 – System
104 – Network
15 106 – Centralized server
108-1, 108-2…108-N – User equipment(s)
110-1, 110-2…110-N – Users
202 – One or more processor(s)
204 – Memory
20 206 – I/O interface(s)
208 – Processing unit(s)
210 – Database
212 – Input unit
214 – Identification unit
25 216 – Other unit (s)
302a, 302b, 302c – One Or More Data Sources
710 – External Storage Device
720 – Bus
12
730 – Main Memory
740 – Read Only Memory
750 – Mass Storage Device
760 – Communication Port
5 770– Processor
DETAILED DESCRIPTION OF THE DISCLOSURE
[0058] In the following description, for the purposes of explanation, various
specific details are set forth in order to provide a thorough understanding of
embodiments of the present disclosure. It will be apparent, however, that
10 embodiments of the present disclosure may be practiced without these specific
details. Several features described hereafter can each be used independently of one
another or with any combination of other features. 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
15 fully addressed by any of the features described herein.
[0059] 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
20 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.
[0060] Specific details are given in the following description to provide a
thorough understanding of the embodiments. However, it will be understood by one
25 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
13
circuits, processes, algorithms, structures, and techniques may be shown without
unnecessary detail in order to avoid obscuring the embodiments.
[0061] 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 5 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
10 procedure, a subroutine, a subprogram, etc. When a process corresponds to a
function, its termination can correspond to a return of the function to the calling
function or the main function.
[0062] The word “exemplary” and/or “demonstrative” is used herein to
mean serving as an example, instance, or illustration. For the avoidance of doubt,
15 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
20 “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.
[0063] Reference throughout this specification to “one embodiment” or “an
25 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.
14
Furthermore, the particular features, structures, or characteristics may be combined
in any suitable manner in one or more embodiments.
[0064] 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 5 “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
10 or more other features, integers, steps, operations, elements, components, and/or
groups 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.
[0065] The widespread adoption of smart mobile devices and ubiquitous
internet access has become a driving force behind the development of mobile
15 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
20 location.
[0066] Indoor-outdoor (IO) sensing is broadly categorized into two main
approaches: GPS-based techniques and smartphone sensor-based methods. GPSbased
techniques typically rely on degrading GPS signals as users transition from
outdoor to indoor environments. However, this approach faces limitations, as GPS
25 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
15
of a smartphone, consuming approximately seven times more energy than
accelerometer and gyroscope sensors.
[0067] 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 5 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.
10 [0068] Cellular signals emanating from cell towers represent another
potential data source for IO detection. The principle behind this approach is the
significant 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
15 battery consumption.
[0069] 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
20 data. Addressing these issues, the present system can accurately tag data collected
from various sources, thereby effectively indicating the user's location.
[0070] 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
25 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,
16
optimization, and location-based services across multiple generations of mobile
technology.
[0071] The various embodiments throughout the disclosure will be
explained in more detail with reference to FIGS. 1-7.
[0072] FIG. 1 illustrates an exemplary 5 network architecture (100) of a
system (102) for identifying a location of a user, in accordance with embodiments
of the present disclosure.
[0073] 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
10 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).
[0074] In an embodiment, the UE (108) may include, but not be limited to,
15 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 global positioning
system (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
20 general-purpose computer, a desktop, a personal digital assistant, a tablet computer,
and a mainframe computer.
[0075] 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,
25 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
17
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.
[0076] In an embodiment, the system (102) may continuously collect
passive data for a selected UE (108) 5 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
10 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.
[0077] Although FIG. 1 shows exemplary components of the network
architecture (100), in other embodiments, the network architecture (100) may
15 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).
20 [0078] FIG. 2 illustrates an exemplary micro service-based architecture
(200) of the system (102), in accordance with an embodiment of the present
disclosure.
[0079] 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
25 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.
18
[0080] 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 5 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,
10 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 machinereadable
storage medium and the hardware for the one or more processor(s) (202)
may comprise a processing resource (for example, one or more processors), to
15 execute 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
20 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.
[0081] The memory (204) may be configured to store one or more
computer-readable instructions or routines in a non-transitory computer readable
25 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 nontransitory
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.
19
[0082] 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 (I/O), storage devices, and the like. The
interface(s) (206) may facilitate communication through the system (102). The
interface(s) (206) may also provide a 5 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).
10 [0083] 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. The
15 UE 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
20 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
25 monitoring of the user's environment without draining battery life or interrupting
the user's activities.
[0084] 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,
30 or stationary. It may also include the user's location coordinates as determined by
20
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.
[0085] Additional data points may be collected, including the device's
altitude, which could help 5 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,
10 3G, 4G, 5G, 6G) may provide additional context about the user's environment.
[0086] 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. This
scoring may be based on one or more predefined conditions that have been
15 determined to be indicative of indoor or outdoor environments.
[0087] 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
20 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.
25 [0088] 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.
21
[0089] 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 loc 5 ation. Conversely, rapid movement along roads or paths may indicate an
outdoor setting.
[0090] 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
10 (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 to
walls 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.
15 [0091] 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
20 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.
[0092] To assign score values based on these conditions, the one or more
25 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
22
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
5 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.
10 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
15 weights (e.g., 0.05 each) as they provide supplementary information.
[0093] The cumulative score may then be calculated as a weighted sum of
these individual scores: Cumulative Score = Σ (Factor Score * Factor Weight).
[0094] This weighted approach allows the system to adapt to various
scenarios and prioritize the most reliable indoor/outdoor status indicators. The
20 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.
[0095] 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
25 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
23
location, while a score of +5 might suggest a likely but less certain outdoor
classification.
[0096] 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 5 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.
10 [0097] 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
15 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
20 locations. In an example, the one or more locations include an indoor location and
an outdoor location.
[0098] 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
25 location-based services, or enhancing network planning and optimization.
[0099] 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.
24
[00100] 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 5 and analysis. This adaptive approach may
allow real-time indoor/outdoor classification updates as the user moves between
different environments.
[00101] 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
10 allow for long-term analysis of user behavior patterns, improve the accuracy of the
classification algorithm over time, and enable the sharing of anonymized data for
broader research or commercial purposes.
[00102] The system (102) for identifying a user's location as indoor or
outdoor may represent a significant advancement in location-based services and
15 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
20 services across various industries.
[00103] 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.
[00104] Although FIG. 2 shows exemplary components of the system (102),
25 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).
25
[00105] 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.
5 [00106] 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.
10 [00107] 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
15 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
20 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.
25 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
30 detector sensor, and a proximity sensor. For example, the plurality of sensors may
26
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 5 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
10 with the data tagging mobile application. In another aspect, the data tagging mobile
application may be configured to process and upload the data collected from the
plurality of mobile applications on a remotely placed server.
[00108] 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).
15 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
20 tagging mobile application may be further configured to trigger the plurality of
mobile applications for capturing the data.
[00109] 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
25 (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).
[00110] 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
27
detect the lighting conditions around the user and adjust the screen brightness
accordingly (known as auto brightness).
[00111] The system (102) may be configured to consider a number of GPS
satellites and their corresponding SNR (Signal-to-Noise Ratio) values in dBm.
int IN_VEHICLE The user equipment is in a vehicle, such as a car.
int ON_BICYCLE The user equipment is on a bicycle.
int ON_FOOT The user equipment is on a user who is walking or
running.
int RUNNING The user equipment is on a user who is running.
int STILL The user equipment is still (not moving).
int TILTING The user equipment’s angle relative to gravity
changed significantly.
int UNKNOWN Unable to detect the current activity.
int WALKING The user equipment is on a user who is walking.
5 Table 1: various activities of the user equipment
[00112] 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
10 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
15 interaction. The passive data generated by these sensors is transmitted to the input
28
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 5 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 cellular
networks or other RF-based systems, facilitating communication over broader
10 distances.
[00113] 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
15 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
20 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
25 processors (202) may be configured to extract a set of features and save them in the
database (210).
[00114] 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,
30 the one or more predefined conditions may include:
29
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
5 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
10 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,
15 Z is a reference signal received power (RSRP) threshold set for band 850,
for the classification of indoor-outdoor samples
N 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
20 AP to be considered.
[00115] 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
25 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
30 assigned to each data record. This score reflects how well the data aligns with the
30
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 5 helps combine information from various sensors, such as
location, activity, and network strength, to comprehensively evaluate. In an aspect,
the score matrix may be given as:
Location and Activity
Status Still and user
lies inside
building
polygon
Walking or
on foot and
user lies
inside
building
polygon
Walking or
on foot and
user lies
outside
building
polygon
User lies
outside
building
polygon
On bicycle
or in
vehicle and
user lies
outside
building
polygon
moving
Score 13 6 -2 -8 -15
Number of Satellites
Status
Number of
Satellites with
(SNR > 20 dB)
dB=0
Number of
Satellites
with (SNR
> 20 dB)
=1 or 2
Number of Satellites
with (SNR > 20 dB)
=3
Number of
Satellites
with (SNR
> 20 dB) >
3
Score 18 3 -6 -15
Light Sensor
Status
Light Intensity
> 10000
Light
Intensity <
10000
Light Intensity < 5000
lumens and > 1000
lumens (Daytime)
Light
Intensity <
1000
31
lumens
(Daytime)
lumens and
> 5000
lumens
(Daytime)
lumens
(Daytime)
Score -22 -4 -1 1
Altitude Coverage
on 850
Density of Wi-Fi Aps AC
charging
source
Status Altitude > 10 m
from the
ground
RSRP <-
100 on
band 5
3 or more Wi-Fi APs
discovered with RSSI > -
70 dBm
Charging
Source is
AC
Score 15 3 11 10
Indoor Small Cell
Status Device Connected to IDSC
Score 19
Table 2: Score matrix
[00116] 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 5 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)
10 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. L1
32
ii. L2
iii. L3
Still and User
lies inside
building
polygon
Walking or
On Foot and
the user lies
inside the
building
polygon
Walking or
On Foot and
user lies
outside
building
polygon
Running and
user lies
outside
building
polygon
On Bicycle or In
a Vehicle and
the user lies
outside the
building
polygon
Moving
Number of
Satellites
with SNR > S
dB = 0
Number of
Satellites
with SNR > S
dB = 1 or 2
Number of Satellites with
SNR > S dB = 3
Number of
Satellites with
SNR > S dB > 3
Light
Intensity >
L1 lumens
(Daytime)
Light
Intensity <
L1 lumens
and > L2
lumens
(Daytime)
Light Intensity < L2 lumens
and >L3 lumens (Daytime)
Light Intensity
< L3 lumens
(Daytime)
Altitude > H
m from
Ground
RSRP < Z on
Band 5
N or more Wi-Fi APs
discovered with RSSI > R
dBm
Charging
Source is AC
Device Connected to IDSC
Table 3: Threshold matrix: threshold values for various parameters
[00117] In an aspect, the one or more processors (202) may be configured to
aggregate the assigned score value to each 5 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
33
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.
[00118] In an operative aspect, 5 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
10 values to get the cumulative score.
[00119] 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
15 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
20 walking, and the Wi-Fi data assigns a score value of 5 based on the signal strength
being above a threshold of 80 dBm.
[00120] 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
25 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
30 system has set a threshold (such as a score of 20), this score could indicate that the
34
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 multiple
sources of data, assign significance to 5 each, and aggregate those evaluations into a
single actionable score that informs further decisions or actions.
[00121] By employing the cumulative score, the system may offer several
technical advantages listed below:
• data aggregation and simplification: The cumulative score consolidates data
10 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
15 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.
20 • 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
25 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
35
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.
[00122] In an example, 5 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
10 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
15 where timely responses are critical. The cumulative score facilitates easier
integration and interoperability with other systems. For example, in IoT 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
20 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.
25 [00123] 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
30 samples. The identification unit (214) may be configured to classify the data
36
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 of
the user’s environment, 5 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,
10 low).
Cumulative Score Category
Greater than 15 Indoor - High
10 to 15 Indoor - Medium
5 to 10 Indoor - Low
-5 to 5 Unknown
-10 to -5 Outdoor - Low
-15 to -10 Outdoor - Medium
Less than -15 Outdoor - High
Table 4: Cumulative score for classifying the data samples (received data)
as indoor data or outdoor data
[00124] For example, the cumulative score of 16 falls into the "Indoor -
High" category according to the predefined mapping. This would indicate that the
15 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
37
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 a
location with a strong signal presence. If the 5 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
10 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,
15 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.
[00125] 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
20 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
25 an example, the database (210) may include oracle database, DB2 database, Postgre
SQL database, Microsoft SQL Server database, Microsoft Access database or
MySQL database.
[00126] 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
30 present disclosure.
38
[00127] 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 based
corresponding to a number of parameters associated with the user. In an example,
the number of parameters may include an activity 5 (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
10 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
15 location, number of satellites, light intensity, altitude, coverage area, density of Wi-
Fi access points, type of charging source, and type of cell connection.
[00128] 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
20 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
25 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.
39
[00129] 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 and
relevance in different contexts. 5 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.
[00130] At step (408), the method (400) includes identifying, by an
identification unit (214), the location of the user as indoor or outdoor by mapping
10 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
15 (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.
[00131] In some embodiments, the method (400) further includes activating
the one or more data sources (302a, 302b, 302c) upon detection of a change in
20 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.
[00132] The method (400) may also include storing the identified location
data in a database (210) and synchronizing the stored data with a remote server.
25 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.
[00133] In another exemplary embodiment, a user equipment (108) is
described that is configured to perform the method (400) for identifying the location
40
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.
[00134] FIG. 5 illustrates an exemplary flow chart (500) illustrating various
steps performed by the system 5 (102) during tagging of the data representing the
location of the user, in accordance with an embodiment of the present disclosure.
[00135] 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
10 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)
15 (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
20 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
25 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.
41
[00136] 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.
[00137] During 5 step 510, the data tagging mobile application may be
configured to determine the tagging of data (indoor or outdoor) based on the final
score.
[00138] 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
10 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).
[00139] 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
15 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
20 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.
[00140] FIG. 6 illustrates another exemplary flow chart (600) illustrating
25 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.
[00141] During step 602, the system (102) (or the data tagging mobile
application installed within the user equipment) may be configured to determine
42
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 5 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.
[00142] 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
10 system (102) may be configured to capture the data.
[00143] 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.
[00144] During step 610, the system (102) may be configured to determine
15 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.
[00145] During step 612, the system (102) may be configured to determine
20 whether the user equipment (108) is in coverage area or not. If the user equipment
(108) has no coverage area, then the system (102) may be configured to capture the
data.
[00146] During step 614, the system (102) may be configured to determine
whether the user equipment (108) has been switched from LTE (Long-Term
25 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.
43
[00147] 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.
[00148] In an operational aspect, the data is 5 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
10 dynamic data collection process, ensuring the system captures relevant information
under various conditions and user activities.
[00149] 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
15 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.
20 [00150] 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
25 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
44
as either 'indoor' or 'outdoor'. Data tagging helps enhance decision-making accuracy
for applications that require understanding the user's environment.
[00151] The present disclosure provides technical advancement related to
indoor/outdoor location detection for mobile devices. This advancement addresses
the limitations of existing solutions 5 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
10 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.
[00152] FIG. 7 illustrates an example computer system (700) in which or
with which the embodiments of the present disclosure may be implemented.
15 [00153] 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
20 processor (770) may include various modules associated with embodiments of the
present 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
25 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.
[00154] 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
45
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 5 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).
[00155] In an embodiment, the bus (720) may communicatively couple the
10 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)
15 to the computer system (700).
[00156] 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
20 connections connected through the communication port(s) (760). Components
described 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.
[00157] The method and system of the present disclosure may be
25 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
46
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 5 the method according to the present
disclosure.
[00158] 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
10 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.
15 ADVANTAGES OF THE PRESENT DISCLOSURE
[00159] 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.
[00160] The present disclosure identifies user data samples, which in turn
20 enhances all planning and optimization projects for network operators, leading to
more efficient resource allocation and improved network performance.
[00161] 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.
25 [00162] The present disclosure tags user data as indoor or outdoor based on
user behaviour measurements and location details, offering valuable insights for
47
applications in fields such as smart home automation, retail analytics, and urban
planning.
[00163] 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 5 its relevance and adaptability in the evolving
telecommunications landscape.
[00164] 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.
10 [00165] 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.
[00166] The present disclosure enables real-time updates to location
classification upon detecting changes in user activity, providing timely and relevant
15 information for location-based applications and services.
48
We claim:
1. A system (102) for identifying a location of a user in a network, comprising:
a memory (204);
one or more processors 5 (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
10 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
15 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.
20 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
25 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 of
49
satellites, 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 5 (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
10 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
15 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
20 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
25 passive data corresponding to each data source based on one or more
predefined conditions according to a score matrix;
50
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 5 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.
10 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
15 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
20 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
25 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.
51
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 5 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
10 claimed in claim 9.