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 AND METHOD FOR ESTIMATING CALL SETUP SUCCESS RATE IN A NETWORK
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

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
5 dress protection, belonging to Jio Platforms Limited (JPL) 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 INVENTION
[0002] The embodiments of the present disclosure generally relate to the
network performance in telecommunication networks. More particularly, the
15 present disclosure relates to a system and a method for estimating the call setup
success rate in a future time instance.
BACKGROUND OF THE INVENTION
[0003] The following description of related art is intended to provide
20 background information pertaining to the field of the disclosure. This section may
include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.
25 [0004] In the field of telecommunications, the call setup success rate
(CSSR) is an essential metric to indicate the health of the network. The call setup success rate typically includes a rate to assess the successful call establishment and to determine influence of customers for the telecom service. The call setup success rate acts as a network key performance indicator (KPI) used to evaluate the

performance of network.
[0005] The network with low call setup success rate suffers with poor
network quality and congestion in the network. In addition, the lower call setup
success rate also affects the performance of the hardware in the network, resulting
5 in bad user experience.
[0006] There is, therefore, a need in the art to provide a system and a method
that can mitigate the problems associated with the prior arts.
OBJECTS OF THE INVENTION
10 [0007] Some of the objects of the present disclosure, which at least one
embodiment herein satisfies are as listed herein below.
[0008] An object of the present disclosure is to provide a system and a
method for estimating the call setup success rate (CSSR) in future time instances.
[0009] Another object of the present disclosure is to improve the user
15 experience in the network.
[0010] Another object of the present disclosure is to improve the CSSR in
the network.
[0011] Another object of the present disclosure is to provide a system and a
method that are economical and easy to implement. 20
SUMMARY OF THE PRESENT DISCLOSURE
[0012] The present disclosure discloses a system for estimating the call
setup success rate (CSSR) in a future time instance. The system comprises a
processor configured to receive a request from a user for predicting the CSSR. The
25 request includes information indicating time period for which the CSSR needs to
be predicted. The time period may also include future time instances for which the CSSR needs to be predicted. The system further includes a forecasting engine

coupled to the processor. The forecasting engine is configured to in response to
request, predict the CSSR for future time instances based on historic CSSR data. In
addition, the forecasting engine is configured to determine if the predicted CSSR is
less than a pre-defined threshold. The forecasting engine is further configured to,
5 based on the determination, generate a notification indicating corrective action.
[0013] In an embodiment, the forecasting engine is further configured to
generate an analysis report indicating a probable cause of falling CSSR.
[0014] In an embodiment, the forecasting engine is configured to employ a
machine learning (ML) model to analyze the historic CSSR data for predicting the
10 CSSR for future time instances.
[0015] In an embodiment, the ML model is trained using historic CSSR data
and a plurality of hyper parameters.
[0016] In an embodiment, the system comprises a load balancer to send the
request to the forecasting engine.
15 [0017] The present disclosure discloses a method for estimating the call
setup success rate (CSSR) in a future time instance. The method comprises receiving a request from a user for predicting the CSSR. The request includes information indicating time period for which the CSSR needs to be predicted. The time period may include future time instance for which the CSSR needs to be
20 predicted. The method further comprises, in response to the request, predicting the
CSSR for future time instances based on historic CSSR data. The method also includes determining if the predicted CSSR is less than a pre-defined threshold. The method also includes based on the determination, generating a notification indicating corrective action.
25 [0018] In an embodiment, the method further comprises generating an
analysis report indicating a probable cause of falling CSSR.
[0019] In an embodiment, to predict the CSSR for future time instances, the

method comprises employing a machine learning (ML) model to analyze the historic CSSR data.
[0020] In an embodiment, the method further comprises training the ML
model using the historic CSSR data and a plurality of hyper parameters.
5 [0021] The present disclosure discloses a computer program product
comprising a non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to receive a request from a user for predicting the CSSR. The request includes information indicating future time instances for which the CSSR needs to be
10 predicted. In addition, the instructions cause the one or more processors to, in
response to request, predict the CSSR for future time instances based on historic CSSR data. Further, the instructions cause the one or more processors to determine if the predicted CSSR is less than a pre-defined threshold. The instructions cause the one or more processors to, based on the determination, generate a notification
15 indicating corrective action.
BRIEF DESCRIPTION OF DRAWINGS
[0022] The accompanying drawings, which are incorporated herein, and
constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems which like reference numerals refer to the same
20 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
25 drawings includes the disclosure of electrical components, electronic components,
or circuitry commonly used to implement such components.
[0023] FIG. 1 illustrates an example network architecture for implementing
a system, in accordance with an embodiment of the present disclosure.

[0024] FIG. 2 illustrates an example block diagram of a system, in
accordance with an embodiment of the present disclosure.
[0025] FIG. 3 illustrates a flow diagram representing an architecture
depicting the operations of the proposed system, in accordance with some
5 embodiments of the present disclosure.
[0026] FIG. 4 illustrates exemplary representation of flow diagram
representing a method for estimating call setup success rate a network, in accordance with some embodiments of the present disclosure.
[0027] FIG. 5 illustrates an example computer system in which or with
10 which the embodiments of the present disclosure may be implemented.
[0028] FIG. 6 depicts an exemplary flowchart representing a method for
estimating call setup success rate a network, in accordance with some embodiments of the present disclosure.
[0029] The foregoing shall be more apparent from the following more
15 detailed description of the disclosure.
LIST OF REFERENCE NUMERALS
100 – Network Architecture
102-1, 102-2…102-N – Users
20 104-1, 104-2…104-N – Computing Devices
106- Network
108 –System
202 – Processor(s)
204 – Memory
25 206 –Interface(s)
208 – Processing Engine
210 - Database
212 – Forecasting Engine

214 – Load Balancer
300 – Flow Diagram
400 – Method
500 – Computer System
5 510 – External Storage Device
520 – Bus
530 – Main Memory
540 – Read Only Memory
550 – Mass Storage Device
10 560 – Communication Port
570 - Processor
DETAILED DESCRIPTION
[0030] In the following description, for explanation, various specific details
15 are outlined in order to provide a thorough understanding of embodiments of the
present disclosure. It will be apparent, however, that embodiments of the present
disclosure may be practiced without these specific details. Several features
described hereafter can each be used independently of one another or with any
combination of other features. An individual feature may not address all of the
20 problems discussed above or might address only some of the problems discussed
above. Some of the problems discussed above might not be fully addressed by any of the features described herein.
[0031] The ensuing description provides exemplary embodiments only and
is not intended to limit the scope, applicability, or configuration of the disclosure.
25 Rather, the ensuing description of the exemplary embodiments will provide those
skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

[0032] Specific details are given in the following description to provide a
thorough understanding of the embodiments. However, it will be understood by one
of ordinary skill in the art that the embodiments may be practiced without these
specific details. For example, circuits, systems, networks, processes, and other
5 components may be shown as components in block diagram form in order not to
obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.
[0033] Also, it is noted that individual embodiments may be described as a
10 process that is depicted as a flowchart, a flow diagram, a data flow diagram, a
structure diagram, or a block diagram. Although a flowchart may describe the
operations as a sequential process, many of the operations can be performed in
parallel or concurrently. In addition, the order of the operations may be re-arranged.
A process is terminated when its operations are completed but could have additional
15 steps not included in a figure. A process may correspond to a method, a function, a
procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
[0034] The word “exemplary” and/or “demonstrative” is used herein to
20 mean serving as an example, instance, or illustration. For the avoidance of doubt,
the subject matter disclosed herein is not limited by such examples. In addition, any
aspect or design described herein as “exemplary” and/or “demonstrative” is not
necessarily to be construed as preferred or advantageous over other aspects or
designs, nor is it meant to preclude equivalent exemplary structures and techniques
25 known to those of ordinary skill in the art. Furthermore, to the extent that the terms
“includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive like the term “comprising” as an open transition word without precluding any additional or other elements.

[0035] Reference throughout this specification to “one embodiment” or “an
embodiment” or “an instance” or “one instance” means that a particular feature,
structure, or characteristic described in connection with the embodiment is included
in at least one embodiment of the present disclosure. Thus, the appearances of the
5 phrases “in one embodiment” or “in an embodiment” in various places throughout
this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[0036] The terminology used herein is to describe particular embodiments
10 only and is not intended to be limiting the disclosure. As used herein, the singular
forms “a”, “an”, and “the” are intended to include the plural forms as well, unless
the context 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
15 components, but do not preclude the presence or addition of one or more other
features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any combinations of one or more of the associated listed items.
[0037] Embodiments herein relate to system and method for estimating the
20 call setup success rate (CSSR) in the future time instances. In telecommunications,
the call setup success rate (CSSR) is the fraction of the attempts to make a call that
result in a connection to the dialled number. In particular, the system analyzes the
historic data associated with the CSSR to predict the CSSR value for any anomaly
or discrepancy in future. The analysis of the historical data is performed using
25 machine learning algorithms. In an embodiment, the system may train machine
learning (ML)/Artificial intelligence (AI) models using historical data and apply
them to predict future values. In order to detect anomaly in CSSR for the near
future, the system determines whether predicted CSSR is not within the predefined
range of threshold e.g., CSSR value being falling below 99% in the near future. In
30 case the predicted CSSR is not within the predefined range of threshold, system

generates a detailed analysis of the probable cause affecting the CSSR. In this manner, system helps the network operation team to rectify the problem.
[0038] The system may also fine-tune the parameters for the machine
learning algorithms to optimize their performance. Upon identification of the
5 probable cause, one or more corrective actions can be taken. Thus, the anomaly can
be predicted prior to occurrence of the anomaly and accordingly rectified.
[0039] The various embodiments throughout the disclosure will be
explained in more detail with reference to FIGs. 1-5.
[0040] FIG. 1 illustrates an example network architecture 100 for
10 implementing a proposed system 108, in accordance with an embodiment of the
present disclosure.
[0041] As illustrated in FIG. 1, one or more computing devices 104-1, 104-
2…104-N) may be connected to a proposed system 108 through a network 106. A person of ordinary skill in the art will understand that the one or more computing
15 devices 104-1, 104-2…104-N may be collectively referred as computing devices
104 and individually referred as a computing device 104. One or more users 102-1, 102-2…102-N may provide one or more requests to the system 108. A person of ordinary skill in the art will understand that the one or more users 102-1, 102-2…102-N may be collectively referred as users 102 and individually referred as a
20 user 102. Further, the computing devices 104 may also be referred as a user
equipment (UE) 104 or as UEs 104 throughout the disclosure.
[0042] In an embodiment, the computing device 104 may include, but not
be limited to, a mobile, a laptop, etc. Further, the computing device 104 may include
one or more in-built or externally coupled accessories including, but not limited to,
25 a visual aid device such as a camera, audio aid, microphone, or keyboard.
Furthermore, the computing device 104 may include a mobile phone, smartphone, virtual reality (VR) devices, augmented reality (AR) devices, a laptop, a general-purpose computer, a desktop, a personal digital assistant, a tablet computer, and a

mainframe computer. Additionally, input devices for receiving input from the user 102 such as a touchpad, touch-enabled screen, electronic pen, and the like may be used.
[0043] In an embodiment, the network 106 may include, by way of example
5 but not limitation, at least a portion of one or more networks having one or more
nodes that transmit, receive, forward, generate, buffer, store, route, switch, process,
or a combination thereof, etc. one or more messages, packets, signals, waves,
voltage or current levels, some combination thereof, or so forth. The network 106
may also include, by way of example but not limitation, one or more of a wireless
10 network, a wired network, an internet, an intranet, a public network, a private
network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, or some combination thereof.
15 [0044] In an embodiment, the system 108 facilitates the prediction of CSSR
in future time instances so that an anomaly can be predicted prior to occurrence of the anomaly. The proactively identification of the anomaly allows the network operators to address the issue and to achieve a better user experience.
[0045] FIG. 2 illustrates an example block diagram 200 of a proposed
20 system 108, in accordance with an embodiment of the present disclosure.
[0046] Referring to FIG. 2, in an embodiment, the system 108 may include
one or more processor(s) 202. 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
25 devices that process data based on operational instructions. Among other
capabilities, the one or more processor(s) 202 may be configured to fetch and execute computer-readable instructions stored in a memory 204 of the system 108. The memory 204 may be configured to store one or more computer-readable instructions or routines in a non-transitory computer readable storage medium,

which may be fetched and executed to create or share data packets over a network
service. The memory 204 may comprise any non-transitory storage device
including, for example, volatile memory such as random-access memory (RAM),
or non-volatile memory such as erasable programmable read only memory
5 (EPROM), flash memory, and the like.
[0047] In an embodiment, the system 108 may include 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 108. The
10 interface(s) 206 may also provide a communication pathway for one or more
components of the system 108. Examples of such components include, but are not limited to, processing engine(s) 208 and a database 210. Further, the processing engine(s) 208 may include one or more engine(s) such as, but not limited to, an input/output engine, an identification engine, and an optimization engine.
15 [0048] In an embodiment, the processing engine(s) 208 may be
implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) 208. In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For
20 example, the programming for the processing engine(s) 208 may be processor-
executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine(s) 208 may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may
25 store instructions that, when executed by the processing resource, implement the
processing engine(s) 208. In such examples, the system may comprise the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the system and the processing resource. In other examples, the
30 processing engine(s) 208 may be implemented by electronic circuitry.

[0049] In an embodiment, the processing engine 208 may include a
forecasting engine that may analyze the historic data associated with the CSSR to predict the CSSR value for any anomaly or discrepancy in future. The analysis of the historical data is performed using machine learning algorithms.
5 [0050] In example, analyzing historic CSSR data (hereinafter
interchangeably referred to as data) to predict future CSSR using the AI/ML techniques may include one or more steps. The one or more steps may include historical data preprocessing, exploratory data analysis, feature engineering, model selection, and evaluation.
10 [0051] In examples, the data collection and preprocessing may include
collecting and preparing the CSSR data for processing. In examples, the CSSR data may of a recent time period, for example, last eighteen (18) months for a given area. The data is prepared to be in a normalized form for further processing. In data cleaning, missing values, outliers, and anomalies in the CSSR data may be
15 identified and updated. For data formatting, the data is organized, for example, in a
time-series format with a consistent time interval (e.g., hourly, daily). Further, feature engineering is performed in which relevant features are generated such as time-based features (hour, day, month), lag features (previous time steps), and rolling statistics (moving averages).
20 [0052] In exploratory data analysis (EDA), the data is analyzed to identify
underlying patterns and to obtain insights. For example, the data is visualized by plotting time-series data to identify trends, seasonality, and cycles. In another examples, statistical analysis is performed to calculate summary statistics (mean, median, variance) and autocorrelation.
25 [0053] Further, feature engineering is used to create additional features that
can help the model process the data better. In examples, time-based features, lag features, rolling statistics and the like, may be used. The time-based features may include adding features such as the day of the week, month, quarter, and holiday indicators. The lag features may include use past values of the data as predictors

(e.g., CSSR at time t-1, t-2). In rolling statistics, rolling means, standard deviations,
and other statistical measures are calculated. Thereafter, an appropriate machine
learning model(s) may be selected. In an example, machine learning models for
time-series forecasting may be used. Some examples of machine learning models
5 include but are not limited to Auto Regressive Integrated Moving Average
(ARIMA), Seasonal Auto Regressive Integrated Moving Average (SARIMA), Exponential Smoothing, Linear Regression, Decision Trees, Random Forests, Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Prophet.
[0054] Once the model(s) are selected, the model(s) are trained and
10 validated. The model training and validation may include splitting the data into
training and testing sets, then training the model and validating its performance. In
example, train-test split may be used where the data is split into training and test
sets while preserving the time order. Further, cross-validation using techniques like
TimeSeriesSplit may be used for cross-validation. Thereafter, model training is
15 performed on the selected models. The models are tuned by optimizing model
parameters using grid search or random search. The tuned models are evaluated using appropriate metrics, which may include, but are not limited to Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE).
20 [0055] The evaluated models may be deployed for forecasting. In
implementations, the model may be used to predict future CSSR values and monitor its performance over time. To fine tune the performance of the models, the model is periodically trained with new data to maintain accuracy. In instances, the models are continuously monitored and improved by tracking predictions against actual
25 CSSR values, updating the model with new data at regular intervals and refining
the model based on feedback and insights.
[0056] In an embodiment, the forecasting engine may train ML/AI models
using historical data and apply them to predict future values. In order to detect anomaly in CSSR for the near future, the forecasting engine determines that CSSR

is not within the predefined range of threshold, for example, CSSR value being falling below 99% in the near future. In such cases, the forecasting engine may generate a detailed analysis of the probable cause affecting the CSSR. In this manner, the system 108 assists the network operation team to rectify the problem.
5 [0057] The forecasting engine may also fine-tune the parameters for the
machine learning algorithms to optimize their performance. Upon identification of the probable cause, one or more corrective actions can be taken.
[0058] Although FIG. 2 shows exemplary components of the system 108,
in other embodiments, the system 108 may include fewer components, different
10 components, differently arranged components, or additional functional components
than depicted in FIG. 2. Additionally, or alternatively, one or more components of the system 108 may perform functions described as being performed by one or more other components of the system 108.
[0059] FIG. 3 illustrates a flow diagram 300 representing an architecture
15 depicting operations of the system 108, in accordance with some embodiments of
the present disclosure. In step 302, the user may send a request to predict CSSR
with a predefined time period. The request may also include information indicating
future time instances for which the CSSR needs to be predicted. Upon receipt of the
request through the user interface (UI), the request may be sent to the forecasting
20 engine 212 through the load balancer (LB) 214 (in step 304). The forecasting engine
212 may obtain the trained ML model from the cache (in step 306) and historical
CSSR data from database (in step 310), which may further obtain the historical
CSSR data from distributed file system (in step 312) or data lake (in step 314). The
forecasting engine 212 may predict the CSSR for the future time instance e.g., next
25 week, next month, etc., (in step 316) and generate a detailed analysis report (in step
318). Based on the report, if the predicted CSSR is not within the predefined threshold range, for example, below 99%, the forecasting engine 212 may generate a notification indicating that a corrective action needs to be taken (in step 320). Otherwise, the flow ends when the predicted CSSR is not within the predefined

threshold range, for example, above 99%.
[0060] FIG. 4 illustrates an exemplary representation of flow diagram
representing a method 400 for estimating call setup success rate a network, in
accordance with some embodiments of the present disclosure. As illustrated, data
5 collection CSSR data is collected from the network functions (NF) and stored in the
database upon performing enrichment and normalization on the collected data (in step 402). Then, machine learning and advanced ML models for prediction of future CSSR value (in step 406) with highest accuracy are trained using historic data and multiple hyper parameters. The forecasting engine uses the ML models for
10 predicting the future CSSR values (in step 404). In example, the estimation may be
auto-estimation (in step 408). In examples, ML models that can be used may include time series forecasting models such as AutoRegressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), Prophet, etc., regression models such as linear regression, random forest regression, gradient boosting machines, etc. or
15 deep learning models such as recurrent neural networks (RNN), long short-term
memory networks (LSTM), etc. In examples, the historical CSSR data may be gathered, and missing values and outliers are identified and normalized to create a standardized CSSR data. Further, feature engineering may be performed that includes creating time-based features (e.g., day of the week, month, quarter). In
20 aspects, lag features may be optionally added (e.g., CSSR values from previous
days/weeks). In examples, model selection and training may be performed. In examples, any of the ML described above or not described can be chosen. The CSSR data may be split into training and test sets. In implementations, one or more models may be trained using the training set. Hyperparameters may be tuned using
25 techniques like Grid Search or Random Search. Further, one or more models on the
test set may be evaluated using various metrics like mean absolute errors, root mean squared errors, mean absolute percentage errors. Furthermore, cross-validation may be performed to ensure robustness. One or more appropriate trained models may be used to predict CSSR values for any given time instance in future. The ML models
30 used may be continuously evaluated and retrained periodically with new CSSR

data.
[0061] Upon training, CSSR future values are computed using the trained
ML model by fetching the data stored in the database. The system 108 may then
create a detailed analysis of the probable cause of falling CSSR value below
5 predetermined threshold in the near future (in step 410). Upon determining that the
predicted value of CSSR is less than a predetermined threshold, alert notification is transmitted to the network operators via a communication channel and over dashboard so as to take corrective action (in step 412).
[0062] FIG. 5 illustrates an example computer system 500 in which or with
10 which the embodiments of the present disclosure may be implemented.
[0063] As shown in FIG. 5, the computer system 500 may include an
external storage device 510, a bus 520, a main memory 530, a read-only memory 540, a mass storage device 550, a communication port(s) 560, and a processor 570. A person skilled in the art will appreciate that the computer system 500 may include
15 more than one processor and communication ports. The processor 570 may include
various modules associated with embodiments of the present disclosure. The communication port(s) 560 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 fiber, a serial port, a parallel port, or other existing or future ports. The
20 communication ports(s) 560 may be chosen depending on a network, such as a
Local Area Network (LAN), Wide Area Network (WAN), or any network to which the computer system 500 connects.
[0064] In an embodiment, the main memory 530 may be Random Access
Memory (RAM), or any other dynamic storage device commonly known in the art.
25 The read-only memory 540 may be any static storage device(s) e.g., but not limited
to, a Programmable Read Only Memory (PROM) chip for storing static information e.g., start-up or basic input/output system (BIOS) instructions for the processor 570. The mass storage device 550 may be any current or future mass storage solution, which can be used to store information and/or instructions. Exemplary mass storage

solutions include, but are not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and/or Firewire interfaces).
5 [0065] In an embodiment, the bus 520 may communicatively couple the
processor(s) 570 with the other memory, storage, and communication blocks. The
bus 520 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
10 well as other buses, such a front side bus (FSB), which connects the processor 570
to the computer system 500.
[0066] In another embodiment, operator and administrative interfaces, e.g.,
a display, keyboard, and cursor control device may also be coupled to the bus 520
to support direct operator interaction with the computer system 500. Other operator
15 and administrative interfaces can be provided through network connections
connected through the communication port(s) 560. Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system 500 limit the scope of the present disclosure.
20 [0067] FIG. 6 depicts an exemplary flowchart representing a method for
estimating call setup success rate a network, in accordance with some embodiments of the present disclosure.
[0068] In an embodiment, the present disclosure discloses a method 600 for
estimating the call setup success rate (CSSR) in a future time instance.
25 [0069] The method 600 may include at step 602, receiving a request from a
user for predicting the CSSR. The request includes information indicating time period for which the CSSR needs to be predicted. In examples, the time period may be future time instances.

[0070] Further, at step 604, the method may include, in response to the
request, predicting the CSSR for future time instances based on historic CSSR data.
[0071] In addition, at step 606, the method 600 may include determining if
the predicted CSSR is less than a pre-defined threshold.
5 [0072] In addition, the method 600 may include at step 608, based on the
determination, generating a notification indicating corrective action.
[0073] 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 is to be implemented merely as illustrative of the disclosure and not as a limitation.
15
ADVANTAGES OF THE INVENTION
[0074] The present disclosure provides a system and a method for
estimating the call setup success rate (CSSR) in future time instances.
[0075] The present disclosure provides a system and a method to improve
20 the user experience in the network.
[0076] The present disclosure provides a system and a method to improve
the CSSR in the network.
[0077] The present disclosure provides a system and a method to provide a
system and a method that are economical and easy to implement.

WE CLAIM:
1. A system (108) for estimating a call setup success rate (CSSR), the system (108)
5 comprising:
a processor (202) configured to receive a request from a user for predicting the CSSR, the request includes information indicating time period for which the CSSR is to be predicted;
a forecasting engine (212) coupled to the processor (202), the forecasting
10 engine (212) is configured to:
in response to the request, predict the CSSR for the indicated time period based on historic CSSR data;
determine if the predicted CSSR is less than a pre-defined threshold;
and
15 based on the determination, generate a notification indicating corrective
action.
2. The system (108) as claimed in claim 1, wherein the forecasting engine (212) is
further configured to generate an analysis report indicating a probable cause of
20 falling CSSR.
3. The system (108) as claimed in claim 1, wherein the forecasting engine (212) is
configured to employ a machine learning (ML) model to analyze the historic CSSR
data for predicting the CSSR
25
4. The system (108) as claimed in claim 3, wherein the ML model is trained using
historic CSSR data and a plurality of hyper parameters.
5. The system (108) as claimed in claim 1 further comprising a load balancer (214)
30 to send the request to the forecasting engine.

6. A method for estimating a call setup success rate (CSSR), the method
comprising:
receiving, by a processor (202), a request from a user for predicting the CSSR,
the request includes information indicating time period for which the CSSR needs
5 to be predicted;
in response to the request, predicting, by a forecasting engine (212), the CSSR for the indicated time period based on historic CSSR data;
determining, by the forecasting engine (212), if the predicted CSSR is less
than a pre-defined threshold; and
10 based on the determination, generating, by the forecasting engine (212), a
notification indicating corrective action.
7. The method as claimed in claim 6, wherein the method further comprises
generating, by the forecasting engine (212), an analysis report indicating a probable
15 cause of falling CSSR.
8. The method as claimed in claim 6, wherein to predict the CSSR, the method
comprises employing a machine learning (ML) model to analyze the historic CSSR
data.
20
9. The method as claimed in claim 8, wherein the method further comprises training
the ML model using the historic CSSR data and a plurality of hyper parameters.
10. A user equipment (104) communicatively coupled with a system (108), the coupling comprises steps of:
receiving a connection request from the system (108);
sending an acknowledgment of the connection request to the system (108);
transmitting a plurality of signals in response to the connection request,
wherein the system (108) is configured for estimating a call setup success rate
30 (CSSR) as claimed in claim 1.
Dated this 14 day of June 2024