System and method for determining confidence factor to evaluate network performance

Abstract

Disclosed is a system (100) and a method (300) for determining a confidence factor to evaluate network performance. The method comprises initiating, by a test unit of a user device (110), a request for performing a network speed test. The method further comprises receiving, by a receiving unit of the user device (110) from a network, values of a plurality of network performance metrics based on results of the network speed test. The method further comprises determining, by a determining unit of the user device (110), a confidence factor based on the values of the plurality of network performance metrics.

Description

SYSTEM AND METHOD FOR DETERMINING CONFIDENCE FACTOR TO EVALUATE NETWORK PERFORMANCETECHNICAL FIELD

[0001] The embodiments of the present disclosure generally relate to the field of telecommunication networks and systems. More particularly, the present disclosure relates to a system and a method for determining a confidence factor to evaluate network performance.BACKGROUND OF THE INVENTION

[0002] The subject matter disclosed in the background section should not be assumed or construed to be prior art merely because of its mention in the background section. Similarly, any problem statement mentioned in the background section or its association with the subject matter of the background section should not be assumed or construed to have been previously recognized in the prior art.

[0003] In the rapidly evolving era of global connectivity, an accurate measurement of performance of communication network is critical for both users and service providers alike. To this end, various metrics such as an upload speed, a download speed, and latency are true indicators of quality and performance of the communication network. These metrics are typically assessed using speed test applications available for mobile and desktop platforms. The speed test applications are being employed extensively nowadays to provide the users with real-time feedback on the performance of the communication network.

[0004] However, a general issue that is quite prevalent in existing measurement techniques utilized for assessing the performance of the communication network is that reliability and accuracy of measurement are influenced by various factors, including user's device capabilities, network congestion, and server locations, whichhas led to disbelief amongst the users regarding validity of results of measurements reported by the speed tests applications.

[0005] Various attempts have been made to address the issue of the reliability and the accuracy of speed test results primarily focusing on improving the measurement techniques. In this regard, many existing solutions have implemented dynamic server selection processes, choosing servers based on proximity and load to mitigate the latency and provide more accurate speed readings. Several other approaches have integrated data from multiple tests over time to average out discrepancies, offering a cumulative representation of the performance of the communication network rather than relying on a single test instance. Despite these approaches, the users often remain uncertain about the reliability of the data, largely because the approaches do not explicitly communicate a level of confidence in the results. Furthermore, existing speed test solutions generally do not offer an adaptive mechanism to adjust a confidence level based on real-time testing conditions and user-specific parameters. The users receive raw numbers for the upload speed, the download speed, and the latency, but the numbers are not accompanied by their accuracy or the reliability under current network conditions.

[0006] In light of the above-mentioned challenges associated with the existing speed test solutions, there lies a clear need for an improved method for evaluating the performance of the communication network.SUMMARY

[0007] The following embodiments present a simplified summary to provide a basic understanding of some aspects of the disclosed invention. This summary is not an extensive overview, and it is not intended to identify key / critical elements or to delineate the scope thereof. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.

[0008] According to an aspect of the present disclosure, disclosed herein is a method for determining a confidence factor to evaluate network performance. Themethod comprises initiating, by a test unit of a user device, a request for performing a network speed test. The method further comprises receiving, by a receiving unit of the user device from a network, values of a plurality of network performance metrics based on results of the network speed test and determining, by a determining unit of the user device, a confidence factor based on the values of the plurality of network performance metrics. The confidence factor corresponds to a measure of an accuracy of the plurality of network performance metrics to evaluate the network performance.

[0009] In one or more implementations, the method further comprises displaying, by a display unit of the user device, the confidence factor and the plurality of network performance metrics on a User Interface (UI) of the user device.

[0010] In one or more implementations, the method further comprises storing, by a storage unit of the user device, the values of the plurality of network performance metrics and the determined confidence factor in a database.

[0011] In one or more implementations, a machine learning model is trained using the stored values of the plurality of network performance metrics and corresponding confidence factor to enable determination of the confidence factor during a network speed test.

[0012] In one or more implementations, the plurality of network performance metrics comprises a latency, an upload speed and a download speed.

[0013] In one or more implementations, the method further comprises determining, by the determining unit, a latency factor based on the values of the plurality of network performance metrics.

[0014] In one or more implementations, the determining the latency factor comprises assigning, by the determining unit, a first value to the latency factor when the latency is less than or equal to a first threshold value and assigning, by the determining unit, a second value to the latency factor when the latency is betweenthe first threshold value and a second threshold value. The determining the latency factor further comprises assigning, by the determining unit, a third value to the latency factor when the latency exceeds the second threshold value.

[0015] In one or more implementations, the first value corresponds to a maximum value, the second value corresponds to an intermediate value and the third value corresponds to a minimum value of the assigned latency factor.

[0016] In one or more implementations, the determining the confidence factor comprises combining, by the determining unit, a half of a latency factor and a half of a difference between an upload speed and a download speed.

[0017] According to another aspect of the present disclosure, a system for determining a confidence factor to evaluate network performance is disclosed. The system comprises a user device. The user device comprises a test unit, a receiving unit and a determining unit. The test unit is configured to initiate a request for performing a network speed test. The receiving unit configured to receive from a network, values of a plurality of network performance metrics based on results of the network speed test. The determining unit configured to determine a confidence factor based on the values of the plurality of network performance metrics. The confidence factor corresponds to a measure of an accuracy of the plurality of network performance metrics to evaluate the network performance.BRIEF DESCRIPTION OF DRAWINGS

[0021] Various embodiments disclosed herein will become better understood from the following detailed description when read with the accompanying drawings. The accompanying drawings constitute a part of the present disclosure and illustrate certain non-limiting embodiments of inventive concepts. Further, components and elements shown in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. For consistency and ease of understanding, similar components and elements are annotated by reference numerals in the exemplary drawings.

[0018] FIG. 1 illustrates a block diagram depicting a network environment for determining a confidence factor to evaluate network performance, in accordance with an embodiment of the present disclosure.

[0019] FIG. 2 illustrates a block diagram depicting a system architecture of a user device, in accordance with an exemplary embodiment of the present disclosure.

[0023] FIG. 3 illustrates a flowchart depicting a method for determining the confidence factor to evaluate the network performance, in accordance with an embodiment of the present disclosure.

[0024] FIG. 4 illustrates a schematic block diagram of a computing system, in accordance with an embodiment of the present disclosure.DETAILED DESCRIPTION OF THE INVENTION

[0025] Inventive concepts of the present disclosure will now be described more fully hereinafter with reference to the accompanying drawings, in which examples of one or more embodiments of inventive concepts are shown. Inventive concepts may, however, be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Further, the one or more embodiments disclosed herein are provided to describe the inventive concept thoroughly and completely, and to fully convey the scope of each of the present inventive concepts to those skilled in the art. Furthermore, it should be noted that the embodiments disclosed herein are not mutually exclusive concepts. Accordingly, one or more components from one embodiment may be tacitly assumed to be present or used in any other embodiment.

[0026] The following description presents various embodiments of the present disclosure. The embodiments disclosed herein are presented as teaching examples and are not to be construed as limiting the scope of the present disclosure. The present disclosure should in no way be limited to the illustrative implementations, drawings, and techniques illustrated below, including the exemplary design andimplementation illustrated and described herein, but may be modified, omitted, or expanded upon without departing from the scope of the present disclosure.

[0027] The following description contains specific information pertaining to embodiments in the present disclosure. The detailed description uses the phrases “in some embodiments” or “some implementations” which may each refer to one or more or all of the same or different embodiments or implementations. The term “some” as used herein is defined as “one, or more than one, or all.” Accordingly, the terms “one,” “more than one,” “more than one, but not all” or “all” would all fall under the definition of “some.” In view of the same, the terms, for example, “in an embodiment” or “in an implementation” refers to one embodiment or one implementation and the term, for example, “in one or more embodiments” refers to “at least one embodiment, or more than one embodiment, or all embodiments.” Further, the term, for example, “in one or more implementations” refers to “at least one implementation, or more than one implementation, or all implementations.

[0028] The term “comprising,” when utilized, means “including, but not necessarily limited to;” it specifically indicates open-ended inclusion in the so-described one or more listed features, elements in a combination, unless otherwise stated with limiting language. Furthermore, to the extent that the terms “includes,” “has,” “have,” “contains,” and other similar words are used in either the detailed description, such terms are intended to be inclusive in a manner similar to the term “comprising.”

[0029] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features.

[0030] The description provided herein discloses exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the presentdisclosure. Rather, the foregoing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing any of the exemplary embodiments. Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it may be understood by one of the ordinary skilled in the art that the embodiments disclosed herein may be practiced without these specific details.

[0031] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein the description, the singular forms "a", "an", and "the" include plural forms unless the context of the invention indicates otherwise.

[0032] The terminology and structure employed herein are for describing, teaching, and illuminating some embodiments and their specific features and elements and do not limit, restrict, or reduce the scope of the present disclosure. Accordingly, unless otherwise defined, all terms, and especially any technical and / or scientific terms, used herein may be taken to have the same meaning as commonly understood by one having ordinary skill in the art.

[0033] The present disclosure relates to a system and a method for determining an accuracy of network performance metrics for evaluating performance of a communication network by generating a confidence factor. The confidence factor corresponds to a measure of the accuracy of the network performance metrics provided by a network speed test. The confidence factor provides users with an insight on an assessment of a network quality, considering variations in the network performance metrics such as an upload speed, a download speed, and a latency.

[0034] An aspect of the present disclosure is to provide a system and a method that enhances a reliability and trustworthiness of network speed measurements by generating the confidence factor based on real-time analysis of the network conditions.

[0035] Another aspect of the present disclosure is to provide a system and a method that operates on both mobile and desktop platforms, ensuring consistent user experience and the reliability across different devices and operating systems.

[0036] Yet another aspect of the present disclosure is to provide a system and a method for accurately calculating the confidence factor associated with the network performance metrics such as the upload speed, the download speed, and the latency.

[0037] Several key terms used in the description play pivotal roles in facilitating the system functionality. In order to facilitate an understanding of the description, the key terms are defined below.

[0038] The latency- The term latency may refer to a time delay experienced in the network during transmission of data from a source to a destination. The latency indicates responsiveness of a network connection.

[0039] The upload speed- The term upload speed may denote a rate at which the data is transmitted from a user device to a remote server or the network.

[0040] The download speed - The term download speed may refer to the rate at which the data is received by the user device from the remote server or the network.

[0041] A speed test - The speed test is performed by the user device or the remote server to measure various network performance metrics including the latency, the upload speed, and the download speed. The speed test involves the transfer of test data between the user device and the server to compute the network performance metrics.

[0042] Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings. FIG. 1 to FIG. 4, discussed below, and the one or more embodiments used to describe the principles of the present disclosure are by way of illustration only and should not be construed in any way to limit the scope of the present disclosure. Those skilled in the art will understand that theprinciples of the present disclosure may be implemented in any suitably arranged system or device.

[0043] FIG. 1 illustrates a block diagram depicting a network environment 100 for determining the confidence factor to evaluate the network performance, in accordance with an embodiment of the present disclosure.

[0044] The embodiments of the network environment 100 (may also be referred as the “system 100”) shown in FIG. 1 is for illustration only. Other embodiments of the network environment 100 may be used without departing from the scope of this disclosure.

[0045] As shown in FIG. 1, the network environment 100 may include a user device 110, a network 120, a server 130, and a database 150. The server 130 communicates with each of the user device 110, and the database 150 via the network 120.

[0046] The user device 110 may include a User Interface (UI) 110-1, a communication unit 110-2, a processor 110-3 (may also be referred to as “one or more processors 110-3”) and a device memory 110-4. The UI 110-1 may enable interaction between the user and the user device 110, displaying real-time results of the speed tests and the generated confidence factor. The communication unit 110-2 within the user device 110 may enable communication of the user device 110 with the server 130 for data exchange. In one or more embodiments, one or more speed test applications may be installed on the user device 110 to communicate with the server 130. The processor 110-3 may execute operating system instructions stored in the device memory 110-4 in order to control the overall operation of the user device 110.

[0047] The processor 110-3 is configured to execute programs and instructions stored in the device memory 110-4. The processor 110-3 is further configured to move data into or out of the device memory 110-4 as required by an executing process. The processor 110-3 may also be configured to execute the one or more applications based on an operating system or in response to signals received fromgNBs or an operator. The processor 110-3 may also be coupled to an I / O interface, which provides the user device 110 with an ability to connect to other devices, such as laptop computers and handheld computers. The I / O interface may act as a communication path between the above-described user device components and the processor 110-3. The processor 110-3 may include an intelligent hardware device including a general-purpose processor, such as, for example, and without limitation, a Central Processing Unit (CPU), an Application Processor (AP), a dedicated processor, or the like, a graphics-only processing unit such as a Graphics Processing Unit (GPU), a microcontroller, a Field-Programmable Gate Array (FPGA), a programmable logic device, a discrete hardware component, or any combination thereof.

[0048] The device memory 110-4 may include any type of computer-readable medium usable by a computer or the processor 110-3, such as a Read-Only Memory (ROM), a Random-Access Memory (RAM), a flash memory, a removable storage drive, a Hard Disc Drive (HDD), a solid-state memory, a magnetic storage drive, a Programmable Read-Only Memory (PROM), an Erasable Programmable Read- Only Memory (EPROM), and / or an Electrically Erasable Programmable Read-Only Memory EEPROM. In an aspect, for example, the device memory 110-4 may be a non-transitory computer-readable storage medium that stores one or more computer-executable codes or instructions.

[0049] The user device 110 may include, but not limited to, smartphones, tablets, laptops, desktop computers, Personal Digital Assistants (PDAs), smartwatches, or any other computing device capable of network connectivity. The user may include, but is not limited to, an end consumer using the speed test application on a personal device, a field engineer testing the speed test applications for assessing the network conditions, a network administrator, or an automated system making data-driven decisions for determining the confidence factor.

[0050] The network 120 enables communication between components of the network environment 100. The network 120 may include suitable logic, circuitry,and interfaces that may be configured to provide several network ports and several communication channels for transmission and reception of data related to operations of various entities of the network environment 100. Each network port may correspond to a virtual address (or a physical machine address) for transmission and reception of the communication data. For example, the virtual address may be an Internet Protocol Version 4 (IPV4) (or an IPV6 address) and the physical address may be a Media Access Control (MAC) address. The network 120 may be associated with an application layer for implementation of communication protocols based on one or more communication requests from the various entities of the network environment 100. The communication data may be transmitted or received via the communication protocols. Examples of the communication protocols may include, but are not limited to, Hypertext Transfer Protocol (HTTP), File Transfer Protocol (FTP), Simple Mail Transfer Protocol (SMTP), Domain Network System (DNS) protocol, Common Management Interface Protocol (CMIP), Transmission Control Protocol and Internet Protocol (TCP / IP), User Datagram Protocol (UDP), Long Term Evolution (LTE) communication protocols, or any combination thereof. In some aspects of the present disclosure, the communication data may be transmitted or received via at least one communication channel of several communication channels in the network 120. The communication channels may include, but are not limited to, a wireless channel, a wired channel, a combination of wireless and wired channel thereof. The wireless or wired channel may be associated with a data standard which may be defined by one of a Local Area Network (LAN), a Personal Area Network (PAN), a Wireless Local Area Network (WLAN), a Wireless Sensor Network (WSN), Wireless Area Network (WAN), Wireless Wide Area Network (WWAN), a Metropolitan Area Network (MAN), a satellite network, the Internet, an optical fiber network, a coaxial cable network, an Infrared (IR) network, a Radio Frequency (RF) network, and a combination thereof. Aspects of the present disclosure are intended to include or otherwise cover any type of communication channel, including known, related art, and / or later developed technologies.

[0051] The server 130 may function as a central entity responsible for storing, processing, and delivering a content to the user device 110 over the network 120. The server 130 responds to requests from the user device 110 and ensures seamless data transmission.

[0052] The server 130 may be a network of computers, a software framework, or a combination thereof, that may provide a generalized approach to create a server implementation. Examples of the server 130 may include, but are not limited to, personal computers, laptops, mini -computers, mainframe computers, any nontransient and tangible machine that can execute a machine-readable code, cloudbased servers, distributed server networks, or a network of computer systems. The server 130 may be realized through various web-based technologies such as, but not limited to, a Java web-framework, a .NET framework, a Personal Home Page (PHP) framework, or any web-application framework.

[0053] The server 130 may include a processor 140 (may also be referred as “one or more processors 140”), a communication interface 142, and a server memory 144. The processor 140 may include various processing circuitry / units and communicates with the server memory 144 and the communication interface 142. The processor 140 is configured to execute the instructions stored in the server memory 144 and to perform various processes. The processor 140 may include the intelligent hardware device including the general -purpose processor, such as, for example, and without limitation, the CPU, the AP, the dedicated processor, or the like, the graphics-only processing unit such as the GPU, the microcontroller, the FPGA, the programmable logic device, the discrete hardware component, or any combination thereof.

[0054] The communication interface 142 may be configured to enable the server 130 to communicate with various entities of the network environment 100 via the network 120. Examples of the communication interface 142 may include, but are not limited to, a modem, a network interface such as an Ethernet card, a communication port, and / or a Personal Computer Memory Card InternationalAssociation (PCMCIA) slot and card, an antenna, a Radio Frequency (RF) transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a Coder-Decoder (CODEC) chipset, a Subscriber Identity Module (SIM) card, and a local buffer circuit. It will be apparent to a person of ordinary skill in the art that the communication interface 142 may include any device and / or apparatus capable of providing wireless or wired communications between the server 130 and various other entities of the network environment 100.

[0055] The server memory 144 may be configured to store the logic, instructions, circuitry, interfaces, and / or codes of the processor 140 for executing various operations. Examples of the server memory 144 may include but are not limited to, the ROM, the RAM, the flash memory, the removable storage drive, the HDD, the solid-state memory, the magnetic storage drive, the PROM, the EPROM, and / or the EEPROM.

[0056] The database 150 may serve as a repository for storing historical network performance data, including the speed test results, latency measurements, and associated confidence factors. In an embodiment, the database 150 may provide the historical data that helps refine the confidence factor. The server 130 accesses the database 150 to retrieve past data for analysis and stores new data to continuously enhance machine learning model accuracy.

[0057] Although FIG. 1 illustrates one example of the network environment 100, various changes may be made to FIG. 1. For example, the network environment 100 may include any number of user devices and servers in any suitable arrangement. Further, in another example, the network environment 100 may include any number of components in addition to the components shown in FIG. 1. Further, various components in FIG. 1 may be combined, further subdivided, or omitted and additional components may be added according to particular needs.

[0058] FIG. 2 illustrates a block diagram depicting a system architecture of the user device 110, in accordance with an exemplary embodiment of the present disclosure.

[0059] The user device 110 may include the UI 110-1, the communication unit 110- 2, the processor 110-3 (may also be referred to as “one or more processors 110-3”) the device memory 110-4, processing units 202. Each of the components of the user device 110 is communicatively coupled to each other via a first communication bus 204.

[0060] The processing units 202 may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the user device 110. In non -limiting examples, described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing unit(s) 202 may be processor-executable instructions stored on a non-transitory machine- readable storage medium and the hardware for the processor 110-3 may execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing units 202. In such examples, the user device 110 may also comprise the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the user device 110 and the processing resource. In other examples, the processing units 202 may be implemented using an electronic circuitry.

[0061] According to an example embodiment, the processing units 202 may include a test unit 208, a receiving unit 210, a determining unit 212, a display unit 214, and a storage unit 216. Various modules of the processing units 202 may be communicatively coupled to each other by way of a second communication bus 206.

[0062] Referring to FIG. 2, the test unit 208 is configured to initiate a request for performing the network speed test. The receiving unit 210 is configured to receive from the network, values of a plurality of network performance metrics based on the results of the network speed test. The determining unit 212 is configured to determine the confidence factor based on the values of the plurality of networkperformance metrics. The display unit 214 is configured to display the confidence factor and the plurality of network performance metrics on the UI 110-1 of the user device 110. The storage unit 216 is configured to store the values of the plurality of network performance metrics and the determined confidence factor in the database 150.

[0063] The determining unit 212 is further configured to determine a latency factor based on the values of the plurality of network performance metrics and is configured to determine the confidence factor by combining a half of the latency factor and a half of a difference between the upload speed and the download speed.

[0064] Although FIG. 2 illustrates one example of the user device 110, various changes may be made to FIG. 2. Further, the user device 110 may include any number of components in addition to those shown in FIG. 2, without deviating from the scope of the present disclosure. Further, various components in FIG. 2 may be combined, further subdivided, or omitted and additional components may be added according to particular needs.

[0065] FIG. 3 illustrates a flowchart depicting a method 300 for determining the confidence factor to evaluate the network performance, in accordance with an embodiment of the present disclosure.

[0066] At block 302, the test unit 208 may initiate the request for performing the network speed test. In an embodiment, the test unit 208, upon initiating the network speed test via the speed test application, send the request to the server 130 where the speed tests are performed.

[0067] At block 304, the receiving unit 210 may receive from / via the network 120, values of the plurality of network performance metrics based on results of the network speed test.

[0068] In an embodiment, upon initiating the network speed test, the speed test application performs the speed test using Application Programming Interfaces(APIs) to gather the values of the plurality of network performance metrics from the server 130. The front-end function is triggered once the values of the plurality of network performance metrics is received and the confidence factor is calculated based on the upload and the download speeds and the latency.

[0069] At block 306, the determining unit 212 is configured to determine the confidence factor based on the values of the plurality of network performance metrics.

[0070] In an implementation, the speed test application first determines the latency and then a page of the speed test application is redirected to a next page to determine the upload speed and the download speed. The captured download speed and the upload speed are parsed into integer values for calculation to ensure that calculations performed later use numeric data types. Further, the speed test application compares the upload speed and the download speed to determine a maximum speed (mm) using Math.max() function between the upload speed and the download speed. A preliminary confidence factor is assigned based on comparisons between the upload speed and the download speed. The higher the speed, the more likely the network is performing optimally.

[0071] Further, the speed test application determines an absolute difference (diffValue) between the upload speed and the download speed. If there is a noticeable difference between the speeds (diffValue), the determining unit 212 is configured to adjust the confidence factor accordingly. The larger is the difference, the lower is the confidence in the network performance. The diffValue is then compared against a series of predefined values. For instance, if the diffValue is less than or equal to 10, the confidence factor is set to 100, indicating minimal discrepancy between the upload and the download speeds. As the diffValue increases, the confidence factor decreases in predefined intervals. For instance, the confidence factor is 90 for the diffValue up to 20, 80 for up to 30, down to the confidence factor of 10 for the diffValue lesser than or equal to 100. Thus, theconfidence factor is inversely proportional to the determined difference between the upload speed and the download speed.

[0072] The determining unit 212 is further configured to determine the latency factor based on the value of the latency. The latency factor is an indicator of the network responsiveness. The latency factor decreases as the latency increases, indicating a lower confidence in the network reliability with higher latency. In an implementation, the determining unit 212 assigns a maximum value to the latency factor when the latency is below or equal to a first predefined threshold value. The determining unit 212 reduces the value of the latency factor when the latency is between the first threshold value and a second threshold value, and the determining unit 212 assigns a minimum value to the latency factor when the latency exceeds the second threshold value.

[0073] For instance, if the latency is less than or equal to 50 milliseconds, the latency factor is set to 100. For the latency greater than 50 milliseconds and less than and equal to 100 milliseconds, the latency factor is reduced to 80, and for the latency greater than 100 milliseconds, the latency factor further reduces to 10. The latency factor plays a critical role in a final computation of the confidence factor.

[0074] Further the determining unit 212 may generate a final confidence factor based on the difference between the upload speed and the download speed and the determined latency factor. The final confidence factor is generated by combining the half of the latency factor and the half of the value derived from the determined difference between the upload speed and the download speed. The final confidence factor is represented by a following equation:

[0075] finalCf = (this. latencyF actor * 0.5) + (cf * 0.5), wherein the finalCf corresponds to the final confidence factor and cf corresponds to the confidence factor.

[0076] In one or more implementations, initially the confidence factor is set to 90%. This setting acts as a default confidence factor prior to an execution of conditionsbased on a specific network measurement. Further, it is checked whether the download speed is greater than or equal to the upload speed. If this condition is met, the confidence factor is adjusted to 100%, reflecting a high confidence in network's download capability relative to its upload capability. Conversely, if the upload speed is found to be greater than the download speed, then the confidence factor is adjusted to the diffValue. If a maximum measured speed equals the download speed it reconfirms the confidence factor at 100%, thereby highlighting the network's efficiency in the download speed.

[0077] The display unit 214 is configured to display the confidence factor and the plurality of network performance metrics on the UI 110-1 of the user device 110. The storage unit 216 may store the generated confidence factor, along with the network performance metrics, in the database 150. In an implementation, the disclosed system and the method may utilize the machine learning models or historical performance data to enhance the accuracy of the confidence factor. The machine learning models may predict network reliability based on specific conditions, such as peak usage times, and adjust the confidence factor accordingly. The machine learning models are trained using the stored values of the plurality of network performance metrics and corresponding confidence factor to enable determination of the confidence factor during a subsequent network speed test.

[0078] FIG. 4 illustrates a schematic block diagram of a computing system 400, in accordance with an embodiment of the present disclosure.

[0079] The computing system 400 includes a network 402, a network interface 404, a processor 406, an Input / Output (I / O) interface 408 and a non-transitory computer readable storage medium 410 (hereinafter may also be referred to as the “storage medium 410” or the “storage media 410”).

[0080] The network interface 404 includes wireless network interfaces such as Bluetooth, Wi-Fi, Worldwide Interoperability for Microwave Access (WiMAX), General Packet Radio Service (GPRS), or Wideband Code Division Multiple Access(WCDMA) or wired network interfaces such as Ethernet, Universal Serial Bus (USB), or Institute of Electrical and Electronics Engineers-864 (IEEE-864).

[0081] The processor 406 may include various processing circuitry / modules and communicate with the storage medium 410 and the VO interface 408. The processor 406 is configured to execute instructions stored in the storage medium 410 and to perform various processes. The processor 406 may include an intelligent hardware device including a general -purpose processor, such as, for example, and without limitation, a Central Processing Unit (CPU), a dedicated processor, or the like, a graphics-only processing unit such as a GPU, a microcontroller, a programmable logic device, a discrete hardware component, or any combination thereof. The processor 406 may be configured to execute computer-readable instructions 410-2 stored in the storage medium 410 to cause the system 200 to perform various functions.

[0082] The storage medium 410 stores a set of instructions i.e., computer program instructions 410-1 (hereinafter may also be referred to as instructions 410-1) required by the processor 406 for controlling its overall operations.

[0083] The storage media 410 may include an electronic storage medium, a magnetic storage medium, an optical storage medium, a quantum storage medium, or the like. For example, the storage media 410 may include, but are not limited to, hard drives, floppy diskettes, optical disks, flash memory, magnetic or optical cards, solid-state memory devices, or other types of physical media suitable for storing electronic instructions. In one or more embodiments, the storage media 410 includes a Compact Disk-Read Only Memory (CD-ROM), a Compact Disk-Read / Write (CD-R / W), and / or a Digital Video Disc (DVD).

[0084] In one or more implementations, the storage medium 410 stores computer program code configured to cause the computing system 400 to perform at least a portion of the processes and / or methods. Accordingly, in at least one implementation, the computing system 400 performs the method for determining the confidence factor to evaluate the network performance.

[0085] Embodiments of the present disclosure have been described above with reference to flowchart illustrations of methods and systems according to embodiments of the disclosure, and / or procedures, algorithms, steps, operations, formulae, or other computational depictions, which may also be implemented as computer program products. In this regard, each block or step of the flowchart, and combinations of blocks (and / or steps) in the flowchart, as well as any procedure, algorithm, step, operation, formula, or computational depiction can be implemented by various means, such as hardware, firmware, and / or software including one or more computer program instructions embodied in computer-readable program code. As will be appreciated, any such computer program instructions may be executed by one or more computer processors, including without limitation a general-purpose computer or special purpose computer, or other programmable processing apparatus to perform a group of operations comprising the operations or blocks described in connection with the disclosed method.

[0086] Further, these computer program instructions, such as embodied in computer-readable program code, may also be stored in one or more computer- readable memory or memory devices (ex. the storage medium 410) that can direct a computer processor or other programmable processing apparatus to function in a particular manner, such that the instructions 410-2 stored in the computer-readable memory or memory devices produce an article of manufacture including instruction means which implement the function specified in the block(s) of the flowchart(s).

[0087] It will further be appreciated that the term “computer program instructions” as used herein refer to one or more instructions that can be executed by the one or more processors (for example, the processor 406) to perform one or more functions as described herein. The instructions 410-2 may also be stored remotely such as on a server, or all or a portion of the instructions can be stored locally and remotely.

[0088] Separate instances of these methods / processes may be executed on or distributed across any number of separate computer systems. A variety of alternative implementations will be understood by those having ordinary skill in the art.

[0089] Now, referring to the technical abilities and advantageous effect of the present disclosure, operational advantages that may be provided by one or more embodiments may include providing the system and the method that generates the confidence factor on front end using the data from the speed test, and provides immediate feedback to the users. Real-time calculation ensures that the users receive up-to-date assessments of their network performance without any delay.

[0090] Another noteworthy advantage provided by the one or more embodiments may include, but not limited thereto, generating the confidence factor based on the upload speed, the download speed, and the latency and using the machine learning models to enhance the accuracy and the reliability of the results. This approach builds user trust in the network performance data displayed on the UI. Further, the system's compatibility with both mobile and desktop applications, along with background API calls, ensures a consistent and integrated user experience across different devices. Further, the storing of the confidence factor in the database enhances data quality by providing a historical record of the network performance metrics. The data can be used for trend analysis and reporting, leading to more accurate assessments and better-informed decisions regarding network optimization.

[0091] Further, the system and the method described herein may be applied to various use cases, including monitoring the network performance in homes, businesses, or large-scale network deployments by Internet Service Providers (ISPs). A real-time generation of the confidence factor adds an extra layer of transparency to an evaluation process, allowing the users to understand both the performance and the reliability of their network at any given time.

[0092] Those skilled in the art will appreciate that the methodology described herein in the present disclosure may be carried out in other specific ways than those set forth herein in the above disclosed embodiments without departing from essential characteristics and features of the present invention. The above-describedembodiments are therefore to be construed in all aspects as illustrative and not restrictive.

[0093] The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. Any combination of the above features and functionalities may be used in accordance with one or more embodiments.

[0094] In the present disclosure, each of the embodiments has been described with reference to numerous specific details which may vary from embodiment to embodiment. The foregoing description of the specific embodiments disclosed herein may reveal the general nature of the embodiments herein that others may, by applying current knowledge, readily modify and / or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications are intended to be comprehended within the meaning of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and is not limited in scope.LIST OF REFERENCE NUMERALS

[0095] The following list is provided for convenience and in support of the drawing figures and as part of the text of the specification, which describe innovations by reference to multiple items. Items not listed here may nonetheless be part of a given embodiment. For better legibility of the text, a given reference number is recited near some, but not all, recitations of the referenced item in the text. The same reference number may be used with reference to different examples or different instances of a given item. The list of reference numerals is:100- Network environment / System110 - User device110-1- User Interface (UI)110-2- Communication Unit110-3- Processor110-4- Device Memory120- Network130- Server140- Processor of the server142- Communication Interface144- Server Memory150- Database202- Processing units204- First communication bus206- Second communication bus208- Test unit210- Receiving unit212- Determining unit214- Display unit216- Storage unit300- Method for determining a confidence factor to evaluate network performance302- 306- Steps to perform method 300400- Computing System402- Network404- Network Interface406- Processor408- I / O interface410- Non transitory computer readable storage medium410-2- Instructions

Claims

1. We Claim:

1. A method (300) for determining a confidence factor to evaluate network performance, the method comprising: initiating, by a test unit (208) of a user device (110), a request for performing a network speed test; receiving, by a receiving unit (210) of the user device (110) from a network, values of a plurality of network performance metrics based on results of the network speed test; and determining, by a determining unit (212) of the user device (110), a confidence factor based on the values of the plurality of network performance metrics, wherein the confidence factor corresponds to a measure of an accuracy of the plurality of network performance metrics to evaluate the network performance.

2. The method (300) as claimed in claim 1, further comprising displaying, by a display unit (214) of the user device (110), the confidence factor and the plurality of network performance metrics on a User Interface (UI) (110-1) of the user device (HO).

3. The method (300) as claimed in claim 1, further comprising storing, by a storage unit (216) of the user device, the values of the plurality of network performance metrics and the determined confidence factor in a database (150).

4. The method (300) as claimed in claim 3, wherein a machine learning model is trained using the stored values of the plurality of network performance metrics and corresponding confidence factor to enable determination of the confidence factor during a network speed test.

5. The method (300) as claimed in claim 1, wherein the plurality of network performance metrics comprises a latency, an upload speed and a download speed.

6. The method (300) as claimed in claim 1, further comprising determining, by the determining unit (212), a latency factor based on the values of the plurality of network performance metrics.

7. The method (300) as claimed in claim 6, wherein the determining the latency factor comprises: assigning, by the determining unit (212), a first value to the latency factor when the latency is less than or equal to a first threshold value; assigning, by the determining unit (212), a second value to the latency factor when the latency is between the first threshold value and a second threshold value; and assigning, by the determining unit (212), a third value to the latency factor when the latency exceeds the second threshold value.

8. The method (300) as claimed in claim 7, wherein the first value corresponds to a maximum value, the second value corresponds to an intermediate value and the third value corresponds to a minimum value of the assigned latency factor.

9. The method (300) as claimed in claim 1, wherein the determining the confidence factor comprises combining, by the determining unit, a half of a latency factor and a half of a difference between an upload speed and a download speed.

10. A system (100) for determining a confidence factor to evaluate network performance, the system comprising a user device (110), wherein the user device (110) comprises: a test unit (208) configured to initiate a request for performing a network speed test; a receiving unit (210) configured to receive from a network, values of a plurality of network performance metrics based on results of the network speed test; a determining unit (212) configured to determine a confidence factor based on the values of the plurality of network performance metrics, wherein theconfidence factor corresponds to a measure of an accuracy of the plurality of network performance metrics to evaluate the network performance.

11. The system (100) as claimed in claim 10, further comprising a display unit (214) configured to display the confidence factor and the plurality of network performance metrics on a User Interface (UI) (110-1) of the user device (110).

12. The system (100) as claimed in claim 10, further comprising a storage unit (216) configured to store the values of the plurality of network performance metrics and the determined confidence factor in a database (150).

13. The system (100) as claimed in claim 12, wherein a machine learning model is trained using the stored values of the plurality of network performance metrics and corresponding confidence factor to enable determination of the confidence factor during a network speed test.

14. The system (100) as claimed in claim 10, wherein the plurality of network performance metrics comprises a latency, an upload speed and a download speed.

15. The system (100) as claimed in claim 10, wherein the determining unit is further configured to determine a latency factor based on the values of the plurality of network performance metrics.

16. The system (100) as claimed in claim 15, wherein to determine the latency factor, the determining unit (212) is configured to: assign a first value to the latency factor when the latency is less than or equal to a first threshold value; assign a second value to the latency factor when the latency is between the first threshold value and a second threshold value; and assign a third value to the latency factor when the latency exceeds the second threshold value.T117. The system (100) as claimed in claim 16, wherein the first value corresponds to a maximum value, the second value corresponds to an intermediate value and the third value corresponds to a minimum value of the assigned latency factor.

18. The system (100) as claimed in claim 10, wherein the determining unit is configured to determine the confidence factor by combining a half of a latency factor and a half of a difference between an upload speed and a download speed.

19. A computer program product comprising computer-executable instructions that are stored on a non-transitory computer-readable medium and that, when executed by at least one processing unit performs operations comprising: initiating a request for performing a network speed test; receiving, from a network, values of a plurality of network performance metrics based on results of the network speed test; and determining a confidence factor based on the values of the plurality of network performance metrics, wherein the confidence factor corresponds to a measure of an accuracy of the plurality of network performance metrics to evaluate the network performance.

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