System and method for analyzing network performance parameters

Abstract

The present disclosure relates to a system (102) and method (500) for analyzing network performance parameters. A user interface module (212) enables reception of comprehensive network speed test requests that specify multiple network performance parameters for analysis. A test execution module (214) triggers coordinated operation of multiple testing units (302a, 302b, 302c) to perform simultaneous network speed tests across different platforms. A data collection module (216) systematically aggregates the generated performance information from all testing units. One or more processors (202) transform the collected information through advanced processing algorithms to generate standardized performance data for each network parameter. An analyzing module (218) performs multi-dimensional analysis of the processed data based on specific attributes to provide comprehensive network performance insights. The integrated approach enables automated cross-platform testing, unified data collection, and sophisticated analysis of network performance characteristics, offering significant advantages over conventional single-platform testing methods.

Description

SYSTEM AND METHOD FOR ANALYZING NETWORK PERFORMANCE PARAMETERSRESERVATION OF RIGHTS

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

[0002] The embodiments of the present disclosure generally relate to telecommunications network. In particular, the present disclosure relates to a system and method for analyzing network performance parameters.DEFINITION

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

[0004] Network performance parameters" refers to measurable attributes of a network connection, including but not limited to download speed, upload speed, latency, jitter, packet loss, and bandwidth.

[0005] "Testing unit" refers to a standardized speed test platform or tool used to measure and evaluate network performance parameters.

[0006] User interface module" refers to a component of the system that enables user interaction, allowing input of test requests and display of results.

[0007] Test execution module" refers to a component responsible for initiating and managing the execution of network speed tests across multiple testing units.

[0008] Data collection module" refers to a component that aggregates and organizes the information generated by various testing units.

[0009] "Analyzing module" refers to a component that processes collected data to identify patterns, trends, and anomalies in network performance.

[0010] "Report generation module" refers to a component that creates visual representations and detailed reports based on analyzed network performance data.

[0011] Attribute" refers to a specific characteristic or factor used to categorize or evaluate network performance data, such as Internet Service Provider (ISP), geographic location, or time frame.

[0012] Processed data" refers to the information that has been organized, normalized, and prepared for analysis after collection from testing units.

[0013] Automated speed test routines" refers to pre-programmed sequences of network performance tests that can be executed without manual intervention at specified intervals.

[0014] Network speed test request" refers to a user-initiated command to evaluate specific network performance parameters using multiple testing platforms.

[0015] "Speed test" refers to the process of measuring various network performance parameters using standardized testing methodologies.

[0016] Standardized format" refers to a uniform data structure used to organize and represent network performance information from diverse sources.

[0017] Customized testing parameters" refers to user-defined settings for speed tests, including test duration, frequency, and specific testing units to be used.

[0018] Continuous monitoring" refers to the ongoing process of regularly executing speed tests and analyzing network performance over extended periods.

[0019] ISP performance" refers to the quality and consistency of internet service provided by a specific Internet Service Provider, as measured by various network performance parameters.

[0020] "Performance degradation" refers to a measurable decline in network quality or speed compared to expected or previous performance levels.

[0021] Trend analysis" refers to the examination of network performance data over time to identify patterns, improvements, or declines in service quality.

[0022] Network anomaly" refers to an unexpected or unusual event or measurement in network performance that deviates significantly from normal patterns.

[0023] Visual representation" refers to graphical displays of network performance data, such as charts, graphs, or heat maps, designed to facilitate easy understanding of complex information.BACKGROUND OF THE DISCLOSURE

[0024] The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

[0025] Networks have become increasingly complex due to higher speeds, increased interconnected units, and the integration of various sub-networks into larger ones. Users send different types of data simultaneously, including text, voice, video, and multimedia files. In both new and existing communication networks, a significant challenge is testing and troubleshooting to ensure network operators identify the causes of performance issues.

[0026] The demand for fast and reliable internet has increased, particularly for activities such as gaming, audio, and video streaming on mobile devices. Users require better network quality to minimize delays, leading to increased interest in real-time monitoring of network speed. Currently, numerous speed testing tools enable users to assess connectivity performance of user devices (such as personal computers, laptops, smartphones, and tablets). These speed testing tools establish aconnection to a nearest geographical test server node, typically within the user's Internet service provider's (ISP’s) infrastructure.

[0027] The primary function of speed tools is to provide users with information regarding the speed of their internet connection. This information is presented through two key metrics: upload speed and download speed. Upload speed represents the rate at which data is transmitted from the user's device to the internet, while download speed measures the rate at which data is received from the internet to the user's device. These metrics serve as fundamental indicators of the efficiency and effectiveness of the user's internet connection.

[0028] However, existing solutions have specific limitations. The existing solutions often provide isolated measurements without offering analysis across multiple testing platforms. The lack of comparative data prevents users and the ISPs from gaining a comprehensive view of network performance. The existing solutions require manual intervention for each test, which limits the ability to perform continuous, automated monitoring over extended periods. Thus, the existing manual approach hinders the detection of performance trends and patterns that emerge over time.

[0029] Additionally, the existing solutions fail to provide customizable testing parameters, which restricts user’s ability to tailor tests to specific needs or network conditions. The absence of user-defined schedules for automated testing limits the flexibility and usefulness of these tools for both individual users and network operators.

[0030] Further, the existing solutions lack advanced analytical capabilities. Many existing solutions fail to offer comparisons based on attributes such as the ISP identifier, user location, or time frame. The deficiency makes it difficult to identify performance discrepancies across different contexts or to make informed decisions about network improvements or ISP selection.

[0031] Furthermore, the existing solutions fail to generate comprehensive reports or visual representations of the network performance data. The absence of clear, actionable insights derived from collected data limits the practical value of these tools for both end-users and network administrators. The existing solutions face difficulty in providing a comprehensive, automated, and customizable solution for analyzing network performance parameters across multiple testing platforms.

[0032] There is, therefore, a need in the art to provide a method and a system that can overcome the shortcomings of the existing prior arts by offering automated, comparative analysis of network performance data from multiple sources, with customizable testing parameters and comprehensive reporting capabilities.SUMMARY OF THE DISCLOSURE

[0033] In an exemplary embodiment, a system for analyzing network performance parameters is described. The system comprises a memory and one or more processors coupled to the memory. The one or more processors are configured to execute a set of instructions stored in the memory. The set of instructions cause the one or more processors to system for analyzing network performance parameters. The one or more processors configured to execute a set of instructions stored therein to receive, by a user interface module, a network speed test request from a user. Trigger, by a test execution module, a plurality of testing units configured to perform a network speed test and generate information related to the one or more network performance parameters associated with the received network speed test request. Collect, by a data collection module, the generated information from the plurality of testing units. Process, by the one or more processors, the collected information to generate processed data corresponding to each of the network performance parameter. Analyze, by an analyzing module, the processed data corresponding to each of the network performance parameter based upon at least one attribute.

[0034] In some embodiments, the system is further configured to compare, by the analyzing module, the processed data corresponding to each testing unit of theplurality of testing units based on the at least one attribute. The system is further configured to generate, by a report generation module, at least one report including visual representation based on the comparison.

[0035] In some embodiments, the one or more network performance parameters comprise of a download speed, an upload speed, latency, a streaming speed, a quality of service, Internet Service Provider (ISP) performance, and bandwidth.

[0036] In some embodiments, the at least one attribute comprises of an ISP identifier, a location of the user, or a time frame.

[0037] In some embodiments, the data collection module is configured to collect the generated information in a pre-defined format. The pre-defined format comprises a structured data format that includes a timestamp of the network speed test; an identifier of the testing unit that performed the network speed test; measured values for each of the network performance parameter; and metadata associated with the network connection used for the network speed test.

[0038] In some embodiments, the one or more processors are further configured to receive, by the user interface module, customized testing parameters for the network speed test from the user. The customized testing parameters comprise at least one of: a selection of specific testing unit to be used for the network speed test; a defined test duration for the network speed test; a specified number of network speed test iterations to be performed; custom thresholds for acceptable performance levels for each of the network performance parameter; and preferred time intervals between successive network speed tests.

[0039] In some embodiments, the one or more processors are further configured to perform, by the test execution module, continuous monitoring of the ISP performance over time by: executing the automated network speed test routines at regular intervals as defined by a predetermined schedule; storing results of thenetwork speed test in the database; and calculating performance metrics for the ISP based on the stored results.

[0040] In another exemplary embodiment, a method for analyzing network performance parameters is described. The method comprises receiving, by a user interface module, a network speed test request from a user. The method further comprises triggering, by a test execution module, a plurality of testing units configured to perform a network speed test and generate information related to the one or more network performance parameters associated with the received network speed test request. The method further comprises collecting, by a data collection module, the generated information from the plurality of testing units. The method further comprises processing, by one or more processors, the collected information to generate processed data corresponding to each of the network performance parameter. The method further comprises analyzing, by an analyzing module, the processed data corresponding to each of the network performance parameter based upon at least one attribute.

[0041] In yet another exemplary embodiment, a user equipment (UE) communicatively coupled with a system is disclosed. The coupling comprises steps of receiving, by the system, a connection request, sending, by the system, an acknowledgment of the connection request to the UE, and transmitting a plurality of signals in response to the connection request. The system is configured for analyzing one or more network performance parameters. The system comprises a memory and one or more processors coupled to the memory. The one or more processors are configured to execute a set of instructions stored in the memory. The set of instructions cause the one or more processors to system for analyzing the one or more network performance parameters. The one or more processors configured to execute the set of instructions stored therein to receive, by a user interface module, a network speed test request from a user. Trigger, by a test execution module, a plurality of testing units configured to perform a network speed test and generate information related to the one or more network performance parameters associated with the received network speed test request. Collect, by a data collectionmodule, the generated information from the plurality of testing units. Process, by the one or more processors, the collected information to generate processed data corresponding to each of the network performance parameter. Analyze, by an analyzing module, the processed data corresponding to each of the network performance parameter based upon at least one attribute.

[0042] In yet another exemplary embodiment, 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 perform a method for analyzing one or more network performance parameters is described. The method comprises receiving, by a user interface module, a network speed test request from a user. The method further comprises triggering, by a test execution module, a plurality of testing units configured to perform a network speed test and generate information related to the one or more network performance parameters associated with the received network speed test request. The method further comprises collecting, by a data collection module, the generated information from the plurality of testing units. The method further comprises processing, by one or more processors, the collected information to generate processed data corresponding to each of the network performance parameter. The method further comprises analyzing, by an analyzing module, the processed data corresponding to each of the network performance parameter based upon at least one attribute.

[0043] The foregoing general description of the illustrative embodiments and the following detailed description thereof are merely exemplary aspects of the teachings of this disclosure and are not restrictive.OBJECTIVE OF THE PRESENT DISCLOSURE

[0044] Some of the objectives of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.

[0045] An objective of the present disclosure is to provide a system and a method that analyzes multiple parameters associated with a network in real-time.

[0046] Another objective of the present disclosure is to provide a system and a method that extracts data from multiple speed testing platforms and generates a comparative view representing parameters associated with the network, thereby reducing a response time.

[0047] Another objective of the present disclosure is to provide a system and a method that employs automation for speed test results comparison, thereby addressing challenges associated with manual testing by improving efficiency, consistency, accuracy, and scalability.

[0048] Another objective of the present disclosure is to provide a system and a method that enables users and service providers to assess and monitor internet service provider’s (ISP’s) performance through automated, multi-platform testing and analysis of various network performance parameters.

[0049] Another objective of the present disclosure is to provide a system and a method that automates speed test results comparison and enhances the assessment process by improving efficiency, reducing errors, and providing users and service providers with timely, customized, and data-driven insights into ISP performance.

[0050] Another objective of the present disclosure is to provide a system and method for analyzing network performance parameters that allows users to select specific parameters for analysis.

[0051] Another objective of the present disclosure is to provide a system and method that triggers multiple testing units to perform speed tests and generate information related to selected network performance parameters.

[0052] Another objective of the present disclosure is to provide a system and method that collects, processes, and analyzes data from multiple testing units to generate insights into network performance parameters.

[0053] Another objective of the present disclosure is to provide a system and method that compares processed data across different testing units based on attributes such as ISP identifier, location, or time frame.

[0054] Another objective of the present disclosure is to provide a system and method that generates visual representations and reports based on the analysis and comparison of network performance data.

[0055] Another objective of the present disclosure is to provide a system and method that allows users to customize testing parameters and schedules for automated speed tests.

[0056] Other objectives and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.BRIEF DESCRIPTION OF DRAWINGS

[0057] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same 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 drawings includes the disclosure of electrical components, electronic components or circuitry commonly used to implement such components.

[0058] FIG. 1 illustrates an exemplary network architecture for analyzing network performance parameters, in accordance with embodiments of the present disclosure.

[0059] FIG. 2 illustrates an exemplary system architecture for analyzing the network performance parameters, in accordance with embodiments of the present disclosure.

[0060] FIG. 3 illustrates an exemplary block diagram of the system for analyzing the network performance parameters, in accordance with embodiments of the present disclosure.

[0061] FIG. 4 illustrates an exemplary flow diagram of a method for analyzing the network performance parameters, in accordance with embodiments of the present disclosure.

[0062] FIG. 5 illustrates another exemplary flow diagram of the method for analyzing the network performance parameters, in accordance with embodiments of the present disclosure.

[0063] FIG. 6 illustrates an exemplary computer system in which or with which embodiments of the present disclosure may be implemented.

[0064] The foregoing shall be more apparent from the following detailed description of the disclosure.LIST OF REFERENCE NUMERALS100 - Network architecture102 - System104- Network106 - Centralized server108-1, 108-2. . . 108-N - User equipment(s) (UEs) / Computing device(s)110-1, 110-2... 110-N - Users202 - One or more processor(s)204- Memory206 - I / O interface(s)208 - Processing module(s)210 - Database212- User interface module214- Test execution module216- Data collection module218-Analyzing module220-Report generation module222-Other modules300- Block diagram302a, 302b, 302c- A plurality of testing units400, 500 -Flow diagram600 - A computer system610 - External Storage Device620 - Bus630 - Main Memory640 - Read Only Memory650 - Mass Storage Device660 - Communication Port(s)670- ProcessorDETAILED DESCRIPTION OF THE PRESENT DISCLOSURE

[0065] 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. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.

[0066] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in thefunction and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

[0067] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.

[0068] Also, it is noted that individual embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

[0069] The word “exemplary” and / or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and / or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similarto the term “comprising” as an open transition word without precluding any additional or other elements.

[0070] Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0071] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and / or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. As used herein, the term “and / or” includes any and all combinations of one or more of the associated listed items. It should be noted that the terms “mobile device”, “user equipment”, “user device”, “communication device”, “device” and similar terms are used interchangeably for the purpose of describing the invention. These terms are not intended to limit the scope of the invention or imply any specific functionality or limitations on the described embodiments. The use of these terms is solely for convenience and clarity of description. The invention is not limited to any particular type of device or equipment, and it should be understood that other equivalent terms or variations thereof may be used interchangeably without departing from the scope of the invention as defined herein.

[0072] As used herein, an “electronic device”, or “portable electronic device”, or “user device” or “communication device” or “user equipment” or “device” refers to any electrical, electronic, electromechanical and computing device. The user device is capable of receiving and / or transmitting one or parameters, performing function / s, communicating with other user devices and transmitting data to the other user devices. The user equipment may have a processor, a display, a memory, a battery and an input-means such as a hard keypad and / or a soft keypad. The user equipment may be capable of operating on any radio access technology including but not limited to IP-enabled communication, Zig Bee, Bluetooth, Bluetooth Low Energy, Near Field Communication, Z-Wave, Wi-Fi, Wi-Fi direct, etc. For instance, the user equipment may include, but not limited to, a mobile phone, smartphone, virtual reality (VR) devices, augmented reality (AR) devices, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, mainframe computer, or any other device as may be obvious to a person skilled in the art for implementation of the features of the present disclosure.

[0073] Further, the user device may also comprise a “processor” or “processing unit” includes processing unit, wherein processor refers to any logic circuitry for processing instructions. The processor may be a general-purpose processor, a special purpose processor, a conventional processor, a digital signal processor, a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits, Field Programmable Gate Array circuits, any other type of integrated circuits, etc. The processor may perform signal coding data processing, input / output processing, and / or any other functionality that enables the working of the system according to the present disclosure. More specifically, the processor is a hardware processor.

[0074] As portable electronic devices and wireless technologies continue to improve and grow in popularity, the advancing wireless technologies for data transfer are also expected to evolve and replace the older generations of technologies. In the field of wireless data communications, the dynamic advancement of various generations of cellular technology are also seen. Thedevelopment, in this respect, has been incremental in the order of second generation (2G), third generation (3G), fourth generation (4G), fifth generation (5G), sixth generation (6G), and more such generations are expected to continue in the forthcoming time.

[0075] While considerable emphasis has been placed herein on the components and component parts of the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiment as well as other 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 interpreted merely as illustrative of the disclosure and not as a limitation.

[0076] The present disclosure provides a system and method for real-time analysis and visualization of multiple network performance parameters. The present disclosure allows users to monitor their internet connection and quickly identify potential issues. The present disclosure enables users to track changes in the connection speed over time, offering insights into possible causes of reduced internet speeds. By visualizing multiple network performance parameters and their details, the present disclosure offers significant improvements over existing speed test tools, providing users with a comprehensive view of their internet connection's performance.

[0077] The various embodiments throughout the disclosure will be explained in more detail with reference to FIGS. 1-6.

[0078] FIG. 1 illustrates an exemplary network architecture (100) of a system (102) for analyzing network performance parameters, in accordance with embodiments of the present disclosure.

[0079] As illustrated in FIG. 1, one or more user equipment (108-1, 108- 2...108-N) may be connected to a system (102) for analyzing network performanceparameters in a network environment through a network (104). A person of ordinary skill in the art will understand that the one or more user equipment (108-1, 108- 2...108-N) may be collectively referred to as user equipment (108) and individually referred to as a user equipment (108). The user equipment (108) is operated by users (110-1, 110-2...110-N).

[0080] In an embodiment, the user equipment (108) may include, but not be limited to, a mobile phone, a tablet, a smartphone, a desktop computer, a laptop computer, or any other device capable of interacting with the system (102) for network performance analysis. These devices serve as the interface between the user and the network performance analysis system, allowing users to initiate tests, view results, and receive recommendations.

[0081] The user equipment (108) should possess certain key capabilities to effectively interact with the system. It must have network connectivity, enabling it to connect to the internet via various means such as Wi-Fi, cellular data, or wired connections, as the device itself will be the subject of the network performance tests. User interface capabilities, including a screen and input method like a touchscreen, keyboard, or mouse, are essential to allow users to interact with the system's interface, select test parameters, and view results.

[0082] Furthermore, the user equipment should have sufficient processing power to run the necessary software or web applications that communicate with the system (102) and potentially perform local calculations or data processing. It should support compatible software, either through a dedicated application installed on the device or by accessing a web-based interface through a browser, which connects to the system (102) for initiating tests and receiving results. Adequate storage capacity is also beneficial, allowing users to store historical test results or downloaded reports for offline viewing and analysis.

[0083] In an embodiment, the network (104) may include, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, acircuit- switched network, a 4G network, a 5G network, and 6G networks, or some combination thereof. The system (102) may be connected to a centralized server (106). This diverse range of network types ensures that the system can analyze performance across various connectivity scenarios. Wireless networks include cellular networks like 4G, which offers broadband internet for mobile devices, 5G, which provides significantly faster data transfer speeds and lower latency, and the emerging 6G technology, expected to offer even more advanced capabilities such as terabit-per- second data rates and microsecond latency. These cellular networks coexist with other wireless technologies like Wi-Fi. Wired networks, including fiber-optic and Ethernet connections, offer stable, high-speed connectivity. The internet represents the global network of interconnected computer networks, while intranets are private networks within organizations. Public networks are accessible to anyone, whereas private networks restrict access to authorized users. Packet- switched networks, common in internet communications, break data into packets for efficient transmission, while circuit- switched networks, often used in traditional telephone systems, establish dedicated circuits for each communication session. By encompassing this wide array of network types, the system can provide comprehensive performance analysis across the entire spectrum of modern networking technologies.

[0084] Although FIG. 1 shows exemplary components of the network architecture (100), in other embodiments, the network architecture (100) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 1. Additionally, or alternatively, one or more components of the network architecture (100) may perform functions described as being performed by one or more other components of the network architecture (100).

[0085] FIG. 2 illustrates an exemplary system architecture (200) for analyzing the network performance parameters, in accordance with embodiments of the present disclosure.

[0086] Referring to FIG. 2, in an embodiment, the system (102) may include one or more processors (202). The one or more processors (202) may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and / or any devices that process data based on operational instructions. Among other capabilities, the one or more processors (202) may be configured to fetch and execute computer-readable instructions stored in a memory (204) of the system (102). 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 perform network performance parameter analysis in the network environment.

[0087] In an embodiment, the system (102) may include VO interface(s) (206). The VO interface(s) (206) may comprise a variety of interfaces, for example, interfaces for data input and output devices, storage devices, and the like. The VO interface(s) (206) may facilitate communication through the system (102). The VO interface(s) (206) may also provide a communication pathway for one or more components of the system (102). Examples of such components include, but are not limited to, processing module(s) (208), and a database (210) for storing network performance data.

[0088] The processing module(s) (208) may include a user interface module (212), a test execution module (214), a data collection module (216), an analyzing module (218), a report generation module (220) and other modules (222). These modules work in concert to perform network performance parameter analysis.

[0089] The user interface module (212) may receive network speed test requests from users (110-1, 110-2...110-N). These users may include network operators, engineers, analysts, and administrators who interact with the system (102) to perform network analysis and optimization tasks. The test execution module (214) may trigger a plurality of testing units to perform speed tests and generate information related to selected network performance parameters. The datacollection module (216) may collect the generated information from the plurality of testing units. The analyzing module (218) may analyze and compare the processed data corresponding to each of the selected network performance parameters based upon at least one attribute. The report generation module (220) may generate at least one report or visual representation based on the comparison, offering insights into network performance, service utilization, and potential areas for network improvement to better serve the user base.

[0090] In an embodiment, the processing module(s) (208) may be implemented as a combination of hardware and programming to implement one or more functionalities of the processing module(s) (208). For example, the programming for the processing module(s) (208) may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing module(s) (208) may comprise a processing resource (for example, one or more processors), to execute such instructions.

[0091] Although FIG. 2 shows exemplary components of the system (102), in other embodiments, the system (102) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 2. The various embodiments may be explained in more detail with reference to FIG. 2 and FIG. 3.

[0092] The system (102) for analyzing network performance parameters may comprise a memory (204) and one or more processors (202) coupled to the memory (204). The one or more processors (202) may be configured to execute a set of instructions stored in the memory (204) to perform various operations related to network performance analysis.

[0093] The system (102) may include a user interface module (212) that may be configured to receive a network speed test request from a user. The user interface module (212) may provide a graphical user interface (GUI) accessible through various devices such as personal computers, smartphones, or tablets, allowing users to interact seamlessly with the system (102). The network speed test requestreceived by the user interface module (212) may comprise a selection of network performance parameters to be analyzed. These network performance parameters may be presented to the user as a list of options or checkboxes, enabling easy selection of desired metrics.

[0094] The network performance parameters selected by the user may include one or more of a download speed, an upload speed, latency, a streaming speed, a quality of service, Internet Service Provider (ISP) performance, and bandwidth.

[0095] Download speed, typically measured in megabits per second (Mbps), may refer to the rate at which data is transferred from the internet to the user's device. For example, a download speed of 100 Mbps would allow a user to download a 1 GB file in approximately 1.5 minutes.

[0096] Upload speed, also measured in Mbps, may indicate the rate at which data is sent from the user's device to the internet. This metric is particularly important for activities such as video conferencing or uploading large files to cloud storage.

[0097] Latency, often measured in milliseconds (ms), may represent the time it takes for data to travel from the source to the destination. Lower latency values, such as 20 ms, may indicate a more responsive connection, which is crucial for online gaming or real-time applications. Streaming speed may be a specialized metric that focuses on the network's ability to handle continuous data flow, such as video streaming. It may be measured in terms of buffer time or the quality of video that can be streamed without interruption.

[0098] Quality of Service (QoS) may be a comprehensive metric that evaluates the overall performance of the network, taking into account factors such as packet loss, jitter, and consistency of service.

[0099] Internet Service Provider (ISP) performance may refer to a set of metrics specific to the Internet Service Provider, which may include uptime, customer service responsiveness, and adherence to advertised speeds.

[0100] Bandwidth, typically measured in Mbps, may represent the maximum amount of data that can be transmitted over an internet connection in a given amount of time.

[0101] By allowing users to select specific parameters, the system (102) may provide flexibility in tailoring the analysis to individual needs and preferences. For instance, a user who frequently engages in online gaming might prioritize latency and download speed, while a content creator might be more interested in upload speed and streaming performance. This customization capability may enable users to focus on the aspects of network performance that are most relevant to their usage patterns and requirements.

[0102] Upon receiving the network speed test request, a test execution module (214) of the system (102) may trigger a plurality of testing units (302a, 302b, 302c) to perform speed tests. The test execution module (214) may act as a central coordinator, initiating and managing the testing process across multiple platforms simultaneously. The test execution module (214) may interpret the user's requests and translate them into specific instructions for each testing unit.

[0103] The plurality of testing units (302a, 302b, 302c) may comprise a set of standardized speed test platforms Each testing unit may have its own methodology and server network, providing a diverse range of data points for analysis. For example, testing unit 302a may utilize a nearby server for testing, while testing unit 302b may use a geographically distant server, offering insights into both local and long-distance network performance.

[0104] The plurality of testing units (302a, 302b, 302c) may generate information related to the selected network performance parameters. The information generated may include raw data such as the number of bytes transferred, the time taken for transfers, and server response times. For instance, if download speed is a selected parameter, each testing unit may perform a series of downloads of varying file sizes and calculate the average speed. Similarly, forlatency, the units may send multiple ping requests and measure the round-trip time for each.

[0105] The use of multiple standardized testing units may enhance the reliability and comprehensiveness of the speed test results. By employing various testing methodologies and server locations, the system (102) may provide a more accurate representation of the user's network performance. This approach may help mitigate potential biases or limitations of any single testing platform. For example, if one testing unit (302a) shows an unusually low speed due to temporary server issues, results from other units (302b, 302c) may help identify this as an outlier rather than a true reflection of the network's performance.

[0106] Moreover, using standardized platforms may ensure consistency in testing over time, allowing for meaningful comparisons of network performance across different time periods or locations. This standardization may also facilitate benchmarking against industry norms or advertised service levels, providing users with valuable context for interpreting their results.

[0107] The system (102) may further include a data collection module (216) that may be configured to collect the generated information from the plurality of testing units. This data collection module (216) may act as a centralized repository, aggregating data from various sources into a unified format for further processing and analysis.

[0108] The data collection module (216) may collect the generated information in a pre-defined format. The pre-defined format may be designed to ensure consistency and compatibility across the plurality of testing units and parameters. For instance, the format may utilize a standardized data structure such as JavaScript Object Notation (JSON) or Extensible Markup Language (XML), which are widely used for data interchange due to their human-readable nature and ease of parsing by machines.

[0109] The pre-defined format may comprise a structured data format that includes several key components. The pre-defined format may include a timestamp of the speed test, recorded in a standardized format, allowing for precise chronological ordering of test results and facilitating time -based analysis. The format may also include an identifier of the testing unit that performed the test, which may be a unique alphanumeric string (e.g., "TU302a") that distinguishes each testing unit, enabling the system to track and compare results across different platforms.

[0110] Furthermore, the format may contain measured values for each of the selected network performance parameters. These values may be recorded with appropriate units and precision. For example, download and upload speeds may be recorded in Mbps with two decimal places (e.g., 95.67 Mbps), while latency may be recorded in milliseconds as whole numbers (e.g., 23 ms).

[0111] The format may also include metadata associated with the network connection used for the test. This metadata may encompass a variety of contextual information that could influence test results, such as the type of network connection, the Internet Service Provider (ISP) name, the geographic location of the test, the device type used for testing, and the operating system and browser version, if applicable.

[0112] The structured approach to data collection may facilitate efficient processing and analysis of the collected information. By organizing data in a consistent, well-defined format, the system (102) may more easily perform operations such as filtering, sorting, and aggregating results. For example, the system could quickly retrieve all test results from a specific ISP or calculate average speeds for a particular geographic area over a given time period. This structured format may also simplify the process of data visualization and report generation, as the data can be easily mapped to various chart types or tabular formats.

[0113] Moreover, the standardized data structure may enhance the scalability and maintainability of the system (102). As new testing units or performanceparameters are added in the future, they can be seamlessly integrated into the existing data format, ensuring backward compatibility and consistent analysis capabilities over time.

[0114] The one or more processors (202) of the system (102) may process the collected information to generate processed data corresponding to each of the selected network performance parameters. The processing stage may serve as a crucial intermediary step between raw data collection and in-depth analysis, transforming diverse inputs into a unified format. For instance, this unified format might consist of standardized data entries, each representing a single speed test. Each entry could include a unique test identifier, a timestamp, an identifier for the testing unit used, the ISP identifier, the user's location, and various network performance measurements. These measurements might include download speed and upload speed in megabits per second, latency in milliseconds, and packet loss as a percentage. By organizing the data in this consistent manner, the system can efficiently analyze and compare results across different tests, time periods, and network conditions. This standardization enables the system to perform comprehensive analyses and generate meaningful insights from the collected network performance data.

[0115] The processing may involve organizing, parsing, and structuring the collected information into a standardized format. Organizing may entail grouping related data points, such as all tests from a specific ISP or geographic location. Parsing may involve extracting relevant information from the raw data, potentially dealing with different data formats from various testing units. Structuring may refer to arranging this extracted data into a predefined, consistent format that facilitates further analysis.

[0116] The standardized format may comprise a tabular structure with a set of predefined columns for each of the selected network performance parameters. This tabular structure may resemble a spreadsheet or a relational database table, where each row represents a single speed test, and columns represent different attributesor measurements. The columns may include Test identifier (ID), Timestamp, Testing Unit, ISP, Location, Download Speed (Mbps), Upload Speed (Mbps), Latency (ms), Jitter (ms), and Packet Loss (%).

[0117] The standardized format may also include normalized values for the selected network performance parameters across the plurality of testing units (302a, 302b, 302c). Normalization may involve converting all measurements to a common scale or unit. For instance, if one testing unit reports download speeds in Mbps and another in Kbps, the system may convert all speeds to Mbps for consistency. Normalization may also involve statistical techniques such as min-max scaling or z-score normalization to make disparate metrics comparable.

[0118] A unified time format for all timestamps may be implemented in the standardized format. This might involve converting all timestamps to Coordinated Universal Time (UTC) and using a consistent format. This unification may enable accurate chronological ordering and time-based analysis across different time zones and daylight-saving time changes.

[0119] Standardized identifiers for ISPs and geographic locations may be incorporated. For ISPs, this might involve mapping various name formats to a single standardized identifier, ensuring consistency across different data sources and user inputs. Geographic locations may be standardized using recognized international codes for regions and cities, facilitating consistent geographical analysis. This standardization process helps eliminate inconsistencies in naming conventions and location descriptions, enabling more accurate and reliable comparisons across different network performance tests and data sets. By using universally recognized coding systems for locations, the system can perform precise geographic -based analyses without being hindered by variations in how places are named or described in different contexts or languages.

[0120] The standardized format may include calculated aggregate metrics for each of the selected network performance parameters across speed tests. These metrics may include averages, medians, percentiles, standard deviations, andminimum and maximum values. For example, for download speeds, the system might calculate an average of 95.5 Mbps, a median of 94.2 Mbps, a 95th percentile of 120.3 Mbps, a standard deviation of 15.7 Mbps, a minimum of 45.6 Mbps, and a maximum of 150.2 Mbps. The system calculates various percentile values, including the 95th percentile, for each of the selected network performance parameters. The 95th percentile represents a value below which 95% of the observations fall. In the context of network performance, this metric is particularly useful for understanding peak performance levels while excluding extreme outliers. It provides insight into the upper range of typical performance, offering a more stable measure of high-end speeds than the maximum value alone. This percentile is often used in network planning and service level agreements as it represents a realistic measure of top performance that users can expect to experience regularly.

[0121] This standardization may enable consistent and comparable analysis across different tests and parameters. It may allow for meaningful comparisons between different ISPs, locations, or time periods, regardless of the original data source. For instance, the system could easily generate reports comparing the median download speeds of different ISPs in a specific city or track the 95th percentile latency for a particular ISP over time.

[0122] Moreover, this standardized format may facilitate more advanced analytical techniques, such as trend analysis, anomaly detection, or predictive modelling. By providing a uniform, well-structured dataset, the system may enable data scientists or automated algorithms to apply sophisticated statistical or machine learning techniques to derive deeper insights from the network performance data.

[0123] The system (102) may include an analyzing module (218) that may be configured to analyze the processed data corresponding to each of the selected network performance parameters based upon at least one attribute. This analyzing module (218) may serve as the analytical engine of the system, employing various statistical and computational techniques to extract meaningful insights from the standardized data.

[0124] The at least one attribute upon which the analysis may be based may comprise an ISP identifier, a location of the user, or a time frame. These attributes may serve as dimensions along which the network performance data can be segmented and compared.

[0125] When analyzing based on the attribute, the system may compare performance metrics across different service providers. This analysis may involve calculating average speeds for each ISP, comparing latency and jitter across ISPs, assessing reliability by examining metrics like packet loss or service uptime for each ISP, and evaluating consistency of service by looking at the standard deviation of performance metrics for each ISP. For example, ISP A may have an average download speed of 95 Mbps, while ISP B may average 85 Mbps. The system might find that ISP C has a median latency of 20 ms, while ISP D's median latency is 35 ms.

[0126] For the location attribute, the analyzing module (218) may examine how network performance varies across different geographical areas. This analysis may include creating performance heat maps to visualize average download speeds across a city or region, identifying underserved areas by highlighting locations with consistently lower performance metrics, comparing urban vs. rural performance, and analyzing the impact of distance from network nodes. The system might find that urban areas have average download speeds of 100 Mbps, while rural areas average 50 Mbps.

[0127] When considering the time frame attribute, the system may analyze how network performance changes over time. This temporal analysis may involve identifying peak usage hours, tracking long-term trends, detecting seasonal variations, and evaluating the impact of network upgrades. The system might detect that average speeds drop by 20% between 7 PM and 10 PM daily or find that network congestion increases during holiday periods.

[0128] By analyzing the data based on these attributes, the system (102) may provide insights into how network performance varies across different serviceproviders, geographical locations, or time periods. These insights may be valuable for various stakeholders, including users, ISPs, policymakers, and researchers. Users may understand which ISP offers the best performance in their area or identify optimal times for bandwidth-intensive activities. ISPs may identify areas for improvement, either in specific locations or during particular time frames. Policymakers may assess digital divide issues by comparing performance across different regions or demographics. Researchers may study patterns and trends in network performance to inform future network designs or policies.

[0129] The analyzing module (218) may employ various analytical techniques to derive these insights, such as descriptive statistics, inferential statistics, time series analysis, and machine learning algorithms. These techniques may include calculating means, medians, and standard deviations to summarize performance, using t-tests or Analysis of Variance (ANOVA) to determine if differences between groups are statistically significant, employing moving averages or exponential smoothing to identify trends and seasonality in performance over time, and utilizing clustering techniques to group similar performance profiles or predictive models to forecast future performance based on historical data.

[0130] Through these analyses, the system (102) may transform raw network performance data into actionable insights, enabling users to make informed decisions about their internet services and helping stakeholders to understand and improve network performance across various dimensions.

[0131] The analyzing module (218) may also be configured to compare the processed data corresponding to each testing unit of the plurality of testing units (302a, 302b, 302c) based on the at least one attribute. This comparative analysis may serve as a crucial mechanism for cross-validation and quality assurance of the network performance measurements.

[0132] The comparison process may involve several sophisticated analytical techniques:i. Correlation Analysis: The system may calculate correlation coefficients between the results of different testing units. For example, if testing unit 302a and 302b show a high positive correlation (e.g., r = 0.95) for download speeds, it may indicate consistency in measurements across platforms. ii. Bland-Altman Analysis: This statistical method may be used to assess agreement between two measurement techniques. The system might plot the differences between two testing units against their means, helping to identify any systematic bias or outliers. iii. Intraclass Correlation Coefficient (ICC): This statistical measure may be employed to assess the reliability and consistency of measurements across all testing units. An ICC close to 1 would indicate high agreement among the testing units. iv. Analysis of Variance (ANOVA): The system may use ANOVA to determine if there are statistically significant differences in the results from different testing units. For instance, it might reveal that testing unit 302c consistently reports higher latency values compared to 302a and 302b.

[0133] The comparison may allow for a comprehensive evaluation of network performance across different testing platforms, potentially highlighting any discrepancies or consistencies in the results. Some potential outcomes of this comparative analysis might include: i. Consistency Verification: If all testing units (302a, 302b, 302c) show similar results for a given network parameter (e.g., download speeds within ±5% of each other), it may increase confidence in the accuracy of the measurements. ii. Outlier Detection: The system might identify that one testing unit consistently reports significantly different results. For example, if testing unit 302a reports download speeds 50% higher than 302b and302c, it may warrant further investigation into the testing methodology or potential issues with that unit. iii. Methodology Insights: Differences in results might reveal insights about the testing methodologies. For instance, if testing unit 302b consistently shows lower latency than 302a and 302c, it might indicate that 302b uses a different server location or routing path for its tests. iv. Environmental Factors: Discrepancies might highlight the impact of environmental factors on different testing methods. For example, testing unit 302c might be more sensitive to network congestion, showing greater variation in results during peak usage hours compared to 302a and 302b. v. ISP-Specific Behaviors: The comparison might reveal that certain ISPs perform differently across testing platforms. For instance, ISP X might show better performance on testing unit 302a, while ISP Y performs better on 302b, potentially indicating optimization for specific testing methodologies. vi. Temporal Patterns: The system might identify that discrepancies between testing units are more pronounced during certain time periods, possibly indicating time-dependent factors affecting measurement accuracy.

[0134] By conducting the comprehensive comparison, the system (102) may enhance the reliability and robustness of its network performance analysis. It may allow users to have greater confidence in the reported results, knowing that they have been cross validated across multiple testing platforms. Moreover, any consistent discrepancies identified may serve as valuable data points themselves, potentially revealing nuances in network behaviour or testing methodologies that single-platform analyses might miss.

[0135] The multi-platform comparative approach may also future proof the system against potential biases or limitations of any single testing methodology. As internet technologies and testing methods evolve, the system's ability to compare and synthesize results from multiple sources may ensure its continued relevance and accuracy in assessing network performance.

[0136] Based on the comparison performed by the analyzing module (218), a report generation module (220) of the system (102) may generate at least one report or visual representation. This report generation module (220) may serve as the interface between the complex analytical processes and the end-user, translating data-driven insights into accessible, actionable information.

[0137] The report generation module (220) may produce various types of reports, including executive summaries, detailed technical reports, and performance dashboards. Executive summaries may provide concise overviews of key performance metrics, suitable for quick review by decision-makers. For example, a summary might state, "Your network averaged 95 Mbps download speed over the past month, with 99.9% uptime. This performance ranks in the top 10% of similar ISP plans in your area."

[0138] Detailed technical reports may contain comprehensive in-depth analysis of all measured parameters, statistical tests, and methodologies used. These reports might include sections on speed test results (download, upload, latency), consistency of service (jitter, packet loss), comparative analysis (ISP benchmarking, geographic comparisons), trend analysis (performance over time), and anomaly detection and explanations.

[0139] Performance dashboards may offer interactive digital interfaces allowing users to explore data dynamically. Users might be able to filter results by date range, location, or ISP, and drill down into specific metrics of interest.

[0140] The visual representations generated by the report generation module (220) may include line graphs illustrating performance trends over time, bar charts comparing performance across different categories, heat maps visualizing performance variations across geographic areas, scatter plots showing relationships between different metrics, box plots displaying the distribution of performance metrics, and radar charts comparing multiple performance metrics across different scenarios.

[0141] The reports or visual representations may provide users with easily understandable summaries of the network performance analysis. For example, a homeowner might receive a monthly report showing their internet performance trends, how it compares to their neighbors, and recommendations for optimal usage times for high-bandwidth activities. A small business owner might get a dashboard comparing different ISP options in their area, with projections of how each would handle their typical workload.

[0142] The information presented in these reports may potentially aid in decision-making regarding network services or troubleshooting. For instance, a user experiencing frequent video call disruptions might see in their report that their upload speed drops significantly during work hours, prompting them to upgrade their service plan. An ISP might use aggregated reports to identify areas with consistently poor performance, informing their network expansion plans. A remote worker might use their performance dashboard to determine the best times for scheduling important video conferences based on historical network performance data.

[0143] By translating complex network performance data into clear, actionable insights, the report generation module (220) may empower users to make informed decisions about their internet services, optimize their network usage, and effectively troubleshoot issues when they arise.

[0144] The system (102) may offer additional customization options to users, enhancing its flexibility and adaptability to diverse user needs. The one or more processors (202) may be further configured to receive, through the user interface module (212), customized testing parameters for the speed tests from the user. This customization feature may transform the system from a one-size-fits-all solution into a highly personalized network analysis tool.

[0145] These customized testing parameters may comprise several key elements. Users may have the ability to select specific testing units (302a, 302b, 302c) to be used for the speed tests. This selection may allow users to choose which testing platforms they prefer or trust most. For instance, a user might select testing unit 302a for its widespread use and recognition, while another user might prefer testing unit 302b due to its simplicity and focus on video streaming performance. More technically inclined users might opt for testing unit (302c) for its detailed network protocol performance measurements. Each testing unit offers distinct features and methodologies for assessing network performance. By allowing users to select their preferred testing units, the system accommodates various user preferences and testing requirements. This flexibility enables users to tailor the testing process to their specific needs, whether they prioritize general popularity, specific use cases like video streaming, or in-depth technical analysis of network protocols. The ability to choose between different testing units also allows for cross- validation of results, potentially uncovering discrepancies or consistencies across different testing methodologies.

[0146] The defined test duration parameter directly impacts the accuracy and comprehensiveness of the speed test results. A user setting a longer duration, such as 30 seconds instead of the default 10 seconds, allows the system to capture more data points and potentially reveal speed fluctuations that shorter tests might miss. For example, a 30-second test might show that while the initial speed burst reaches 100 Mbps, the sustained speed over 30 seconds averages to 80 Mbps, providing a more realistic picture of the user's typical experience.

[0147] The number of test iterations specified by the user affects the statistical reliability of the results. For instance, if a user sets the system to perform 10 iterations of each test over a 24-hour period, the resulting data will provide insight into performance variations throughout the day. This could reveal patterns such as consistent speed drops during peak usage hours (e.g., 8-10 PM), which might not be apparent from a single test or fewer iterations.

[0148] Custom thresholds for acceptable performance levels enable the system to tailor its analysis and alerts to the user's specific needs. For example, if a user sets a minimum acceptable download speed of 50 Mbps, the system will flag any tests that fall below this threshold. This could result in a report highlighting that while the average speed meets this threshold, 20% of tests fall below it during evening hours, suggesting potential issues with network congestion during these times.

[0149] The time intervals between successive tests, as defined by the user, impact the granularity of the performance data collected. For instance, a user who sets tests to run every hour will get a more detailed view of performance fluctuations compared to one who sets daily tests. This could be particularly revealing for users trying to diagnose intermittent issues. For example, hourly tests might reveal that speeds consistently drop every day between 3-4 PM, coinciding with when local schools let out, a pattern that might be missed with less frequent testing.

[0150] These customized parameters work in concert to provide a tailored testing experience. For example, a user might configure the system to run 30-second tests using at least two testing units, performing 5 iterations every 2 hours, with custom thresholds of 100 Mbps for download and 20 Mbps for upload speeds. This configuration would provide a comprehensive view of network performance throughout the day, with enough data points to identify trends and issues, whilefocusing on the speeds that matter most to that particular user. The resulting analysis might reveal that while first testing unit shows consistent performance, second testing unit results drop below the threshold every evening, suggesting a potential issue with the path to second testing unit servers during high-traffic periods.

[0151] The system may also allow users to define the test duration for each speed test. This customization may enable users to specify how long each individual test should run. Users might opt for a quick 5-second test for routine checks, a more comprehensive 30-second test for detailed analysis, or an extended 5-minute test to capture performance variations over a longer period. The flexibility in test duration may cater to various user needs, from quick daily checks to in-depth troubleshooting sessions.

[0152] Users may also have the option to specify the number of test iterations to be performed. This feature may allow users to determine how many times the test should be repeated. A user might choose a single test for a quick snapshot of current performance or opt for 5 iterations to get an average performance metric. Some users might even set up 24 hourly tests over a day to capture daily performance fluctuations. This iterative testing capability may provide more robust and representative data, especially in scenarios where network performance may vary over time.

[0153] The system may further allow users to set custom thresholds for acceptable performance levels for each of the selected network performance parameters. This customization may enable users to define their own standards for what constitutes acceptable performance. For example, a user might set a minimum acceptable threshold of 50 Mbps for download speed, 10 Mbps for upload speed, a maximum acceptable threshold of 50 ms for latency, and a maximum acceptable threshold of 1% for packet loss. These custom thresholds may allow for moremeaningful analysis and reporting, tailored to each user's specific needs and expectations.

[0154] Additionally, users may have the ability to set preferred time intervals between successive tests. This feature may allow users to specify how frequently they want tests to be conducted. Some users might prefer tests every hour for detailed monitoring, while others might opt for daily tests at a specific time to capture peak usage performance. Others might choose weekly tests for regular check-ups. This scheduling flexibility may enable users to align their network performance monitoring with their usage patterns and troubleshooting needs.

[0155] The customization may allow users to tailor the testing process to their specific requirements or network conditions. An online gamer, for instance, might configure the system to run short latency tests every 30 minutes during their typical gaming hours, with a strict latency threshold of 20 ms. A small business owner might set up daily comprehensive tests during off-hours, with thresholds matching their Service Level Agreement (SLA) with their ISP. A home user interested in troubleshooting intermittent issues might configure hourly tests over a week, using multiple testing units for cross-validation.

[0156] The benefits of this customization may be manifold. By allowing users to define their own parameters, the system ensures that the tests are relevant to each user's specific use case and expectations. Users can avoid unnecessary testing by scheduling tests at intervals that make sense for their usage patterns, thereby improving efficiency. Custom thresholds allow for more meaningful alerts and reports, based on what constitutes good or poor performance for each specific user. The ability to run multiple iterations or tests at specific times can help users identify patterns in network performance issues, aiding in troubleshooting efforts. By allowing selection of specific testing units, users can compare results across different testing methodologies. Moreover, users can balance the thoroughness oftesting with the resources they're willing to allocate to network performance monitoring.

[0157] Through the customization, the system (102) may provide a more tailored and valuable service to each user, adapting to their unique network environments, usage patterns, and performance expectations. This level of personalization may significantly enhance the utility of the network performance analysis, making it a more effective tool for a wide range of users, from casual home internet users to IT professionals managing complex network infrastructures. The flexibility and adaptability offered by these customization options may ultimately lead to more informed decision-making and more effective network management for all users of the system.

[0158] The system (102) may also support automated testing capabilities, enhancing its ability to provide continuous and comprehensive network performance monitoring. The one or more processors (202) may be configured to execute, through the test execution module (214), automated speed test routines. These routines may represent a significant advancement in network performance testing, moving beyond manual, ad-hoc tests to a more systematic and consistent approach.

[0159] These automated speed test routines may be designed with several key functionalities in mind. First, they may initiate speed tests at predetermined intervals without manual intervention. This capability may allow for regular, consistent testing without requiring the user to remember or manually initiate each test. For instance, the system might automatically run a speed test every hour, providing a detailed picture of network performance throughout the day.

[0160] Secondly, the automated routines may sequentially activate each of the plurality of testing units (302a, 302b, 302c) to perform their respective speed tests. This sequential activation may ensure that each testing platform is utilized,providing a comprehensive view of network performance across different testing methodologies. For example, the system might first run a test using testing unit 302a, followed by 302b, and then 302c, cycling through all available testing platforms.

[0161] The automated routines may also be responsible for collecting test results from each of the activated testing units. This collection process may involve gathering various performance metrics such as download speed, upload speed, latency, and packet loss from each testing platform. The system may normalize these results to ensure consistency across different testing units, allowing for easier comparison and analysis.

[0162] Finally, the automated routines may store the collected test results in a database (210). This storage capability may be crucial for building a historical record of network performance over time. The database (210) may be structured to allow for efficient retrieval and analysis of past test results, enabling trend analysis and long-term performance tracking.

[0163] This automation may enable continuous monitoring of network performance without requiring constant user input. Users may benefit from having a constant stream of up-to-date performance data without needing to manually initiate tests or record results. This continuous monitoring may be particularly valuable for identifying intermittent issues or performance patterns that might be missed with sporadic manual testing.

[0164] To further enhance the automation capabilities, the system (102) may allow users to define schedules for the automated speed test routines. This scheduling feature may provide an additional layer of customization, allowing users to tailor the timing and frequency of tests to their specific needs and network usage patterns.

[0165] The one or more processors (202) may be configured to receive, through the user interface module (212), a user-defined schedule for executing the automated speed test routines. This user-defined schedule may comprise several key elements. First, it may include a start date and time for beginning the automated speed tests. This feature may allow users to align the start of testing with specific events or time periods of interest. For example, a user might schedule tests to begin at the start of a new billing cycle with their ISP.

[0166] The user-defined schedule may also specify the frequency of test execution. Users might choose to run tests hourly, daily, or at any other interval that suits their needs. For instance, a user might schedule tests to run every 15 minutes during their typical working hours to closely monitor their network performance during critical periods.

[0167] Additionally, the schedule may include a duration for running the automated speed tests. This duration setting may allow users to define how long the automated testing should continue. Users might set up short-term intensive testing periods, such as hourly tests for a week, or long-term monitoring, such as daily tests over several months.

[0168] This scheduling feature may provide users with the flexibility to conduct tests during specific time periods of interest or to align with their network usage patterns. For example, a business user might schedule more frequent tests during business hours and fewer tests during off-hours. A home user might set up tests to run more often during evening hours when they typically stream video or play online games.

[0169] The combination of automated testing routines and user-defined scheduling may create a powerful tool for comprehensive network performance monitoring. Users may benefit from consistent, regular testing without the need for constant manual intervention, while still maintaining the flexibility to customize thetesting schedule to their specific needs and interests. This approach may lead to more thorough and relevant network performance data, enabling users to make more informed decisions about their internet services and usage patterns.

[0170] The system (102) may be capable of performing continuous monitoring of ISP performance over time. The one or more processors (202) may be configured to execute the automated speed test routines at regular intervals as defined by a predetermined schedule, store results of each speed test in the database (210), calculate performance metrics for each ISP based on the stored results, and generate trend analysis reports showing ISP performance changes over the monitored time period. This long-term monitoring and analysis may provide valuable insights into the consistency and reliability of ISP services over extended periods.

[0171] The analyzing module (218) of the system (102) may be further configured to generate suggestions for the user based on the comparison of network performance data. This capability may transform the system from a mere data collection and analysis tool into an intelligent advisor, providing actionable insights to users. These suggestions may be derived from the comprehensive analysis of collected data, taking into account various factors such as historical performance, user requirements, and industry benchmarks.

[0172] The system employs a multi-faceted approach to generate suggestions based on the comparison results. This process involves several key algorithms and decision-making steps to ensure that the suggestions are relevant, actionable, and data driven.

[0173] Furthermore, the system may offer advice on troubleshooting steps for intermittent connectivity issues. By analyzing patterns in network disruptions and correlating them with other data points, the system may provide targeted troubleshooting suggestions. For example, if the system detects that connectivityissues often coincide with specific times of day, it might suggest checking for interference from neighboring networks or scheduling automatic router reboots.

[0174] By providing the actionable suggestions, the system (102) may assist users in optimizing their network performance and addressing potential issues. This proactive approach may empower users to take informed actions to improve their internet experience, rather than simply presenting them with raw data or complex analytics.

[0175] In addition to user- focused suggestions, the analyzing module (218) may also generate comprehensive network statistics and diagnostics data. This feature may transform the system into a powerful tool for in-depth network analysis, capable of providing detailed insights into various aspects of network performance.

[0176] The analyzing module (218) may also focus on identifying patterns in network performance fluctuations. By applying advanced pattern recognition algorithms to the collected data, the system may uncover recurring trends or cyclical variations in network performance. This could include daily or weekly patterns, such as consistent speed drops during peak usage hours.

[0177] The system (102) may be designed to operate within a larger network architecture (100). This network architecture (100) may include a network (104) that facilitates communication between the system (102) and various user equipments (108-1, 108-2...108-N). The user equipments (108-1, 108-2...108-N) may be used by users (110-1, 110-2...110-N) to interact with the system (102) and initiate network performance analyses.

[0178] The system (102) may be implemented on a centralized server (106) within the network architecture (100). This centralized approach may allow for efficient data processing and analysis, as well as easy access for multiple users across different locations. The centralized server (106) may house the variouscomponents of the system (102), including the memory (204), one or more processors (202), I / O interfaces (206), and the database (210).

[0179] The system (102) may utilize various processing modules (208) to carry out its functions. These processing modules (208) may include, but are not limited to, the user interface module (212), test execution module (214), data collection module (216), analyzing module (218), and report generation module (220). Additional modules (222) may also be included to handle other specific tasks or future expansions of the system's capabilities.

[0180] In conclusion, the system (102) for analyzing network performance parameters may provide a comprehensive solution for monitoring, analyzing, and optimizing network performance. By offering features such as customizable testing parameters, automated testing routines, continuous monitoring, and detailed analysis and reporting, the system (102) may empower users to gain valuable insights into their network performance and make informed decisions about their internet services and network configurations.

[0181] FIG. 3 illustrates an exemplary block diagram (300) of the system (102) for analyzing the network performance parameters, in accordance with embodiments of the present disclosure.

[0182] The block diagram (300) may include a plurality of testing units (302a, 302b, 302c) namely testing unit 1 (302a), testing unit 2 (302b) and testing unit 3 (302c), a data collection module (216), one or more processors (202), an analyzing module (218), and a database (210). In an example, the data collection module (216), the one or more processors (202), the analyzing module (218), and the memory may be embedded into a single entity known as a network analyzing apparatus. In another example, the system (102) may be embedded into a computing device. The system (102) (automated network testing system (102)) may include a network analysis server placed at a remote location that provides an integratedplatform for automating various tests on the network to ensure a quality of the network services provided to the user. The network analysis server may be remotely accessible through a user equipment (108). In one example embodiment, a network operator may access the network analysis server from a remote location. In another example embodiment, the network operator may access the network analysis server by coupling a personal computing device directly to the network analysis server. In an aspect, the network analysis server may provide a user interface module (212) that may be accessible through the user equipment (108). The user interface module (212) may include various data fields that are adapted to receive data from a user. In an example embodiment, the data may include a user identifier data, a network data and / or the type of network test to be executed. In one embodiment, the user interface module (212) may provide a user with a predefined list of network tests to choose from and the user may select the type of network test from the list of predefined tests. In an example, the predefined list of network tests includes a download speed test, an uplink speed test, a jitter test, and a latency test.

[0183] One or more user equipments (108) may be configured to submit requests for accessing a network performance report provided by the network analysis server. For example, the one or more user equipments (108) may allow users (e.g., individuals, employees of telecommunication service providers or network operators, etc.) or automatic control units (e.g., self-optimizing network (SON) modules) to interact with the network information provided by the network analysis server. In an embodiment, the one or more user equipments (108) may be computing devices. For example, one or more of the user devices may be an electronic device, such as a cell phone, a smart phone, a tablet, a laptop, a personal digital assistant (PDA), a computer, a desktop, a workstation, a digital media player, a server, a terminal, a kiosk, or the like. The one or more user equipments (108) may include a microphone, a speaker, a wireless module, a camera, and / or a display.

[0184] The plurality of testing units (302a, 302b, 302c) may be configured to perform speed tests in the network and generate information related to theselected network performance parameters. In an aspect, the plurality of testing units (302a, 302b, 302c) may include various standardized speed test platforms. In an operative aspect, the plurality of testing units (302a, 302b, 302c) may be configured to generate the information about the selected network performance parameters. In an example, the selected network performance parameters include one or more of a download speed, an upload speed, latency, a streaming speed, a quality of service, ISP performance, and bandwidth. In an aspect, the plurality of testing units (302a, 302b, 302c) may be placed at different places covering a large area. In an embodiment, the plurality of testing units (302a, 302b, 302c) may be configured to communicate with the one or more processors (202) over the network.

[0185] In an operative aspect, the user may be configured to generate a network speed test request for performing network analysis. In an aspect, the user may be configured to generate the network speed test request by using a network analysis mobile application installed in the user equipment (UE). The network analysis mobile application may be configured to communicate with the network analysis server. In some examples, the network analysis mobile application may be a software or a mobile application from an application distribution platform. For example, the network speed test request may include the address information of the user equipment, MCC (Mobile Country Code), MNC (Mobile Network Code), LAC (Location Area Code), and Cell-ID (CID) representing the current location of the user equipment (UE) (108). The address information of the user equipment (UE) (108) is a geographical location of the user equipment (UE) (108) and an identification information of the user equipment (UE) (108), such as an Internet Protocol (IP) address of the user equipment (UE) (108). In an example, the IP address is a logical address provided by the IP Protocol to each network and each host on the Internet.

[0186] A memory, of the user equipment (UE) (108), is configured to store program instructions. The memory is configured to store the data received from the network analysis mobile application. The program instructions include a programthat implements a method to initiate the speed test in accordance with embodiments of the present disclosure and may implement other embodiments described in this specification. The memory may be configured to store preprocessed data. The memory may include any computer-readable medium known in the art including, for example, volatile memory, such as Static Random Access Memory (SRAM) and Dynamic Random Access Memory (DRAM) and / or nonvolatile memory, such as Read Only Memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes.

[0187] In an aspect, the network analysis mobile application may be configured to, via a processor, fetch and execute computer-readable instructions stored in the memory of the UE. The processor may be configured to execute a sequence of instructions of the method to initiate the speed test, which may be embodied in a program or software. The instructions can be directed to the processor, which may subsequently program or otherwise be configured to implement the methods of the present disclosure. In some examples, the processor is configured to control and / or communicate with large databases, perform high- volume transaction processing, and generate reports from large databases. The processor may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and / or any devices that manipulate signals based on operational instructions.

[0188] In an aspect, the user may be configured to customize the testing parameters (download speed, uplink speed, packet loss, latency, and jitter) and metrics (connection metrics like jitter, packet loss, and latency) through the network analysis mobile application. This includes prioritizing download speed, latency, or overall performance consistency based on individual preferences. The packet loss occurs when data packets do not reach their destination due to network congestion. A high percentage of packet loss indicates that the user is transmitting more information than his network can handle. This causes packets to be dropped andresults in poor-quality audio or video. Furthermore, latency is defined as the time taken for information to be delivered. High latency causes lag in the network, which leads to timing issues in Voice over Internet Protocol (VoIP) conversations and slow videos in streaming. Jitter refers to the variability in the time taken for data packets or information to arrive. When there is high jitter, meaning a high variation in delivery time, it can result in choppy voice calls or glitchy -looking video quality.

[0189] The network analysis server or the one or more processors (202) may be configured to receive the generated network speed test request from the user. On receiving the generated request, the one or more processors (202) may be configured to send a triggering signal to the plurality of testing units (302a, 302b, 302c). In an example, on receiving the triggering signals, the plurality of testing units (302a, 302b, 302c) may be configured to generate the information in real time. The one or more processors (202) may be configured to receive the information from the plurality of testing units (302a, 302b, 302c).

[0190] The data collection module (216) may be configured to collect the information received from the plurality of testing units (302a, 302b, 302c) and provide collected information. In an example, the data collection module (216) may be configured to collect the information in a pre-defined format (for example, an excel format). In an aspect, the data collection module (216) may be a computing system. In an example, the data collection module (216) may include a wireless transceiver for wirelessly communicating with the plurality of testing units (302a, 302b, 302c), a memory, and a table for storing information about a history of the changes and additions to the received data from the plurality of testing units (302a, 302b, 302c).

[0191] The one or more processors (202) may be configured to receive the collected information from the data collection module (216) and to process the received information to generate a processed data. In an aspect, the one or more processors (202) may be configured to perform various steps of organizing, parsing,and structuring the received information into a standardized format. In an example, the one or more processors (202) may be configured to generate the processed data corresponding to each parameter of the selected network performance parameters.

[0192] The analyzing module (218) may be configured to analyze the processed data (standardized format) corresponding to each parameter based upon at least one attribute. In an example, the at least one attribute may include an Internet Service Provider (ISPs), a location of the user, or a time frame. In an aspect, the analyzing module (218) may include a report generation module (220).

[0193] The analyzing module (218) may be configured to identify variations and differences in internet speeds and performance. In an aspect, the analyzing module (218) may be configured to compare the processed data corresponding to each testing unit based on the attribute and generate a comparison result. For example, the analyzing module (218) is configured to compare the uplink speed corresponding to a defined ISP based on the information received from the plurality of testing units (302a, 302b, 302c). In an example, this comparison is usually conducted based on specific network metric chosen for evaluation. In an aspect, the specific network metric may be chosen by the user or by a network operator.

[0194] The report generation module (220) may be configured to receive the comparison result and may be configured to generate at least one report or visual representation based on the comparison result. In an example, the at least one report or visual representation may illustrate the differences among ISPs, geographical locations, or different time periods. The at least one report or visual representation may be configured to provide valuable insights into the performance of ISPs and their services. In an aspect, the user may be configured to define a required format of the at least one report or visual representation.

[0195] FIG. 4 illustrates an exemplary flow diagram (400) of a method for analyzing the network performance parameters, in accordance with an embodiment of the present disclosure.

[0196] At step 402, the system (102) may be configured to receive, by a user interface module (212), a network speed test request from a user.

[0197] At step 404, the system (102) may be configured to run command to execute the network speed test. The system (102) may be configured to fetch the information stored in the plurality of testing units (302a, 302b, 302c). For example, the information may be stored in the database (210) associated with the system (102). In another example, the system (102) may be configured to periodically fetch the information from the plurality of testing units (302a, 302b, 302c). The system (102) may be configured to execute the instructions stored in the database (210) to perform the method for analyzing the selected network performance parameters. In an aspect, the system (102) may be configured to execute the automated speed test routines on the fetched information. In another aspect, the system (102) may be configured to execute the automated speed test routines on the fetched information corresponding to each testing unit.

[0198] At step 406, the system (102) may be configured to confirm whether the test (automated speed test routines) executed properly or not.

[0199] At step 410, if the test did not execute properly, the system (102) may be unable to generate the at least one report (an excel file) corresponding to that testing unit.

[0200] At step 408, if the test was executed properly, the system (102) may be configured to perform the automation test for other testing units (online testing applications).

[0201] At step 412, the system (102) may be configured to perform the automation test for each online testing application (testing unit) and generate the report showing a comparative analysis of the selected network performance parameters corresponding to each testing unit (application).

[0202] FIG. 5 illustrates an exemplary flow diagram of the method (500) for analyzing the network performance parameters, in accordance with embodiments of the present disclosure.

[0203] At step (502), the method (500) includes receiving, by a user interface module (212), a network speed test request from a user. The network speed test request includes one or more network performance parameters for analysis. The selected network performance parameters may comprise one or more of a download speed, an upload speed, latency, a streaming speed, a quality of service, ISP performance, and bandwidth. For instance, a user might select download speed, which refers to the rate at which data is transferred from the internet to the user's device, typically measured in megabits per second (Mbps). Upload speed, conversely, measures the rate at which data is sent from the user's device to the internet. Latency, often measured in milliseconds, represents the time delay between sending a request and receiving a response. This step allows users to tailor their network performance analysis to their specific needs, such as a gamer focusing on latency or a content creator prioritizing upload speeds.

[0204] The receiving process performed by the user interface module (212) involves a comprehensive sequence of operations. Initially, the user interface module (212) performs user authentication by validating user credentials including username and password combinations. The authentication process further encompasses verification of device authorization and checking of appropriate access permissions to ensure secure system access.

[0205] Following authentication, the user interface module (212) constructs a structured request object. This request object encompasses critical information including the user identifier, detailed device information such as device type, operating system version, and browser specifications. The request object further incorporates the network connection type, whether it be WiFi, cellular, or ethemet, along with a precise timestamp of the request and the user's selected test parameters.

[0206] The user interface module (212) then executes a thorough request validation process. This validation encompasses verification of all required parameters, ensuring each parameter adheres to specified formats and ranges. The validation process also confirms that request size constraints are met and verifies the overall syntax of the request to maintain data integrity.

[0207] Once validated, the user interface module (212) processes the request through a series of operations. These operations include parsing the request parameters and converting them into standardized formats suitable for system processing. The module generates a unique test session identifier and appropriately queues the request for execution in the system.

[0208] The network speed test request in the present disclosure represents a significant advancement over conventional speed tests through several innovative aspects. The system implements comprehensive multi-platform integration, wherein it simultaneously engages multiple standardized testing platforms. This integration enables the aggregation of results across varying testing methodologies and facilitates detailed comparative analysis across different platforms.

[0209] The system introduces advanced customization capabilities that surpass traditional speed tests. Users can select specific testing units according to their requirements and define custom test durations suited to their needs. The system allows users to specify iteration counts for repeated testing and configureacceptable performance thresholds. Additionally, users can establish automated testing intervals through detailed scheduling options.

[0210] A distinctive feature of the system lies in its comprehensive analysis capabilities. The system performs cross -platform result validation to ensure accuracy and conducts sophisticated trend analysis over time. It enables detailed geographic performance comparisons and generates ISP-specific insights. The system further supports customized reporting options to present results according to user preferences.

[0211] The automated execution capabilities of the system represent a significant departure from conventional manual speed tests. The system supports scheduled automated testing, enabling consistent performance monitoring without manual intervention. It maintains detailed historical performance data and continuously monitors network performance metrics. The system automatically generates alerts when performance metrics deviate from configured thresholds, enabling proactive network management.

[0212] At step (504), the method (500) includes triggering, by a test execution module (214), a plurality of testing units (302a, 302b, 302c) configured to perform a network speed test and generate information related to the one or more network performance parameters. These testing units may comprise standardized speed test platforms. Each platform may use different methodologies to measure network performance, providing a comprehensive view of the user's internet connection.

[0213] The triggering process executed by the test execution module (214) encompasses a sophisticated sequence of operations. Initially, the test execution module (214) performs resource availability verification by checking the status and capacity of each testing unit in the system. The module evaluates current systemload, network conditions, and testing unit readiness before initiating the test sequence.

[0214] The test execution module (214) implements a distributed triggering mechanism that coordinates test execution across multiple testing units. The module first establishes secure communication channels with each testing unit through encrypted protocols. It then transmits test configuration parameters including the specified test duration, iteration count, and performance thresholds to each testing unit. The module employs an intelligent load balancing algorithm to distribute testing tasks efficiently among available units.

[0215] The triggering sequence incorporates precise timing control mechanisms. The test execution module (214) synchronizes the initiation of tests across different units to ensure temporal consistency in measurements. This synchronization involves calculating network latency compensation factors and implementing countdown mechanisms that coordinate simultaneous test execution across distributed testing units.

[0216] The plurality of testing units (302a, 302b, 302c) represents diverse testing platforms, each specialized for specific aspects of network performance measurement. For instance, testing units may comprise an available speed test platform, which specializes in measuring download speeds, upload speeds, and latency through a globally distributed server network. This unit employs multiple connections to saturate the network pipe and determine maximum throughput capacity. In an embodiment, the testing units may implement the HTML5-based WebTest platform, which performs browser-centric performance measurements including Transmission Control Protocol (TCP) connection establishment times, Domain Name System (DNS) resolution speeds, and end-to-end application layer performance metrics. This unit evaluates network performance from the perspective of web application responsiveness and user experience metrics.

[0217] The test execution module (214) coordinates these diverse testing units through a standardized execution protocol. For each testing unit, the module initializes test-specific parameters such as server selection criteria, measurement intervals, and data sampling rates. It maintains continuous communication with each unit throughout the test execution phase, monitoring test progress and handling any execution exceptions that may arise.

[0218] The triggering process incorporates sophisticated error handling mechanisms. The test execution module (214) continuously monitors the status of each testing unit during test execution. If any unit encounters errors or becomes unresponsive, the module implements automatic failover procedures to maintain testing continuity. These procedures may include re-routing tests to alternate testing units or adjusting test parameters to accommodate the changed testing environment.

[0219] At step (506), the method (500) includes collecting, by a data collection module (216), the generated information from the plurality of testing units (302a, 302b, 302c). This collection process involves gathering data in a predefined format, which includes a timestamp of the speed test, an identifier of the testing unit, measured values for each parameter, and relevant metadata. For example, the collected data might include a download speed of 100 Mbps measured at 2:00 PM by testing unit 302a, along with information about the user's ISP and location.

[0220] The data collection module (216) implements a comprehensive data collection process that operates through multiple coordinated stages. The module initiates data collection by establishing dedicated communication channels with each testing unit using secure socket connections. These channels employ encryption protocols to ensure data integrity and security during transmission. The module implements a polling mechanism that continuously monitors each testing unit for newly generated test results while maintaining optimal network resource utilization.

[0221] The collection process incorporates sophisticated data buffering mechanisms to handle varying data generation rates from different testing units. The data collection module (216) employs adaptive buffering algorithms that automatically adjust buffer sizes based on incoming data volume and system resource availability. This ensures efficient collection of data even during periods of high testing activity or when dealing with testing units that generate data at different rates.

[0222] The generated information collected from the testing units encompasses a comprehensive set of network performance metrics. For example, from testing unit 302a, the collected data includes precise measurements of download speeds recorded in megabits per second (Mbps), with timestamps accurate to the millisecond level. For example, a typical data point might include a download speed of 95.67 Mbps recorded at 14:23:45.234 GMT, along with the associated server location and network path information. Testing unit 302b generates detailed upload speed measurements, including granular data about upload performance characteristics. This information includes not only the peak upload speed but also stability metrics such as speed variations over the test duration. For instance, the unit might record an average upload speed of 35.42 Mbps with a standard deviation of 2.1 Mbps over a 30-second test period.

[0223] The data collected from testing unit 302c includes comprehensive latency measurements, capturing both round-trip times and jitter metrics. This information encompasses minimum, maximum, and average latency values, typically ranging from 15ms to 200ms depending on network conditions. The unit also generates packet loss statistics, recording both the percentage of lost packets and the temporal distribution of losses during the test period.

[0224] The testing units themselves are configured with specific data generation capabilities tailored to different aspects of network performance measurement. Testing unit 302a specializes in throughput measurement, utilizingmultiple parallel connections to saturate the network path and determine maximum achievable speeds. This unit employs adaptive algorithms that adjust the number of parallel connections based on network conditions to optimize measurement accuracy.

[0225] Testing unit 302b focuses on real-world performance metrics, simulating actual user activities such as web browsing, video streaming, and file transfers. This unit generates detailed timing information for each simulated activity, providing insights into practical network performance under various usage scenarios. The unit incorporates mechanisms to account for factors such as TCP slow start and congestion control algorithms.

[0226] Testing unit 302c concentrates on network quality metrics, generating detailed information about connection stability and reliability. This unit performs continuous monitoring of network characteristics such as packet interarrival times, routing path stability, and DNS resolution performance. The unit also generates metadata about network conditions during testing, including information about network congestion and routing efficiency.

[0227] The data collection module (216) processes this diverse information through a normalization pipeline that standardizes data formats across different testing units. The module applies timestamp synchronization to align data points from different sources, converts all measurements to consistent units, and aggregates related metrics into coherent data structures. This normalization ensures that collected data can be effectively analyzed regardless of its source testing unit.

[0228] At step (508), the method (500) includes processing, by one or more processors (202), the collected information to generate processed data corresponding to each of the network performance parameter. This processing step involves organizing the raw data into a standardized format for easier analysis. For instance, all speed measurements might be converted to Mbps, and timestampsstandardized to UTC. The processors might also calculate aggregate metrics, such as average download speeds or 95th percentile latency values, providing a more comprehensive view of network performance over time.

[0229] The processing operation executed by the one or more processors (202) encompasses a sophisticated multi-stage data transformation pipeline. Initially, the processors perform data sanitization by removing invalid or corrupted entries from the collected information. This sanitization process involves validation of data formats, identification of outliers through statistical analysis, and verification of data completeness. For instance, if a speed test result lacks essential parameters such as timestamp or measurement values, the processors flag and handle such incomplete entries according to predefined rules.

[0230] The one or more processors (202) then execute data normalization procedures on the sanitized information. This normalization process converts all speed measurements to consistent units, typically megabits per second (Mbps), regardless of the original reporting format from different testing units. The processors apply temporal normalization to align timestamps across different time zones and testing platforms, converting all temporal data to Coordinated Universal Time (UTC) for standardized analysis.

[0231] The collected information comprises comprehensive network performance data obtained through the data collection module (216). This information includes primary network metrics such as download speeds, typically ranging from 1 Mbps to 1000 Mbps, upload speeds varying between 0.5 Mbps to 500 Mbps, and latency measurements ranging from 1 millisecond to 1000 milliseconds. The collected information also encompasses secondary metrics including packet loss rates, jitter measurements, and connection stability indicators.

[0232] The processors implement advanced aggregation algorithms to combine related measurements from multiple testing sources. For example, when processing download speed data, the processors calculate weighted averages basedon measurement reliability indicators, determine median values to account for outliers, and compute statistical variance to assess measurement stability. This aggregation process generates consolidated metrics that provide a more accurate representation of actual network performance.

[0233] The one or more processors (202) apply sophisticated statistical analysis to the normalized data. This analysis includes calculation of moving averages to identify performance trends, computation of percentile distributions to understand performance variations, and generation of correlation coefficients to identify relationships between different performance parameters. For instance, the processors might identify that peak download speeds correlate inversely with network latency during specific time periods.

[0234] The processing operation incorporates contextual enrichment of the collected data. The processors augment raw performance metrics with relevant contextual information such as geographic location data, Internet Service Provider (ISP) identifiers, and network topology information. This enrichment process enables more meaningful analysis by considering environmental factors that may impact network performance.

[0235] The processors generate derived metrics through mathematical transformations of the collected data. These transformations include calculation of quality scores based on weighted combinations of primary metrics, generation of performance indices that normalize measurements across different network conditions, and computation of trend indicators that predict future performance patterns based on historical data.

[0236] During the final processing stage, the one or more processors (202) organize the processed data into optimized data structures designed for efficient analysis and retrieval. These structures incorporate indexing schemes based on temporal, geographic, and performance-based parameters, enabling rapid access to specific subsets of processed data for subsequent analysis operations. Theprocessors also generate metadata about the processing operation itself, including information about applied transformations, data quality metrics, and processing timestamps.

[0237] At step (510), the method (500) includes analyzing (510), by an analyzing module (218), the processed data corresponding to each of the network performance parameter based upon at least one attribute. These attributes might include the Internet Service Provider (ISP), the user's location, or specific time frames. For example, the module might analyze how download speeds vary between different ISPs in the user's area, or how latency changes during peak usage hours.

[0238] The analyzing module (218) executes a comprehensive analytical process that operates through multiple sophisticated phases to evaluate the processed network performance data. The analysis begins with a dimensional decomposition phase, wherein the analyzing module (218) separates the processed data into distinct analytical dimensions based on the specified attributes. These dimensions include temporal aspects such as time-of-day and day-of-week patterns, geographical distributions of performance metrics, and service provider- specific characteristics.

[0239] The analyzing module (218) implements pattern recognition algorithms to identify significant trends and anomalies within each network performance parameter. This analytical process employs time series analysis techniques to detect periodic patterns, seasonal variations, and long-term trends in network performance metrics. The module utilizes advanced statistical methods such as exponential smoothing and autoregressive modeling to forecast future performance patterns based on historical data patterns.

[0240] During the correlation analysis phase, the analyzing module (218) examines relationships between different network performance parameters. The module calculates correlation coefficients to quantify the strength and direction of relationships between metrics such as download speed and latency. For example,the module might identify that increased network latency correlates with decreased download speeds during specific time periods or in particular geographic regions.

[0241] The analyzing module (218) performs comparative analysis across different attribute categories. When analyzing based on Internet Service Provider (ISP) attributes, the module compares performance metrics between different service providers, accounting for factors such as service tier differences and geographical coverage variations. The module generates normalized performance indices that enable fair comparisons across different service providers and network configurations.

[0242] The analyzing module (218) incorporates threshold -based analysis to identify performance violations and service level agreement (SLA) breaches. This analytical process involves comparing measured performance metrics against predefined thresholds and generating alert signals when performance falls outside acceptable ranges. The module tracks both the frequency and duration of threshold violations to assess overall service quality.

[0243] Performance stability analysis represents a critical component of the analytical process. The analyzing module (218) evaluates the consistency of network performance by calculating stability metrics such as standard deviation of performance measurements, frequency of performance fluctuations, and duration of performance degradation events. This analysis helps identify network configurations or conditions that lead to unstable performance.

[0244] The analyzing module (218) generates synthetic performance indicators by combining multiple primary metrics through weighted aggregation schemes. These synthetic indicators provide comprehensive measures of network quality that account for multiple performance aspects simultaneously. The module adjusts weighting factors based on the relative importance of different performance parameters in specific usage contexts.

[0245] The final phase of analysis involves the generation of actionable insights from the analyzed data. The analyzing module (218) identifies performance optimization opportunities, generates recommendations for network configuration improvements, and provides predictive alerts for potential performance issues. These insights are tailored to specific user requirements and network environments, enabling informed decision-making for network management and optimization.

[0246] In some embodiments, the method (500) may further include comparing, by the analyzing module (218), the processed data corresponding to each testing unit of the plurality of testing units (302a, 302b, 302c) based on the at least one attribute. This comparison allows for cross-validation of results between different testing platforms.

[0247] The analyzing module (218) executes a sophisticated comparison process that evaluates processed data across multiple testing units. The comparison process initiates with data alignment, where the module synchronizes measurements from different testing units based on temporal proximity. The module employs a temporal correlation algorithm that matches test results occurring within configurable time windows, typically ranging from milliseconds to minutes, ensuring meaningful comparisons across testing platforms.

[0248] The analyzing module (218) implements a multi-dimensional comparison framework that evaluates performance variations across testing units. For each network performance parameter, the module calculates deviation metrics that quantify differences between measurements from different testing units. These calculations account for the inherent characteristics and measurement methodologies of each testing unit. For instance, when comparing download speed measurements, the module applies normalization factors that account for differences in server locations and testing methodologies between testing units 302a, 302b, and 302c.

[0249] The report generation module (220) executes a comprehensive report creation process that transforms analytical results into informative visual representations. The process begins with data aggregation, where the module consolidates comparison results into structured datasets optimized for visualization. The module then applies formatting rules that determine visual attributes such as color schemes, chart dimensions, and labeling conventions based on the type of data being represented.

[0250] The report generation module (220) implements an intelligent template selection system that chooses appropriate visualization formats based on data characteristics and comparison results. The module maintains a repository of specialized templates for different types of network performance comparisons. For instance, when comparing latency measurements across testing units, the module might select a multi-series line chart template that effectively illustrates temporal variations and cross -platform differences.

[0251] The visual representations generated by the report generation module (220) encompass a diverse range of graphical formats tailored to specific comparison scenarios. For example, when visualizing download speed comparisons across testing units, the module generates multi-layer heat maps where each layer represents data from a different testing unit. These heat maps employ color gradients ranging from cool blues (indicating lower speeds) to warm reds (indicating higher speeds), with intensity variations representing speed magnitudes.

[0252] Another example of visual representation includes performance comparison matrices, where the module generates grid-based visualizations comparing results from each testing unit against others. Each cell in the matrix displays key comparison metrics such as correlation coefficients, mean absolute differences, and statistical significance indicators. The module enhances these matrices with interactive elements that allow users to drill down into specific comparison details.

[0253] The module also generates time-series overlay charts that superimpose performance measurements from different testing units on a common temporal axis. These charts incorporate synchronized zoom capabilities and interactive tooltips that display detailed comparison metrics at specific time points. For instance, a time-series overlay might show how latency measurements from testing unit 302a correlate with measurements from units 302b and 302c throughout a 24-hour period.

[0254] Geographic performance comparison maps represent another sophisticated visualization type. These maps display color-coded regions indicating areas where testing units show significant measurement variations. The module enhances these maps with interactive elements that reveal detailed comparison statistics for specific geographic locations, including performance difference percentages and confidence intervals for the comparisons.

[0255] The report generation module (220) further produces advanced statistical visualization elements such as box-and-whisker plots that compare the distribution of measurements across testing units. These plots provide immediate visual insight into measurement consistency and variability across platforms, highlighting median values, quartile ranges, and statistical outliers for each testing unit's measurements.

[0256] In some embodiments, the method (500) may further include generating, by a report generation module (220), at least one report or visual representation based on the comparison. These reports might include line graphs showing performance trends over time, bar charts comparing different ISPs, or heat maps displaying performance variations across geographic areas. For example, a user might receive a monthly report showing how their internet speeds compare to the average in their neighborhood, along with recommendations for improvement.

[0257] In some embodiments, the method (500) may further include receiving customized testing parameters from the user. These parameters might include selecting specific testing units, defining test durations, or setting custom performance thresholds.

[0258] Additionally, the method may involve executing automated speed test routines. These routines could perform tests at regular intervals without user intervention, providing continuous monitoring of network performance. For example, the system might run tests every four hours, cycling through all available testing units and storing the results for long-term trend analysis.

[0259] In some embodiments, the one or more network performance parameters comprise one or more of a download speed, an upload speed, latency, a streaming speed, a quality of service, Internet Service Provider (ISP) performance, and bandwidth.

[0260] In some embodiments, the at least one attribute comprises an ISP identifier, a location of the user, or a time frame.

[0261] In some embodiments, the method (500) may further include collecting the generated information comprises collecting the information in a predefined format. The pre-defined format comprises a structured data format that includes: a timestamp of the network speed test; an identifier of the testing unit that performed the network speed test; measured values for each of the network performance parameter; and metadata associated with the network connection used for the network speed test.

[0262] In some embodiments, the method (500) may further include receiving, by the user interface module, customized testing parameters for the network speed test from the user. The customized testing parameters comprise: a selection of specific testing unit to be used for the network speed test; a definednetwork speed test duration for the network speed test; a specified number of network speed test iterations to be performed; custom thresholds for acceptable performance levels for each of the network performance parameter; and preferred time intervals between successive network speed tests.

[0263] The user interface module (212) provides an interactive configuration interface through which users can specify detailed customized testing parameters. For example, when a network administrator accesses the system through a web-based interface, they encounter a structured parameter configuration panel. Within this panel, the user interface module (212) presents dropdown menus for testing unit selection.

[0264] The user interface module (212) implements intuitive duration selection controls through which users can specify test durations. For instance, a quality assurance engineer might configure a short-duration test of 30 seconds for quick performance checks, a medium-duration test of 5 minutes for detailed performance analysis, or an extended test of 30 minutes for comprehensive network stability assessment. The module provides visual feedback showing the implications of different duration settings on test accuracy and resource utilization.

[0265] For iteration configuration, the user interface module (212) presents numerical input fields with intelligent validation. A typical configuration might involve a network analyst setting up 10 iterations during peak hours (9 AM to 5 PM) and 5 iterations during off-peak hours (5 PM to 9 AM) to establish performance baselines under different load conditions. The module automatically validates these inputs to ensure they align with system capabilities and resource constraints.

[0266] The user interface module (212) enables sophisticated threshold configuration through interactive sliding controls. For example, a service provider representative might set minimum acceptable thresholds of 100 Mbps for downloadspeed, 20 Mbps for upload speed, and maximum acceptable thresholds of 50 milliseconds for latency and 1% for packet loss. The module provides real-time visual indicators showing how these thresholds compare to historical performance data and industry standards.

[0267] The time interval configuration functionality within the user interface module (212) allows users to establish complex testing schedules. For instance, a network operations team might configure tests to run every 15 minutes during business hours, every hour during evening hours, and every 4 hours during overnight periods. The module provides a calendar-based interface for setting these intervals, complete with conflict detection and resolution capabilities.

[0268] In some embodiments, the method (500) may further include performing, by the test execution module, continuous monitoring of the ISP performance over time. The continuous monitoring is performed by executing the automated network speed test routines at regular intervals as defined by a predetermined schedule; storing results of each speed test in the database; and calculating performance metrics for each ISP based on the stored results.

[0269] The test execution module (214) implements continuous monitoring of ISP performance through a sophisticated real-time surveillance system. This monitoring system maintains persistent observation of network performance metrics through a series of coordinated measurement cycles. The module establishes multiple monitoring threads that operate concurrently, each dedicated to specific performance aspects such as throughput, latency, and connection stability. For instance, one thread continuously tracks download and upload speeds, while another monitors latency and packet loss, ensuring comprehensive coverage of all critical performance indicators.

[0270] The test execution module (214) employs an adaptive monitoring mechanism that automatically adjusts its measurement intensity based on network conditions. During periods of performance degradation, the module automaticallyincreases monitoring frequency to capture detailed performance data. For example, if download speeds drop below 80% of the expected threshold, the system intensifies monitoring from standard 15-minute intervals to 5-minute intervals until performance stabilizes. This adaptive approach ensures detailed capture of performance anomalies while optimizing system resource utilization during normal operation.

[0271] The regular intervals for automated network speed test routines incorporate a highly configurable scheduling framework. These intervals are fully tuneable through a dynamic configuration interface that allows administrators to define custom testing frequencies. The system supports various interval configurations, including fixed-time intervals (e.g., every 30 minutes), variable intervals based on time-of-day (e.g., every 10 minutes during peak hours, every hour during off-peak), and condition-based intervals that adjust according to network performance patterns.

[0272] Network administrators can fine-tune these intervals through multiple parameters. The primary interval duration can be set between 1 minute and 24 hours, with granular adjustments possible in one-minute increments. Secondary interval parameters allow for burst testing modes where multiple tests are executed in rapid succession followed by longer pause periods. For example, an administrator might configure five tests executed within one minute, repeated every hour, to obtain more statistically significant measurements.

[0273] The predetermined schedule operates through a sophisticated calendar-based system that defines testing patterns across different time scales. This schedule incorporates multiple layers of timing definitions, including daily schedules that specify peak and off-peak testing frequencies, weekly schedules that account for weekend versus weekday traffic patterns, and monthly schedules that accommodate planned maintenance windows or expected usage pattern variations.

[0274] The schedule system supports complex recurring patterns. For instance, it can be configured to execute tests every 5 minutes during business hours (9 AM to 5 PM) on weekdays, every 15 minutes during evening hours (5 PM to 11 PM), and every hour during overnight periods (11 PM to 9 AM). Weekend schedules might implement different intervals, such as uniform 30-minute testing intervals throughout the day. The schedule also accommodates special event periods where more frequent testing may be required, such as during major software deployments or network upgrades.

[0275] The predetermined schedule includes built-in intelligence for handling exceptions and conflicts. It automatically adjusts for daylight saving time transitions, accounts for system maintenance windows, and includes failover logic for missed test executions. The schedule also incorporates priority levels for different types of tests, ensuring critical performance measurements are prioritized during periods of high system load.

[0276] In yet another exemplary embodiment, a user equipment (UE) communicatively coupled with a system is disclosed. The coupling comprises steps of receiving, by the system, a connection request, sending, by the system, an acknowledgment of the connection request to the UE, and transmitting a plurality of signals in response to the connection request. The system is configured for analyzing network performance parameters. The system comprises a memory and one or more processors coupled to the memory. The one or more processors are configured to execute a set of instructions stored in the memory. The set of instructions cause the one or more processors to system for analyzing network performance parameters, the system comprising a memory, and one or more processors coupled to the memory. The one or more processors configured to execute a set of instructions stored therein to receive, by a user interface module, a network speed test request from a user. The network speed test request includes one or more network performance parameters for analysis. Trigger, by a test execution module, a plurality of testing units configured to perform a network speed test andgenerate information related to the one or more network performance parameters associated with the received network speed test request. Collect, by a data collection module, the generated information from the plurality of testing units. Process, by the one or more processors, the collected information to generate processed data corresponding to each of the network performance parameter. Analyze, by an analyzing module, the processed data corresponding to each of the network performance parameter based upon at least one attribute.

[0277] In yet another exemplary embodiment, 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 perform a method for analyzing one or more network performance parameters is described. The method comprises receiving, by a user interface module, a network speed test request from a user. The method further comprises triggering, by a test execution module, a plurality of testing units configured to perform a network speed test and generate information related to the one or more network performance parameters associated with the received network speed test request. The method further comprises collecting, by a data collection module, the generated information from the plurality of testing units. The method further comprises processing, by one or more processors, the collected information to generate processed data corresponding to each of the network performance parameter. The method further comprises analyzing, by an analyzing module, the processed data corresponding to each of the network performance parameter based upon at least one attribute.

[0278] The present disclosure provides technical advancement in the field of network performance analysis and optimization. It addresses limitations of existing solutions by offering a multi-faceted, automated approach to speed testing and analysis. By implementing customizable parameters, continuous monitoring, and advanced data processing techniques, the invention significantly improves the accuracy and depth of network performance evaluation. This enables users to gain a more comprehensive understanding of their internet connection, optimize their online experiences, and make more informed decisions about their internet services.The system's ability to provide long-term trend analysis and actionable recommendations represents a substantial improvement over traditional, single- point-in-time speed tests, potentially leading to better internet experiences for users and more efficient network management for service providers.

[0279] FIG. 6 illustrates an example computer system (600) in which or with which the embodiments of the present disclosure may be implemented.

[0280] As shown in FIG. 6, the computer system (600) may include an external storage device (610), a bus (620), a main memory (630), a read-only memory (640), a mass storage device (650), a communication port(s) (660), and a processor (670). A person skilled in the art will appreciate that the computer system (600) may include more than one processor and communication ports. The processor (670) may include various modules associated with embodiments of the present disclosure. The communication port(s) (660) may be any of an RS-232 port for use with a modem-based dialup connection, a 10 / 100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fibre, a serial port, a parallel port, or other existing or future ports. The communication ports(s) (660) 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 (600) connects.

[0281] In an embodiment, the main memory (630) may be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. The read-only memory (640) 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 (670). The mass storage device (650) 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).

[0282] In an embodiment, the bus (620) may communicatively couple the processor(s) (670) with the other memory, storage, and communication blocks. The bus (620) may be, e.g. a Peripheral Component Interconnect PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), Universal Serial Bus (USB), or the like, for connecting expansion cards, drives, and other subsystems as well as other buses, such a front side bus (FSB), which connects the processor (670) to the computer system (600).

[0283] In another embodiment, operator and administrative interfaces, e.g., a display, keyboard, and cursor control device may also be coupled to the bus (620) to support direct operator interaction with the computer system (600). Other operator and administrative interfaces can be provided through network connections connected through the communication port(s) (660). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system (600) limit the scope of the present disclosure.

[0284] The method and system of the present disclosure may be implemented in a number of ways. For example, the methods and systems of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above unless specifically stated otherwise. Further, in some embodiments, the present disclosure may also be embodied as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers a recordingmedium storing a program for executing the method according to the present disclosure.

[0285] While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the disclosure and not as limitation.ADVANTAGES OF THE PRESENT DISCLOSURE

[0286] The present disclosure analyzes multiple network parameters in realtime, providing users with an immediate understanding of their network performance. This real-time analysis enables rapid identification of issues and allows for prompt action, minimizing downtime and improving overall user experience.

[0287] The present disclosure extracts data from multiple speed testing platforms and generates a comparative view representing network parameters, reducing response time significantly. By aggregating data from diverse sources, the system delivers a comprehensive and accurate representation of network performance. This multi-platform approach identifies discrepancies between different testing methodologies, ensuring more reliable results.

[0288] The present disclosure employs automation for speed test results comparison, addressing challenges associated with manual testing by enhancing efficiency, consistency, accuracy, and scalability. This automation eliminates human error in data collection and analysis, ensures regular test execution withoutuser intervention, and enables processing of large data volumes that manual handling cannot efficiently manage.

[0289] The present disclosure equips users and service providers with tools to assess and monitor internet service performance continuously. This capability empowers users to make informed decisions about internet service plans and usage patterns, while providing service providers with valuable insights for network optimization and customer service enhancement. The continuous monitoring feature identifies long-term trends and recurring issues that sporadic manual tests often miss.

[0290] The present disclosure automates speed test results comparison and enhances the assessment process by increasing efficiency, minimizing errors, and delivering timely, customized, data-driven insights into Internet Service Provider (ISP) performance to users and service providers. This automation saves time and enables routine execution of complex analyses. The system generates customized insights tailored to specific user needs or service provider requirements, increasing data relevance and actionability.

[0291] The present disclosure features a user-friendly interface that translates complex network performance data into accessible formats for both technical and non-technical users. By presenting information through clear visual representations such as graphs and charts, the system enables users to quickly comprehend their network performance status without requiring extensive technical knowledge.

[0292] The present disclosure offers a scalable solution adaptable to evolving network technologies and testing methodologies. As new speed testing platforms emerge or existing ones update their methods, the system incorporates these changes readily, ensuring sustained relevance and accuracy.

[0293] The present disclosure contributes to improved internet service quality by equipping users and service providers with precise tools to identify and address performance issues. This data-driven approach to network management leads to more efficient resource allocation, enhanced customer service, and an overall higher quality internet experience for users.

Claims

CLAIMSWe claim:

1. A system (102) for analyzing one or more network performance parameters, the system (102) comprising: a memory (204); one or more processors (202) coupled to the memory (204) and configured to execute a set of instructions stored therein to: receive, by a user interface module (212), a network speed test request from a user; trigger, by a test execution module (214), a plurality of testing units (302a, 302b, 302c) configured to perform a network speed test and generate information related to the one or more network performance parameters associated with the received network speed test request; collect, by a data collection module (216), the generated information from the plurality of testing units (302a, 302b, 302c); process, by the one or more processors (202), the collected information to generate processed data corresponding to each of the network performance parameter; and analyze, by an analyzing module (218), the processed data corresponding to each of the network performance parameter based upon at least one attribute.

2. The system (102) as claimed in claim 1, further configured to: compare, by the analyzing module (218), the processed data corresponding to each testing unit of the plurality of testing units (302a, 302b, 302c) based on the at least one attribute; and generate, by a report generation module (220), at least one report including visual representation based on the comparison.

3. The system (102) as claimed in claim 1, wherein the one or more network performance parameters comprise of a download speed, an upload speed, latency, a streaming speed, a quality of service, Internet Service Provider (ISP) performance, and bandwidth.

4. The system (102) as claimed in claim 1, wherein the at least one attribute comprises of an ISP identifier, a location of the user, or a time frame.

5. The system (102) as claimed in claim 1, wherein the data collection module (216) is configured to collect the generated information in a pre-defined format, wherein the pre-defined format comprises a structured data format that includes: a timestamp of the network speed test; an identifier of the testing unit that performed the network speed test; measured values for each of the network performance parameter; and metadata associated with a network connection used for the network speed test.

6. The system (102) as claimed in claim 1, wherein the one or more processors (202) are further configured to: receive, by the user interface module (212), customized testing parameters for the network speed test from the user, wherein the customized testing parameters comprise at least one of: a selection of specific testing unit (302a, 302b, 302c) to be used for the network speed test; a defined test duration for the network speed test; a specified number of network speed test iterations to be performed; custom thresholds for acceptable performance levels for each of the network performance parameter; and preferred time intervals between successive network speed tests.

7. The system (102) as claimed in claim 3, wherein the one or more processors (202) are further configured to: perform, by the test execution module (214), continuous monitoring of the ISP performance over time by: executing automated network speed test routines at regular intervals as defined by apredetermined schedule; storing results of the network speed test in a database (210); and calculating performance metrics for the ISP based on the stored results.

8. A method (500) for analyzing one or more network performance parameters, the method (500) comprising: receiving (502), by a user interface module (212), a network speed test request from a user; triggering (504), by a test execution module (214), a plurality of testing units (302a, 302b, 302c) configured to perform a network speed test and generate information related to the one or more network performance parameters associated with the received network speed test request; collecting (506), by a data collection module (216), the generated information from the plurality of testing units (302a, 302b, 302c); processing (508), by one or more processors (202), the collected information to generate processed data corresponding to each of the network performance parameter; and analyzing (510), by an analyzing module (218), the processed data corresponding to each of the network performance parameter based upon at least one attribute.

9. The method (500) as claimed in claim 8, further comprising: comparing, by the analyzing module (218), the processed data corresponding to each testing unit of the plurality of testing units (302a, 302b, 302c) based on the at least one attribute; and generating, by a report generation module (220), at least one report including visual representation based on the comparison.

10. The method (500) as claimed in claim 8, wherein the one or more network performance parameters comprise of a download speed, an upload speed,latency, a streaming speed, a quality of service, Internet Service Provider (ISP) performance, and bandwidth.

11. The method (500) as claimed in claim 8, wherein the at least one attribute comprises an ISP identifier, a location of the user, or a time frame.

12. The method (500) as claimed in claim 8, wherein collecting the generated information comprises collecting the information in a pre-defined format, wherein the pre-defined format comprises a structured data format that includes: a timestamp of the network speed test; an identifier of the testing unit that performed the network speed test; measured values for each of the network performance parameter; and metadata associated with a network connection used for the network speed test.

13. The method (500) as claimed in claim 8, further comprising: receiving, by the user interface module (212), customized testing parameters for the network speed test from the user, wherein the customized testing parameters comprise at least one of: a selection of specific testing unit (302a, 302b, 302c) to be used for the network speed test; a defined network speed test duration for the network speed test; a specified number of network speed test iterations to be performed; custom thresholds for acceptable performance levels for each of the network performance parameter; and preferred time intervals between successive network speed tests.

14. The method (500) as claimed in claim 10, further comprising: performing, by the test execution module (214), continuous monitoring of the ISP performance over time by: executing automated network speed test routines at regular intervals as defined by a predetermined schedule; storing results of the network speed test in a database (210); and calculating performance metrics for the ISP based on the stored results.

15. A user equipment (UE) (108) communicatively coupled with a system (102), the coupling comprises steps of: receiving, by the system (102), a connection request; sending, by the system (102), an acknowledgment of the connection request to the UE (108); and transmitting a plurality of signals in response to the connection request, wherein the system (102) is configured for analyzing one or more network performance parameters as claimed in claim 1.

16. A computer program product comprising a non-transitory computer- readable medium comprising instructions that, when executed by one or more processors (202), cause the one or more processors (202) to perform a method (500) for analyzing one or more network performance parameters, the method (500) comprising: receiving (502), by a user interface module (212), a network speed test request from a user; triggering (504), by a test execution module (214), a plurality of testing units (302a, 302b, 302c) configured to perform a network speed test and generate information related to the one or more network performance parameters associated with the received network speed test request; collecting (506), by a data collection module (216), the generated information from the plurality of testing units (302a, 302b, 302c); processing (508), by one or more processors (202), the collected information to generate processed data corresponding to each of the network performance parameter; and analyzing (510), by an analyzing module (218), the processed data corresponding to each of the network performance parameter based upon at least one attribute.

Images