Description:FIELD OF INVENTION
[001] The present invention relates to a scaling potential analyzing system and a method thereof. Particularly, the present invention relates to analyzing the scaling potential of an organization by a system integrated through a machine learning model-based system.
BACKGROUND OF THE INVENTION
[002] Organizational scaling potential analyzer is a tool to evaluate expansion of an organization in terms of operations, products and services. The Organizational scaling potential analyzer provides the quantitative value that helps in identifying key areas for expansion of the organization. Additionally, the organizational scaling potential analyzer enable the organization in informed decision making by providing data driven decision making capability. Further, the analyzer is utilized to identify growth opportunities by determining areas where organization can effectively scale. Furthermore, the analyzer reduce the likelihood of failures while scaling by extensively analyzing various factors.
[003] However, the present devices used for analyzing organizational scaling potential face several limitations that affect their accuracy. Data quality and consistency are significant issues, as present devices are unable to process inconsistent data format and dealing with inconsistent data formats that lead to inaccuracies. Incomplete or outdated data can distort results, but current devices often lack the capability to effectively handle and update this data. The lack of standardization is another major limitation, as present devices are unable to reconcile varying definition and calculations used by the different organizations for the same metric, making comparison difficult and unreliable. Data integration challenges also pose a problem, as presently available devices struggle to integrate data from multiple sources which is a complex and error prone process. Moreover, the dynamic nature of market condition means that rapid changes can quickly render the current analysis obsolete, necessitating periodic updates. Current devices are often not designed to manage these updates efficiently, making them resource intensive and difficult to handle.

[004] In addition to these limitations, present analyzer have several disadvantages. High cost associated with implementing and maintaining these systems are a significant barrier, encompassing expenses for collecting data, investing in software, and allocating human resources for analysis. The process for collecting and analyzing data is also time consuming, leading to delays in obtaining actionable insights. Furthermore, current devices have limited scope, often focusing narrowly on specific metrics and potentially neglecting broader performance indicators. Lastly, these devices tends to be inflexible, with static systems that do not adapt well to evolving organizational needs.

[005] There are several patent applications that disclose a system and method to obtain different metrics. One such United States patent application US4035627A discloses a battery powered hand-held calculator for performing arithmetic, trigonometric and logarithmic functions and displaying the results thereof is provided with a clock mode which performs the function of a clock and displays real time or the function of a stopwatch and stores and displays the times at which recorded events have taken place. However, the cited invention does not provide advance metrics to assess organizational scaling potential.

[006] Therefore, in order to overcome the challenges associated with the state of the art, there is a need to develop an efficient system for analyzing scaling potential of an organization, thereby helping organization to expand for long-term success and stability.
OBJECTIVE OF THE INVENTION
[007] The primary objective of the present invention is to provide a scaling potential analyzing system and a method thereof.
[008] Another objective of the present invention is to provide an efficient system integrated with a machine learning model for analyzing the scaling potential of an organization, enabling the organization to accordingly make more informed and strategic decisions.
[009] Another objective of the present invention is to provide a streamlined and automated assessment of the scaling potential of the organization.

[0010] Another objective of the present invention is to leverage advanced machine learning models to analyze complex datasets, identify patterns, and precisely analyse scaling potential of the organization.

[0011] Another objective of the present invention is to provide an extensive analysis of scaling potential of the organization based on a wide range of parameters.

[0012] Another objective of the present invention is to utilize machine learning models that indicate and forecast future performance trends.

[0013] Another objective of the present invention is to reduce manual effort by leveraging advanced analytical capabilities.

[0014] Yet another objective of the present invention is to provide comprehensive assessment that support strategic planning and resource allocation.

[0015] Other objectives and advantages of the present invention will become apparent from the following description taken in connection with the accompanying drawings, wherein, by way of illustration and example, the aspects of the present invention are disclosed.
BRIEF DESCRIPTION OF DRAWINGS
[0016] The present invention will be better understood after reading the following detailed description of the presently preferred aspects thereof with reference to the appended drawings, in which the features, other aspects, and advantages of certain exemplary embodiments of the invention will be more apparent from the accompanying drawing in which:
[0017] Figure 1 illustrates a block diagram of a system for analysing scaling potential of an organization.
SUMMARY OF THE INVENTION
[0018] The present invention relates to a scaling potential analyzing system and a method thereof. The system comprising: a computing device to receive input data and instructions entered by a user; an input device connected to the computing device and communicably connected to the processor for the user to provide input data and instructions to initiate analysis of the input data for analyzing scaling potential of an organization; a processor integrated into the computing device to process the input data received from the user; a server wirelessly connected to the processor to receive and store the processed input data; a machine learning module installed in the server to analyze scaling potential of the organization based on the processed input data; and a display connected to the computing device to display the analyzed scaling potential data; and a memory integrated into the computing device and connected to the processor to store the input data and analyzed scaling potential related data. The present invention also provides a method for analyzing the scaling potential of the organization. The method comprising steps of: providing input data and instructions to the computing device through the input device; storing the input data in the memory integrated into the computing device; transmitting the input data to the processor through the computing device to process/analyze the input data based on the instructions received by the user; transmitting the processed input data through the processor integrated in the computing device to the server wirelessly connected to the computing device; analyzing the processed input data received by the server through the machine learning module installed in the server to analyze scaling potential of the organization; storing the analyzed scaling potential data by the server; transmitting the analyzed scaling potential data of the organization from the server to the computing device wirelessly to display the analyzed scaling potential data of the organization on the display connected to the computing device. The present invention assists organizations to improve the precision of analysis of scaling potential of the organization and reduce manual effort by leveraging advanced analytical capabilities.

DETAILED DESCRIPTION OF INVENTION
[0019] The following detailed description and embodiments set forth herein below are merely exemplary out of the wide variety and arrangement of instructions which can be employed with the present invention. The present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. All the features disclosed in this specification may be replaced by similar other or alternative features performing similar or same or equivalent purposes. Thus, unless expressly stated otherwise, they all are within the scope of the present invention.
[0020] Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the invention. In addition, descriptions of well-known functions and constructions are omitted for clarity and conciseness.
[0021] The terms and words used in the following description and claims are not limited to the bibliographical meanings but are merely used to enable a clear and consistent understanding of the invention. Accordingly, it should be apparent to those skilled in the art that the following description of exemplary embodiments of the present invention are provided for illustration purpose only and not for the purpose of limiting the invention.
[0022] It is to be understood that the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise.
[0023] It should be emphasized that the term “comprises/comprising” when used in this specification is taken to specify the presence of stated features, integers, steps, or components but does not preclude the presence or addition of one or more other features, steps, components, or groups thereof.
[0024] Accordingly, the present invention relates to a scaling potential analyzing system and a method thereof. Particularly, the present invention relates to analyzing the scaling potential of an organization by a system integrated through a machine learning model-based system.
[0025] In an embodiment, as shown in Figure 1, the system (100) comprises a computing device (102), a processor (104) integrated into the computing device (102), a display (106) connected to the computing device (102); an input device (108) connected to the computing device (102) and communicably connected to the processor (104), a memory (110) integrated into the computing device (102) and connected to the processor (104), a server (112) wirelessly connected to the processor (104), and a machine learning module (114) installed in the server (112).
[0026] The computing device (102) may be configured to receive input data as well as the instructions for the processor (104) to initiate analysis of the input data for analyzing scaling potential of an organization, wherein the input data and instructions are provided by a user through an input device (108). In an exemplary embodiment, the computing device (102) may be selected from a group consisting of, such as, but not limited to, a computer, laptop, calculator, etc.
[0027] The input data provided by the user comprises of a plurality of pre-defined parameters that are used for analyzing the scaling potential of the organization. In an exemplary embodiment, the input data may be selected from a group consisting of such as, but not limited to, number of documented operational processes, percentage of processes with clearly defined steps and objectives, process documentation completeness score, frequency of process review and updates, time saved through process automation, error reduction rate due to standardized processes, employee adherence to defined processes, process cycle time improvements, number of bottlenecks identified and resolved, customer satisfaction scores related to process outcomes, employee feedback on process effectiveness, percentage of processes with established performance metrics, process consistency across different teams/departments, time to onboard new employees on existing processes, number of process improvement suggestions implemented, reduction in ad-hoc decision making, percentage of processes with clear ownership and accountability, rate of successful process outcomes, time spent on non-value-added activities within processes, number of processes integrated with organizational goals, efficiency gains from process optimization, percentage of processes with built-in quality checks, number of processes monitored through dashboards, reduction in operational costs due to streamlined processes, time saved in leadership decision-making due to established processes, rate of process scalability during growth phases, number of processes that have become "muscle memory" for the organization, percentage reduction in process variations, time spent on higher-order strategic thinking by leadership, number of cross-functional processes implemented successfully, or a combination thereof.
[0028] In an exemplary embodiment, the input device (108) may be selected from a group consisting of, such as, but not limited to, a physical keyboard, touch-screen keyboard, mouse, etc.
[0029] The processor (104) may be configured to process/analyze input data on receiving the user’s instructions from the computing device (102) and thereafter wirelessly transmit data to the server (112) for analysis of scaling potential of organization by the machine learning module (114). In an exemplary embodiment, the processed input data may be transmitted to the server (112) through Wi-Fi, Bluetooth, Ethernet, GPRS module, and the like.
[0030] The server (112) receives processed data from the processor (104) and stores the processed input data. In an exemplary embodiment, the server (112) may store data in an architecture selected from a group consisting of, such as, but not limited to, Recurrent Neural Network (RNN) model architecture, an auto encoder, and the like. The machine learning module (114) installed in the server (112) analyses scaling potential of organization based on the processed input data. The server (112) wirelessly transmits the analyzed scaling potential data to the computing device (102) through such as, but not limited to, Wi-Fi, Bluetooth, etc., for displaying the analyzed scaling potential data of organization through the display (106) for the user.

[0031] The machine learning module (114) may be trained on datasets encompassing a wide range of pre-defined parameters to analyze scaling potential of the organization, thereby providing an assessment of scaling potential of the organizations among the consumers. The machine learning module (114) is trained to assign weightage to each pre-defined parameter, which involves segregating the data for training the machine learning module (114) into training data, which is used to train the machine learning module (114) and testing data, which is used to determine the performance of the trained machine learning module (114). In an exemplary embodiment, the machine learning module (114) may be selected from a group consisting of such as, but not limited to, linear regression, polynomial regression, random forest model, and the like, thereby making the process of assigning weight dynamic and automatic.

[0032] The machine learning module (114) also provides data-driven recommendations based on the input data. The machine learning module (114) is trained on training data of various industries and companies so as to evaluate a calibrated score reflective of the organization’s scaling potential. In an exemplary embodiment, the machine learning module (114) is configured to evaluate the calibrated score based upon assigned weight, through a model selected from a group consisting of such as, but not limited to, linear regression, polynomial regression, random forest model, and the alike.

[0033] The memory (110) may be configured to store the input data provided by the user as well as the evaluated/analyzed organizational scaling potential data. In an exemplary embodiment, the memory may be selected from a group consisting of, such as, but not limited to, flash memory, secondary memory, cache memory, and the alike.
[0034] In an embodiment, the present invention also provides a method for analyzing scaling potential of the organization using the system of the present invention. The method comprises the following steps:
• providing input data and instructions to the computing device (102) through the input device (108);
• storing the input data in the memory (110) integrated into the computing device (102);
• transmitting the input data to the processor (104) through the computing device (102) to process/analyze the input data based on the instructions received by the user;
• transmitting the processed input data through the processor (104) integrated in the computing device (102) to the server (112) wirelessly connected to the computing device (102);
• analyzing the processed input data received by the server (112) through the machine learning module (114) installed in the server (112) to provide analysis of scaling potential of the organization;
• storing the analyzed scaling potential of the organization, by the server (112);
• transmitting the analyzed scaling potential from the server (112) to the computing device (102) wirelessly to display the analyzed scaling potential of the organization on the display (106) connected to the computing device (102).
[0035] The input data provided by the user comprises of a plurality of pre-defined parameters that are used for analyzing scaling potential of the organization. In an exemplary embodiment, the input data may be selected from a group consisting of, such as, but not limited to, number of documented operational processes, percentage of processes with clearly defined steps and objectives, process documentation completeness score, frequency of process review and updates, time saved through process automation, error reduction rate due to standardized processes, employee adherence to defined processes, process cycle time improvements, number of bottlenecks identified and resolved, customer satisfaction scores related to process outcomes, employee feedback on process effectiveness, percentage of processes with established performance metrics, process consistency across different teams/departments, time to onboard new employees on existing processes, number of process improvement suggestions implemented, reduction in ad-hoc decision making, percentage of processes with clear ownership and accountability, rate of successful process outcomes, time spent on non-value-added activities within processes, number of processes integrated with organizational goals, efficiency gains from process optimization, percentage of processes with built-in quality checks, number of processes monitored through dashboards, reduction in operational costs due to streamlined processes, time saved in leadership decision-making due to established processes, rate of process scalability during growth phases, number of processes that have become "muscle memory" for the organization, percentage reduction in process variations, time spent on higher-order strategic thinking by leadership, number of cross-functional processes implemented successfully, or a combination thereof.

[0036] In an exemplary embodiment, the computing device may be selected from a group consisting of such as, but not limited to, a computer, laptop, calculator, etc.

[0037] In another exemplary embodiment, the input device (108) may be selected from a group consisting of, such as, but not limited to, a physical keyboard, touch-screen keyboard, mouse, etc.

[0038] In yet another exemplary embodiment, the memory may be selected from a group consisting of, such as, but not limited to, flash memory, secondary memory, cache memory, and the alike.

[0039] The instructions provided by the user include the instructions for analyzing the input data by the processor (104) for analysis of scaling potential of the organization.

[0040] In yet another exemplary embodiment, the server (112) may store data in an architecture selected from a group consisting of, such as, but not limited to, Recurrent Neural Network (RNN) model architecture, an auto encoder, and the like.

[0041] The advantages of the present invention are enlisted herein:
• The present invention provides an efficient system integrated with a machine learning model for analyzing the scaling potential of an organization, enabling the organization to accordingly make more informed and strategic decisions.
• The present invention provides a streamlined and automated assessment of the scaling potential of the organization.
• The present invention leverages advanced machine learning models to analyse complex datasets, identify patterns, and precisely analyse scaling potential of the organization.
• The present invention provides an extensive analysis of scaling potential of the organization based on a wide range of parameters.
• The present invention utilizes machine learning models that indicate and forecast future performance trends.
• The present invention reduces manual effort by leveraging advanced analytical capabilities.
• The present invention provides comprehensive assessment that support strategic planning and resource allocation.

[0042] While this invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
, Claims:WE CLAIM:

1. A scaling potential analyzing system, comprising:
a. a computing device (102) to receive input data and instructions by a user;
b. an input device (108) connected to the computing device (102) and communicably connected to the processor (104) for the user to provide input data and instructions to initiate analysis of the input data for analysis of scaling potential of an organization;
c. a processor (104) integrated into the computing device (102) to process the input data received from the user;
d. a server (112) wirelessly connected to the processor (104) to receive and store the processed input data;
e. a machine learning module (114) installed in the server (112) to analyze scaling potential of the organization based on the processed input data;
f. a display (106) connected to the computing device (102) to display the analyzed scaling potential of the organization; and
g. a memory (110) integrated into the computing device (102) and connected to the processor (104) to store the input data and analyzed organizational scaling potential data.
2. The system (100) as claimed in claim 1, wherein the computing device (102) is selected from a group consisting of a computer, calculator, and laptop.
3. The system (100) as claimed in claim 1, wherein the input data comprises of, number of documented operational processes, percentage of processes with clearly defined steps and objectives, process documentation completeness score, frequency of process review and updates, time saved through process automation, error reduction rate due to standardized processes, employee adherence to defined processes, process cycle time improvements, number of bottlenecks identified and resolved, customer satisfaction scores related to process outcomes, employee feedback on process effectiveness, percentage of processes with established performance metrics, process consistency across different teams/departments, time to onboard new employees on existing processes, number of process improvement suggestions implemented, reduction in ad-hoc decision making, percentage of processes with clear ownership and accountability, rate of successful process outcomes, time spent on non-value-added activities within processes, number of processes integrated with organizational goals, efficiency gains from process optimization, percentage of processes with built-in quality checks, number of processes monitored through dashboards, reduction in operational costs due to streamlined processes, time saved in leadership decision-making due to established processes, rate of process scalability during growth phases, number of processes that have become "muscle memory" for the organization, percentage reduction in process variations, time spent on higher-order strategic thinking by leadership, number of cross-functional processes implemented successfully, or a combination thereof.
4. The system (100) as claimed in claim 1, wherein the input device (108) is selected from a group consisting of, a physical keyboard, touch-screen keyboard, mouse, or a combination thereof.
5. The system (100) as claimed in claim 1, wherein the machine learning module (114) is configured for continuous refinement through iterative training to alleviate reliance on human intervention.
6. The system (100) as claimed in claim 1, wherein the machine learning module (114) is configured to attribute weightage to pre-defined parameters based on a machine learning model.
7. The system (100) as claimed in claim 1, wherein the machine learning model is trained on datasets encompassing a wide range of pre-defined parameters to analyze scaling potential of the organization.
8. The system (100) as claimed in claim 7, wherein the machine learning model is selected from a group consisting of linear regression, polynomial regression, and random forest model.
9. A method for analyzing scaling potential of the organization using the system as claimed in claim 1, comprising steps of:
• providing input data and instructions to the computing device (102) through the input device (108);
• storing the input data in the memory (110) integrated into the computing device (102);
• transmitting the input data to the processor (104) through the computing device (102) to process/analyze the input data based on the instructions received by the user;
• transmitting the processed input data through the processor (104) integrated in the computing device (102) to the server (112) wirelessly connected to the computing device (102);
• analyzing the processed input data received by the server (112) through the machine learning module (114) installed in the server (112) to provide analysis of scaling potential of the organization;
• storing the analyzed scaling potential of the organization, by the server (112);
• transmitting the analyzed scaling potential from the server (112) to the computing device (102) wirelessly to display the analyzed scaling potential of the organization on the display (106) connected to the computing device (102).
10. The method as claimed in claim 9, wherein the method comprise a step of providing iterative training to machine learning module (114) for continuous refinement.