Description:FIELD OF INVENTION
[001] The present invention relates to a stakeholder network strength analyzing system and a method thereof. Particularly, the present invention relates to analyzing network strength of stakeholder(s) by a system integrated through a machine learning model-based system.
BACKGROUND OF THE INVENTION

[002] An organizational growth and impact are defined by the strength of the network. By maximizing the network strength, the impact level and advancement opportunities can be significantly improved. Analyzing the network strength of stakeholders is effective in business operations. Stakeholders groups may include people, parties, companies and any entity that may have an interest, on a project. Some examples of stakeholders may include employees, customers, suppliers, communities, members, shareholders, referral sources, distribution partners, financiers, functional areas, departments, etc.
[003] Commonly, there are some computer implemented programs known for analyzing network strength of an organization. One Indian patent application document 202211019219 disclosed a programmable calculator that includes a display to post input and receive output. The calculator disclosed in the cited document makes computations easier by computing difficult mathematical problems and retrieving mistakes much easier. However, the cited document fails to disclose advanced metric based computations, such as, size, conversion rate, average response time, percentage related to the stakeholders’ network. Also the other commonly known system does not provide a system for precise and quantitative network strength data of an organization using advanced metrics. 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 stakeholder network strength advanced computation method that determines the size of the professional network of an organization to significantly improve its proficiency and profitability.
OBJECTIVE OF THE INVENTION
[004] The primary objective of the present invention is to provide a stakeholder network strength analyzing system and a method thereof.
[005] Another objective of the present invention is to provide a machine learning-based stakeholder network strength analyzing system, to measure size of professional network.
[006] Another objective of the present invention is to analyse network growth rate and number of industry events attended.
[007] Yet another objective of the present invention is to utilize network strength scores indicating number of referrals given, number of successful referrals received, number of mentorship relationships established, number of partnerships formed, number of collaborative projects initiated with network members, number of different industries/roles represented number of introductions made within the network, and number of categories of strategic connections identified.
[008] Another objective of the present invention is to analyze the frequency of interactions with key network members, diversity of network, engagement rate on professional social media platforms, frequency of network newsletter or updates sent.
[009] Yet another objective of the present invention is to evaluate percentage of identified categories with established connections.
[0010] Yet another objective of the present invention is to assist organizations to improve the precision of network strength evaluation and reduce manual effort by leveraging advanced analytical capabilities.
[0011] 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
[0012] 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:
[0013] Figure 1 illustrates a block diagram of the system for analyzing the network strength of the stakeholders.
SUMMARY OF THE INVENTION
[0014] The present invention relates to a stakeholder network strength analyzing system and a method thereof. The system comprises of: a computing device (102) to receive input data and instructions by a user; an input device (108) connected to the computing device (102); a processor (104) integrated into the computing device (102) to process the input data received from the user; a server (112) wirelessly connected to the processor (104) to receive and store the processed input data; a machine learning module (114) installed in the server (112) to evaluate network strength of stakeholders’ based on the processed input data; a display (106); and a memory (110) integrated into the computing device (102) and connected to the processor (104) to store the input data and evaluated network strength data of stakeholders’. The input data may be selected from a group consisting of such as, but not limited to, size of professional network (total connections), network growth rate (new connections per month), number of industry events attended, number of speaking engagements at relevant forums, frequency of interactions with key network members, number of successful referrals received, number of referrals given, conversion rate of network connections to business opportunities, number of partnerships formed, revenue generated through network connections, number of mentorship relationships established, diversity of network (number of different industries/roles represented), engagement rate on professional social media platforms, number of collaborative projects initiated with network member, frequency of network newsletter or updates sent, open and response rates for network communications, number of introductions made within the network, average response time to network communications, number of categories of strategic connections identified, percentage of identified categories with established connections, or a combination thereof.
[0015] The present invention also relates to a method for analyzing network strength of stakeholders' using the system. The method comprises the steps of: providing input data and instructions to a computing device (102) through an input device (108); storing the input data in a 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 a server (112) wirelessly connected to the computing device (102); analyzing the processed input data received by the server (112) through a machine learning module (116) installed in the server (112) to analyze network strength of stakeholders; storing the analyzed network strength data of stakeholders by the server (112) through a storage architecture; transmitting the analyzed network strength data of stakeholders’ from the server (112) to the computing device (102) wirelessly to display the analyzed network strength data of stakeholders’ on a display (106) installed and connected to the computing device (102). The present invention provides machine learning based data driven insights, enabling an organization to determine the network strength of an organization.
DETAILED DESCRIPTION OF INVENTION
[0016] 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.
[0017] 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.
[0018] 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.
[0019] It is to be understood that the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise.
[0020] 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, integers, steps, components, or groups thereof.
[0021] Accordingly, the present invention relates to a stakeholder network strength analyzing system and a method thereof. Particularly, the present invention relates to analyzing network strength of stakeholder(s) by a system integrated through a machine learning model-based system.
[0022] 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).
[0023] The computing device (102) may be configured to receive input data as well as the instructions for the processor (104) to initiate the analysis of the input data for analyzing network strength of stakeholders’, 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.
[0024] The input data provided by the user comprises of a plurality of pre-defined parameters that are used for analyzing the network strength of stakeholders’. In an exemplary embodiment, the input data may be selected from a group consisting of such as, size of professional network (total connections), network growth rate (new connections per month), number of industry events attended, number of speaking engagements at relevant forums, frequency of interactions with key network members, number of successful referrals received, number of referrals given, conversion rate of network connections to business opportunities, number of partnerships formed, revenue generated through network connections, number of mentorship relationships established, diversity of network (number of different industries/roles represented), engagement rate on professional social media platforms, number of collaborative projects initiated with network member, frequency of network newsletter or updates sent, open and response rates for network communications, number of introductions made within the network, average response time to network communications, number of categories of strategic connections identified, percentage of identified categories with established connections, or a combinations thereof.
[0025] 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.
[0026] 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 evaluation of network strength of stakeholders' 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.
[0027] The server (112) receives processed data from the processor (104) and stores the processed input data through an architecture. The machine learning module (114) installed in the server (112) evaluates network strength data of stakeholders' based on the processed input data. The server (112) wirelessly transmits the analyzed network strength data of stakeholders' to the computing device (102) through such as, but not limited to, Wi-Fi, Bluetooth, etc., for displaying the analyzed network strength data of stakeholders' through the display (106) for the user. In an exemplary embodiment, the architecture to store the processed input data may be selected from, but not limited to, Recurrent Neural Network (RNN), Autoencoder, and the like.
[0028] The machine learning module (114) may be trained on datasets encompassing a wide range of pre-defined parameters to analyze/evaluate network strength of stakeholders, thereby providing an assessment of the network strength of stakeholders', of the organizations. 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). The machine learning module (114) may also be configured. 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.
[0029] The memory (110) may be configured to store the input data provided by the user as well as the evaluated/analyzed network strength data of stakeholders’. 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 like.
[0030] The instructions provided by the user include the instructions for analyzing the input data by the processor for analyzing stakeholders’ network strength.
[0031] In an embodiment, the present invention also provides a method for analyzing network strength of stakeholders’ using the system of the present invention. The method comprises the following steps:
• providing input data and instructions to a computing device (102) through an input device (108);
• storing the input data in a 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 a server (112) wirelessly connected to the computing device (102);
• analyzing the processed input data received by the server (112) through a machine learning module (116) installed in the server (112) to analyze network strength of stakeholders’;
• storing the analyzed network strength data of stakeholders’ by the server (112) through a storage architecture;
• transmitting the analyzed stakeholders’ network strength data from the server (112) to the computing device (102) wirelessly to display the analyzed network strength data of stakeholders’ on a display (106) installed and connected to the computing device (102).
[0032] The input data provided by the user comprises of a plurality of pre-defined parameters that are used for analyzing the network strength of stakeholders'. In an exemplary embodiment, the input data may be selected from a group consisting of such as, size of professional network (total connections), network growth rate (new connections per month), number of industry events attended, number of speaking engagements at relevant forums, frequency of interactions with key network members, number of successful referrals received, number of referrals given, conversion rate of network connections to business opportunities, number of partnerships formed, revenue generated through network connections, number of mentorship relationships established, diversity of network (number of different industries/roles represented), engagement rate on professional social media platforms, number of collaborative projects initiated with network member, frequency of network newsletter or updates sent, open and response rates for network communications, number of introductions made within the network, average response time to network communications, number of categories of strategic connections identified, percentage of identified categories with established connections, or a combinations thereof.
[0033] 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.
[0034] In another 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.
[0035] 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 like.
[0036] In an embodiment, the stakeholders may be such as, but not limited to, employees, a communities, suppliers, workers, customers, members, shareholders, referral sources, distribution partners, financiers, functional areas, departments, etc.
[0037] The advantages of the present invention are enlisted herein:
• The present invention provides a network strength calculating device of an organization for strategic operations of an organization.
• The present invention covers wide range of parameters to analyse organizational stakeholder network strength using advanced metrics.
• The present invention provides a network strength analysing system to determine the size of the professional network of an organization to significantly improve its proficiency and profitability.
• The present invention provides a quantitative measurement of the plurality of stakeholder within the network of an organization.
• The present invention provides machine learning based data driven insights, enabling an organization to precisely determine the network strength of an organization.
[0038] 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:1. A stakeholder network strength analyzing system, comprising:
• a computing device (102) to receive input data and instructions by a user;
• 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 analyzing network strength of stakeholders’;
• a processor (104) integrated into the computing device (102) to process the input data received from the user;
• a server (112) wirelessly connected to the processor (104) to receive and store the processed input data through a model architecture;
• a machine learning module (114) installed in the server (112) to evaluate network strength of stakeholders’ based on the processed input data; and
• a display (106) installed and connected to the computing device (102) to display the analyzed stakeholders’ network strength data; and
• a memory (110) integrated into the computing device (102) and connected to the processor (104) to store the input data and evaluated network strength data of stakeholders’.
2. The system as claimed in claim 1, wherein the computing device (102) is selected from computer, laptop, calculator.
3. The system as claimed in claim 1, wherein the input data comprises of a selected from a group consisting of: size of professional network (total connections), network growth rate (new connections per month), number of industry events attended, number of speaking engagements at relevant forums, frequency of interactions with key network members, number of successful referrals received, number of referrals given, conversion rate of network connections to business opportunities, number of partnerships formed, revenue generated through network connections, number of mentorship relationships established, diversity of network (number of different industries/roles represented), engagement rate on professional social media platforms, number of collaborative projects initiated with network member, frequency of network newsletter or updates sent, open and response rates for network communications, number of introductions made within the network, average response time to network communications, number of categories of strategic connections identified, percentage of identified categories with established connections, or a combinations thereof.
4. The system (100) as claimed in claim 1, wherein the input device (108) is a physical keyboard, touch-screen keyboard, and mouse.
5. The system (100) as claimed in claim 1, wherein the server (112) employs a Recurrent Neural Network (RNN) model architecture for data storage trained on a dataset, encompassing a wide range of pre-defined input parameters to evaluate/analyze network strength of stakeholders’ to improve decision-making by the organization.
6. The system (100) as claimed in claim 1, wherein machine learning module (114) is configured for continuous refinement through iterative training to alleviate reliance on human intervention.
7. The system (100) as claimed in claim 1, wherein machine learning module (114) is configured to attribute weightage to pre-defined input data as compared to the standard industrial norms.
8. The system (100) as claimed in claim 1, wherein the stakeholder is, employee, community, supplier, worker, customer, member, shareholder, referral source, distribution partner, financier, department.
9. The system (100) as claimed in claim 1, wherein the machine learning module (114) may be selected from a group consisting of linear regression, polynomial regression, and random forest model.
10. The method for analyzing network strength of stakeholders’ using the system (100) as claimed in claim 1 comprises the steps of:
• providing input data and instructions to a computing device (102) through an input device (108);
• storing the input data in a 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 a server (112) wirelessly connected to the computing device (102);
• analyzing the processed input data received by the server (112) through a machine learning module (116) installed in the server (112) to analyze network strength of stakeholders’;
• storing the analyzed network strength data of stakeholders’ by the server (112) through a storage architecture;
• transmitting the analyzed stakeholders’ network strength data from the server (112) to the computing device (102) wirelessly to display the analyzed network strength data of stakeholders’ on a display (106) installed and connected to the computing device (102).