DESC:FIELD OF INVENTION
[0001] The embodiments of the present disclosure generally relate to optimally bringing the efficiencies of modern computing and networking to the administration and support of electronic interactions and consequences and further relate to a secure architecture enabling distributed, trusted administration for social protection to enable benefit dispersal by a user.

BACKGROUND OF THE INVENTION
[0002] 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.
[0003] Efficient, effective societies require capabilities enabling their inhabitants to control the nature and consequences of their participation in interactions. Every community needs certain basic services, facilities and installations. Support and administrative services are also very important to ensure that people are compensated for their efforts. Such support and administrative services provide great economies in terms of scale and scope—making our economy much more efficient. In recent years, the identification of needy citizens based on a deprivation index has become increasingly important. However, the traditional way to identify citizens to provide support mainly relies on manual review and collective voting, which easily causes subjectivity and randomness. To alleviate the problem above, an automatic identification model for determining deprivation indices for benefit dispersal accordingly. The development and operation of a set of administrative and support services that support these objectives and facilitate the emergence of more diverse, flexible, scalable, and efficient business models for Deprivation index Calculation, from user eligibility to benefit dispersal based on the deprivation index of an individual.
[0004] There is therefore a need in the art to provide a system and a method that can facilitate mitigating the problems associated with the prior art.

OBJECTS OF THE PRESENT DISCLOSURE
[0005] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.
[0006] It is an object of the present disclosure to enhance high chances of accurate and reduce missing data in case of only surveyed, applied citizens.
[0007] It is an object of the present disclosure to reduce ultimate repercussions of errors on policy and policy-level changes, and using only the verified and latest data thus enrolled or terminated citizens with a time validity.
[0008] It is an object of the present disclosure to use only shareable data.
[0009] It is an object of the present disclosure to solve many data issues by the implementation as only citizens that are being enrolled/terminated for at least one scheme are picked.
[0010] It is an object of the present disclosure to provide a refined survey/new service.

SUMMARY
[0011] This section is provided to introduce certain objects and aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.
[0012] In an aspect, the present disclosure provides for a system for determining beneficiary need score of a plurality of users. The system may include one or more processors operatively coupled to a plurality of first computing devices, the one or more processors coupled with a memory that stores instructions which when executed by the one or more processors causes the system to receive a plurality of first set of data packets from a plurality of first computing devices, the plurality of first set of data packets pertaining to information on one or more welfare-based schemes to be availed by the plurality of users operating the plurality of first computing devices. The system may also receive a plurality of second set of data packets from the plurality of first computing devices, the second set of data packets pertaining to a set of responses generated by the plurality of first computing devices to a set of queries provided by a second computing device associated with an entity and based on user information of the plurality of users. The system may further be configured to extract, by using an artificial intelligence (AI) engine, a plurality of first set of attributes from the plurality of first set of data packets, the first set of attributes pertaining to an eligibility criteria of the plurality of users for the one or more welfare-based schemes based on a predefined set of eligibility rules. In an embodiment, the AI engine may be associated with the one or more processors. The system may be further configured to determine, by using the AI engine, the eligibility criteria of the plurality of users based on the extracted first set of attributes and thus, based on the determined eligibility criteria, the AI engine may extract a second set of attributes from the second set of data packets, the second set of attributes pertaining to user information associated with account details of each user. Furthermore, the system may compute, by the AI engine, a quantum of need for each of the users for the one or more welfare-based schemes based on the extracted first and second set of attributes and thereby generate, by the AI engine, a beneficiary score for each user based upon the computed quantum of need.
[0013] In an embodiment, the system may be further configured to authenticate and verify, by using the AI engine each user such that the plurality of users can access the system to apply for the one or more welfare schemes.
[0014] In an embodiment, the system may be further configured to check if the user has already availed the one or more welfare-based schemes based on the extracted first and second set of attributes.
[0015] In an embodiment, the system may be further configured to reject a user if any user information is determined to be false.
[0016] In an embodiment, the set of queries provided by the entity may pertain to questions based on the eligibility rule of the one or more welfare-based schemes.
[0017] In an embodiment, the system may be further configured to obtain a registration data based on a request from an unregistered user through a respective first computing device. In an embodiment, login credentials may be generated based on acknowledgement of a request and verification of registration data of the unregistered user, wherein the unregistered user enters the generated login credentials to access the system.
[0018] In an embodiment, the registration data obtained by means of a set of predefined forms may be required to be filled by the unregistered user to become an enrolled user, wherein the registration data is saved in a database having a specific time frame to further get for verification and authentication of the new enrolled user.
[0019] In an embodiment, during verification, the registration data provided by the enrolled user may be verified by a second user associated with the entity.
[0020] In an embodiment, the computation of the beneficiary score may be further based on a set of preference parameters associated with one or more preference rules associated with the predefined scheme.
[0021] In an embodiment, the beneficiary score may be further used to make selection of most eligible users out of plurality of users.
[0022] In an embodiment, the system may be further configured to be flexible and scalable to include a plurality of parameters associated with geographical, time, environment constraints to assign or filter out one or more unnecessary benefits associated with the predefined scheme to the most eligible users out of the plurality of users.
[0023] In an aspect, the present disclosure provides for a user equipment (UE) for determining beneficiary need score of a plurality of users. The UE may include a processor and a receiver operatively coupled to a plurality of first computing devices, the processor coupled with a memory that stores instructions which when executed by the processor causes the UE to receive a plurality of first set of data packets from a plurality of first computing devices, the plurality of first set of data packets pertaining to information on one or more welfare-based schemes to be availed by the plurality of users operating the plurality of first computing devices. The UE may also receive a plurality of second set of data packets from the plurality of first computing devices, the second set of data packets pertaining to a set of responses generated by the plurality of first computing devices to a set of queries provided by a second computing device associated with an entity and based on user information of the plurality of users. The UE may further be configured to extract, by using an artificial intelligence (AI) engine, a plurality of first set of attributes from the plurality of first set of data packets, the first set of attributes pertaining to an eligibility criteria of the plurality of users for the one or more welfare-based schemes based on a predefined set of eligibility rules. In an embodiment, the AI engine may be associated with the one or more processors. The UE may be further configured to determine, by using the AI engine, the eligibility criteria of the plurality of users based on the extracted first set of attributes and thus, based on the determined eligibility criteria, the AI engine may extract a second set of attributes from the second set of data packets, the second set of attributes pertaining to user information associated with account details of each user. Furthermore, the UE may compute, by the AI engine, a quantum of need for each of the users for the one or more welfare-based schemes based on the extracted first and second set of attributes and thereby generate, by the AI engine, a beneficiary score for each user based upon the computed quantum of need.
[0024] In an aspect, the present disclosure provides for a method for determining beneficiary need score of a plurality of users. The method may include the step of receiving, by one or more processors, a plurality of first set of data packets from a plurality of first computing devices, the plurality of first set of data packets pertaining to information on one or more welfare-based schemes to be availed by the plurality of users operating the plurality of first computing devices. The one or more processors may be operatively coupled to the plurality of first computing devices and may be coupled with a memory that stores instructions which may be executed by the one or more processors. The method may further include the steps of extracting, by using an artificial intelligence (AI) engine, a plurality of first set of attributes from the plurality of first set of data packets, the first set of attributes pertaining to an eligibility criteria of the plurality of users for the one or more welfare-based schemes based on a predefined set of eligibility rules. In an embodiment, the AI engine may be associated with the one or more processors. The method may further include the step of determining, by using the AI engine, the eligibility criteria of the plurality of users based on the extracted first set of attributes. Further, based on the determined eligibility criteria, the method may include the step of extracting, by the AI engine a second set of attributes from the second set of data packets, the second set of attributes pertaining to user information associated with account details of each user. The method may also include the step of computing, by the AI engine, a quantum of need for each of the users for the one or more welfare-based schemes based on the extracted first and second set of attributes and then the step of generating, by the AI engine, a beneficiary score for each user based upon the computed quantum of need.

BRIEF DESCRIPTION OF DRAWINGS
[0025] The accompanying drawings, which are incorporated herein, and constitute a part of this invention, 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 invention. 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 invention of such drawings includes the invention of electrical components, electronic components or circuitry commonly used to implement such components.
[0026] FIG. 1 illustrates an exemplary network architecture in which or with which the system of the present disclosure can be implemented, in accordance with an embodiment of the present disclosure
[0027] FIG. 2A illustrates an exemplary representation of system based on an artificial intelligence (AI) based architecture, in accordance with an embodiment of the present disclosure.
[0028] FIG. 2B illustrates an exemplary representation of a user equipment (UE) based on an artificial intelligence (AI) based architecture, in accordance with an embodiment of the present disclosure.
[0029] FIG. 3 illustrates exemplary representation of a Backend Graph, in accordance with an embodiment of the present disclosure.
[0030] FIG. 4 illustrates an exemplary representation of Defining Nodes Attributes, in accordance with an embodiment of the present disclosure.
[0031] FIGs. 5A-5C illustrate exemplary representations of Assigning Probabilities, in accordance with an embodiment of the present disclosure.
[0032] FIGs. 6A-6B illustrate exemplary representations of drip benefit recommendation for default values from a backend graph, in accordance with an embodiment of the present disclosure.
[0033] FIGs. 7A-7B illustrate exemplary representations of filtration of results Based on Deprivation index, in accordance with an embodiment of the present disclosure.
[0034] FIGs. 8A-8H illustrate exemplary representations of recommended deprivation index and some of the input parameters for Beneficiaries, in accordance with an embodiment of the present disclosure.
[0035] FIG. 9 refers to the exemplary computer system in which or with which embodiments of the present invention can be utilized, in accordance with embodiments of the present disclosure.
[0036] The foregoing shall be more apparent from the following more detailed description of the invention.

DETAILED DESCRIPTION OF INVENTION
[0037] 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.
[0038] 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 the function and arrangement of elements without departing from the spirit and scope of the invention as set forth.
[0039] 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.
[0040] The present invention provides a robust and effective solution to an entity or an organization by enabling the entity to implement a system for process flow for Deprivation index Calculation, from user eligibility to benefit dispersal based on the deprivation index of a user. Thus, the system and method of the present disclosure may be beneficial for both entities and users.
[0041] Referring to FIG. 1 that illustrates an exemplary network architecture (100) in which or with which system (110) of the present disclosure can be implemented, in accordance with an embodiment of the present disclosure. As illustrated in FIG. 1, by way of example and not by not limitation, the exemplary architecture (100) may include a plurality of users (102-1, 102-2…102-N) (collectively referred to as citizens (102) or users (102) and individually as citizen (102) or user (102)) associated with a plurality of first computing devices (104-1, 104-2,…104-N) (also referred to as user devices (104) or user computing devices (104) collectively and user device (104) individually), at least a network (106), at least a centralized server 112 and at least a second computing device (116) associated with an entity (114). More specifically, the exemplary architecture (100) includes a system (110) equipped with an artificial intelligence (AI) engine (214) (Ref. FIG. 2A) for enhancing features required for determining a deprivation index (interchangeably referred to as beneficiary score herein) for each user (102). The user device (104) may be communicably coupled to the centralized server (112) through the network (106) to facilitate communication therewith. As an example, and not by way of limitation, the user computing device (104) may be operatively coupled to the centralised server (112) through the network (106) and may be associated with the entity (114). Examples of the user computing devices (104) can include, but are not limited to, a computing device (104) associated with welfare based assets, a smart phone, a portable computer, a personal digital assistant, a handheld phone and the like.
[0042] The system (110) may be further operatively coupled to a second computing device (116) associated with the entity (114). The second computing device (116) may further be associated with a second user (118). The second user (118) can be anyone managing the system (110), a field inspector, an analyst, a system manager and the like. The system (110) may further be operatively coupled to a third computing device (108) (also referred to as the user computing device or user equipment (UE) hereinafter) associated with an entity (114). The entity (114) may include a company, a hospital, an organisation, a university, a lab facility, a business enterprise, or any other secured facility associated with welfare and social policy benefits. In some implementations, the system (110) may also be associated with the UE (108). The UE (108) can include a handheld device, a smart phone, a laptop, a palm top and the like. Further, the system (110) may also be communicatively coupled to the one or more first computing devices (104) via a communication network (106).
[0043] In an embodiment, the system (110) may receive a plurality of first set of data packets pertaining to a plurality of users or users (102) associated with a plurality of first computing devices (104). In an exemplary embodiment, the user can be a citizen of a country and may also be referred to as beneficiary herein. The AI engine (214) operatively coupled to the system (110) may receive the plurality of first set of data packets from a plurality of user computing devices, the plurality of data packets pertaining to information on welfare-based schemes that can be availed by the user (102). The AI engine (214) may also receive a plurality of second set of data packets from the plurality of computing devices, the second set of data packets pertaining to a set of responses generated by the user to a set of queries provided by the entity (114) and user information of the user. The set of queries provided by the entity may pertain to questions based on a scheme's eligibility rule but not limited to the like. The system may then extract a plurality of first set of attributes from the first set of data packets, the first set of attributes pertaining to eligibility criteria of the user based on a predefined set of eligibility rules.
[0044] The system (110) may then check an eligibility criteria of the user based on the extracted first set of attributes. For example, the first step for the users will be to go through the eligibility check of a scheme they are interested in applying for by answering some questions based on the scheme's eligibility rules. The eligibility rules form the basis of the Eligibility Check. The Eligibility Rules can be strictly “policy-documents-based rules” for a particular scheme, that can be objective in nature, non-changing, fixed, and static, and can only be changed if the policy itself is changed for a scheme. For Example: As per Drip Irrigation Policy Document, any beneficiary can apply for up to a maximum of 5 hectares of his/her farmland.
[0045] In an exemplary embodiment, if the users are eligible, the system (110) may then extract a second set of attributes from the second set of data packets, the second set of attributes pertaining to user information associated with Account Details such as Account Number, Bank Name, and IFSC code, copy of Bank Passbook. The user information may further include Details of Supplier/Vendor ( Name of Supplier/Vendor, GSTIN), details of farmland/(s) ( district, block, crop spacing), Crop for which benefit is being availed, Soil health Card Score and the like, Estimated Cost- Copy of quotation and Design of drip irrigation system, Copy of Bank Passbook, Copy of quotation, Photograph of the plot(s) to be developed with geo-tagging, Design of drip irrigation system, Land Record Documents but not limited to the like. Based on the extracted first and second set of attributes, the AI engine (214) may then authenticate and verify the user such that the user can avail the system (110) to apply for a predefined scheme. The system (110) may also check if the user has already availed the predefined scheme based on the extracted first and second set of attributes. The system (110) may be further configured to reject the user if any user information is found to be false.
[0046] If the user information is not present, the user (102) can be registered through the user device and login details can be generated and stored along with the user information. The user can be asked to fill a set of predefined forms that can be saved in a database having a specific time frame to further get for verification and authentication later. For example, after the Eligibility Check, users will be authenticated from using but not limited to DigiLocker Login and will complete form using a Family ID database but not limited to it.
[0047] In an exemplary embodiment, the system (110) may evaluate how much the user is actually in need of the predefined scheme (also referred to as the benefit herein) once the application of the user is verified successfully enrolled. The system (110) may generate a score to each enrolled user based upon how much they need the predefined scheme.
[0048] In an exemplary embodiment, during verification, the details and documents provided by the user will get verified. A second user (also referred to as the field inspector) associated with the entity can verify the user information. For example, the field inspector may verify quotation (manual verification), design of drip irrigation system (manual verification), accounts details (manual verification), supplier/vendor Details (manual verification), Land Related Details can be verified by the entity such as a Land record department (manual verification). Whether or not the user has already availed the benefit and when the benefit was availed earlier will be verified by the Department (manual verification). The field inspector will verify and upload the photograph of the plot to be developed with geotagging. Once all fields are verified the field inspector will approve the quotation. If in case any parameter is found to be false, application will automatically get rejected with the reason for rejection
[0049] In an exemplary embodiment, a method may be performed in the system (110) for determining the deprivation index and may include the step of identifying a set of preference parameters (interchangeably simply referred to as the preference parameters hereinafter). The preference parameters can be used for computation of the deprivation index for the user for the predefined scheme. The score is further used to make selection of most needy beneficiaries out of all the enrolled beneficiaries. The preference parameters are based on a set of preference rules (simply referred to as preference parameters) because the preference parameters are subjective in nature. The preference rules offer flexibility to change as per requirements and time. The Preference Rules can be either Policy Driven or Inferred but not limited to the like. The Policy-Driven Preference Rules are mandated by policy documents to be used while identifying beneficiaries and delivering benefits. For Example, in Drip Irrigation, Policy Document quoted :“More focus be given on promotion of micro irrigation for water intensive/guzzling crops to minimise water requirement”. The rule mentioned above is not exclusive as it does not make the beneficiaries who are farming less water intensive crops ineligible, however it mandates to give more preference to beneficiaries availing benefits for water intensive crops including sugarcane, Cotton and the like. The Inferred Preference Rules, although made by the people who are domain experts, however there may be certain points which may have got missed while designing the policy due to geographical, time, environment constraints and the like. There can also be situations where an administrator may need more parameters to filter out the neediest benefits. The inferred rules fill these gaps by giving flexibility to administrators to add additional parameters to filter upon the neediest beneficiaries out of the enrolled beneficiaries. For example, the requirement to give preference to disabled female applicants in benefit dispersal. As these rules are not being stated, the perceptions and judgements behind the creation of these rules can widely differ by choices and time-based conditions hence the implementation of these rules are done in such a way that they offer flexibility for modifications.
[0050] In an exemplary embodiment, the system (110) may be configured to provide flexibility to modify the preference parameters as per requirements at any stage of the project.
[0051] The system (110) may further be configured to construct a graph after getting the right preference parameters. The system (110) may further defining nodes and attributes and assign probabilities to the nodes and attributes.
[0052] In an embodiment, the first computing device (104) and the second computing device (116) may communicate with the system (110) via set of executable instructions residing on any operating system, including but not limited to, Android TM, iOS TM, Kai OS TM and the like. In an embodiment, the first computing device (104) ) and the second computing device (116) may include, but not limited to, any electrical, electronic, electro-mechanical or an equipment or a combination of one or more of the above devices such as 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 computing device, wherein the computing device may include one or more in-built or externally coupled accessories including, but not limited to, a visual aid device such as camera, audio aid, a microphone, a keyboard, input devices for receiving input from a user such as touch pad, touch enabled screen, electronic pen and the like. It may be appreciated that the user computing device (104) may not be restricted to the mentioned devices and various other devices may be used. A smart computing device may be one of the appropriate systems for storing data and other private/sensitive information.
[0053] In an exemplary embodiment, a network (106) may include, by way of example but not limitation, at least a portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, etc. one or more messages, packets, signals, waves, voltage or current levels, some combination thereof, or so forth. A network 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, a circuit-switched network, an ad hoc network, an infrastructure network, a public-switched telephone network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, some combination thereof.
[0054] In another exemplary embodiment, the centralized server (112) may include or comprise, by way of example but not limitation, one or more of: a stand-alone server, a server blade, a server rack, a bank of servers, a server farm, hardware supporting a part of a cloud service or system, a home server, hardware running a virtualized server, one or more processors executing code to function as a server, one or more machines performing server-side functionality as described herein, at least a portion of any of the above, some combination thereof.
[0055] In an embodiment, the system (110) may include one or more processors coupled with a memory, wherein the memory may store instructions which when executed by the one or more processors may cause the system to facilitate determination of beneficiary score. FIG. 2A with reference to FIG. 1, illustrates an exemplary representation of system (110) for facilitating determining beneficiary score of a plurality of users based on an artificial intelligence (AI) based architecture, in accordance with an embodiment of the present disclosure. In an aspect, the system (110) may comprise one or more processor(s) (202). The one or more processor(s) (202) may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that process data based on operational instructions. Among other capabilities, the one or more processor(s) (202) may be configured to fetch and execute computer-readable instructions stored in a memory (204) of the system (110). The memory (204) may be configured to store one or more computer-readable instructions or routines in a non-transitory computer readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory (204) may comprise any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as EPROM, flash memory, and the like.
[0056] In an embodiment, the system (110) may include an interface(s) 206. The interface(s) 206 may comprise a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, and the like. The interface(s) 206 may facilitate communication of the system (110). The interface(s) 206 may also provide a communication pathway for one or more components of the system (110). Examples of such components include, but are not limited to, processing engine(s) 208 and a database 210.
[0057] The processing engine(s) (208) may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) (208). In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) (208) may be processor executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine(s) (208) may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) (208). In such examples, the system (110) may comprise the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the system (110) and the processing resource. In other examples, the processing engine(s) (208) may be implemented by electronic circuitry.
[0058] The processing engine (208) may include one or more engines selected from any of a data acquisition engine (212), an artificial intelligence (AI) engine (214), and other engines (216).
[0059] FIG. 2B illustrates an exemplary representation (250) of the user equipment (UE) (108), in accordance with an embodiment of the present disclosure. In an aspect, the UE (108) may comprise a processor (222). The processor (222) may be implemented as one or more microprocessors, edge processors, 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 processor(s) (222) may be configured to fetch and execute computer-readable instructions stored in a memory (224) of the UE (108). The memory (224) may be configured to store one or more computer-readable instructions or routines in a non-transitory computer readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory (224) may comprise any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as EPROM, flash memory, and the like.
[0060] In an embodiment, the UE (108) may include an interface(s) 206. The interface(s) 206 may comprise a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, and the like. The interface(s) 206 may facilitate communication of the UE (108). Examples of such components include, but are not limited to, processing engine(s) 228 and a database (230).
[0061] The processing engine(s) (228) may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) (228). In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) (228) may be processor executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine(s) (228) may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) (228). In such examples, the UE (108) may comprise the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the UE (108) and the processing resource. In other examples, the processing engine(s) (228) may be implemented by electronic circuitry.
[0062] The processing engine (228) may include one or more engines selected from any of a data acquisition engine (232), an artificial intelligence (AI) engine (234), and other engines (236).
[0063] FIG. 3 illustrates exemplary representation of a Backend Graph, in accordance with an embodiment of the present disclosure. As illustrated, the backend graph is depicted for Drip Irrigation Scheme. The graph may be constructed after the parameters are put in a network. Certain points that are kept in mind while obstructing graph, for effective procedure. The number of parent nodes to not be kept more than 3 because the more the number of nodes the less effective will be the effect of each node on the child/benefit node and will thus make our result ineffective. The probability/weightage distribution sum for each node cannot be greater than 1. Preference to be given to the graph’s depth as compared to the graph’s width for more effective results. The resultant graph/network thus formed by connecting the preferences parameters, may be called an Inference Graph obtained through the concept of Bayesian Networks.
EXEMPLARY SCENARIO
[0064] FIG. 4 illustrates an exemplary representation of Defining Nodes Attributes, in accordance with an embodiment of the present disclosure. As illustrated in the figure, after construction of the graph as shown in FIG. 3, each node may be defined with attributes as per the parameters. The information passed into the system is either processed or is in raw form based upon the probability distribution requirements. Example: Here the “Consuming_Crop” Parameter is being defined as Extremely High, High, Medium, Low. The Crop Input given by the beneficiary will be processed in the backend to get replaced by either of the attributes, so that it can be passed on to the deprivation indexr.
[0065] FIGs. 5A-5C illustrate exemplary representations of Assigning Probabilities, in accordance with an embodiment of the present disclosure. In an embodiment, once all the attributes are defined, the complete Graph is assigned with conditional probability distributions (CPDs). These are the distributions which help the graph in computing recommendations. The nodes in the graph are of at least two types. As illustrated in FIG. 5A, probabilities assigned for a set of base nodes are shown. The base nodes have no parent node coming in. The probabilities assignment for Base nodes are done in either of one way as shown in FIG. 5A. The probability assigned is calculated by the computation of actual citizen data. A simple Example is : If 1000 Beneficiary applicants are there for a drip scheme, the probability for base node - “Gender” will be the proportional sum of number of people belonging to a gender. While FIG. 5B illustrates probability assignments for the base nodes derived from government and associated organization of related surveys. There may be some data values that are not present with us at the initial stage. To overcome this data barrier, we use researched values computed by govt and related organisations with those parameters for which we do not have the actual data time being. For Example, if the economic profile data for enrolled applicants is not known, the generic population data can be assumed until the actual data is obtained. These probability distributions can be easily replaced with actual distribution once access to the data is obtained.
[0066] FIG. 5C illustrates probability assignment Weightages for Non-Base Nodes in Child node. The nodes are the result of a combination of 2 or more base nodes. The purpose of these nodes is to increase the depth in tree structure and reduce tree width, so as to make the maximum impact of parameters probability on the ultimate benefit node. The probabilities assigned to these nodes are weightages.
[0067] FIGs. 6A-6B illustrate exemplary representations of drip benefit recommendation for default values from a backend graph, in accordance with an embodiment of the present disclosure. After, assigning accurate weightages and probabilities, there is a need to go through the recommendations to have a cross-check on the done work. The inference graph backend provides the flexibility to manually change the input values to test its results on the just parent node, successive nodes and the ultimate final node. FIG. 6A shows the default input values and corresponding recommendation for the drip irrigation scheme. It can be seen from FIG. 6A that the drip benefit recommendation for default values is : 54.69% in favour. Changing the parameters in favour of the beneficiary, FIG. 6B shows that changing certain parameters in favour of the beneficiary has changed the recommended benefit to 86.47% in favour. Thus, ensuring the accuracy of the graph.
TABLE 1 highlights the list of Base Parameters that are being used in designing Inference Graph for Drip Irrigation Scheme.

Parameter Name Policy / Inferred Explanation of Parameter Preference Given
Crop Water Requirement Policy Policy document mandates to promote micro irrigation technologies in water intensive/consuming crops like sugarcane, banana, cotton etc and give adequate focus to extend coverage of field crops under micro irrigation technologies.
Apart from horticulture and water guzzling crops, cereals and pulses may also be brought under the ambit of Micro irrigation.
Attributes: ExtremelyHigh, High, Medium, Low (Processed)
Preference: Water Guzzling Crops, Horticulture, Pulses
Groundwater Level Policy Policy Document mandates to promote micro irrigation technologies in water scarce, water stressed and critical ground water blocks/districts
Attributes: Low,Medium,High (Processed)
Preference : Low Groundwater Levels areas
Fertigation Component Availability Policy Policy Document mandates to make potential use of micro irrigation systems for promoting fertigation.
Attributes: Yes,No Preference : Applicants with Fertigation Facility
Source of Water (for Irrigation)
Policy Policy Document mandates : “As small farm holdings may not have individual source of water, it would be preferable to encourage a group of farmers to avail the benefits of drip irrigation through a common water source”
It is thus preferable to have the common water source near the farmland
Attributes : Yes,No Preference : Presence of common Water source near the farmland
Caste Policy Policy Document mandates to ensure 16% and 8% of the total allocation are in proportion of SC/ST population in the district should be utilised for Special Component Plan (SCP) & Tribal Sub Plan (TSP) respectively.

Attributes: SC,ST,OBC,General,DNT,NT Preference: SC,ST,DNT,NT
Small and Marginal Farmer
Policy Policy Document mandates to ensure that at least 50% of the allocation is utilised for small, marginal farmers
The beneficiary farmer is a small and marginal farmer or not.The farmers having land holding upto 2 hectares.
Attributes: Yes,No Preference: Small and Marginal Farmers
Gender Policy Policy Document mandates to ensure at-least 30% should be women beneficiaries/farmers.

Attributes: Male,Female,Transgender Preference: Females and Transgenders
Soil Score Inferred The soil health card score of farmland for which benefit is being availed.
Attributes : Poor,Fair,Good Preference : Fair and Good Soil Score
Total Landholding ( in hectares) Inferred The Total Landholding for which benefit is being applied for.
Attributes: Low,Medium,High. Preference : 0-2 Hectares of level landholding.
Land Ownership Inferred The Land Ownership of farmland for which benefit is being availed.
Attributes: Self-Owned Land or Leased Land. Preference : Leased Land
Annual Income Inferred The Annual Income of the farmer. Preference : Low Income Farmers (
Economic Classification Inferred The Economic Classification in which Family falls in. Attributes : BPL,APL,EWS Preference : BPL and EWS
House Type Inferred The type of House beneficiary resides in ,Attributes: Pakka or Kutcha House Preference : Kutcha House Type Residents
Area Of Residence Inferred The area of Residence of beneficiary.
Attributes: Rural or Urban Preference : Rural
Is Disabled
Inferred The beneficiary suffers from any type of disability.
Attributes: Yes, No Preference : Disables Applicant: Yes
Contract Farming Inferred The Type of farming is contract farming or not.
Attributes: Yes, No Preference : Contract Farming: Yes
Marital Status Inferred The marital status of the beneficiary.
Attributes: Married, Widow, Widower, Separated, Unmarried Preference: Widow, Widower
Ever Applied Same Benefit Inferred The applicant has ever availed the benefit for this scheme.
Attributes: Yes, No Preference : First Time Applicants
Farmland: Multiple Location Inferred The being is being availed for fields at multiple locations or not.
Attributes: Yes, No Preference: Applicant applying for a single field.

[0068] Alternatively, assigned probability and weightages can be cross checked is by using an Explainable AI. The Explainable AI (XAI) may pertain to techniques in the application of artificial intelligence technology such that the results of the solution can be understood by the users. The Explainable AI tries to understand the steps and models involved in making decisions. Explainable AI removes the black box from decision making. The System Provides the feature of Explainable AI which does the Reverse Analysis of Recommendation. The Explainable AI provides the flexibility to change the probability and weightages as per the data/requirements at any stage of the project.
[0069] In an exemplary embodiment, the final scores are being displayed, with scores corresponding to citizens identity (ID). All this process supports complete data privacy and security of the user as the data passed is encrypted by randomly-generated token numbers (cannot be accessed/meddled by a third party) and is hidden as data being passed only corresponding to the particular fields and an ID allotted to the user for this process only. Thus, no Personal information including Name, Email, Block etc are displayed or processed.
[0070] FIGs. 7A-7B illustrate exemplary representations of filtration of results Based on Deprivation index, in accordance with an embodiment of the present disclosure. The final evaluated results are being displayed in the dynamic dashboard created for the system. The dashboard provides following features: Filtration of Results Based on Deprivation index: For Example, FIG. 7A illustrates all those beneficiaries, that have got scores above 60, are being selected for benefit dispersal. Filtering Results Based on On-Demand/Generic Parameters: This Dynamic Dashboard/B.I Tool provides the flexibility to choose the most needy benefit as per: the deprivation indexed result, Combination of deprivation index results + demographic parameters and demographic Parameters. All the filtrations are accompanied with dynamic change in demographics and statistical data analysis so as to make filtration procedure easy and insightful. For example, FIG. 7B shows the insights for all the applications. If only male applicants are chosen, then the score can be 60 or above and the selection in that case gets reduced to 35 applicants.
[0071] FIGs. 8A-8H illustrate exemplary representations of recommended deprivation index and some of the input parameters for Beneficiaries, in accordance with an embodiment of the present disclosure. FIG. 8A highlights display of final deprivation index, corresponding to user/beneficiary id while FIG. 8B highlights demographic composition of beneficiaries evaluated. In an exemplary embodiment, feature for mass approval and Dispersal may be activated once the program officer is satisfied with the selection criteria and filters. He then can disperse the benefit to the most needy beneficiaries in one click. The budget will simultaneously get updated and the dispersal procedure can be progressed with different sets of parameters and filters. FIGs. 8C-8H illustrate exemplary example representations of various scenarios of results Based on Deprivation index, in accordance with an embodiment of the present disclosure.
[0072] FIG. 9 illustrates an exemplary computer system in which or with which embodiments of the present invention can be utilized in accordance with embodiments of the present disclosure. As shown in FIG. 9, computer system 900 can include an external storage device 910, a bus 920, a main memory 930, a read only memory 990, a mass storage device 950, communication port 960, and a processor 970. A person skilled in the art will appreciate that the computer system may include more than one processor and communication ports. Processor 990 may include various modules associated with embodiments of the present invention. Communication port 960 may be chosen depending on a network, any network to which computer system connects. Memory 930 can be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. Read-only memory 990 can be any static storage device(s). Mass storage 950 may be any current or future mass storage solution, which can be used to store information and/or instructions. Bus 920 communicatively couples processor(s) 970 with the other memory, storage and communication blocks.
[0073] Optionally, operator and administrative interfaces, e.g. a display, keyboard, and a cursor control device, may also be coupled to bus 920 to support direct operator interaction with a computer system. Other operator and administrative interfaces can be provided through network connections connected through communication port 960. Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system limit the scope of the present disclosure.
[0074] 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 invention. These and other changes in the preferred embodiments of the invention 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 invention and not as limitation.
[0075] 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, IC layout design, and/or trade dress protection, belonging to Jio Platforms Limited (JPL) or its affiliates (herein after 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.

ADVANTAGES OF THE PRESENT DISCLOSURE
[0076] The present disclosure provides for a system and method that enhances high chances of accurate and reduce missing data in case of only surveyed, applied citizens.
[0077] The present disclosure provides for a system and method that reduces ultimate repercussions of errors on policy and policy-level changes, and using only the verified and latest data thus enrolled or terminated citizens with a time validity.
[0078] The present disclosure provides for a system and method that solves many data issues by the implementation as only citizens that are being enrolled/terminated for at least one scheme are picked.
[0079] The present disclosure provides for a refined survey/new service.

WE CLAIMS:

1. A system (110) for determining beneficiary score of a plurality of users (102), said system (110) comprising;
one or more processors (202) operatively coupled to a plurality of first computing devices (104), the one or more processors (202) coupled with a memory (204), wherein said memory (204) stores instructions which when executed by the one or more processors (202) causes said system (110) to:
receive a plurality of first set of data packets from a plurality of first computing devices (104), the plurality of first set of data packets pertaining to information on one or more welfare-based schemes to be availed by the plurality of users (102) operating the plurality of first computing devices (104);
receive a plurality of second set of data packets from the plurality of first computing devices (104), the second set of data packets pertaining to a set of responses generated by the plurality of first computing devices (104) to a set of queries provided by a second computing device (116) associated with an entity (114) and based on user information of the plurality of users (102);
extract, by using an artificial intelligence (AI) engine (214), a plurality of first set of attributes from the plurality of first set of data packets, the first set of attributes pertaining to an eligibility criteria of the plurality of users (102) for the one or more welfare-based schemes based on a predefined set of eligibility rules, wherein the AI engine is associated with the one or more processors (202);
determine, by using the AI engine (214), the eligibility criteria of the plurality of users (102) based on the extracted first set of attributes;

based on the determined eligibility criteria, extract, by the AI engine (214) a second set of attributes from the second set of data packets, the second set of attributes pertaining to user information associated with account details of each user;
compute, by the AI engine, a quantum of need for each of the users for the one or more welfare-based schemes based on the extracted first and second set of attributes;
generate, by the AI engine, a beneficiary score for each user based upon the computed quantum of need.
2. The system (110) as claimed in claim 1, wherein the system is further configured to authenticate and verify, by using the AI engine (214) each said user such that the plurality of users accesses the system (110) to apply for the one or more welfare schemes.
3. The system (110) as claimed in claim 1, wherein the system is further configured to check if the user has already availed the one or more welfare-based schemes based on the extracted first and second set of attributes.
4. The system (110) as claimed in claim 1, wherein the system is further configured to reject a user if any user information is determined to be false.
5. The system as claimed in claim 1, wherein the set of queries provided by the entity (114) pertain to questions based on the eligibility rule of the one or more welfare-based schemes.
6. The system (110) as claimed in claim 1, wherein the system is configured to obtain a registration data based on a request from an unregistered user through a respective first computing device (104), wherein login credentials are generated based on acknowledgement of a request and verification of registration data of the unregistered user, wherein the unregistered user enters the generated login credentials to access the system (110).
7. The system (110) as claimed in claim 6, wherein the registration data obtained by means of a set of predefined forms are required to be filled by the unregistered user to become an enrolled user, wherein the registration data is saved in a database having a specific time frame to further get for verification and authentication of the new enrolled user.
8. The system (110) as claimed in claim 6, wherein during verification, the registration data provided by the enrolled user is verified by a second user (118) associated with the entity (114).
9. The system as claimed in claim 1, wherein the computation of the beneficiary score is further based on a set of preference parameters associated with one or more preference rules associated with the predefined scheme.
10. The system (110) as claimed in claim 1, wherein the beneficiary score is further is used to make selection of most eligible users out of plurality of users.
11. The system (110) as claimed in claim 1, wherein the system is configured to be flexible and scalable to include a plurality of parameters associated with geographical, time, environment constraints to assign or filter out one or more unnecessary benefits associated with the predefined scheme to the most eligible users out of the plurality of users.
12. A user equipment (UE) (108) for determining beneficiary score of a plurality, said UE (108) comprising;
An edge processor (222) and a receiver operatively coupled to a plurality of first computing devices (104), the processor (222) coupled with a memory (224), wherein said memory (224) stores instructions which when executed by the processor (222) causes said UE (108) to:
receive, by the receiver, a plurality of first set of data packets from a plurality of first computing devices (104), the plurality of first set of data packets pertaining to information on one or more welfare-based schemes to be availed by the plurality of users (102) operating the plurality of first computing devices (104);

receive, by the receiver, a plurality of second set of data packets from the plurality of first computing devices (104), the second set of data packets pertaining to a set of responses generated by the plurality of first computing devices (104) to a set of queries provided by a second computing device (116) associated with an entity (114) and based on user information of the plurality of users (102);
extract, by using an artificial intelligence (AI) engine, a plurality of first set of attributes from the plurality of first set of data packets, the first set of attributes pertaining to an eligibility criteria of the plurality of users (102) for the one or more welfare-based schemes based on a predefined set of eligibility rules, wherein the AI engine (234) is associated with the processor (222);
determine, by using the AI engine (234), the eligibility criteria of the plurality of users (102) based on the extracted first set of attributes;
based on the determined eligibility criteria, extract, by the AI engine (234), a second set of attributes from the second set of data packets, the second set of attributes pertaining to user information associated with account details of each user;
compute, by the AI engine (234), a quantum of need for each of the users for the one or more welfare-based schemes based on the extracted first and second set of attributes;
generate, by the AI engine (234), a beneficiary score for each user based upon the computed quantum of need.
13. A method for determining beneficiary score of a plurality of users, said method comprising the steps of
receiving, by one or more processors (202), a plurality of first set of data packets from a plurality of first computing devices (104), the plurality of first set of data packets pertaining to information on one or more welfare-based schemes to be availed by the plurality of users (102) operating the plurality of first computing devices (104), wherein the one or more processors (202) are operatively coupled to the plurality of first computing devices (104), the one or more processors (202) coupled with a memory (204), wherein said memory (204) stores instructions which are executed by the one or more processors (202);
receiving, by the one or more processors (202), a plurality of second set of data packets from the plurality of first computing devices (104), the second set of data packets pertaining to a set of responses generated by the plurality of computing devices (104) to a set of queries provided by a second computing device (116) associated with an entity (114) and based on user information of the plurality of users (102);
extracting, by using an artificial intelligence (AI) engine (214), a plurality of first set of attributes from the plurality of first set of data packets, the first set of attributes pertaining to an eligibility criteria of the plurality of users (102) for the one or more welfare-based schemes based on a predefined set of eligibility rules, wherein the AI engine (214) is associated with the one or more processors (202);
determining, by using the AI engine (214), the eligibility criteria of the plurality of users (102) based on the extracted first set of attributes;
based on the determined eligibility criteria, extracting, by the AI engine (214) a second set of attributes from the second set of data packets, the second set of attributes pertaining to user information associated with account details of each user;
computing, by the AI engine, a quantum of need for each of the users for the one or more welfare-based schemes based on the extracted first and second set of attributes;
generating, by the AI engine (214), a beneficiary score for each user based upon the computed quantum of need.
14. The method as claimed in claim 13, wherein the method further comprises the step of: authenticating and verifying, by using the AI engine (214) each said user such that the plurality of users accesses the system to apply for the one or more welfare-based schemes.
15. The method as claimed in claim 13, wherein the method further comprises the step of:
checking if the user has already availed the one or more welfare-based schemes based on the extracted first and second set of attributes.
16. The method as claimed in claim 13, wherein the method further comprises the step of:
rejecting a user if any user information is determined to be false.
17. The method as claimed in claim 13, wherein the set of queries provided by the entity (114) pertain to questions based on the eligibility rule of the one or more welfare-based schemes.
18. The method as claimed in claim 13, wherein the method further comprises the step of:
obtaining a registration data based on a request from an unregistered user through a respective first computing device (104), wherein login credentials are generated based on acknowledgement of a request and verification of registration data of the unregistered user, wherein the unregistered user enters the generated login credentials to access the method.
19. The method as claimed in claim 18, wherein the registration data obtained by means of a set of predefined forms are required to be filled by the unregistered user to become an enrolled user, wherein the registration data is saved in a database having a specific time frame to further get for verification and authentication of the new enrolled user.
20. The method as claimed in claim 19, wherein during verification, the registration data provided by the enrolled user is verified by a second user associated with the entity (114).
21. The method as claimed in claim 13, wherein the computation of the beneficiary score is further based on a set of preference parameters associated with one or more preference rules associated with the predefined scheme.
22. The method as claimed in claim 13, wherein the beneficiary score is further is used to make selection of most eligible users out of plurality of users.
23. The method as claimed in claim 13, wherein the method further comprises the step of:
configuring the system (110) to be flexible and scalable to include a plurality of parameters associated with geographical, time, environment constraints to assign or filter out one or more unnecessary benefits associated with the predefined scheme to the most eligible users out of the plurality of users.