DESC:RESERVATION OF RIGHTS
[0001] A portion of the disclosure of this patent document contains material, which is subject to intellectual property rights such as but are not limited to, copyright, design, trademark, integrated circuit (IC) layout design, and/or trade dress protection, belonging to Jio Platforms Limited (JPL) or its affiliates (hereinafter referred as owner). The owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all rights whatsoever. All rights to such intellectual property are fully reserved by the owner.

FIELD OF INVENTION
[0002] The embodiments of the present disclosure generally relate to systems and methods for generating heuristics based global optimization using Genetic Algorithms (GA). More particularly, the present disclosure relates to a system and a method for generating an optimal set of non-fungible tokens (NFT’s).

BACKGROUND
[0003] The following description of the 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 is used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of the prior art.
[0004] Systems and methods implementing computerized auction are being increasingly employed by individuals and organizations for exchanging non-fungible tokens (NFT’s). Further, computerized auction systems may be accessible to vast global markets to be utilized by a large number of auction participants, namely offerors and bidders. The computerized auction systems operate across networks to offer bidders a convenient way to search, view and acquire an endless range of NFTs.
[0005] In a conventional computerized NFT auction, the bidder faces two conflicting objectives namely, the quality of the NFT (quality of all selected NFTs i.e., NFT set) and the probability of bid success for the NFT set on a fixed budget. The two objectives are inversely correlated i.e., a NFT with a high score will have lesser probability of bid success at lower bids. Therefore technical difficulties may be encountered while balancing the quality of the NFT set without exceeding the fixed budget.
[0006] There is, therefore, a need in the art to provide a system and a method that can mitigate the problems associated with the prior arts.

OBJECTS OF THE INVENTION
[0007] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are listed herein below.
[0008] It is an object of the present disclosure to provide a system and a method providing non-fungible token (NFT) selection and an auction system using artificial intelligence (AI) to make an informed decision in bidding of NFT across various categories.
[0009] It is an object of the present disclosure to provide a system and a method where a decision maker generates a scaled number weight for optimizing between a NFT-set quality and a probability of bid success. Based on these inputs, the AI engine generates a list of NFT’s across categories with their optimal bids.
[0010] It is an object of the present disclosure to provide a system and a method where the decision maker utilizes multiple scenarios during the auctions, thereby generating a better viewpoint during bidding process.
[0011] It is an object of the present disclosure to provide a system and a method where some of the NFT’s in a few categories are locked and the NFT’s for other categories are auctioned for generating an optimized solution.

SUMMARY
[0012] 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.
[0013] In an aspect, the present disclosure relates to generating an optimal set of non-fungible tokens (NFT’s). The system includes a processor, and a memory operatively coupled to the processor, where the memory stores instructions to be executed by the processor. The processor receives an input from one or more computing devices associated with a user. The input is based on one or more NFT’s associated with one or more categories. The one or more computing devices are connected to the processor via a network. The processor generates a quality score for the one or more NFT’s associated with the one or more categories. The processor generates a bidding model based on the quality score of the one or more NFT’s. The processor receives one or more constraints and one or more features associated with the one or more NFT’s. The processor predicts via an optimization engine an optimal price, a NFT set, and a selection success rate associated with the NFT set based on the bidding model, the one or more constraints, and the one or more features.
[0014] In an embodiment, the one or more constraints received by the processor may include at least one of a budget constraint, a maximum number of NFT’s for the one or more categories, a minimum number of NFT’s for the one or more categories, and an individual NFT budget constraint.
[0015] In an embodiment, the one or more features received by the processor may include at least one of a demographic feature and a calendar feature associated with the one or more NFT’s.
[0016] In an embodiment, the optimization engine used by the processor (202) may be an artificial intelligence (AI) engine.
[0017] In an embodiment, the processor may use a genetic technique via the AI engine to predict the optimal price, the NFT set, and the selection success rate associated with the NFT set based on the bidding model, the one or more constraints, and the one or more features.
[0018] In an embodiment, the one or more categories may include at least one of a healthcare category, a sports category, an appliances category, and a retail category.
[0019] In an embodiment, the NFT set may include a total quality associated with one or more selected NFT’s via the bidding model.
[0020] In an aspect, the present disclosure relates to a method for generating an optimal set of non-fungible tokens (NFT’s). The method includes receiving, by a processor associated with a system, an input from one or more computing devices associated with a user. The input is based on one or more NFT’s associated with one or more categories. The method includes generating, by the processor, a quality score for the one or more NFT’s associated with the one or more categories. The method includes generating, by the processor, a bidding model based on the quality score of the one or more NFT’s. The method includes receiving, by the processor, one or more constraints and one or more features associated with the one or more NFT’s. The method includes predicting, by the processor, via an optimization engine an optimal price, a NFT set, and a selection success rate associated with the NFT set based on the bidding model, the one or more constraints, and the one or more features.
[0021] In an embodiment, the method may include receiving, by the processor, at least one of a budget constraint, a maximum number of NFT’s for the one or more categories, a minimum number of NFT’s for the one or more categories, and an individual NFT budget constraint.
[0022] In an embodiment, the method may include receiving, by the processor, the one or more features at least one of a demographic feature and a calendar feature associated with the one or more NFT’s.
[0023] In an embodiment, the optimization engine used by the processor may be an AI engine.
[0024] In an embodiment, the method may include using, by the processor, a genetic technique via the AI engine to predict the optimal price, the NFT set, and the selection success rate associated with the NFT set based on the bidding model, the one or more constraints, and the one or more features.
[0025] In an embodiment, the one or more categories may include at least one of a healthcare category, a sports category, an appliances category, and a retail category.
[0026] In an embodiment, the NFT set may include a total quality associated with one or more selected NFT’s via the bidding model.
[0027] In an aspect, a user equipment (UE) for sending requests includes one or more processors communicatively coupled to a processor in a system. The one or more processors are coupled with a memory and said memory stores instructions to be executed by the one or more processors. The one or more processors transmit an input to the processor via a network. The input is based on one or more NFT’s associated with one or more categories. The processor is configured to receive the input from the UE. The processor is configured to generate a quality score for the one or more NFT’s associated with the one or more categories. The processor is configured to generate a bidding model based on the quality score of the one or more NFT’s. The processor is configured to receive one or more constraints and one or more features associated with the one or more NFT’s. The processor is configured to predict via an optimization engine an optimal price, a NFT set, and a selection success rate associated with the NFT set based on the bidding model, the one or more constraints, and the one or more features.

BRIEF DESCRIPTION OF DRAWINGS
[0028] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings includes the disclosure of electrical components, electronic components, or circuitry commonly used to implement such components.
[0029] FIG. 1 illustrates an example network architecture (100) for implementing a proposed system (108), in accordance with an embodiment of the present disclosure.
[0030] FIG. 2 illustrates an example block diagram (200) of a proposed system (108), in accordance with an embodiment of the present disclosure.
[0031] FIG. 3 illustrates an example block diagram (300) for selection of optimal non-fungible tokens (NFT’s), in accordance with an embodiment of the present disclosure.
[0032] FIG. 4 illustrates an example system architecture (400) for generating optimal NFT’s, in accordance with an embodiment of the present disclosure.
[0033] FIG. 5 illustrates an example block diagram (500) of an auction engine, in accordance with an embodiment of the present disclosure.
[0034] FIG. 6 illustrates an example chromosomes of a genetic algorithm (600) signifying NFT’s, in accordance with an embodiment of the present disclosure.
[0035] FIG. 7 illustrates an example crossover process (700) of the genetic algorithm, in accordance with an embodiment of the present disclosure.
[0036] FIG. 8 illustrates an example mutation process (800) of the genetic algorithm, in accordance with an embodiment of the present disclosure.
[0037] FIG. 9 illustrates an example simulation (900) generated by the auction engine, in accordance with an embodiment of the present disclosure.
[0038] FIG. 10 illustrates an example graph representation (1000) for generating optimal NFT’s, in accordance with an embodiment of the present disclosure.
[0039] FIG. 11 illustrates an example representation (1100) of a frozen NFT, in accordance with an embodiment of the present disclosure.
[0040] FIG. 12 illustrates an example representation (1200) of a fixed NFT, in accordance with an embodiment of the present disclosure.
[0041] FIG. 13 illustrates an example representation (1300) of a fixed NFT with category and bid price, in accordance with an embodiment of the present disclosure.
[0042] FIG. 14 illustrates an example block diagram (1400) for NFT quality prediction, in accordance with an embodiment of the present disclosure.
[0043] FIG. 15 illustrates an example block diagram (1500) for NFT selection success probability, in accordance with an embodiment of the present disclosure.
[0044] FIGs. 16A-16C illustrate example block diagrams (1600) of a sample user interface (UI) screen representing the auction engine, in accordance with embodiments of the present disclosure.
[0045] FIG. 17 illustrates an example computer system (1700) in which or with which embodiments of the present disclosure may be implemented.
[0046] The foregoing shall be more apparent from the following more detailed description of the disclosure.

DEATILED DESCRIPTION
[0047] 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.
[0048] 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 disclosure as set forth.
[0049] 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 to avoid obscuring the embodiments.
[0050] Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
[0051] The word “exemplary” and/or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and/or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising” as an open transition word without precluding any additional or other elements.
[0052] Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[0053] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a”, “an”, and “the” are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.
[0054] The various embodiments throughout the disclosure will be explained in more detail with reference to FIGs. 1-17.
[0055] FIG. 1 illustrates an example network architecture (100) for implementing a proposed system (108), in accordance with an embodiment of the present disclosure.
[0056] As illustrated in FIG. 1, the network architecture (100) may include a system (108). The system (108) may be connected to one or more computing devices (104-1, 104-2…104-N) via a network (106). The one or more computing devices (104-1, 104-2…104-N) may be interchangeably specified as a user equipment (UE) (104) and be operated by one or more users (102-1, 102-2...102-N). Further, the one or more users (102-1, 102-2…102-N) may be interchangeably referred as a user (102) or users (102).
[0057] In an embodiment, the computing devices (104) may include, but not be limited to, a mobile, a laptop, etc. Further, the computing devices (104) may include a smartphone, virtual reality (VR) devices, augmented reality (AR) devices, a general-purpose computer, desktop, personal digital assistant, tablet computer, and a mainframe computer. Additionally, input devices for receiving input from the user (102) such as a touch pad, touch-enabled screen, electronic pen, and the like may be used. A person of ordinary skill in the art will appreciate that the computing devices (104) may not be restricted to the mentioned devices and various other devices may be used.
[0058] In an embodiment, the 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. The network (106) may also 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, or some combination thereof.
[0059] In an embodiment, the system (108) may receive an input from one or more computing devices (104) associated with a user (102).The input may be based on one or more non-fungible tokens (NFT’s) associated with one or more categories. The system (108) may generate a quality score for the one or more NFT’s associated with the one or more categories. The one or more categories may include but not limited to a healthcare category, a sports category, an appliances category, and a retail category.
[0060] In an embodiment, the system (108) may generate a bidding model based on the quality score of the one or more NFT’s. The system (108) may receive one or more constraints and one or more features associated with the one or more NFT’s. The one or more constraints received by the system (108) may include but not limited a budget constraint, a maximum number of NFT’s for the one or more categories, a minimum number of NFT’s for the one or more categories, and an individual NFT budget constraint. The one or more features received by the system (108) may include but not limited to a demographic feature and a calendar feature associated with the one or more NFT’s.
[0061] In an embodiment, the system (108) may predict via an optimization engine an optimal price, a NFT set, and a selection success rate associated with the NFT set based on the bidding model, the one or more constraints, and the one or more features. The optimization engine used by the system (108) may be an artificial intelligence (AI) engine. Further, the NFT set may include a total quality associated with one or more selected NFT’s via the bidding model.
[0062] Although FIG. 1 shows exemplary components of the network architecture (100), in other embodiments, the network architecture (100) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 1. Additionally, or alternatively, one or more components of the network architecture (100) may perform functions described as being performed by one or more other components of the network architecture (100).
[0063] FIG. 2 illustrates an example block diagram (200) of a proposed system (108), in accordance with an embodiment of the present disclosure.
[0064] Referring to FIG. 2, the system (108) may comprise one or more processor(s) (202) that 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 (108). 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 random-access memory (RAM), or non-volatile memory such as erasable programmable read only memory (EPROM), flash memory, and the like.
[0065] In an embodiment, the system (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 (I/O) devices, storage devices, and the like. The interface(s) (206) may also provide a communication pathway for one or more components of the system (108). Examples of such components include, but are not limited to, processing engine(s) (208) and a database (210), where the processing engine(s) (208) may include, but not be limited to, a data ingestion engine (212), an AI engine (214), and other engine(s) (216). In an embodiment, the other engine(s) (216) may include, but not limited to, a data management engine, an input/output engine, and a notification engine.
[0066] In an embodiment, 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 (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 system (108) and the processing resource. In other examples, the processing engine(s) (208) may be implemented by electronic circuitry.
[0067] Although FIG. 2 shows exemplary components of the system (108), in other embodiments, the system (108) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 2. Additionally, or alternatively, one or more components of the system (108) may perform functions described as being performed by one or more other components of the system (108).
[0068] In an embodiment, the processor (202) may receive an input via the data ingestion engine (212). The input may be received from one or more computing devices (104) associated with a user (102). The processor (202) may store the input in the database (210). The input may be based on one or more non-fungible tokens (NFT’s) associated with one or more categories. The processor (202) may generate a quality score for the one or more NFT’s associated with the one or more categories. The one or more categories may include but not limited to a healthcare category, a sports category, an appliances category, and a retail category.
[0069] In an embodiment, the processor (202) may generate a bidding model based on the quality score of the one or more NFT’s. The processor (20) may receive one or more constraints and one or more features associated with the one or more NFT’s. The one or more constraints received by the processor (202) may include but not limited a budget constraint, a maximum number of NFT’s for the one or more categories, a minimum number of NFT’s for the one or more categories, and an individual NFT budget constraint. The one or more features received by the processor (202) may include but not limited to a demographic feature and a calendar feature associated with the one or more NFT’s.
[0070] In an embodiment, the processor (202) may predict via an optimization engine an optimal price, a NFT set, and a selection success rate associated with the NFT set based on the bidding model, the one or more constraints, and the one or more features. The optimization engine used by the processor (202) may be an AI engine (214). Further, the NFT set may include a total quality associated with one or more selected NFT’s via the bidding model.
[0071] FIG. 3 illustrates an example block diagram (300) for selection of optimal non-fungible tokens (NFT’s), in accordance with an embodiment of the present disclosure.
[0072] As illustrated in FIG. 3, in an embodiment, the NFT pool (302) may contain NFT’s mapped to categories. The categories may map across healthcare, sports, appliances, retail and Energy. As illustrated, P1 and P2 may correspond to Category 1, P3 and P4 may correspond to Category 2 and P5, P6 may correspond to Category 3. The auction system (304) may receive a NFT pool as an input and recommend optimal NFTs (306) based on the constraints.
[0073] In an embodiment, every NFT may be associated with a quality score that measures the value of the NFT in that category. Further, the system (108) use optimization for recommending the best combinations of NFTs and bid prices in the auction while considering the constraints of having minimum number of NFTs for each category.
[0074] In an embodiment, the system (108) may generate a simulation platform to test out different cases for various decision makers. The system (108) may include simulation on pre-slotted NFTs and finding the optimal NFTs for the other slots.
[0075] FIG. 4 illustrates an example system architecture (400) for generating optimal NFT’s, in accordance with an embodiment of the present disclosure.
[0076] As illustrated in FIG. 4, in an embodiment, the system (108) may use a NFT quality model (402) to predict the quality of the NFT and generate a score associated with the NFT. Further, the system (108) may use a NFT bid/auction model (404) to predict a success rate of the selection of the NFT at a particular price. Further, the system (108) may use Master data (406) with features and the attributes to understand the NFT. The system (108) may include external factors (408) into account that may include but not limited to demographics and calendar features. The system (108) may determine an optimal NFT price (412), a NFT quality score (414), and a selection success rate (416) via an auction engine (410).
[0077] In an embodiment, the auction engine (410) may use a genetic technique that may generate an initial population of N chromosomes. The auction engine (410) may analyse the feasibility of the chromosome (inputs) based on the constraints. The auction engine (410) may evaluate a fitness f(n) of each chromosome n of the population, N. The auction engine (410) may create a new generation, i.e. a new population by repeating the following steps until the termination criteria is met. The auction engine (410) may select two parent chromosomes from a population according to their fitness. Several methods including Roulette wheel and weighted ranking approaches may be implemented. Further, the following may be implemented by the auction engine (410).
a. The auction engine (410) may analyze the feasibility of the chromosome (inputs) based on the constraints
b. The auction engine (410) may use a crossover function, cross(p,q) that takes two chromosomes p and q (denoted as parent chromosomes) as inputs and generates a new set of off springs (children) chromosomes.
c. The auction engine (410) may analyze the feasibility of the chromosome (inputs) based on the constraints
d. The auction engine (410) may compute a mutation function, mutate(k) that takes a chromosome k as input and adds random noise to the input features to generate a new chromosome. The noise helps in capturing the variability of the input space, making the method generalize and concentrate on a global solution instead of a local one.
e. The auction engine (410) may analyze the feasibility of the chromosome (inputs) based on the constraints.
f. The auction engine (410) may ass feasible children chromosomes (off springs) to a ‘new’ population.
g. The auction engine (410) may replace newly generated population used in the technique in an iterative manner.
h. The auction engine (410) may use a terminating condition to satisfy, stop, and return the best solution in a current population. The terminating condition may depend on how the average quality of the population changes with a generation. If the termination condition is not met, the new population may go to a step where the fitness may be calculated.
[0078] FIG. 5 illustrates an example block diagram (500) of an auction engine, in accordance with an embodiment of the present disclosure.
[0079] As illustrated in FIG. 5, in an embodiment, the system (108) may use an initial population (502), calculate fitness (504), select (506), crossover (508), use the mutation function (510) and determine is a termination criteria is met. Based on appositive determination, the system (108) may terminate the process or the system (108) may go back using the initial population (502).
[0080] FIG. 6 illustrates an example chromosomes of a genetic algorithm (600) signifying NFT’s, in accordance with an embodiment of the present disclosure.
[0081] As illustrated in FIG. 6, in an embodiment, the system (108) may define a chromosome associated with a problem. The chromosome may signify which NFTs may be selected, the category and a bid price associated with the selected NFT’s. The system (108) may generate two categories of genes which may be ordered pairs. The first gene may be the category while the second gene may be the bid price of the NFT selected in the first gene.
[0082] Further, in an embodiment, the system (108) may use a fitness function for evaluating the score chromosome as follows.
•
[0083] FIG. 7 illustrates an example crossover process (700) of the genetic algorithm, in accordance with an embodiment of the present disclosure.
[0084] As illustrated in FIG. 7, in an embodiment, the system (108) may use the crossover step where unique gene sequences from two parents may be used to construct children chromosomes.
[0085] FIG. 8 illustrates an example mutation process (800) of the genetic algorithm, in accordance with an embodiment of the present disclosure.
[0086] As illustrated in FIG. 8, in an embodiment, the system (108) may use a mutation step where random number of genes in a chromosome may be randomly mutated.
[0087] Further, in an embodiment, the system (108) may use a termination criterion where a time limit and a maximum number of iterations may be considered.
[0088] FIG. 9 illustrates an example simulation (900) generated by the auction engine, in accordance with an embodiment of the present disclosure.
[0089] As illustrated in FIG. 9, in an embodiment, the system (108) may various scenarios based on a set quality weight and a bid success weight. Further, keeping the constraints fixed, the optimizer may provide a bank or pool of NFT squads with a category and a bid price. The higher the total NFT quality weight, the lower the weight for bid success as the two objectives may be inversely correlated.
[0090] FIG. 10 illustrates an example graph representation (1000) for generating optimal NFT’s, in accordance with an embodiment of the present disclosure.
[0091] As illustrated in FIG. 10, in an embodiment, the system (108) may generate a selected set where the selected set may include a high chance (probability) of success in selection but include a lower overall quality. Further, the selected set may include an equal weightage of bid selection success probability and a quality of the selected set. The selected set may be the best quality set but may include a lower chance of getting selected in the bid.
[0092] FIG. 11 illustrates an example representation (1100) of a frozen NFT, in accordance with an embodiment of the present disclosure.
[0093] As illustrated in FIG. 11, in an embodiment, the system (108) may receive a total NFT quality weight, a bid success weight, and determine the NFTs to be included in the squad. Further, the system (108) may freeze the NFTs set to be included. The system (108) may generate an optimal set and bid prices for the frozen NFTs and also suggest other NFTs in the non-frozen slots of the squad with their bid price and category.
[0094] FIG. 12 illustrates an example representation (1200) of a fixed NFT, in accordance with an embodiment of the present disclosure.
[0095] As illustrated in FIG. 12, in an embodiment, the system (108) may use the total NFT quality weight, the bid success weight, the NFT to be included in the set, bid prices for the NFT to be included in the set, and a category to be frozen as inputs. Also the other NFTs of the squad, their category and the bid price may be determined. Here, instead of the slot, the role may also be fixed for the selected NFTs.
[0096] FIG. 13 illustrates an example representation (1300) of a fixed NFT with category and bid price, in accordance with an embodiment of the present disclosure.
[0097] As illustrated in FIG. 13, in an embodiment, the system (108) may determine the fixed NFT and the bid price.
[0098] FIG. 14 illustrates an example block diagram (1400) for NFT quality prediction, in accordance with an embodiment of the present disclosure.
[0099] As illustrated in FIG. 14, in an embodiment, the system (108) may use market factors such as but not limited to category sentiment and other geo-political factors as features. Further, the system (108) may use calendar and demographics factors. Additionally, the system (108) may use attributes of the NFT, such as but not limited to indivisibility, uniqueness, and authenticity. The system (108) may generate a NFT quality model. The NFT quality model may be an inference model which may receive the NFT as input and predict the quality/value of the NFT. The higher the score, the higher may be the value of the NFT. The NFT value may include a real-value between 0 to 1.0.
[00100] FIG. 15 illustrates an example block diagram (1500) for NFT selection success probability, in accordance with an embodiment of the present disclosure.
[00101] As illustrated in FIG. 15, in an embodiment, the system (108) may use market factors such as category sentiment and other geo-political factors as features in the model. Further, the system (108) may use calendar and demographics factors and attributes of the NFT, such as but not limited to indivisibility, uniqueness, and authenticity. The system (108) may generate the NFT bid model that takes the NFT and a price as input and predicts the probability/chances of selection success. Higher the score, the higher may be the probability of selection of the NFT.
[00102] FIGs. 16A-16C illustrate example block diagrams (1600) of a sample user interface (UI) screen representing the auction engine, in accordance with embodiments of the present disclosure.
[00103] As illustrated in FIGs. 16A-16C, in an embodiment, the system (108) may use a user interface (UI) screen that may include simulation with inputs of the total NFT bid success and the total NFT quality. The UI screen may include a provision to fix the NFT, their bid price, and category. The output may also be displayed on this screen after the ‘Simulate’ button is pressed.
[00104] In an embodiment, the system (108) may use a NFT configuration to enable the user (102) to change the parameters of a certain NFT in the pool. Further, the system (108) may include a global configuration with different constraint inputs and parameters for the simulation model.
[00105] FIG. 17 illustrates an exemplary computer system (1700) in which or with which embodiments of the present disclosure may be implemented.
[00106] As shown in FIG. 17, the computer system (1700) may include an external storage device (1710), a bus (1720), a main memory (1730), a read-only memory (1740), a mass storage device (1750), a communication port(s) (1760), and a processor (1770). A person skilled in the art will appreciate that the computer system (1700) may include more than one processor and communication ports. The processor (1770) may include various modules associated with embodiments of the present disclosure. The communication port(s) (1760) may be any of an RS-232 port for use with a modem-based dialup connection, a 10/100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. The communication ports(s) (1760) may be chosen depending on a network, such as a Local Area Network (LAN), Wide Area Network (WAN), or any network to which the computer system (1700) connects.
[00107] In an embodiment, the main memory (1730) may be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. The read-only memory (1740) may be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chip for storing static information e.g., start-up or basic input/output system (BIOS) instructions for the processor (1770). The mass storage device (1750) may be any current or future mass storage solution, which can be used to store information and/or instructions. Exemplary mass storage solutions include, but are not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and/or Firewire interfaces).
[00108] In an embodiment, the bus (1720) may communicatively couple the processor(s) (1770) with the other memory, storage, and communication blocks. The bus (1720) may be, e.g. a Peripheral Component Interconnect PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), (USB), or the like, for connecting expansion cards, drives, and other subsystems as well as other buses, such a front side bus (FSB), which connects the processor (1770) to the computer system (1700).
[00109] In another embodiment, operator and administrative interfaces, e.g., a display, keyboard, and cursor control device may also be coupled to the bus (1720) to support direct operator interaction with the computer system (1700). Other operator and administrative interfaces can be provided through network connections connected through the communication port(s) (1760). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system (1700) limit the scope of the present disclosure.
[00110] While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be implemented merely as illustrative of the disclosure and not as a limitation.

ADVANTAGES OF THE INVENTION
[00111] The present disclosure provides a system and a method that takes informed decisions using the various parameters to process a highly uncertain auction problem with conflicting objectives.
[00112] The present disclosure provides a system and a method that provides good solutions quickly during an auction process.
[00113] The present disclosure provides a system and a method where a decision maker generates scenario simulations and allows a wide range of possibilities arising in the auction process.
,CLAIMS:1. A system (108) for generating an optimal set of non-fungible tokens (NFT’s), the system (108) comprising:
a processor (202); and
a memory (204) operatively coupled with the processor (202), wherein said memory (204) stores instructions, which when executed by the processor (202), cause the processor (202) to:
receive an input from one or more computing devices (104) associated with a user (102), wherein the input is based on one or more NFT’s associated with one or more categories, and wherein the one or more computing devices (104) are connected to the processor (202) via a network (106);
generate a quality score for the one or more NFT’s associated with the one or more categories;
generate a bidding model based on the quality score of the one or more NFT’s;
receive one or more constraints and one or more features associated with the one or more NFT’s; and
predict via an optimization engine an optimal price, a NFT set, and a selection success rate associated with the NFT set based on the bidding model, the one or more constraints, and the one or more features.

2. The system (108) as claimed in claim 1, wherein the one or more constraints received by the processor (202) comprise at least one of: a budget constraint, a maximum number of NFT’s for the one or more categories, a minimum number of NFT’s for the one or more categories, and an individual NFT budget constraint.

3. The system (108) as claimed in claim 1, wherein the one or more features received by the processor (202) comprise at least one of: a demographic feature and a calendar feature associated with the one or more NFT’s.

4. The system (108) as claimed in claim 1, wherein the optimization engine used by the processor (202) is an artificial intelligence (AI) engine (214).

5. The system (108) as claimed in claim 4, wherein the processor (202) uses a genetic technique via the AI engine (214) to predict the optimal price, the NFT set, and the selection success rate associated with the NFT set based on the bidding model, the one or more constraints, and the one or more features.

6. The system (108) as claimed in claim 1, wherein the one or more categories comprise at least one of: a healthcare category, a sports category, an appliances category, and a retail category.

7. The system (108) as claimed in claim 1, wherein the NFT set comprises a total quality associated with one or more selected NFT’s via the bidding model.

8. A method for generating an optimal set of non-fungible tokens (NFT’s), the method comprising:
receiving, by a processor (202), associated with a system (108), an input from one or more computing devices (104) associated with a user (102), wherein the input is based on one or more NFT’s associated with one or more categories;
generating, by the processor (202), a quality score for the one or more NFT’s associated with the one or more categories;
generating, by the processor (202), a bidding model based on the quality score of the one or more NFT’s;
receiving, by the processor (202), one or more constraints and one or more features associated with the one or more NFT’s; and
predicting, by the processor (202), via an optimization engine an optimal price, a NFT set, and a selection success rate associated with the NFT set based on the bidding model, the one or more constraints, and the one or more features.

9. The method as claimed in claim 8, comprising receiving, by the processor (202), at least one of: a budget constraint, a maximum number of NFT’s for the one or more categories, a minimum number of NFT’s for the one or more categories, and an individual NFT budget constraint.

10. The method as claimed in claim 8, comprising receiving, by the processor (202), the one or more features received by the processor (202) comprise at least one of: a demographic feature and a calendar feature associated with the one or more NFT’s.

11. The method as claimed in claim 8, wherein the optimization engine used by the processor (202) is an artificial intelligence (AI) engine (214).

12. The method as claimed in claim 11, comprising using, by the processor (202), a genetic technique via the AI engine (214) to predict the optimal price, the NFT set, and the selection success rate associated with the NFT set based on the bidding model, the one or more constraints, and the one or more features.

13. The method as claimed in claim 8, wherein the one or more categories comprise at least one of: a healthcare category, a sports category, an appliances category, and a retail category.

14. The method as claimed in claim 8, wherein the NFT set comprises a total quality associated with one or more selected NFT’s via the bidding model.

15. A user equipment (UE) (104) for sending requests, the UE (104) comprising:
one or more processors communicatively coupled to a processor (202) associated with a system (108), wherein the one or more processors are coupled with a memory, and wherein said memory stores instructions, which when executed by the one or more processors, cause the one or more processors to:
transmit an input to the processor (202) via a network (106), wherein the input is based on one or more non-fungible tokens (NFT’s) associated with one or more categories,
wherein the processor (202) is configured to:
receive the input from the UE (104);
generate a quality score for the one or more NFT’s associated with the one or more categories;
generate a bidding model based on the quality score of the one or more NFT’s;
receive one or more constraints and one or more features associated with the one or more NFT’s; and
predict via an optimization engine an optimal price, a NFT set, and a selection success rate associated with the NFT set based on the bidding model, the one or more constraints, and the one or more features.