WO2023150185A2

WO2023150185A2 - Nft appraisal algorithm and method

Info

Publication number: WO2023150185A2
Application number: PCT/US2023/012144
Authority: WO
Inventors: Hans MULLINGS
Original assignee: Mullings Hans
Priority date: 2022-02-01
Filing date: 2023-02-01
Publication date: 2023-08-10
Also published as: WO2023150185A3

Abstract

A method of appraising non-fungible tokens (NFTs) utilizing a specialized appraisal algorithm that includes receiving a first digital input equating to a lowest commercially and digitally advertised monetary sale value within an NFT collection associated with an appraising NFT, receiving a second digital input equating to a mathematical average of two effectuated monetary sales within the NFT collection, receiving a third digital input equating to a highest monetary offer for the appraising NFT within a defined time period before receiving the third digital input, receiving a fourth digital input equating to a mathematical average of all effectuated monetary sales within the NFT collection within a defined time period before receiving the fourth digital input, calculating an appraised monetary value for the appraising NFT by adding the weighted first, second, third, and fourth digital inputs, wherein the inputs are respectively weighted in the range of 10-30%, 30-35%, 25-30%, and 10%.

Description

NFT APPRAISAL ALGORITHM AND METHOD

FIELD OF THE INVENTION

The present invention relates generally to non-fungible tokens, and more particularly, to specific algorithms and methods researched and tested to provide the truest and fairest value of a non-fungible token.

BACKGROUND OF THE INVENTION

Non-fungible tokens (“NFTs”) are noninterchangeable digital assets stored on a blockchain. Types of NFT data units may be associated with digital files such as photos, videos, and audio. Because each token is uniquely identifiable, NFTs differ from blockchain cryptocurrencies. Due to their uniqueness and artistic aspect, valuing an NFT can be a difficult, contentious, and often-debated process with little consensus around the fair market value of a given NFT. Valuation matrices include varied factors such as the credibility of the artist in the physical world, the nature of the artwork, the effort put in the creation of the artwork, the story behind the artwork, the social currency of the artist, and the utility of the artwork, with no guidance as to how much weight each individual factor should be given. This discord and uncertainty around the value of a given NFT and, more importantly, around the optimal method of valuating a given NFT, can cause investors to make poor purchase or investment decisions in NFTs, resulting in pecuniary loss, frustration, and volatile market conditions.

[0001] Therefore, a need exists to overcome the problems with the prior art as discussed above.

SUMMARY OF THE INVENTION

The invention provides a method to determine the best, true, and fair value for a NFT, which are considered to be a digital asset that can be linked or connected to represent in-real-life physical item. The method utilizes one or more algorithms having factors that are specially weighted to determine that value. The weighted percentage, for each factor in the algorithms, will be considered to be the “default” or “standard” in NFT appraisal algorithms. With the use of an application user interface (“API”), third-party applications will have the ability to adjust weights in the algorithms, custom to their third-party application needs (example: for bank loan purposes).

[0002] With the foregoing and other objects in view, there is provided, in accordance with the invention, a method, preferably executed through a specialized computer program or software application, of appraising non-fungible tokens (NFTs) utilizing a specialized appraisal algorithm that includes the steps of receiving a first digital input equating to a lowest commercially and digitally advertised monetary sale value within an NFT collection associated with an appraising NFT and weighting the received first digital input by a value of 10-30%, receiving a second digital input equating to a mathematical average of two effectuated monetary sales within the NFT collection and weighting the received second digital input by a value of 30-35%, receiving a third digital input equating to a highest monetary offer for the appraising NFT within a defined time period before receiving the third digital input and weighting the received third digital input by a value of 25-30%, receiving a fourth digital input equating to a mathematical average of all effectuated monetary sales within the NFT collection within a defined time period before receiving the fourth digital input and weighting the received fourth digital input by a value of 10%, and calculating an appraised monetary value for the appraising NFT by adding the weighted first, second, third, and fourth digital inputs.

In accordance with another feature, an embodiment of the present invention includes comparing at least one of a public key address and/or token ID of the appraised NFT with the other NFTs in the respective public key address and/or token ID of the same NFT collection for identical identity.

In accordance with a further feature, an embodiment of the present invention also includes selectively and digitally filtering a plurality of commercial monetary sale values within the NFT collection to ascertain the lowest commercial monetary sale value utilized as received first digital input.

In accordance with an additional feature, an embodiment of the present invention also includes weighting the received first digital input by a value of 10%, receiving the second digital input equating to the mathematical average of two effectuated monetary sales within the NFT collection for a particular property trait or rarity rank window and weighting the received second digital input by a value of 35%, weighting the received third digital input by a value of 25%, receiving the fourth digital input equating to the mathematical average of all effectuated monetary sales within the NFT collection for the particular property trait or rarity rank window and weighting the received fourth digital input by the value of 10%, and receiving a fifth digital input equating to a lowest commercially and digitally advertised monetary sale value within an NFT collection for the particular property trait or rarity rank window and weighting the received fifth digital input by a value of 20%. In accordance with yet another feature, an embodiment of the present invention also includes selectively and digitally filtering a plurality of commercial monetary sale values within the NFT collection to ascertain the particular property trait or rarity rank window associated with an identifiable property trait or rarity rank window of the appraised NFT.

In accordance with an additional feature, an embodiment of the present invention also includes weighting the received first digital input by a value of 30%, receiving the second digital input equating to the mathematical average of two most-recent effectuated monetary sales within the NFT collection before receiving the second digital input and weighting the received second digital input by a value of 30%, and weighting the received third digital input by a value of 30%.

In accordance with a further feature of the present invention, the defined time period before receiving the third and fourth digital inputs is 30 days.

In accordance with an additional feature, an embodiment of the present invention also includes searching a plurality of online NFT marketplaces to ascertain the highest monetary offer for the appraising NFT.

Although the invention is illustrated and described herein as embodied in a method of controlling a grading attachment on a skid steer vehicle, it is, nevertheless, not intended to be limited to the details shown because various modifications and structural changes may be made therein without departing from the spirit of the invention and within the scope and range of equivalents of the claims. Additionally, well-known elements of exemplary embodiments of the invention will not be described in detail or will be omitted so as not to obscure the relevant details of the invention.

Other features that are considered as characteristic for the invention are set forth in the appended claims. As required, detailed embodiments of the present invention are disclosed herein; however, it is to be understood that the disclosed embodiments are merely exemplary of the invention, which can be embodied in various forms. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one of ordinary skill in the art to variously employ the present invention in virtually any appropriately detailed structure. Further, the terms and phrases used herein are not intended to be limiting; but rather, to provide an understandable description of the invention. While the specification concludes with claims defining the features of the invention that are regarded as novel, it is believed that the invention will be better understood from a consideration of the following description in conjunction with the drawing figures, in which like reference numerals are carried forward. The figures of the drawings are not drawn to scale.

Before the present invention is disclosed and described, it is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. The terms “a” or “an,” as used herein, are defined as one or more than one. The term “plurality,” as used herein, is defined as two or more than two. The term “another,” as used herein, is defined as at least a second or more. The terms “including” and/or “having,” as used herein, are defined as comprising (i.e., open language). The term “coupled,” as used herein, is defined as connected, although not necessarily directly, and not necessarily mechanically. The term “providing” is defined herein in its broadest sense, e.g., bringing/coming into physical existence, making available, and/or supplying to someone or something, in whole or in multiple parts at once or over a period of time. Also, for purposes of description herein, the terms “upper”, “lower”, “left,” “rear,” “right,” “front,” “vertical,” “horizontal,” and derivatives thereof relate to the invention as oriented in the figures and is not to be construed as limiting any feature to be a particular orientation, as said orientation may be changed based on the user’s perspective of the device. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, brief summary or the following detailed description.

As used herein, the terms “about” or “approximately” apply to all numeric values, whether or not explicitly indicated. These terms generally refer to a range of numbers that one of skill in the art would consider equivalent to the recited values (i.e., having the same function or result). In many instances these terms may include numbers or percentages that are rounded to the nearest significant figure and any % indicated, unless specifically indicated, is an approximation within a range of +/- 5%. The terms “program,” “software application,” and the like as used herein, are defined as a sequence of instructions designed for execution on a computer system. A “program,” “computer program,” or “software application” may include a subroutine, a function, a procedure, an object method, an object implementation, an executable application, an applet, a servlet, a source code, an object code, a shared library/dynamic load library and/or other sequence of instructions designed for execution on a computer system. BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying figures, where like reference numerals refer to identical or functionally similar elements throughout the separate views and which together with the detailed description below are incorporated in and form part of the specification, serve to further illustrate various embodiments and explain various principles and advantages all in accordance with the present invention.

FIG. 1 is a block diagram of an NFT appraisal algorithm and method, in accordance with an exemplary embodiment of the present invention;

FIG. 2 is a block diagram of the NFT appraisal algorithm and method, when all NFTs in the same collection (contract address) are identical with the same property traits, in accordance with the present invention;

FIG. 3 is a block diagram of the NFT appraisal algorithm and method, when there is no prior sale history for the current NFT being appraised and the NFT is not part of a collection, in accordance with the present invention;

FIG. 4 is a block diagram of the NFT appraisal algorithm and method, when valuing an NFT when there is only one NFT in its collection (contract address) and there must be other NFT collections (contract addresses) from the same creator, in accordance with the present invention;

FIG. 5 is a block diagram of an NFT appraisal algorithm and method for appraising an NFT representing a location, plot, or address, in accordance with the present invention;

FIG. 6 is a block diagram of an NFT appraisal algorithm and method for appraising an NFT generating revenue or rewards exchanged into a currency with value or dividend, in accordance with the present invention;

FIG. 7 is a block diagram of an NFT appraisal algorithm and method for appraising an NFT embodied in a domain name (such as ENS), in accordance with the present invention; and

FIG. 8 depicts a process flow diagram depicted an exemplary method of appraising NFTs utilizing a specialized appraisal algorithm in accordance with one embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

The present invention provides a novel and efficient method of calculating the best, true, and most fair value of a given NFT, that overcomes the heretofore-mentioned disadvantages of the heretofore- known devices and methods of this general type and that provides a more reliable and accurate measure of the market value of an NFT. The present invention beneficially aids potential buyers and investors in evaluating the list price of an NFT so as to facilitate financially sound investment or purchase decisions and reduce or eliminate pecuniary loss, frustration, and volatile market conditions.

The present invention provides an NFT appraisal algorithm and method 100 (referred to as “method 100” hereinafter for brevity) for valuating and/or calculating the market value of a given NFT. Embodiments of the invention provide a method 100 that may be applied in connection with various types and categories of NFTs including, without limitation, when all NFTs in the same collection (contract address) are identical with the same property traits, when there is only one NFT in its collection (contract address), where the NFT represents a location, plot, or address; where the NFT generates revenue or rewards that can be exchanged into a currency with value or dividend, and when an NFT is embodied in a domain name (such as ENS).

Referring now to FIG. 1, one exemplary method 100 includes weighted inputs consisting of a first input 102 consisting of a collection or contract address floor price (weighted at 10%); a second input 104 consisting of the average of the last two similar NFT sales for an NFT with similar properties and rarity rank, if applicable (weighted at 35%); a third input 106 consisting of the highest offer for said NFT within the last 30 days (weighted at 25%); a fourth input consisting of the filtered floor price on an NFT with similar properties and rarity rank, if applicable within the same collection or contract (weighted at 20%); and a fifth input 110 consisting of the average price for all NFTs sold in the same collection or contract address within the last 30 days (weighted at 10%). The first input 102 refers to the lowest listed “buy now” price at which an NFT can be bought within a collection, rather than the average price of the NFT. In one embodiment, the software application can digitally capture the “buy now” price from a third-party domain by utilizing a text recognition API. As part of the first input 102, the method may include recognizing the collection contract address (also commonly referred to as public key address that may be configured to deploy an NFT contract on the blockchain) & token ID (including metadata) for the NFT that is being appraised and filtering in that collection to find all that are listed for sale on current marketplaces, wherein the lowest listed price, from the filtering, gives a user the “collection floor price”.

Regarding the second input 104, or the average of the last two comparable sales, the algorithm will utilize the last two sales for NFTs with the same specific property trait and within the calculated rarity rank window. Regarding the former, the method includes finding, within a filtered search, the two most recent sales of NFTs with the same specific property trait being used for the appraisal and calculate the average of these two sales. Regarding the latter, the method includes, finding, within a filtered search, the two most recent sales of NFTs within the same calculated rarity rank window being used for the appraisal and calculate the average of these two sales. The second input is weighted 35% in the algorithm.

Regarding the third input 106, the method includes searching the offer history for the specific NFT being appraised on current marketplaces and using the highest offer ascertained within the last 30 days. This value is weighted 25% in the algorithm.

Regarding the fourth input 108, the method includes ascertaining the floor price for a specific property trait that will be the same specific property trait utilized for the second input 104 and the floor price within the calculated rarity rank window that will be the same rarity rank window utilized for the second input 104. Regarding the specific property trait, the method includes the user selecting the specific property trait that the NFT being appraised has, adding a filter to see all NFTs within the same collection that only have the same specific property trait chosen, finding the lowest listed price from NFTs within this filtered selection, wherein this will provide the “floor price” on a specific property trait. If the NFT does not have any unique property traits, then it cannot be appraised using the method 100, but rather requires methodology 200. To determine or define the rarity rank window, the method includes adding a filter to see only items listed within a specific rarity rank window and identifying a high point and a low point. The high point will default to the highest rarity rank and the lower point will be the rarity rank number for the specific NFT being appraised. Note that that if the filtered window does not have a floor price, the method will require the expansion of the rarity rank window until a floor price for rarity is ascertaining, wherein the process will start at the nearest 10th percentage, increase percentage window in increments of 5% (example: Top 5%, Top 10%, Top 15%, etc.). This value is weighted 20% in the algorithm.

Expanding on the fifth input 110, the process will calculate the average price of all sales within the same contract address for the NFT being appraised within the last 30 Days from the date of the appraisal. This value is weighted 10% in the algorithm. Below is an example calculation using the method 100 (as depicted in FIG. 1), valuing 1 Ethereum at $5,000 USD:

Contract Floor Price (10%): 2.47 ETH = $12,350 USD Last 2 Comparable Sales (35%): 1st Comparable Recent Sale: 7.2 ETH = $36,000 USD 2nd Comparable Recent Sale: 6.5 ETH = $32,500 USD Average of Price of both sales equal: 6.85 ETH = $34,250 USD Highest Offer Within the Last 30 Days (25%): 6.95 ETH = $34,750 USD

Floor Price on Similar Ranking (20%): 7.45 ETH = $37,250 USD

Average Price of all NFTs Sold in the Same Collection Within the Last 30 Days (10%): Average Price of all Sales (past 30 days): 4.68 ETH = $23,400 USD

Total Appraisal Value:

0.1(2.47) + 0.35(6.85) + 0.25(6.95) + 0.2(7.45) + 0.1(4.68) = 6.34 Ethereum = $31,700 USD

In one embodiment, when no rarity ranking is applicable or there are no unique property traits associated with NFT, the method 100 is modified as follows into the following method/algorithm 200 represented in FIG. 2. Specifically, the first input 202 is weighted at 30%; the second input 204 is weighted at 30%; the third input 206 is weighted at 30%; the fourth input 108 in FIG. 1 is eliminated; and the fifth input 110 in FIG. 1, which is now the fourth input 208 in the method 200 is weighted at 10%. Below is an example calculation using the method 200 without any rarity ranking, valuing 1 Ethereum at $5,000 USD:

Contract Floor Price (30%): 5.53 ETH = $ 27,650 USD

Average of Last 2 Sales Within the Same Collection (30%):

1st Most Recent Sale: 5.37 ETH = $26,850 USD 2nd Most Recent Sale: 5.2 ETH = $26,000 USD Average of Price of both sales equal: 5.285 ETH = $26,425 USD Highest Offer on the Specific NFT Within the Last 30 Days (30%):

4.95 ETH = $24,750 USD

Average Price of all NFTs Sold in the Same Collection Within the Last 30 Days (10%):

Average Price of all Sales (past 30 days): 4.68 ETH = $23,400 USD

Total Appraisal Value:

0.3(5.53) + 0.3(5.285) + 0.3(4.95) + 0.1(4.68) = 5.1975 Ethereum = $25,987.50 USD

With reference to FIG. 3, when there is no prior sale history for the current NFT being appraised and the NFT is not part of a collection, a modified method 300 of FIG. 1 is disclosed, wherein the first input 102 is eliminated, the second input 104 is eliminated, the third input 106 is modified to consist of the average of 2 or more most recent offers made on the specific NFT being appraised (weighted at 50% or 1/2), the fourth input 108 is eliminated, and the fifth input 110 is modified to consist of the average price for the last two or more recent sales from the creator, if applicable (weighted at 50% or 1/2). Below is an example calculation using the method 100 when no rarity ranking is applicable and the NFT is not part of a collection (as depicted in FIG. 3), valuing 1 Ethereum at $5,000 USD:

Average of 3 Highest Offers Within the Last 30 Days (60%):

1st Highest Offer: 4.1 ETH = $20,500 USD

2nd Highest Offer: 3.99 ETH = $19,950 USD

3rd Highest Offer: 3.8 ETH = $19,000 USD Average: 3.963 ETH = $19,815 USD

Average Price of Last 2 or More Most Recent Sales From Creator (40%):

1st Most Recent Sale: 5.2 ETH = $26,000 USD 2nd Most Recent Sale: 4.5 ETH = $22,500 USD Average: 4.85 ETH = $24,250 USD

Total Appraisal Value:

((1/2)3.963) + (1/2)4.85) = 4,4065 Ethereum = $22,032.50 USD

With reference to FIG. 4, an exemplary method 400 of valuing an NFT when there is only one NFT in its collection (contract address) and there must be other NFT collections (contract addresses) from the same creator, as reference. Said another way, the creator the NFT desired to be appraised must have other similar, comparable, NFT collections (contract addresses) for comparison and these other NFT collections must have (1) some level of trade volume and (2) active offers within the last 21 days. The method 400 includes and/or consists of utilizing a first input 402 equates to the last price the NFT being appraised sold for (weighted at 33.33% or 1/3), wherein if the NFT has no sales history, then the weighted percentage is split evenly among the second and third inputs 404, 406. The second input 404 equates to the average of two or more most recent offers made on the specific NFT being appraised (weighted at 33.33% or 1/3), wherein the recent offers must be from different wallet addresses or user accounts and submitted within the last 21 days from the appraisal. The third input 406 equates to the average of two or more most recent sales from the same creator in other NFT collections (weighted at 33.33% or 1/3). Below is an example calculation using the method 400, valuing 1 Ethereum at $5,000 USD:

The Last Price the Specific Appraised NFT Sold For (1/3):

5 ETH = $20,500 USD

Average of Two or More Recent Offers Made on Specific Appraised NFT (1/3):

1st Recent Offer: 4.1 ETH = $20,500 USD 2nd Recent Offer: 3.99 ETH = $19,950 USD 3rd Highest Offer: 3.8 ETH = $19,000 USD Average: 3.963 ETH = $19,815 USD

Average of Two or More Recent Sales from Same Creator of Appraised NFT in other NFT Collections (1/3):

1st Most Recent Sale: 5.2 ETH = $26,000 USD

2nd Most Recent Sale: 4.5 ETH = $22,500 USD Average: 4.85 ETH = $24,250 USD Total Appraisal Value (with sales history):

(1/3)5 + ((1/3)3.963) + ((1/3)4.85) = 4.6043 Ethereum = $23,021.50 USD

As best depicted in FIG. 5, the present invention also provides an NFT appraisal algorithm and method 500 for appraising an NFT representing a location, plot, or address that includes weighted inputs consisting of: a first input 502 consisting of the average of the three most recent sales for “comparable” size NFTs (weighted at 50%) and a second input 504 consisting of the average of the two or more highest offers made on the NFT being appraised within the last 30 days (weighted at 50%). Below is an example calculation using the method 500 for appraising an NFT representing a location, plot, or address (as depicted in FIG. 5), valuing 1 Ethereum at $5,000 USD:

Average of 3 Most Recent Sales Closest to Plot or Land Comparable in Size (50%):

1st Most Recent: 11.1 ETH = $55,500 USD 2nd Most Recent: 12.3 ETH = $61,500 USD 3rd Most Recent: 9.87 ETH = $49,350 USD Average: 11.09 ETH = $55,450 USD

Average of 2 or More Highest Offers Made on the Specific NFT Being Appraised Within the Last 30 Days (50%):

1st Highest Offer: 10.5 ETH = $52,500 USD

2nd Highest Offer: 9.8 ETH = $49,000 USD Average: 10.15 ETH = $50,750 USD Total Appraisal Value:

0.5(11.09) + 0.5(10.15) = 10.62 Ethereum = $53,100 USD

As best depicted in FIG. 6, the present invention also provides an NFT appraisal algorithm and method 600 for appraising an NFT generating revenue or rewards exchanged into a currency with value or dividend that includes weighted inputs consisting of: a first input 602 consisting of one of the annual revenue generated from NFT or projected annual revenue based on three months’ minimum earnings received (weighted at 75%); and a second input 604 consisting of the average of two or more highest offers made on the NFT being appraised within the last 30 days (weighted at 25%). Below is an example calculation using the method 600 for appraising an NFT generating revenue or rewards exchanged into a currency with value or dividend (as depicted in FIG. 6), valuing 1 Ethereum at $5,000 USD:

5a. Annual Revenue Generated From NFT (75%): Annual Revenue: 22.5 ETH = $112,500 USD

Average of 2 or More Highest Offers Made on the Specific NFT Being Appraised Within the Last 30 Days (25%): 1st Highest Offer: 19.5 ETH = $97,500 USD 2nd Highest Offer: 15 ETH = $75,000 USD Average: 17.25 ETH = $86,250 USD Total Appraisal Value:

0.75(22.5) + 0.25(17.25) = 21.1875 Ethereum = $105,937.50 USD

5b. Projected Annual Income (75%):

Provided Proof of 3 Month’s Revenue: 5.625 ETH = $28,125 USD Annual Revenue: 22.5 ETH = $112,500 USD

Average of 2 or More Highest Offers Made on the Specific NET Being Appraised Within the Last 30 Days (25%):

1st Highest Offer: 19.5 ETH = $97,500 USD 2nd Highest Offer: 15 ETH = $75,000 USD Average: 17.25 ETH = $86,250 USD Total Appraisal Value:

0.75(22.5) + 0.25(17.25) = 21.1875 Ethereum = $105,937.50 USD

As best depicted in FIG. 7, the present invention also provides an NFT appraisal algorithm and method 700 for appraising an NFT embodied in a domain name (such as ENS). Like the above, the method 700 includes or consists of weighted inputs, including a first input 702 equating to or consisting of a filtered floor price for a specific property trait or by length of a domain name by character or digit count (weighted at 50%). The first input 702 may include adding a filter to see only items listed within the same character or digit length count window of the appraised NFT. To define the character or digit length count window, a high point is calculated by using a default character or a digit length count of 1 and a low point is calculated (that is variable) that is the length count number of the appraised NFT. The second input 704 equates to or consists of an average of two or more highest offers made on the appraised NFT within the last 30 days (weighted at 50%).

Below is an example calculation using the method 700, valuing 1 Ethereum at $5,000 USD:

Filtered Floor Price for Property Trait or by Length of Domain Name (50%): 7.45 ETH = $37,250 USD

Average of 2 or More Highest Offers Made on the Appraised NFT Within Last 30 Days (50%): 1st Highest Offer: 6.99 ETH = $34,950 USD 2nd Highest Offer: 6.95 ETH = $34,750 USD Average: 6.97 ETH = $34,85 USD Total Appraisal Value:

0.5(7.45) + 0.5(6.97) = 7.21 Ethereum = $36,050 USD

The above-described methodology will now be described in connection with the process flow diagram depicted in FIG. 8, which exemplifies a method of appraising NFTs utilizing a specialized appraisal algorithm. The process will be described in conjunction with above-described methodologies. Although FIG. 8 shows a specific order of executing the process steps, the order of executing the steps may be changed relative to the order shown in certain embodiments. Also, two or more blocks shown in succession may be executed concurrently or with partial concurrence in some embodiments. Certain steps may also be omitted in FIG. 8 for the sake of brevity. In some embodiments, some or all of the process steps included in FIG. 8 can be combined into a single process.

The process may begin at step 800 and immediately proceed to the step 802 of receiving a first digital input equating to a lowest commercially and digitally advertised monetary sale value within an NFT collection associated with an appraising NFT and weighting the received first digital input by a value of 10-30%. This digital input may be received, like other digital inputs described herein, into a temporary or permanent non -transitory computer readable medium or memory for utilizing by a computer processor effectuating the aforementioned algorithms. In one embodiment, the process may include comparing a public key address and/or a token ID of the appraised NFT with a respective public key address and token ID of the NFT collection for identical identity. Additionally, the process may include selectively and digitally filtering (e.g., using and through a digital or graphical user interface) a plurality of commercial monetary sale values within the NFT collection to ascertain the lowest commercial monetary sale value utilized as received first digital input.

Next, the process proceeds to step 804 of receiving a second digital input equating to a mathematical average of two effectuated monetary sales within the NFT collection and weighting the received second digital input by a value of 30-35%. Next, the process may include the step 806 of receiving a third digital input equating to a highest monetary offer for the appraising NFT within a defined time period before receiving the third digital input and weighting the received third digital input by a value of 25-30%. Next, the process may proceed to step 808 receiving a fourth digital input equating to a mathematical average of all effectuated monetary sales within the NFT collection within a defined time period before receiving the fourth digital input and weighting the received fourth digital input by a value of 10%. The process may continue to step 810 calculating an appraised monetary value, utilizing at least one computing or computer processor, for the appraising NFT by adding the weighted first, second, third, and fourth digital inputs (as exemplified above).

As best depicted in FIG. 1, the method may also include the steps of weighting the received first digital input by a value of 10%, receiving the second digital input equating to the mathematical average of two effectuated monetary sales within the NFT collection for a particular property trait or rarity rank window and weighting the received second digital input by a value of 35%, weighting the received third digital input by a value of 25%, receiving the fourth digital input equating to the mathematical average of all effectuated monetary sales within the NFT collection for the particular property trait or rarity rank window and weighting the received fourth digital input by the value of 10%, and receiving a fifth digital input equating to a lowest commercially and digitally advertised monetary sale value within an NFT collection for the particular property trait or rarity rank window and weighting the received fifth digital input by a value of 20%.

Additionally, the process may include selectively and digitally filtering a plurality of commercial monetary sale values within the NFT collection to ascertain the particular property trait or rarity rank window associated with an identifiable property trait or rarity rank window of the appraised NFT.

As best depicted in FIG. 2, the process may also include weighting the received first digital input by a value of 30%, receiving the second digital input equating to the mathematical average of two most- recent effectuated monetary sales within the NFT collection before receiving the second digital input and weighting the received second digital input by a value of 30%, and weighting the received third digital input by a value of 30%.

Additionally, the defined time period before receiving the third and fourth digital inputs may be 30 days, i.e., approximately 30 days +/- 2 days. The process may also include searching a plurality of online NFT marketplaces to ascertain the highest monetary offer for the appraising NFT. The process may terminate at step 812. Various modifications and additions can be made to the exemplary embodiments discussed without departing from the scope of the present disclosure. For example, while the embodiments described above refer to particular features, the scope of this disclosure also includes embodiments having different combinations of features and embodiments that do not include all of the above-described features.

Claims

CLAIMS What is claimed is:

1. A method of appraising non-fungible tokens (NFTs) utilizing a specialized appraisal algorithm comprising: receiving a first digital input equating to a lowest commercially and digitally advertised monetary sale value within an NFT collection associated with an appraising NFT and weighting the received first digital input by a value of 10-30%; receiving a second digital input equating to a mathematical average of two effectuated monetary sales within the NFT collection and weighting the received second digital input by a value of 30-35%; receiving a third digital input equating to a highest monetary offer for the appraising NFT within a defined time period before receiving the third digital input and weighting the received third digital input by a value of 25-30%; receiving a fourth digital input equating to a mathematical average of all effectuated monetary sales within the NFT collection within a defined time period before receiving the fourth digital input and weighting the received fourth digital input by a value of 10%; and calculating an appraised monetary value for the appraising NFT by adding the weighted first, second, third, and fourth digital inputs.

2. The method according to claim 1, further comprising: comparing at least one of a public key address and/or token ID of the appraised NFT with other NFTs in the respective public key address and/or token ID of the same NFT collection for identical identity.

3. The method according to claim 1, further comprising: selectively and digitally filtering a plurality of commercial monetary sale values within the NFT collection to ascertain the lowest commercial monetary sale value utilized as received first digital input.

4. The method according to claim 1, further comprising: weighting the received first digital input by a value of 10%; receiving the second digital input equating to the mathematical average of two effectuated monetary sales within the NFT collection for a particular property trait or rarity rank window and weighting the received second digital input by a value of 35%; weighting the received third digital input by a value of 25%; receiving the fourth digital input equating to the mathematical average of all effectuated monetary sales within the NFT collection for the particular property trait or rarity rank window and weighting the received fourth digital input by the value of 10%; and receiving a fifth digital input equating to a lowest commercially and digitally advertised monetary sale value within an NFT collection for the particular property trait or rarity rank window and weighting the received fifth digital input by a value of 20%.

5. The method according to claim 4, further comprising: selectively and digitally filtering a plurality of commercial monetary sale values within the NFT collection to ascertain the particular property trait or rarity rank window associated with an identifiable property trait or rarity rank window of the appraised NFT.

6. The method according to claim 1, further comprising: weighting the received first digital input by a value of 30%; receiving the second digital input equating to the mathematical average of two most-recent effectuated monetary sales within the NFT collection before receiving the second digital input and weighting the received second digital input by a value of 30%; and weighting the received third digital input by a value of 30%.

7. The method according to claim 1, wherein the defined time period before receiving the third and fourth digital inputs is 30 days.

8. The method according to claim 4, wherein the defined time period before receiving the third and fourth digital inputs is 30 days.

9. The method according to claim 6, wherein the defined time period before receiving the third and fourth digital inputs is 30 days.

10. The method according to claim 1, further comprising: