DETAILED ACTION
Status of the Application
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status
This action is a Final Action on the merits in response to the application filed on 11/03/2025.
Claims 1, 4, 8, 10, 13, 16, and 19 have been amended.
Claims 1-20 remain pending in this application.
Response to Amendment
Applicant’s amendments are acknowledged.
The 35 U.S.C. 101 rejections of claims in the previous office action have been maintained.
The 35 U.S.C. 103 rejections of claims in the previous office action are withdrawn in light of applicant’s amendments, however a new 103 rejections was added.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 are directed towards a method, claims 10-15 are directed towards a system and claims 16-20 are directed towards a machine-readable storage medium, all of which are among the statutory categories of invention.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claims are directed to a judicial exception without significantly more.
Regarding claims 1-20, under Step 2A claims 1-20 recite a judicial exception (abstract idea) that is not integrated into a practical application.
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites at least one step or act, including using an encoder to minimize errors. Thus, the claim is to a process, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
With respect to claims 1-20 the independent claims (claims 1, 10, and 16) are directed to managing of digital objects, In independent claim 1, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention:
receiving, by a computing system, an identifier of a target non-fungible token (NFT);
accessing trading data for a plurality of NFTs;
determining, based on the trading data, a trading volume of the target NFT;
when the trading volume is below a first threshold level:
identifying a subset of similar NFTs including at least a predefined number of NFTs that satisfy a chosen similarity criterion based on their respective similarity scores with the target NFT,
wherein the subset of similar NFTs is associated with at least a predefined number of reported trades within a predefined period, and
predicting the price of the target NFT based on second trading data associated with the identified subset of similar NFTs;
when the trading volume is above the first threshold level and below a second threshold level:
predicting the price of the target NFT based on trading volume of the target NFT;
when the trading volume is above the second threshold level:
these steps fall within and recite an abstract ideas because they are directed to a method of organizing human activity which includes commercial or legal interaction such as agreements in the form of contracts, legal obligations; sales activities; business relations (See MPEP 2106.04(a)(2), subsection II).
If a claim limitation, under its broadest reasonable interpretation, covers commercial or legal interaction, then it falls within the “method of organizing human activity” groupings of abstract ideas. Therefore, If the identified limitation(s) falls within any of the groupings of abstract ideas enumerated in the MPEP 2106, the analysis should proceed to Prong Two. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). The claim recites the additional elements of computing system, NFT, encoder, autoencoder, decoder, memory, device , machine-readable medium. The claims recite the steps are performed by the computing system, NFT, encoder, autoencoder, decoder, memory, device , machine-readable medium.
The limitations of
wherein the similarity scores are based at least in part on features extracted from the target NFT using an encoder stage of a trained autoencoder neural network, wherein the autoencoder is trained to minimize reconstruction error between input and output vectors; and
predicting the price of the target NFT based on the trading data and a cryptoasset market movement indicator, wherein the cryptoasset market movement is derived from a traded price of another cryptoasset.
are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
Further, the limitations are recited as being performed by computing system, NFT, encoder, autoencoder, decoder, memory, device , machine-readable medium. The computing system, NFT, encoder, autoencoder, decoder, memory, device , machine-readable medium are recited at a high level of generality. In limitation (a), computing system, NFT, encoder, autoencoder, decoder, memory, device , machine-readable medium are used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f). Additionally, claim 1 recites autoencoder. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application.
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. As explained with respect to Step 2A, Prong Two, the additional elements are the apparatus, memory, processor, image, computer, computer-readable medium, display. The additional elements were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering and outputting. Then, the machine learning techniques recited in the claim are disclosed at a high-level of generality (see at least Specification [0066 “ The visual and/or non-visual features may be extracted from the NFT images by a feature extractor (e.g., represented by a trainable neural network), as described in more detail herein below”]) and does not amount to significantly more than the abstract idea
However, a conclusion that an additional element is insignificant extra solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). As discussed in Step 2A, Prong Two above, the recitations of
wherein the similarity scores are based at least in part on features extracted from the target NFT using an encoder stage of a trained autoencoder neural network, wherein the autoencoder is trained to minimize reconstruction error between input and output vectors; and
predicting the price of the target NFT based on the trading data and a cryptoasset market movement indicator, wherein the cryptoasset market movement is derived from a traded price of another cryptoasset.
are recited at a high level of generality. These elements amount to receiving data and are well understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. 10 As discussed in Step 2A, Prong Two above, the recitation of an computing system, NFT, encoder, autoencoder, decoder, memory, device , machine-readable medium to perform limitations amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
Dependent claims 2-9, 11-15, and 17-20 do not contain any new additional elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims. In this case, the claims are rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Thus, the claim is not patent eligible.
Regarding the dependent claims, dependent claims 4, 13, 19 recite autoencoder for selecting data. The dependent claims 2-9, 11-15, and 17-20 recite limitations that are not technological in nature and merely limits the abstract idea to a particular environment. Claims 2-9, 11-15, and 17-20 recites computing system, NFT, encoder, autoencoder, decoder, memory, device , machine-readable medium which are considered an insignificant extra-solution activities of collecting and analyzing data; see MPEP 2106.05(g). Claims 2-9, 11-15, and 17-20 recites computing system, NFT, encoder, autoencoder, decoder, memory, device , machine-readable medium, which merely recites an instruction to apply the abstract idea using a generic computer component; MPEP 2106.05(f). Additionally, claims 2-9, 11-15, and 17-20 recite steps that further narrow the abstract idea. No additional elements are disclosed in the dependent claims that were not considered in independent claims 1, 10, and 16. Therefore claims 2-9, 11-15, and 17-20 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over WIPO Patent Publication WO 2023069689A2, Quigley, et al. to hereinafter Quigley in view of United State Patent Publication US 20230117399, Jakobsson, et al. to hereinafter Jakobsson in view of United State Patent Publication US 20220172050, Dalli, et al.
Referring to Claim 1, Quigley teaches a method, comprising:
receiving, by a computing system, an identifier of a target non-fungible token (NFT) (
Quigley: Sec. 0154, Tokens may be fungible tokens or non-fungible tokens. Fungible tokens may refer to tokens that are interchangeable. For example, fungible tokens may all have the same identifier. Non-fungible tokens are unique tokens. Non-fungible tokens are transferrable but not interchangeable.
Quigley: Sec. 0191, the token generation system 302 may generate a token identifier that identifies the token. In scenarios where the tokens are non-fungible tokens, the token generation system 302 may generate a unique identifier for each respective token corresponding to the virtual representation.
The token generation system 302 may generate the token identifier using any suitable technique. For example, the token generation system 302 may implement random number genesis, case genesis, simple genesis, and/or token bridge genesis to generate a value that identifies the token.
Quigley: Sec. 0462, attributes of a collectible NFT may include a token identifier, a schema identifier, a template identifier, a name of the NFT, a name of the NFT, a collection of the NFT, and/or the like.);
accessing trading data for a plurality of NFTs (
Quigley: Sec. 0240, the analytics system 602 may trade and analyze data relating to specific types of tokens, such as tokenized tokens, various types of NFTs, fungible tokens, and the like.
Quigley: Sec. 0244, the analytics system 602 may combine pricing-related analytics relating to a set of tokens released by or on behalf of a company with off-chain data relating to the performance of the company (e.g., stock prices, sales history, user engagement, social media mentions, and/or the like). In this example, the analytics system 602 may provide analytics insights relating to the performance of the company vis-a-vis the release of the tokens In another example, the analytics system 602 may combine baseball statistics received from a “stats” oracle with sales, trading, and/or transfer data relating to baseball-related NFTs (e.g., digital NFT-based trading cards and/or NFTs linked to and redeemable for physical baseball memorabilia) to identify correlations between player performances and a corresponding performance of baseball-related NFTs. For example, the analytics system 602 may determine correlations between player performance and NFT trading volume, pricing of the NFTs, overall circulation of the NFTs, and/or the like. In this example, the analytics system 602 may provide insight to future pricing of similar NFTs or on how the price of an NFT may change when the player is playing well or poorly.);
determining, based on the trading data, a trading volume of the target NFT; (
Quigley: Sec. 0244, transaction analytics corresponding to the set of tokens (e.g., the trading volume of the set of tokens, the types of transactions involving the tokens, and/or the like),);
identifying a subset of similar NFTs including at least a predefined number of NFTs that satisfy a chosen similarity criterion based on their respective similarity scores (See Jakobsson) with the target NFT (
Quigley: Sec. 0007, tokens associated with similar distributed ledger transactions. In some of these embodiments, the tokens are non-fungible tokens.
Quigley: Sec. 0833, data indicating that a DRM NFT is listed on a secondary marketplace 9012 for a particular average price to adjust the sales price for similar DRM NFTs (e.g., such that a high secondary sale value may lead to raising prices for pre-sales, whereas a low secondary sale value may lead to lowering pre-sale prices), mint additional DRM NFTs, and/or the like.),
wherein the subset of similar NFTs is associated with at least a predefined number of reported trades within a predefined period (
Quigley: Sec. 0240, how many tokens have been redeemed from a set of tokens, how often a token was transferred before redemption, and/or the like; trading data of certain tokens, such as how often a particular token is traded for, the token(s) that are traded, the accounts of users that traded away or for certain tokens, a time of the trade, the values of the tokens that were traded, and/or the like.
Quigley: Sec. 0463, In addition to programmatically generating new collectible tokens with various attributes, the tokenization platform 100 and/or mystery box system 806 may be configured to provide mystery' boxes that enhance the functionality and value of the collectible tokens. In some embodiments, the mystery box system 806 (e.g., as shown in FIGs. 8 and 31) is configured to provide mystery boxes that “contain” digital token-based trading cards (e.g., such that an unboxing smart contract may redeem the mystery box for a predefined number of digital token-based trading cards, as described in more detail below).
Quigley: Sec. 0485, The analytics and reporting system 112 may generate various analytics and statistical data measuring the usage of tokens for a given token collection. For example, the analytics and reporting system 112 may calculate supply data for tokens (e.g., how many have been issued, how much supply is left), popularity data for tokens (e.g., how frequently users purchase and trade certain tokens), value data for tokens (e.g., how much tokens sell for on marketplaces 3106). );
predicting the price of the target NFT based on second trading data associated with the identified subset of similar NFTs (
Quigley: Sec. 0244, the analytics system 602 may process this on-chain data to determine pricing analytics corresponding to a particular token or set of tokens (e.g., an average price of a particular set of tokens, a predicted future price of the particular set of tokens, a range of prices of the set of tokens, and/or the like)…the analytics system 602 may provide insight to future pricing of similar NFTs or on how the price of an NFT may change when the player is playing well or poorly.
Quigley: Sec. 0246, the analytics system 602 may process the collected data to determine pricing analytics (e.g., an average price of a collection of tokens or certain tokens within a collection, a predicted future price of a collection of tokens or certain tokens within a collection, a range of prices of a collection of tokens or certain tokens within a collection, and/or the like), trading analytics (e.g., level of demand for a collection of tokens or certain tokens, trading volume for a collection or certain tokens, a demand curve for a certain type of tokens relative to the price of the tokens), behavioral analytics (e.g., most browsed collections, time spent browsing on a collection or particular type of token, probability of redemption of a collection of tokens or certain tokens, effects of certain external events on the popularity and/or pricing of a collection of tokens or a particular type of token, a measure of conversion of attention to a real-world entity or event and the purchases of the digital tokens, indicators of when or why certain tokens are redeemed, and/or the like),
Quigley: Sec. 0248, pricing analytics that indicate one or more of an average price of a digital token, a predicted future price of a digital token, a current market price of a digital token, and/or the like. ).
when the trading volume is:
predicting the price of the target NFT based on trading volume of the target NFT (
Quigley: Sec. 0244, the analytics system 602 may process this on-chain data to determine pricing analytics corresponding to a particular token or set of tokens (e.g., an average price of a particular set of tokens, a predicted future price of the particular set of tokens, a range of prices of the set of tokens, and/or the like), transaction analytics corresponding to the set of tokens (e.g., the trading volume of the set of tokens, the types of transactions involving the tokens, and/or the like), redemption analytics (e.g., percentage of tokens that are redeemed, the rate at which tokens are being redeemed, the locations of people redeeming the tokens, and/or the like) and/or other suitable analytics.);
when the trading volume is:
predicting the price of the target NFT based on the trading data and a cryptoasset market movement indicator, wherein the cryptoasset market movement is derived from a traded price of another cryptoasset (
Quigley: Sec. 0244, the analytics system 602 may process this on-chain data to determine pricing analytics corresponding to a particular token or set of tokens (e.g., an average price of a particular set of tokens, a predicted future price of the particular set of tokens, a range of prices of the set of tokens, and/or the like), transaction analytics corresponding to the set of tokens (e.g., the trading volume of the set of tokens, the types of transactions involving the tokens, and/or the like), redemption analytics (e.g., percentage of tokens that are redeemed, the rate at which tokens are being redeemed, the locations of people redeeming the tokens, and/or the like) and/or other suitable analytics.
Quigley: Sec. 0463, digital token-based trading cards and other collectible tokens may be digital assets that are cryptographically linked with a fungible token or a non-fungible token.
Quigley: Sec. 0704, For example, the marketplace 3106 may transfer an NFT ticket 8022 A (and/or any other NFT or token being transferred or sold as described herein) and an amount of currency (e.g., cryptocurrency, tokenized tokens, fiat currency, or the like) corresponding to the sales price (which may be the sales price plus or minus certain fees) to the sales smart contract 8024. Then, the sales smart contract 8024 may distribute the received currency (e.g., cryptocurrency, tokenized tokens, or fiat currency) to the seller and/or any other parties that receive a portion of the sales price as specified by the sales rules, and may deliver the sold NFT ticket 8022A to the buyer.).
Quigley describes market pricing of a crypto asset.
Quigley does not explicitly teach ; when the trading volume is below a first threshold level; similarity score; above the first threshold level and below a second threshold level; above the second threshold level.
However Jakobsson teaches these limitations
when the trading volume is (See Quigley) below a first threshold level (
Jakobsson: Sec. 0222, When vector distances have moderate differences, then the two or more vector descriptions may be determined to be similar. Moderate differences may be based on vector distances falling under a first pre-specified threshold but above a second pre-specified threshold.):
similarity score (
Jakobsson: Sec. 0223, two content elements have similarity scores that satisfy a first rule, then a first action may be taken. Additionally or alternatively, when similarity scores satisfy a second rule, then a second action may be taken. One example rule may be that the score indicates a difference less than a threshold, such as 2.5. )
above the first threshold level and below a second threshold level; above the second threshold level (
Jakobsson: Sec. 0148, the third party may then verify the other NFTs to ensure that the terms stated in the contract of the seventh NFT agree. If the third party determines that the contract exceeds a threshold in terms of value to risk, as assessed in the seventh NFT, then executable elements of the seventh NFT may cause transfers of funds to an escrow party specified in the contract of the sixth NFT.
Jakobsson: Sec. 0210, determinations may be based on comparisons with one or more threshold values.
Jakobsson: Sec. 0223, Such determinations may be based on vector distances falling below particular thresholds. When vector distances have moderate differences, then the two or more vector descriptions may be determined to be similar. Moderate differences may be based on vector distances falling under a first pre-specified threshold but above a second pre-specified threshold. Finally, vector distances above particular thresholds may be determined to be dissimilar.):
Jakobsson describes the thresholds of various levels can includes between and above first and second levels.
Quigley and Jakobsson are both directed to the analysis of blockchain (See Quigley at 0003, 0004, 01118; Jakobsson at 0048, 0055, 0056). Quigley discloses that additional elements, such as digital wallet and marketplace can be considered (See Quigley at 0183, 0284). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Quigley, which teaches detecting and updating digital technology transaction problems in view of Jakobsson, to efficiently apply the analysis of blockchain to enhancing the capability to comparing NFT data. (See Jakobsson at 0051, 0059, 0066, 0210, 266, 0272).
Quigley in view of Jakobsson of does not explicitly teach wherein the similarity scores are based at least in part on features extracted from the target NFT using an encoder stage of a trained autoencoder neural network, wherein the autoencoder is trained to minimize reconstruction error between input and output vectors.
However, Dalli teaches wherein the similarity scores (See Jakobsson) are based at least in part on features extracted from the target NFT using an encoder stage of a trained autoencoder neural network, wherein the autoencoder is trained to minimize reconstruction error between input and output vectors (
Dalli: Sec. 0003, The neural network architectures of the encoder architecture and the decoder architecture may have multiple hidden layers to introduce nonlinearity to such architectures, resulting in deep neural network architectures. The parameters of an autoencoder architecture may be optimized using a cost function to minimize the error and applying the backpropagation algorithm.
Dalli: Sec. 0075, The parameters of an exemplary embodiment may be optimized using a cost function to minimize the error by applying the backpropagation algorithm.
Dalli: Sec. 0175, the explainable generator XG(z e) may refer to a recurrent neural network (RNN) architecture, as shown in FIG. 16. An exemplary explainable generative adversarial network may be modeled after an Encoder-Decoder RNN, where XG(z,e) 2710, as shown in FIG. 16, computes a bottleneck vector and the decoder 3002 is utilized to generate samples.
Dalli: Sec. 0214, utilized in the creation of virtual reality simulations, augmented reality simulations, virtual collaboration spaces, and metaverses. It is further contemplated that such generated data samples may be tagged with some secure traceable digital code, distributed ledger entry or non-fungible token (NFT).)
Quigley, Jakobsson, and Dalli are all directed to the analysis of blockchain (See Quigley at 0003, 0004, 01118; Jakobsson at 0048, 0055, 0056; Dalli at 0214, Claim 6). Quigley discloses that additional elements, such as digital wallet and marketplace can be considered (See Quigley at 0183, 0284). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Quigley in view of Jakobsson, which teaches detecting and updating digital technology transaction problems in view of Dalli, to efficiently apply the analysis of blockchain to improving the processing of data to include the use of autoencoder. (See Dalli at 0051, 0059, 0066, 0210, 266, 0272).
Referring to Claim 2, Quigley teaches the method of claim 1, wherein identifying the subset of similar NFTs further comprises:
identifying a plurality of candidate NFTs (
Quigley: Sec. 0024, a plurality of NFTs corresponding to the specified number
Quigley: Sec. 0935, obtain the other NFT, or plurality of NFTs);
Quigley does not explicitly teach computing, for each candidate NFT of the plurality of candidate NFTs, a similarity score with the target NFT, wherein the similarity score reflects a distance metric between a first vector representing visual features of the candidate NFT and a second vector representing visual features of the target NFT; responsive to determining that the similarity score of a candidate NFT with the target NFT exceeds a predefined threshold similarity score, including the candidate NFT into the subset.
However Jakobsson teaches these limitations
computing, for each candidate NFT of the plurality of candidate NFTs, a similarity score with the target NFT, wherein the similarity score reflects a distance metric between a first vector representing visual features of the candidate NFT and a second vector representing visual features of the target NFT (
Jakobsson: Sec. 0222, Finally, vector distances above particular thresholds may be determined to be dissimilar. Alternatively or additionally, quantitative similarity scores can be computed by systems operating in accordance with various embodiments. Through such operations, the similarity of two or more vector descriptors may be classified as belonging to set categories, where there can be two or more such categories that identify distance. In such cases, there may be a multitude of differing quantitative similarity scores possible. These are only two of the many alternative ways to score similarity between two or more vectorized descriptions, and accordingly, their associated content.);
responsive to determining that the similarity score of a candidate NFT with the target NFT exceeds a predefined threshold similarity score, including the candidate NFT into the subset (
Jakobsson: Sec. 0222, When In instances where vector distances are especially small, systems may thereby determine vector descriptions to be very similar. Such determinations may be based on vector distances falling below particular thresholds. When vector distances have moderate differences, then the two or more vector descriptions may be determined to be similar. Moderate differences may be based on vector distances falling under a first pre-specified threshold but above a second pre-specified threshold. Finally, vector distances above particular thresholds may be determined to be dissimilar. Alternatively or additionally, quantitative similarity scores can be computed by systems operating in accordance with various embodiments.
Jakobsson: Sec. 0223, Methods in accordance with some embodiments of the invention may involve techniques to map similarity scores to actions. For example, when two content elements have similarity scores that satisfy a first rule, then a first action may be taken. Additionally or alternatively, when similarity scores satisfy a second rule, then a second action may be taken. One example rule may be that the score indicates a difference less than a threshold, such as 2.5. Another example rule may be that the score indicates that the two content elements are especially similar according to a particular assessment, as shown above. In accordance with some embodiments, exclusion zones may be associated with tokens. As such, a third example rule may be that the score indicates that the two or more content elements have a similarity score suggesting a distance smaller than a certain value expressed in one of the corresponding token).
Quigley and Jakobsson are both directed to the analysis of blockchain (See Quigley at 0003, 0004, 01118; Jakobsson at 0048, 0055, 0056). Quigley discloses that additional elements, such as digital wallet and marketplace can be considered (See Quigley at 0183, 0284). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Quigley, which teaches detecting and updating digital technology transaction problems in view of Jakobsson, to efficiently apply the analysis of blockchain to enhancing the capability to comparing NFT data. (See Jakobsson at 0051, 0059, 0066, 0210, 266, 0272).
Referring to Claim 3, Quigley teaches the method of claim 2, wherein computing the similarity score further comprises:
evicting, from a feature cache, a cache entry storing a feature vector of an NFT having a most recent transaction date satisfying a staleness condition that compares, to a low sensitivity threshold, a time decay factor computed based on the most recent transaction date (
Quigley: Sec. 0636, numbers lower than a threshold may be kept after redemption
Quigley: Sec. 0863, a threshold number of transactions, wallets that have sent or received at least a certain number, amount, and/or value of tokens, wallets that have sent or received a transaction within a recent period of time,
Quigley: Sec. 0905, The data browser 10010G may sort the list by various criteria, such as a number of transactions, a frequency of transactions, most recent transactions, or any other criteria as requested by the user device 10032. The data browser 10010G may allow the user device 10032 to re-sort the list by different criteria in order to provide various views.
Quigley: Sec. 0912, a data processor 10004 may continually update some values of the database based on a most recent transaction pertaining to the value.);
storing, in the cache entry, the first vector representing visual features of the candidate NFT (
Quigley: Sec. 0232, the machine learning system 502 may receive vectors containing user data (e.g., transaction history, preferences, wish list virtual assets, and the like), virtual asset data (e.g., price, color, fabric, and the like), and outcomes (e.g., redemption, exchanges, and the like). Each vector may correspond to a respective outcome and the attributes of the respective user and respective item. The machine learning system 502 takes in the vectors and generates predictive model based thereon.
Quigley: Sec. 1063, The storage hardware may include cache memory, which may be collocated with or integrated with processing hardware.).
Referring to Claim 4, Quigley teaches the method of claim 2, wherein computing the similarity score further comprises:
identifying, in the autoencoder comprising a multi-layer encoder stage and a multi-layer decoder stage, a plurality of layers of the encoder stage (
Quigley: Sec. 0191, the token generation system 302 may embed or otherwise encode the public key used to digitally sign the token in the token.
Quigley: Sec. 1074, logically into layers. In a layered architecture, a second layer may be logically placed between a first layer and a third layer. The first layer and the third layer would then generally interact with the second layer and not with each other. In various embodiments, this is not strictly enforced - that is, some direct communication may occur between the first and third layers);
computing, for each layer of the plurality of layers, a corresponding value of a loss function for a pricing model utilized for predicting the price of the target NFT (
Quigley: Sec. 0785, the analytics and reporting system 112 may predict the future value of pre-sale tokens on secondary' markets based on data indicating a speed of pre-sales, a number of pre-sales, other distributed ledger activity linked to a campaign, and/or any other data that may tend to indicate a campaign that will become popular. Accordingly, using these and other techniques, sellers may be able to more accurately predict a number of pre-sales tokens that are likely to sell, estimate demand for the goods/services at issue, and/or the like.
Quigley: Sec. 0154, the analytics and reporting system 112 may use one or more trained machine learning models to estimate the likelihood of increased sales based on price adjustments based on marketplace activity for DRM NFTs corresponding to a particular digital asset.);
selecting, among the plurality of layers, a layer corresponding to a minimum value of the loss function(
Quigley: Sec. 0389, the contingent sale request may include other suitable information, such as a contingent sale type (e.g., auction or set price sale), a location of the collateral item, a sought price for the collateral item (if a set price sale), a minimum price for the collateral item (if an auction), a length of the contingency (e.g., the amount of time that the borrower needs to secure and repay the loan), a reward offer (e.g., a predefine reward amount or a percentage of the purchase price, desired loan amount, or repayment amount), and/or the like. In response the marketplace can facilitate the contingent sale, which may result in the collateral item being sold (e.g., a contingent buyer buys the collateral item at a set price or wins an auction) with a set of contingencies or no sale.
Quigley: Sec. 0701, such as a flat fee or percentage that must be paid to the initial seller, conditions for applying the flat fee or percentage (e.g., only if the price is greater than the initial sale price by a certain amount), a minimum price, a maximum price, or other such controls. In embodiments, these sales rules may be designed to discourage high prices for certain types of tickets or events, or conversely to encourage resales by allowing the original ticket issuer to charge a (relatively) lower price while being assured that profits from resales will still be captured.);
utilizing an output of the selected layer to compute the first vector representing visual features of the candidate NFT and the second vector representing visual features of the target NFT (
Quigley: Sec. 0181, the buyer marketplace system 204 may retrieve the virtual representations implicated by the search and may present the virtual representations in a visual manner.
Quigley: Sec. 0218, digital wallet of a user may provide visual indicia that may be associated with the token when being transferred to another person. For example, the visual indicia that may be associated with a token may include emojis, images, gifs, videos, and the like. These visual indicia may be used by the user when transmitting a token to another user. For example, if the token corresponds to a bouquet of flowers and the visual indicia is an emoji of a flower, the user may send the token in a message using the flower emoji. In this example, the user may access the token in the digital wallet (e.g., via a native application on a user device 190) and may select an option to send the token to a recipient.
Quigley: Sec. 0906, The visualizer may display the wallets in a list or other format and may sort the wallets by various criteria, such as by amount or value of tokens currently owned by the wallet, transaction frequency, types of tokens owned, or any other information associated with each wallet.).
Referring to Claim 5, Quigley teaches the method of claim 1, wherein the active trading frequency criterion specifies a threshold number of reported transactions for the target NFT within a specified period of time (
Quigley: Sec. 0902, user requests may specify one or more of type(s) of entity (e.g., fungible tokens, non-fungible tokens, smart contracts, exchanges, wallets, collections, types of tokens), time periods, numbers or frequencies of transactions, various transaction attributes such as purchase amounts, and/or other such criteria for finding any of the entities stored in the data lakes 10012 based on any attribute or other criteria associated with the entities.
Quigley: Sec. 0865, the intelligence system 10008 may cluster the various wallets into three clusters: a first cluster of “crypto novices” that own and trade fewer tokens and tend to stick to trading more popular tokens, a second cluster of “crypto investors” that trade relatively frequently, own or trade moderate amounts, sometimes engage with less popular tokens, etc., and a third cluster of “crypto whales” that trade large amounts, own or trade large sums, engage with a large variety' of tokens, etc. In this example, the intelligence system 10008 may automatically identify these clusters based on a set of attributes indicating token ownership amounts, trading amounts, types of tokens traded, etc
Quigley: Sec. 0896, receive notifications whenever a transaction originating from a specified user wallet is detected. In embodiments, users may subscribe to receive notifications based on the outputs of the intelligence system 10008. For example, users may subscribe to receive notifications when a new token collection is trending (e.g., the token collection’s popularity', as measured by a frequency of transactions involving tokens that match the token collection, increases above a certain level), as detected by the token analytics 008C.
Quigley: Sec. 0920, wallets may be clustered based on amounts of tokens owned and/or volume of trading, types of the tokens and/or other token attributes, trading frequency or other behavior attributes, wallet attributes such as wallet capabilities, user attributes fbr users associated with the wallets, attributes associated with social media accounts linked to the wallets, and/or the like, which may generate (as one example) three clusters corresponding to “crypto novices,” “crypto investors,” and “crypto whales.”
Quigley: Sec. 0154, wallets associated with sets of behavior attributes such as trading frequencies, whether the wallet lends associated NFTs, time of day and/or time zones that the wallet tends to be active, whether the wallet responds to advertising or other offers, a price sensitivity of the wallet, etc. ).
Referring to Claim 6, Quigley teaches the method of claim 1, further comprising:
determining, for a pricing model utilized for predicting the price of the target NFT, values of one or more coefficients of that minimize a difference between observed and predicted price based on a plurality of observed trades (
Quigley: Sec. 0385, the allowed mechanics of a pre-loan liquidation event (e.g., auctions, set-price sales, or the like); and other suitable rules and regulations.
Quigley: Sec. 0389, the contingent sale request may include other suitable information, such as a contingent sale type (e.g., auction or set price sale), a location of the collateral item, a sought price for the collateral item (if a set price sale), a minimum price for the collateral item (if an auction), a length of the contingency (e.g., the amount of time that the borrower needs to secure and repay the loan), a reward offer (e.g., a predefine reward amount or a percentage of the purchase price, desired loan amount, or repayment amount), and/or the like. In response the marketplace can facilitate the contingent sale, which may result in the collateral item being sold (e.g., a contingent buyer buys the collateral item at a set price or wins an auction) with a set of contingencies or no sale.
Quigley: Sec. 0453, the contingent sale request may include other suitable information, such as a contingent sale type (e.g., auction or set price sale), a location of the collateral item, a sought price for the collateral item (if a set price sale), a minimum price for the collateral item (if an auction), a length of the contingency (e.g., the amount of time that the borrower needs to secure and repay the loan), a reward offer (e.g. , a predefine reward amount or a percentage of the purchase price, desired loan amount, or repayment amount), and/or the like. In response the marketplace can facilitate a contingent sale, which may result in the collateral item being sold (e.g., a contingent buyer buys the collateral item at a set price or wins an auction) with a set of contingencies or no sale. In embodiments, the pre-loan liquidation smart contract may receive the results of the contingent sale from the marketplace.).
Referring to Claim 7, Quigley teaches the method of claim 1, further comprising:
visually representing the predicted price of the target NFT (
Quigley: Sec. 0244, the analytics system 602 may use the collected off-chain data types in conjunction with token-specific on-chain data to provide analytics reports relating to specific sets of tokens.. the analytics system 602 may process this on-chain data to determine pricing analytics corresponding to a particular token or set of tokens (e.g., an average price of a particular set of tokens, a predicted future price of the particular set of tokens, a range of prices of the set of tokens, and/or the like),
Quigley: Sec. 0900, then the machine learning 10008B may determine, based on the past history of transactions for the wallet and based on a current market price for the tokens, a prediction that a user is likely to sell one or more tokens at the current market price (e.g., the machine learning prediction may have a confidence level above a certain level),).
Referring to Claim 8, Quigley teaches the method of claim 1, wherein the other cryptoasset is a predefined cryptoasset (
Quigley: Sec. 0017, The method further includes receiving, by the set of smart contracts, a transaction corresponding to the NFT, wherein the transaction delivers a first purchase amount of cryptocurrency tokens to the set of smart contracts and indicates an address of a distributed ledger wallet of a first buyer of the NFT, wherein the set of smart contracts is configured to lock the cryptocurrency tokens until a redemption of the NFT. The method further includes modifying, by the set of smart contracts, the ownership attribute of the NFT to indicate the distributed ledger wallet of the first buyer of the NFT.
Quigley: Sec. 0018, the distributed ledger wallet of the first buyer of the NFT and the distributed ledger wallet of the redeemer of the NFT are the same distributed ledger wallet Additionally or alternatively, the distributed ledger wallet of the first buyer of the NFT and the distributed ledger wallet of the redeemer of the NFT are different wallets, the method further comprising, prior to receiving the redemption request receiving, by the set of smart contracts, a second transaction delivering a second purchase amount of cryptocurrency tokens and indicating a second distributed ledger wallet of a second buyer of the NFT;
Quigley: Sec. 0942, advertising an exchange that provides NFT lending, and the smart contract parameters 10606 may indicate that the tokenized ads 10624 should be airdropped to any wallet after that wallet purchases a certain type of NFT or an NFT meeting certain criteria (e.g., the NFT may be in demand on a certain marketplace).
Quigley: Sec. 0704, The sales smart contract 8024 may further include a sales function 8724, which be used (e.g., by a marketplace 3106) to share profits according to one or more sales rules 8712. For example, the marketplace 3106 may transfer an NFT ticket 8022 A (and/or any other NFT or token being transferred or sold as described herein) and an amount of currency (e.g., cryptocurrency, tokenized tokens, fiat currency, or the like) corresponding to the sales price (which may be the sales price plus or minus certain fees) to the sales smart contract 8024. Then, the sales smart contract 8024 may distribute the received currency (e.g., cryptocurrency, tokenized tokens, or fiat currency) to the seller and/or any other parties that receive a portion of the sales price as specified by the sales rules, and may deliver the sold NFT ticket 8022A to the buyer
Quigley: Sec. 0706, At 8804, the sales smart contract 8024 may receive an amount of an amount of currency (e.g., cryptocurrency, tokenized tokens, fiat currency, or the like) corresponding to the sale from the marketplace 3106 via a second distributed ledger transaction. For example, the marketplace may receive the amount of currency (e.g., cryptocurrency, tokenized tokens, fiat currency, or the like) from the buyer as part of a resale process, take out any fees (e.g., marketplace fees), and transfer the remaining amount to the sales smart contract 802
Quigley: Sec. 0707, At 8806, the sales smart contract 8024 may receive an invocation of a sales function 8724 from the marketplace 3106 via a third distributed ledger transaction, which may specify one or more of an identifier of the NFT ticket 8022A that was received at 8802, an account of the seller of the NFT ticket 8022A, an account of the buyer of the NFT ticket 8022A, an account of the original creator of the NFT ticket 8022A, an indicator of a sales rule 8712, and/or an indicator of the amount of currency that was received at 8804.).
Quigley describe the NFT being an original cryptoasset, in which the Examiner is interpreting as a predefined.
Referring to Claim 9, Quigley teaches the method of claim 1, further comprising:
Quigley does not explicitly teach responsive to failing to identify a subset including at least a predefined number of NFTs that are similar to the target NFT based on their respective pairwise similarity scores with the target NFT, predicting the price of the target NFT based on traits of the target NFT.
However Jakobsson teaches responsive to failing to identify a subset including at least a predefined number of NFTs that are similar to the target NFT based on their respective pairwise similarity scores with the target NFT, predicting the price of the target NFT based on traits of the target NFT (
Jakobsson: Sec. 0249, context of an NFT platform network architecture and in contexts unrelated to similarity assessment for fungible tokens and/or NFTs. Moreover, any of the systems and methods described herein with reference to FIGS. 19-22 can be utilized within any of the NFT platforms described above.
Jakobsson: Sec. 0266, Comparisons may be based on rules defining what standards for matches, including but not limited to similarity scores. Comparisons may, additionally or alternatively, rely on previously adjudicated examples (e.g., manual review) based on criteria that can then be optionally compiled into rules and policies.
Jakobsson: Sec. 0222, When vector distances have moderate differences, then the two or more vector descriptions may be determined to be similar. Moderate differences may be based on vector distances falling under a first pre-specified threshold but above a second pre-specified threshold. Finally, vector distances above particular thresholds may be determined to be dissimilar. ).
Quigley and Jakobsson are both directed to the analysis of blockchain (See Quigley at 0003, 0004, 01118; Jakobsson at 0048, 0055, 0056). Quigley discloses that additional elements, such as digital wallet and marketplace can be considered (See Quigley at 0183, 0284). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Quigley, which teaches detecting and updating digital technology transaction problems in view of Jakobsson, to efficiently apply the analysis of blockchain to enhancing the capability to comparing NFT data. (See Jakobsson at 0051, 0059, 0066, 0210, 266, 0272).
Claims 10-15 recite limitations that stand rejected via the art citations and rationale applied to claims 1-4, 7, 9. Regarding a system comprising:
a memory; a processing device, communicably coupled to the memory, (
storage hardware include a database (such as a relational database or a NoSQL database), a data store, a data lake, a column store, a data warehouse. Examples of storage hardware include nonvolatile memory devices, volatile memorydevices, magnetic storage media, a storage area network (SAN), network-attached storage (NAS), optical storage media, printed media (such as bar codes and magnetic ink), and paper media (such as punch cards and paper tape). The storage hardware may include cache memory, which may be collocated with or integrated with processing hardware. Storage hardware may have read-only, write-once, or read/write properties. Storage hardware may be random access or sequential access.);
Claims 16-20 recite limitations that stand rejected via the art citations and rationale applied to claims 1-4, 9. Regarding a non-transitory machine-readable storage medium comprising executable instructions which, when executed by a processing device (
Quigley: Sec. 0287, executing a machine- readable program or other computer-executable instractions, such as routines, instructions, programs, or other code.
Quigley: Sec. 1066, Software includes instractions that are machine-readable and/or executable. Instructions may be logically grouped into programs, codes, methods, steps, actions, routines, functions, libraries, objects, classes, etc. );
Response to Arguments
Applicant’s arguments filed 11/03/2025 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 11/03/2025.
Regarding the 35 U.S.C. 101 rejection, at pg. 9-12 Applicant argues with respect to claims at issue are not directed to an abstract idea
In response to the 35 USC § 101 claim rejection argument, the Examiner respectfully disagrees. The Examiner did consider each claim and every limitation both individually and as a whole, since the grounds of rejection clearly indicates that an abstract idea has been identified from elements recited in the claims. Using the two-part analysis, the Office has determined there are no elements, in the claim sufficient enough to ensure that the claims amounts to significantly more than the abstract idea itself. As recited, the claims are directed towards:
receiving, by a computing system, an identifier of a target non-fungible token (NFT);
accessing trading data for a plurality of NFTs;
determining, based on the trading data, a trading volume of the target NFT;
when the trading volume is below a first threshold level:
identifying a subset of similar NFTs including at least a predefined number of NFTs that satisfy a chosen similarity criterion based on their respective similarity scores with the target NFT,
wherein the subset of similar NFTs is associated with at least a predefined number of reported trades within a predefined period, and
wherein the similarity scores are based at least in part on features extracted from the target NFT using an encoder stage of a trained autoencoder neural network, wherein the autoencoder is trained to minimize reconstruction error between input and output vectors; and
predicting the price of the target NFT based on second trading data associated with the identified subset of similar NFTs;
when the trading volume is above the first threshold level and below a second threshold level:
predicting the price of the target NFT based on trading volume of the target NFT;
when the trading volume is above the second threshold level:
predicting the price of the target NFT based on the trading data and a cryptoasset market movement indicator, wherein the cryptoasset market movement is derived from a traded price of another cryptoasset.
The claim(s) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer as recited is a generic computer component that performs functions.
Examiner finds the claim recite concepts which are now described in the 2019 PEG as certain methods of organizing human activity. In particular the claims recites limitations for managing of digital objects, which constitutes methods related to commercial or legal interaction such as agreements in the form of contracts, legal obligations; sales activities; business relations which are still considered an abstract idea under the 2019 PEG. The display is comprised of generic computer elements to perform an existing business process. Examiner finds the claims recite mere instructions to implement the abstract idea on a computer and uses the computer as a tool to perform the abstract idea without reciting any improvements to a technology, technological process or computer-related technology.
Regarding, the steps at pg. 11 that Applicant points to as practical application are merely narrowing the abstract idea to a particular technological environment, which has been found to be ineffective to render an abstract idea eligible. Furthermore, the Examiner respectfully disagrees because the steps of:
“Specifically, claim 1 as amended herein recites a particular approach for valuing NFTs. The specific approach recited in claim 1 describes a particular approach to selecting a valuation method based on trading volumes, and recites specific technical features for valuation, such as "when the trading volume is below a first threshold level: identifying a subset of similar NFTs including at least a predefined number of NFTs that satisfy a chosen similarity criterion based on their respective similarity scores with the target NFT, wherein the subset of similar NFTs is associated with at least a predefined number of reported trades within a predefined period; and predicting a price of the target NFT based on second trading data associated with the identified subset of similar NFTs, wherein the similarity scores are based at least in part on features extracted from the target NFT using an encoder stage of a trained autoencoder neural network, wherein the autoencoder is trained to minimize reconstruction error between input and output vectors."”
seems to describe a “particular way” of managing of digital objects. The Applicant is basically relying on the system elements such as identifying scores and predicting prices as integrating the abstract idea into a practical application but those system elements aren't really utilized in any particular manner, and the specification indicates that at 0082 " selecting a layer of the decoder stage of an autoencoder that is employed for extracting visual features from an NFT image by a computing system operating in accordance with aspects of the present disclosure.” which indicates the lack of particularity in the application to the technological environment. These citations are a strong indicator that the technical application is NOT particular, and furthermore the claim invention does not “improves the functioning of a computer or improves another technology or technical field.” or “an improvement to another technology or technical field.
As, the claims are clear steps for managing of digital objects and not the improvement of the autoencoder. The Examiner would like to point the Applicant to the 2019 PEG, in which managing of digital object data will fall under. The 2019 PEG which states:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
The amendments have been considered but are insufficient to overcome the 101 rejection. Additionally, please refer above to the 35 U.S.C. 101 rejection for further explanation and rationale.
Regarding the 35 U.S.C. 103 rejection, at pg. 13 Applicant argues “Applicant respectfully submits that the cited references fail to teach or suggest all the elements of the independent claims as amended herein.”; ”In contrast to the cited references, the claims as amended herein describe a multi-factor approach to identifying similar content suitable for NFT valuation when trading volume of a specific NFT is lower than a threshold value, wherein the selected similar NFTs satisfy specific similarity, trade volume, and trade recency requirements.”.
In response, the Examiner respectfully disagrees to Applicant's argument that the references fail to show certain features of applicant’s invention, it is noted that the features upon which applicant relies (i.e., the claims as amended herein describe a multi-factor approach to identifying similar content suitable for NFT valuation when trading volume of a specific NFT is lower than a threshold value, wherein the selected similar NFTs satisfy specific similarity, trade volume, and trade recency requirements..) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Additionally, Jakobsson describes the thresholds of various levels can includes between and above first and second levels.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Torres et al., U.S. Pub. 20220300802, (discussing the determining of the similarity score).
Sharda et al., W.O. Pub. 2023049638, (discussing the managing and predicting values of NFT).
Kang et al., Blockchain Interoperability Landscape, https://arxiv.org/pdf/2212.09227, 2022 IEEE International Conference on Big Data, 2022 (discussing the monitoring and analyzing of Blockchain).
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/UCHE BYRD/Examiner, Art Unit 3624 /PATRICIA H MUNSON/Supervisory Patent Examiner, Art Unit 3624