DETAILED ACTION
Acknowledgements
This Non-Final Office Action is in reply to Applicant’s RCE filed January 29, 2026.
Claims 1-3, 5-8, 12-14, 16, and 20 are currently amended.
Claims 1–20 are currently pending.
Claims 1–20 have been examined.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on January 29, 2026 has been entered.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 5, 6, 16 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 5 and 6 both refer to “the recommending”. However, the current amendment to claim 1 adds “as a part of recommending a sequence of digital assets” in line 10 and an additional step of “recommending” in line 22. Claim 4, which depends from claim 1, further recites a “recommending” step in line 3. Claims 5 and 6, which depend from claim 4,are amended to each recite “as a basis for the recommending,” but it is not clear which previously recited “recommending” is referred to. As a result, the scope of claims 5 and 6 is unclear. For purposes of examination, claims 5 and 6 will be interpreted as referring to the “recommending” step in claim 4.
Claim 16 is substantially similar to claim 5 and is rejected for the same reason.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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, 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 7, 8, 9, 12, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Andon (US 20200273048 A1) in view of Nice (US 20150278908 A1) in view of Weber (US 20220391895 A1).
Regarding claim 1
Andon teaches:
A computer-implemented method comprising:
presenting a virtual artifact in a virtual environment; {[0115] “In one context, the character [virtual artifact] 226 may be an athlete and the environment 228 may be a sporting environment. FIG. 9 illustrates such a character 226 as a footballer, and the environment 228 as a football pitch within a stadium.”}
managing, using transaction data of a Non-Fungible Token (NFT, digital asset) stored on a blockchain implementation, a usage of a virtual attribute of the digital asset, wherein the virtual attribute is usable with a plurality of virtual artifacts in a plurality of virtual environments, and wherein the managing comprises one of creating, modifying, copying, and transferring of the digital asset; {[0015] “The cryptographic digital asset is transferred to a digital wallet of the second user, and the unique digital asset code is transmitted to a distributed blockchain ledger and recorded on a distinct record block to confirm the transfer of the cryptographic digital asset.”; [0061] “If the CryptoKick [digital asset] is imported into a separate video game, in some configurations, different attributes [virtual attribute] of the CryptoKick may impart changes in the ability level of a user's character [virtual artifact] outfitted with the asset.”}
presenting, responsive to the manipulation, the virtual attribute in association with the virtual artifact during the period in the virtual environment; {[0061] “If the CryptoKick [digital asset] is imported into a separate video game, in some configurations, different attributes [virtual attribute] of the CryptoKick may impart changes in the ability level of a user's character [virtual artifact] outfitted with the asset.”; [0115] “The character's attributes 230 may include, for example, speed, ball control, passing, defense, kicking power, balance, and stamina (among others). In an example, the character 226 may be outfit/skinned with a digital collectable (e.g., an article of apparel 234) that may be uniquely backed by a token on the blockchain 60. In an embodiment, the digital collectable may have been acquired in any one of the manners described herein.”; [0116] “Further building upon the notion of the CryptoKick as property, in an example, a user or company may rent out or lease out the use of the digital collectable within a video game for a period of time. In an example, the leasing may be constrained so that only one instance of a particular user's asset exists in any particular context. For example, a user may own full rights to an exclusive CryptoKick. That user may concurrently lease out the CryptoKick for use in Basketball Game A for 1 week, Soccer Game B for 2 weeks, and 1st Person Shooter Game C for 3 weeks.”}
Andon does not teach, however Nice teaches:
generating a goal embedding vector representing a performance objective of a user to be achieved in the virtual environment; {[0003] “a user may be associated with a user vector [goal embedding vector] whose elements measure the extent of interest the user has in items that are high in corresponding factors.”}
iteratively determining in a first iteration, as a part of recommending a sequence of digital assets, and by comparing the goal embedding vector with at least one asset embedding of the digital asset, that the digital asset has a first high score to satisfy the performance objective in the first iteration in the sequence of digital assets; {[0003] “An item may be associated with an item vector [asset embedding] whose elements measure the extent to which the item possesses some factors. Similarly, a user may be associated with a user vector [goal embedding vector] whose elements measure the extent of interest the user has in items that are high in corresponding factors. […] The dot product [comparing] of the vectors may describe the interaction between the user and item and may be used to determine whether to make a recommendation to a user. […] The dot product […] represents the score between the user i and the item j. The score represents the strength of the relationship between the user i and the item j and may be used to make a recommendation (e.g., recommend item with highest score).”}
iteratively determining in a second iteration, by comparing the goal embedding vector with at least one asset embedding of a second digital asset, that the second digital asset has a second high score to satisfy the performance objective in the second iteration; {[0004] “after all the items have been scored, the highest scoring items may be selected and recommended.”}
recommending, responsive to the second high score, the second digital asset in the sequence of digital asserts, such that using the second digital asset in the second iteration moves the user closer to the performance objective in the virtual environment relative to using the digital asset in the first iteration. {[0004] “after all the items have been scored, the highest scoring items may be selected and recommended.”}
Andon, as discussed above, teaches a marketplace for selling and/or leasing cryptographic digital assets. Nice teaches an item recommendation system to help consumers discover items to buy or rent ([0001] “Conventional recommendation systems provide a powerful discovery experience by suggesting items to users that the users have consumed in the past. Recommendation systems provide information about matches between users (e.g., shoppers) and items (e.g., books, videos, games) based on user interests, preferences, history, or other factors. For example, if a system has data that a user has previously accessed (e.g., purchased, rented, borrowed, played) a set of items, then a recommendation system may identify similar items and recommend them to the user based on the data about the user's own actions (e.g., ‘if you liked this, you might like that’)”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the recommendation system of Nice with the digital asset marketplace of Andon because it would help consumers discover items to lease/buy and therefore have the advantage of potentially increased sales/leases.
Andon in view of Nice does not teach, however Weber teaches:
adding, responsive to the first high score, to the blockchain implementation digital rights data relative to the digital asset {[0056] “NFTs may host individual data licenses (access conditions) including all relevant parameters required to define the license agreement, like duration [time period]”}, the adding enabling a manipulation of the digital asset using the managing relative to a period specified in the digital rights data;
What the adding enables is an intended result not given patentable weight.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to add the access conditions defining a duration of the lease agreement to the leasing method of Andon in view of Nice because it enables [0056] “for automated and still individualizable data access management” and “automated distribution, tracking and enforcement of license conditions according to specific and configurable terms.”
Regarding claim 7
Andon teaches:
The computer-implemented method of claim 1, further comprising:
allowing, as a part of the presenting, an exclusive use of the digital asset by the virtual artifact during the period. {[0116] “a user or company may rent out or lease out the use of the digital collectable within a video game for a period of time. In an example, the leasing may be constrained so that only one instance of a particular user's asset exists in any particular context.”}
Regarding claim 8
Andon teaches:
The computer-implemented method of claim 1, further comprising:
allowing, according to the transaction data, a first use of the digital asset by the virtual artifact for the period; and
further allowing, according to a second transaction data, a second use of the digital asset by a second virtual artifact during at least a portion of the period. {[0116] “a user or company may rent out or lease out the use of the digital collectable within a video game for a period of time. In an example, the leasing may be constrained so that only one instance of a particular user's asset exists in any particular context. For example, a user may own full rights to an exclusive CryptoKick. That user may concurrently lease out the CryptoKick for use in Basketball Game A for 1 week [first use], Soccer Game B for 2 weeks [second use], and 1st Person Shooter Game C for 3 weeks.”}
Regarding claim 9
Andon teaches:
The computer-implemented method of claim 1, wherein the digital asset comprises a virtualized ability of the virtual artifact in the virtual environment. {[0061] “If the CryptoKick [digital asset] is imported into a separate video game, in some configurations, different attributes of the CryptoKick may impart changes in the ability level of a user's character [virtualized artifact] outfitted with the asset. In one example, the attributes of the user's character may be positively influenced by the rarity or exclusivity of the various attributes or by the overall combined rarity or exclusivity of the asset. For example, a rare CryptoKick may impart better jumping ability [virtualized ability] or lateral quickness, a rare CryptoThread may impart better strength or speed, and a rare CryptoLid may impart better vision.”}
Regarding claim 12
Claim 12 (media) is substantially similar to claim 1 and is treated the same with respect to prior art rejections.
Regarding claim 17
Andon teaches:
The computer program product of claim 12, wherein the program instructions are stored in the at least one of the one or more storage media of a local data processing system, {[0016] “Aspects of this disclosure are also directed to a non-transitory, computer-readable medium (CRM) that stores instructions executable by one or more processors of one or more computing devices”; [0080] “The 3rd party integration service 66 may operate as an API on an app provided on the user's device [local data processing system], or as a dedicated cloud based service.”}
and wherein the program instructions are transferred over a network from a remote data processing system.
This is not given patentable weight because it describes where the program instructions come from and does not describe any structure.
Regarding claim 18
Andon teaches:
The computer program product of claim 12, wherein the program instructions are stored in the at least one of the one or more storage media of a server data processing system, {[0016] “Aspects of this disclosure are also directed to a non-transitory, computer-readable medium (CRM) that stores instructions executable by one or more processors of one or more computing devices [server data processing system]”; [0080] “The 3rd party integration service 66 may operate as an API on an app provided on the user's device, or as a dedicated cloud based service.”}
“Cloud based service” implies a data processing system which is a server.
and wherein the program instructions are downloaded over a network to a remote data processing system for use in a computer readable storage device associated with the remote data processing system.
This is not given patentable weight because it describes where the program instructions come from and does not describe any structure.
Regarding claim 19
Andon teaches:
The computer program product of claim 12, wherein the computer program product is provided as a service in a cloud environment. {[0080] “The 3rd party integration service 66 may operate as an API on an app provided on the user's device, or as a dedicated cloud based service.”}
The above limitation is an intended use of the computer-readable media of claim 12, and is not given any patentable weight. However, it is taught by Andon.
Regarding claim 20
Claim 20 (system) is substantially similar to claim 1 and is treated the same with respect to prior art rejections.
Claims 2 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Andon in view of Nice in view of Weber, as applied to claims 1 and 12 above, and further in view of Ye “Customized Regression Model for Airbnb Dynamic Pricing,”.
Regarding claim 2
Andon in view of Nice in view of Weber does not teach, however Ye teaches:
The computer-implemented method of claim 1, further comprising:
determining a price for use of the digital asset by predicting a future price for use of the digital asset using a regression model. {abstract “Second, a regression model predicts the optimal price for each listing-night […] The unique nature of Airbnb listings makes it very difficult to estimate an accurate demand curve that's required to apply conventional revenue maximization pricing strategies.”}
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the regression model pricing of Ye with the NFT rental and owner determined price of Andon because both references deal with renting unique things and the regression model solves the difficulty in pricing them using conventional strategies.
Regarding claim 13
Claim 13 is substantially similar to claim 2 and is treated the same with respect to prior art rejections.
Claims 3 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Andon in view of Nice in view of Weber, as applied to claims 1 and 12 above, and further in view of Dudash (US 20230325461 A1).
Regarding claim 3
Andon in view of Nice in view of Weber does not teach, however Dudash teaches:
The computer-implemented method of claim 1, further comprising:
determining the period using a time scheduling model implemented as a quadratic unconstrained binary optimization problem. {[0070] “QUBO problems have NP (non-deterministic polynomial) hardness; as such, theoretical computational NP-hard problems such as the traveling salesman problem, the protein folding problem, and the genotype imputation problem, as well as practical NP-hard problems such as airline scheduling problems and traffic routing problems, may be represented by QUBO problems. Representing NP-hard problems as QUBO problems and finding solutions using quantum annealers has been shown in many cases to be more efficient than solving NP-hard problems using classical computers.”}
Dudash teaches that scheduling may be represented by QUBO problems. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the QUBO scheduling model of Dudash with the NFT rental of Andon because it would provide a way to determine available time slots while avoiding scheduling conflicts.
Regarding claim 14
Claim 14 is substantially similar to claim 3 and is treated the same with respect to prior art rejections.
Claims 4-6 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Andon in view of Nice in view of Weber, as applied to claims 1 and 12 above, and further in view of Ross, JR, (US 20230005045 A1).
Regarding claims 4-6
Andon does not teach, however Ross teaches:
Claim 4:
The computer-implemented method of claim 1, further comprising: recommending, from the set of digital assets, for lease at a price and during the period, the digital asset.
Claim 5:
The computer-implemented method of claim 4, further comprising:
using information about a plurality of digital assets previously leased by a plurality of users as a basis for the recommending. {[0059] “In some aspects, the collaborative filtering algorithm can identify users who buy similar items in order to recommend products”}
Claim 6:
The computer-implemented method of claim 4, further comprising:
using information about a leasing history of a user to which the digital asset is leased out in the transaction data as a basis for the recommending. {[0060] “In some aspects, the content-based filtering algorithm can recommend items based upon user's purchase history”}
Ross teaches recommending products based on the history of the user receiving the recommendation, and also on the history of a plurality of users. Ross does not explicitly teach using leasing history or recommending leasing an item (as opposed to purchasing). However, Ross teaches [0058] “Identification of desirable content is the first stage of determining a content recommendation.” It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the identification of desirable content and recommendations of Ross with the NFT rental of Andon because it would alert potential renters to things they may desire and therefore increase sales.
Regarding claims 15-16
Claims 15-16 are substantially similar to claims 4-5, respectively, and are treated the same with respect to prior art rejections.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Andon in view of Nice in view of Weber, as applied to claim 1 above, and further in view of Ching (US 20230368600 A1).
Regarding claim 10
Andon in view of Nice in view of Weber does not teach, however Ching teaches:
The computer-implemented method of claim 1, wherein the digital asset comprises an appearance of the virtual artifact in the virtual environment. {[0133] “In other implementations, the items being offered may not be social gaming currency amounts but may instead be digital assets, e.g., skins [appearance of the virtual artifact], game enhancements, avatars, etc., for use in a social gaming system.”}
Ching teaches a digital asset which gives a user an avatar inside of a game. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Ching with the NFT rental of Andon because Andon teaches renting digital assets and Ching teaches a specific type of digital asset.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Andon in view of Nice in view of Weber, as applied to claim 1 above, and further in view of Eby (US 20230316263 A1).
Regarding claim 11
Andon in view of Nice in view of Weber does not teach, however Eby teaches:
The computer-implemented method of claim 1, wherein the digital asset comprises an audio characteristic of the virtual artifact in the virtual environment. {[0008] “In these situations, an NFT [digital asset] could be used to represent [comprise] a unique authentication credential for an individual, such as the virtual equivalent of an avatar's biometric information (e.g., an iris scan, fingerprint, voice print [audio characteristic], etc. of an avatar [virtual artifact] in a virtual world).”}
Eby teaches a digital asset which represents a voice print of an avatar. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Eby with the NFT rental of Andon because Andon teaches renting digital assets and Eby teaches a specific type of digital asset.
Response to Arguments
35 USC § 103
Applicant has amended the independent claims to include new limitations related to making asset recommendations. Applicant argues the new limitations are not taught by the cited art. A new reference, Nice, has been cited. Nice teaches generating user vectors and item vectors, using the dot product of the vectors as a score, and recommending the highest scoring items. This teaches all of the newly added limitations.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Harma (US 20220309115 A1) teaches:
[0117] “In this method, each user (including at least the user of the recommendation device 100) is represented as a vector, such as a 10-dimensional vector, i.e. a ‘user vector’ [goal embedding vector]. Each content item is represented by a vector of a similar dimension, i.e. a ‘content vector’ [asset embedding]. The scalar product of these two vectors serves as the score predicting an expected preference of the user for that content item, i.e. the score indicating whether that content item should be presented/recommended to the user.”
Mahmood (US 20220277205 A1) teaches:
[0099] “In some implementations, method 600 may further include receiving a request for a recommendation of content of the content type. The request may include a particular user identifier. In these implementations, method 600 may further include mapping the particular user identifier to a particular cluster of the plurality of user clusters based on similarity between a user feature vector [goal embedding vector] associated with the particular user identifier (e.g., determined based on current user data) and the respective cluster feature vectors [asset embedding] for the plurality of clusters (e.g., as stored in the database). The mapping may be performed such that the particular user identifier is associated with a particular cluster that has a cluster feature vector that is most similar to the user feature vector. In these implementations, method 600 may further include providing one or more recommended content items from the list of content items of the particular cluster as the recommendation. In the implementations where a ranked list [score] is generated per user per cluster, one or more content items from the list of content items for the particular user of the particular cluster may be provided as the recommendation.”
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/S.M.D./Examiner, Art Unit 3698
/PATRICK MCATEE/Supervisory Patent Examiner, Art Unit 3698