DETAILED ACTION
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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 November 12, 2025 has been entered.
Status of Claims
Claims 1, 8 and 14 have been amended and are hereby entered.
Claims 4, 6, 11, 13, 17, 19 and 21 – 23 were cancelled.
Claims 1 - 3, 5, 7 - 10, 12, 14 - 16, 18 and 20 are pending and have been examined.
This action is made NON-FINAL.
Response to Arguments
Applicant's arguments filed November 12, 2025 have been fully considered but they are not persuasive.
Regarding to Applicant's arguments for Double-Patenting Rejection in p. 9: The Applicant did not take any action and/or did not file an Electronic-Terminal Disclosure or e-td to obviate the Obviousness-type Double Patenting (ODP) rejection since this is a provisional ODP. Applicant’s arguments related to refraining from responding to this rejection has been considered, but are not persuasive to overcome the ODP rejection. Thus, the outstanding ODP was updated according to Applicant’s amendments and is maintained.
Regarding to Applicant's arguments against the 101 rejection of pending claims on pages 9 - 11: Applicant’s arguments directed to the 101 analysis were considered. However, these arguments are not persuasive and the Examiner respectfully disagrees for the following reasons:
For Step 2A-Prong 1 starting in p. 9: The Applicant argues that the independent claims 1, 8 and 14 and its features are not directed to any abstract idea group based on the USTPO guidance and the Ex Parte Desjardins appeal court case as well as Enfish case and because no “human mind could practically perform such functions.” as claimed in the limitations. However, Examiner find this argument unpersuasive. Because a mental process was recited in the particular claim steps directed in part to “link the generated metadata to the generated non-fungible token by referencing the generated metadata using the unique CID” and “based on received infrared data, determine validity of the physical product as compared to the physical product's physical attributes stored in the unique CID…” which can be practically performed in the human mind or in pen and paper. Because linking or referencing data with a unique CID and determine validity of the physical product based on the comparison of data collected requires evaluation and judgement. Also, these steps can either be done with the help of physical aid such as pen and paper or can be performed by humans without or with the assistance (e.g. tool) a computer. But also, and in light of the USPTO memorandum from August 4, 2025, the particular “way” that these claim limitations are recited and are generally applying (i.e. “invoking”) the ML algorithm to recognize physical attributes and determine the validity of the corresponding physical product can still be read as mental process and does not negate the mental nature of these specific limitation(s). Also, these steps do not further limit or differentiate how the ML algorithm of the “intelligent feature recognition engine” is performing these functions differently from being practically performed in the human mind or with pen and paper. Thus, the claim limitations still recite the abstract idea of a mental process even if they require at least one of: (B) physical aid (e.g. pen and paper) and/or (C) a computer (see MPEP 2106.04(a)(2)(III)(B & C)) to determine validity of the physical product.
Similarly, the abstract idea (e.g. judicial exception) of a certain method of organizing human activity in the form of “commercial or legal interactions” was recited in the specific claim limitation steps directed in part to “receiving” provenance data of a physical product attributes and other types of data to “upload” the data, “generate a unique content identifier (CID)” that is included with a minting function in a smart contract that is “deployed” in the blockchain, to “call” the function and “generate” an NFT and link the metadata from the NFT to other types of data to “determine” validity of the physical product. These steps encompass advertising, marketing or sales activities or behaviors while ensuring good business relations by determining legitimate or authentic products via agreements/contracts and/or legal obligations (e.g. “smart contracts”) when later advertised and sold to customers.
For Step 2A-Prong 2 and Step 2B starting in p. 11: The Applicant argues that the pending independent claims “steps define a computer-centric technological process that coordinates decentralized storage, cryptographic identifiers, blockchain execution, and AI-based sensory verification” and the “claimed method improves how distributed-ledger and machine learning systems interoperate by reducing data duplication, enabling on-chain traceability, and enhancing recognition accuracy, thus addressing a specific technological problem rather than claiming an abstract concept.” However, Examiner find these arguments unpersuasive and respectfully disagrees. Because the identified limitations in the claims, even individually and in combination, did not integrate a judicial exception into a practical application since the steps were merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer, general blockchain technology and machine learning (ML) techniques with trained algorithms as a tool to perform an abstract idea (see MPEP 2106.05(f) and 2106.04(d)(I)). Specifically, the claims’ limitations are reciting the use of a generic computer and general blockchain technology broadly recited, that further performs the generic computer functions of generating and uploading metadata file with a generated and linked unique CID in smart contract that once deployed mints the non-fungible tokens and deploy the smart contract in the blockchain to generate them specifically for the physical product and achieve the intended result of “determine validity of the physical product as compared to the physical product's physical attributes” stored in the CID linked and NFT specific to the physical product, based on infrared data received. Lastly, these limitations and their additional elements, individually and in combination, are not “significantly more” as these and its additional elements of the computer, the intelligent feature recognition engine of the mobile application, and the at least one machine learning algorithm trained are recited in a high level of generality that cannot provide an inventive concept at Step 2B, and are not integrating the abstract idea into a practical application. (see MPEP 2106.05). These claims, when compared to the Enfish case (see MPEP 2106.05(a)(I)) as asserted by the Applicant in p.10 from Remarks, does not reflect an improvement to the way the computer is working (i.e. functionality) to specifically achieve the validity determination of the physical product as compared to the physical product's physical attributes and further improve “how distributed-ledger and machine learning systems interoperate by reducing data duplication, enabling on-chain traceability, and enhancing recognition accuracy ”, as alleged. Moreover, the specification in ¶0013 – 14, ¶0082 and ¶00129 – 130 lacks discussion of or generally discusses the prior art and how the invention improved the way the computer validates the authenticity of a physical product while using ML algorithms trained that are broadly claimed and software with broadly recited infrared sensors to recognize specific features such as woven patterns from infrared data of the physical product. Thus, the claim limitations are recited in a high level of generality by disclosing the end result without providing details on how this alleged “improvement” to the computer functioning and/or to the existing technology of “decentralized computing and AI-based sensing” is achieved to advance “how blockchain-based verification systems operate through ML-enabled physical-attribute recognition”, as alleged. Thus, for all the reasons stated above, the Examiner respectfully disagrees, and maintains 35 USC § 101 rejection for these pending claims.
Regarding to Applicant's arguments of rejection under 35 USC § 103 for the pending claims on pages 12 – 14: Applicant’s arguments with respect to claims 1, 8 and 14 have been considered but are not persuasive and the Examiner disagrees. Because Applicant is focusing on each prior art teaching, rather than focusing on the actual language claimed in each claim limitation and how their corresponding limitation steps are different from the prior art teachings while considering the broadest reasonable interpretation (BRI) of each claim. Thus, under the BRI of the claim limitations pointed by the Applicant are still reasonably taught by at least the combination of Suk and Vosseller. The Examiner notes that the recitation of the “physical product's physical attributes further comprise materials with different capacities for specific heat absorption configured to cause the physical product to emit a specific pattern of infrared thermal imaging”, is considered non-functional descriptive matter that does not hold patentable weight and is not part of the invention scope since the infrared data was received from a “mobile application” that was “captured by an infrared sensor of the user device”, as claimed (see ¶0042 and ¶0044 from Vosseller). As for the step directed in part to “utilizing the intelligent feature recognition engine with at least one machine learning algorithm trained to recognize, from the infrared data, specific features of the physical product” including a “woven pattern”, this limitation is taught under the broadest reasonable interpretation (BRI) of the claim as the system includes an “image copy detection system based on a convolutional neural network (CNN) model” developed that can also be trained by “automatically extracting shape values of each image” that is further “converted into 44 different formats” (e.g. interpreted as recognizing infrared data with specific features) to detect “duplicate images by rotation, scaling, and other content manipulations” which can be used in addition to the “verifying that a product is genuine in the certified authority server 500” and the analysis of “similarity with a genuine product image for a real asset using deep learning” (i.e. directed to the specific features of a physical product such a woven patterns) using the CNN model (see ¶0054; Suk), in accordance to the example given in ¶0013 – 14 for the “intelligent feature recognition” claimed and ¶0082 for pattern forms of scannable region from Applicant disclosure. For more details refer to the 35 USC § 103 section. Finally, the Examiner respectfully disagrees, and maintains 35 USC § 103 rejection for these pending claims.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
At least the instant independent claims 1, 8 and 14 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 21, 29 and 37 of Co-pending Application No. 18/201,570 (reference application referred as ‘570 hereafter) in view of Suk (U.S. Pub No. 20230145439 A1) in further view of Vosseller (U.S. Pub No. 20230109574 A1). Although the claims at issue are not identical, they are not patentably distinct from each other because they are not patentably distinct from each other because the differences between the claims are considered to be obvious as set forth below:
Instant claims
Co-pending or reference claims
(18/201,570)
Claims 1, 8 and 14: A system for generation and monitoring of unique distributed token for resource verification, the system comprising: (claim 1)
a processing device;
a non-transitory storage device containing instructions when executed by the processing device, causes the processing device to perform the steps of:
receive provenance data from a first entity, wherein the provenance data comprises information as to a physical product's physical attributes;
generate a metadata file in a form of a script object notation including the provenance data for the physical product;
upload the generated metadata file to a decentralized storage system to generate a unique content identifier (CID) for the metadata file;
include the unique CID in a smart contract comprising a function configured to mint non-fungible tokens;
deploy the smart contract to a blockchain network to call the function;
generate, via calling the function, a non-fungible token specific to the physical product;
link the generated metadata to the generated non-fungible token by referencing the generated metadata using the unique CID;
provide access to a mobile application via a user device, wherein the mobile application comprises an intelligent feature recognition engine;
receive, via the mobile application, infrared data associated with an intensity of infrared light emitted by a physical attribute of the physical attributes captured by an infrared sensor of the user device; and
based on received infrared data, determine validity of the physical product as compared to the physical product's physical attributes stored in the unique CID linked in the non-fungible token specific to the physical product, wherein determining validity of the physical product as compared to the physical product's physical attributes further comprises utilizing the intelligent feature recognition engine with at least one machine learning algorithm trained to recognize, from the infrared data, specific features of the physical product, including a woven pattern; and
wherein the physical product's physical attributes further comprise materials with different capacities for specific heat absorption configured to cause the physical product to emit a specific pattern of infrared thermal imaging due to boundaries of different materials woven into the physical product.
Claims 21, 29 and 37: A system for processing a resource transfer between a simulated and real environment, the system comprising: (claim 21)
a processing device;
a non-transitory storage device comprising instructions that, when executed by the processing device, causes the processing device to:
receive provenance data from a first entity, wherein the provenance data comprises information of a selected resource;
generate a metadata file in a form of a script object notation including the provenance data for the selected resource;
upload the generated metadata file to a decentralized storage system to generate a unique content identifier;
generate a resource non-fungible token (NFT) on a distributed ledger network associated with the unique content identifier to link the resource NFT to the selected resource;
provide a virtual environment associated with a transferor to a user device of a user, wherein the virtual environment comprises a plurality of resource options;
receive a resource selection from the user for the selected resource, wherein the user interacts with an interactable element associated with the selected resource for the resource selection in the virtual environment;
access the resource NFT associated with the selected resource from the distributed ledger network;
retrieve a list of authorized transferors, via a link stored in the resource NFT,
associated with the selected resource, wherein the list of authorized transferors comprises one or more cryptographic addresses associated with one or more authorized transferors;
authenticate a cryptographic address of the transferor with the list of authorized transferors;
receive a request to authenticate the selected resource from the user;
authenticate the selected resource; and
process a resource transfer of the selected resource from the transferor to the user.
Consequently, for pending claims 1, 8 and 14 in view of co-pending ‘570 application and its claims 21, 29 and 37, the differences between the claims are the recitation of the following limitations:
include the unique CID in a smart contract comprising a function configured to mint non-fungible tokens;
deploy the smart contract to a blockchain network to call the function;
provide access to a mobile application via a user device, wherein the mobile application comprises an intelligent feature recognition engine;
receive, via the mobile application, infrared data associated with an intensity of infrared light emitted by a physical attribute of the physical attributes captured by an infrared sensor of the user device; and
based on received infrared data, determine validity of the physical product as compared to the physical product's physical attributes stored in the unique CID linked in the non-fungible token specific to the physical product, wherein determining validity of the physical product as compared to the physical product's physical attributes further comprises utilizing the intelligent feature recognition engine with at least one machine learning algorithm trained to recognize, from the infrared data, specific features of the physical product, including a woven pattern; and
wherein the physical product's physical attributes further comprise materials with different capacities for specific heat absorption configured to cause the physical product to emit a specific pattern of infrared thermal imaging due to boundaries of different materials woven into the physical product.
The co-pending ‘570 application and its claims 21, 29 and 37 did not taught the steps indicated above. However, these instant application’s limitations were further evaluated by Suk (U.S. Pub No. 20230145439 A1) which taught the inclusion of the CID in a smart contract as the “a unique identification code and a surface fingerprint” (directed to the CID) that is stored and packed in the NFT when issuing the NFT by the system (see Fig. 5, S4300 – S4400; ¶0080; Suk) which is requested by a user when selling the real asset or product and a “smart contract” is created in response (directed to mint the NFTs) and contains these unique identifier in its contents which are related to the “NFT transaction” (see ¶0058; Suk). Regarding the limitation of deploying the smart contract to a blockchain, this was taught by Suk as the “smart contract may be distributed on the block chain network to confirm whether the NFT transaction progresses smoothly” based on the “transaction conditions” of the smart contract (e.g. contents) being met (see ¶0058; Suk). The step directed to providing the mobile application access with an intelligent feature recognition engine was taught as the “NFT transaction platform” of the user terminal which includes an “Image Similarity Analysis Model Using Deep Learning” (see ¶0081 and ¶0052 – 56; Suk). For the determining validity of the physical product as compared to the physical product's physical attributes stored in the CID linked in its NFT, was directed to when the “purchaser terminal 400 confirms whether the photographing device is interlocked (S4900) and then performs control to photograph the surface fingerprint (S4900)” and further, “in operation S4700, the surface fingerprint photographed in this way may be output from the purchaser terminal 400 through the surface fingerprint comparison process (S4910) according to whether the product is genuine or not (S4930)” (see ¶0081 and ¶0087; Suk). As for the step directed in part to “utilizing the intelligent feature recognition engine with at least one machine learning algorithm trained to recognize, from the infrared data, specific features of the physical product” including a “woven pattern”, this limitation is taught under the broadest reasonable interpretation (BRI) of the claim as the system includes an “image copy detection system based on a convolutional neural network (CNN) model” developed that can also be trained by “automatically extracting shape values of each image” that is further “converted into 44 different formats” (e.g. interpreted as recognizing infrared data with specific features) to detect “duplicate images by rotation, scaling, and other content manipulations” which can be used in addition to the “verifying that a product is genuine in the certified authority server 500” and the analysis of “similarity with a genuine product image for a real asset using deep learning” (i.e. directed to the specific features of a physical product such a woven patterns) using the CNN model (see ¶0054; Suk), in accordance to the example given in ¶0013 – 14 for the “intelligent feature recognition” claimed and ¶0082 for pattern forms of scannable region from Applicant disclosure. As for the specific feature of a woven patterns from the infrared data of a physical product, is interpreted as Suk system, in ¶0038 already stores a “real asset” including the “surface fingerprint” which may be “unique surface characteristics of a real thing itself” that further is “a mark of an artificially generated unique fingerprint pattern” that can be any type of pattern “identified in an a surface fingerprint” by utilizing “one image processing technology of a flat image, a three-dimensional image, a perspective (x-ray) image, and a holographic image” (see ¶0018; Suk).
Therefore, it would have been obvious to one skilled in the art at the time of filing because would have provided to the instant 570’ application with the abilities of including of the CID in a smart contract, deploying the smart contract to a blockchain, providing access mobile application that includes an intelligent feature recognition engine, receiving image data, radio frequency data, or infrared data via the mobile application and determining validity of the physical product as compared to the physical product's physical attributes stored in the non-fungible token specific to the physical product while utilizing intelligent feature recognition engine with at least one machine learning algorithm trained to recognize the specific features of a physical product (i.e. woven patterns), as taught by Suk in order to maintain “validity, identity, integrity, and continuity by authenticating a surface fingerprint with DID during NFT transactions of real assets” and “help maintain the business credit of a seller of a genuine product to contribute to industrial development and protect interests of consumers.” (¶0002 and ¶0092; Suk).
Finally, the steps of receiving infrared data via the mobile application associated with an intensity of infrared light emitted by a physical attribute of the physical attributes captured by an infrared sensor of the user device and “wherein the physical product's physical attributes further comprise materials with different capacities for specific heat absorption configured to cause the physical product to emit a specific pattern of infrared thermal imaging” were not taught by neither the co-pending ‘570 application and Suk. Thus, the second reference of Vosseller teaches it as “the fingerprint capture system 130 is depicted obtaining sensor-captured features 202 of physical item 204” wherein the “sensor-captured features 202” may include data derived from various electromagnetic spectrum features captured by the sensors about the physical item 204, measured temperatures at different locations of the physical item 204 (or a map of them), a LIDAR scan of the physical item 204, or measurements (or estimated values) of one or more elements or compounds at different locations of the physical item 204” directed to infrared data wherein is obtained from sensors used by the system are “imaging sensors (e.g., one or more high-resolution digital cameras, one or more low-resolution digital cameras), temperature sensors, LIDAR, biochemical sensors”, etc. (see ¶0042; Vosseller). Also, for the step further defining the physical product’s physical attributes that are materials with different capacities for specific heat absorption and emit a specific pattern of infrared thermal imaging interpreted under BRI, this is directed to as “the sensor-captured features 202 may include, but are not limited to, images (e.g., high-resolution images of the physical item 204's features), videos of the physical item 204, data derived from various electromagnetic spectrum features captured by the sensors about the physical item 204, measured temperatures at different locations of the physical item 204 (or a map of them), a LIDAR scan of the physical item 204, or measurements (or estimated values) of one or more elements” (see ¶0044; Vosseller) and the detection of specific pattern emission of infrared thermal imaging claimed is directed to the system’s ability to further compare “the digital fingerprint 206 and the distinguishing feature data 138” to identify “those authentic items and/or differentiating them from items that are not authentic (e.g., knockoffs)” based on the “sensory capacity to detect one or more of the same features and/or compare digital fingerprints to the distinguishing feature data 138 at the level required to identify a physical item as authentic” wherein “different types of sensors” can be used such as “imaging sensors, to capture features of the physical item, including temperature sensors, LIDAR, and biochemical sensors”.
Thus, it would have been obvious to one skilled in the art at the time of filing because would have provided to the instant 570’ application and Suk with the abilities of specifically receiving infrared data related to intensity of infrared light emitted by a physical attribute captured by an infrared sensor of the user device and specifically having physical product’s physical attributes that further comprise of materials with heat absorption capacities with specific heat absorptions in order to emit specific pattern of infrared thermal imaging, as taught by Vosseller in order to identify “those authentic items and/or differentiating them from items that are not authentic (e.g., knockoffs)” and achieve the “sensory capacity” that humans don’t have to “detect one or more of the same features and/or compare digital fingerprints to the distinguishing feature data 138 at the level required to identify a physical item as authentic” wherein such level may be in a more granular, precise and accurate level (¶0047; Vosseller). This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
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 - 3, 5, 7 - 10, 12, 14 - 16, 18 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of this claimed invention recited in the claims begins in view of independent claim 1, the most representative claim of the independent claims set 1, 8 and 14, as follows:
At Step 1: Claims 1, 8 and 14 falls under statutory category of a system, an article of manufacture, and a process, respectively.
At Step 2A Prong 1: Claim 1 (representative of claims 8 and 14) recites an abstract idea in the following limitations:
receive provenance data from a first entity, wherein the provenance data comprises information as to a physical product's physical attributes;
generate a metadata file in a form of a script object notation including the provenance data for the physical product;
upload the generated metadata file…to generate a unique content identifier (CID) for the metadata file;
include the unique CID in a smart contract comprising a function configured to mint non-fungible tokens;
deploy the smart contract to a blockchain network to call the function;
generate, via calling the function, a non-fungible token specific to the physical product;
link the generated metadata to the generated non-fungible token by referencing the generated metadata using the unique CID;
provide access…
receive…infrared data associated with an intensity of infrared light emitted by a physical attribute of the physical attributes captured by an infrared sensor of the user device; and
based on received infrared data, determine validity of the physical product as compared to the physical product's physical attributes stored in the unique CID linked in the non-fungible token specific to the physical product, wherein determining validity of the physical product as compared to the physical product's physical attributes further comprises utilizing the intelligent feature recognition engine with at least one machine learning algorithm trained to recognize, from the infrared data, specific features of the physical product, including a woven pattern; and
wherein the physical product's physical attributes further comprise materials with different capacities for specific heat absorption configured to cause the physical product to emit a specific pattern of infrared thermal imaging due to boundaries of different materials woven into the physical product.
Generally, these limitations, describe a method and a system for identifying and generating an NFT for a physical product to associate and validate the product based on their attributes and image information. As disclosed in the specification in ¶0003, this invention provides “authenticity verification for products or services, particularly with regard to products or services provided in simulated environments and products in the real world that have some tie to simulated environments”. However, the abstract idea(s) of a certain method of organizing human activity (See MPEP 2106.04(a)(2), subsection II) is recited in claim 1 in the form of “commercial or legal interactions”. Specifically, the abstract idea is recited in the steps of “receiving” provenance data of a physical product attributes and other types of data to upload the data, “generate a unique content identifier (CID)” to it with a minting function in a smart contract that is “deployed” in the blockchain, to “call” the function and “generate” an NFT and link the metadata from the NFT to other types of data to “determine” validity of the physical product. Thus, these claims are encompassing advertising, marketing or sales activities or behaviors while ensuring good business relations by determining legitimate or authentic products via agreements/contracts and/or legal obligations (e.g. smart contracts) when later advertised and sold to customers.
Specifically, the steps of “link the generated metadata to the generated non-fungible token by referencing the generated metadata using the unique CID” and “based on received infrared data, determine validity of the physical product as compared to the physical product's physical attributes stored in the unique CID…” fall under the abstract idea of mental processes that can be practically be performed in the human mind or in pen and paper (See MPEP 2106.04(a)(2), subsection III). Because linking or referencing data with a unique CID and determine validity of the physical product based on the comparison of data collected requires evaluation and judgement. Also, these steps can either be done with the help of physical aid such as pen and paper or can be performed by humans without or with the assistance (e.g. tool) a computer. Thus, the steps do not negate and further still reads in the mental nature of the limitation(s), when associating and comparing such information, as well as the concept is merely claimed to be performed on a generic computer and is merely using a computer as a tool to perform the concept (see MPEP 2106.04(a)(2)(III)(B & C)).
Step 2A Prong 2: For independent claims 1, 8 and 14, The judicial exception(s) or abstract idea previously identified is not integrated into a practical application (see MPEP 2106.04 (d)). The claims recite the additional element(s) of one or more a processing device; a non-transitory storage device (from claim 1); A computer program product, a non-transitory computer- readable medium (from claim 8) a decentralized storage system, a blockchain network, a mobile application, a user device, an intelligent feature recognition engine and an infrared sensor of the user device and at least one machine learning algorithm trained (from claims 1, 8 and 14). These additional elements, individually and in combination, and while considering the claims as a whole, are merely used as a tool to perform the abstract idea (refer to MPEP 2106.05(f)). Specifically, at least the steps of “generate a metadata file in a form of a script object notation including the provenance data”, “upload the generated metadata file to a decentralized storage system to generate a unique content identifier (CID) for the metadata file”, “include the unique CID in a smart contract comprising a function configured to mint non-fungible tokens”, “deploy the smart contract to a blockchain network to call the function”, and “generate, via calling the function, a non-fungible token specific to the physical product” are recited as being performed by the computer and general blockchain technology such as minting functions. But also, the step of “…utilizing the intelligent feature recognition engine with at least one machine learning algorithm trained to recognize, from the infrared data, specific features of the physical product…” recited as being performed by the computer with the use of a machine learning (ML) techniques and trained algorithms. Thus, this step is merely used along with the computer (e.g. including “intelligent feature recognition engine” of the “mobile application” via the user device) as a tool to perform the abstract idea to recognize specific features of the physical product. The computer and the ML algorithm are recited at a high level of generality that are being used as a tool to perform the generic computer functions for generating and uploading metadata file with a generated and linked unique CID in smart contract that once deployed, mints the non-fungible tokens to generate them specifically for the physical product and further recognize the specific features of the physical product from infrared data to determine its validity. Thus, these steps mentioned above are further describing and applying the abstract idea without placing any limits on how the technological components are being improved, while distinguishing in the claim language, the performing limitations from functions that generic computer components, general ML algorithms and generic blockchain technology can perform.
As for the steps of “receive provenance data…”, “upload the generated metadata file…”, “provide access…” and “receive image data…” in all claims are really nothing more than links to computer for implementing the use of ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components (refer to MPEP 2106.05 f (2)).
Moreover, the steps of “deploy the smart contract to a blockchain network to call the function” to “generate…a non-fungible token specific to the physical product” and “based on received infrared data, determine validity of the physical product as compared to the physical product's physical attributes…further comprises utilizing the intelligent feature recognition engine with at least one machine learning algorithm trained to recognize, from the infrared data, specific features of the physical product….”, are also “merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application” (MPEP 2106.05(h)). In this case, the blockchain technology to “call a function configured to mint non-fungible tokens” is broadly recited and lacks details on how this “call” of the function to generate the NFT is specifically performed as well as the step of using an “intelligent feature recognition engine” from a “mobile application” with a ML algorithm trained to recognize features derived from infrared data for a physical product. All these steps are simply limited to creating the NFT on a blockchain network field and recognizing features from a physical product with recognition algorithms, specifically for infrared data, that attempts to limit the use of the abstract idea to computer environments and/or the blockchain along with ML techniques being used (see MPEP 2106.05(h) for examples (viii), (ix) and (x)). Therefore, this is indicative of the fact that the claim set has not integrated the abstract idea into a practical application and therefore, the claims are found to be directed to the abstract idea identified by the Examiner.
Step 2B: For independent claims 1, 8 and 14, The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Because these steps and their additional element(s) recited of one or more a processing device; a non-transitory storage device (from claim 1); A computer program product, a non-transitory computer- readable medium (from claim 8) a decentralized storage system, a blockchain network, a mobile application, a user device, an intelligent feature recognition engine and an infrared sensor of the user device and at least one machine learning algorithm trained (from claims 1, 8 and 14), including the step for “include the unique CID in a smart contract” with a “function configured to mint non-fungible tokens”, “deploy the smart contract” to call the function for the NFT generation and “determine validity of the physical product as compared to the physical product's physical attributes…utilizing the intelligent feature recognition engine with at least one machine learning algorithm trained to recognize…specific features of the physical product”. These additional elements are not sufficient to amount significantly more than the judicial exception or abstract idea (see MPEP 2106.05). Because, as indicated in Step 2A Prong 2, these additional element(s) claimed are merely, instructions to “apply” the abstract ideas, which cannot provide an inventive concept. Also, the recitation of a computer to perform the claim limitations amounts to no more than mere instructions to apply the exception using a generic computer component. Thus, even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer, which do not provide an inventive concept at Step 2B.
For dependent claims 2 – 3, 5, 7, 9 – 10, 12, 15 – 16, 18 and 20, these claims cover or fall under the same abstract idea of a method of organizing human activity and mental processes. They describe additional limitations steps of:
Claims 2 – 3, 5, 7, 9 – 10, 12, 15 – 16, 18 and 20: further describes the abstract idea of the method for the generation and monitoring of unique distributed token for resource verification and the unique code embedded in the physical product with different physical attributes including one of the various codes associated to the product that are determined to match or not if tampered to further flag the product and notify the user of a fake product and contact a merchant via a link access. Thus, being directed to the abstract idea group of “commercial or legal interactions” as it encompasses advertising, marketing or sales activities or behaviors while promoting of good business relations based on ensuring legitimate or authentic products via agreements/contracts and/or legal obligations when advertised and sold to customers via the system and involves observation, evaluation, judgment, and opinion for such product comparisons and legitimacy determinations.
Step 2A Prong 2 and Step 2B: For dependent claims 2 – 3, 5, 7, 9 – 10, 12, 15 – 16, 18 and 20, these claims do not include additional elements. Rather, what is claimed simply further defines the same abstract idea that was set forth in the independent claims. Nothing additional is claimed that is not part of the abstract idea.
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.
Claims 1 - 3, 8 - 10, 12 and 14 - 16 are rejected under 35 U.S.C. 103 as being unpatentable over Suk (U.S. Pub No. 20230145439 A1) in view of Damrow (U.S. Pub No. 20230351347 A1) in further view of Vosseller (U.S. Pub No. 20230109574 A1).
Regarding claims 1, 8 and 14:
Suk teaches:
a processing device; a non-transitory storage device containing instructions when executed by the processing device, causes the processing device to perform the steps of: (In ¶0027; Fig. 1 (100, 400 and 300): teaches “user terminal 100” that can access “service providing server 300” (directed to a processing device and a a non-transitory storage device) of “an application related to a real asset authentication service using a DID and NFT” and a “purchaser terminal 400 confirms a genuine product” with a “a photographing device”.)
receive provenance data from a first entity, wherein the provenance data comprises information as to a physical product's physical attributes; (In ¶0038; Fig. 5 (S4200); Fig. 6 (S5100): teaches that the “authenticity of a product, which is a real asset, is received from at least one certified authority server 500, when authentication information including a unique identification code of the product and a surface fingerprint according to surface characteristics of the product is stored in the authentication information database based on genuine information” which is directed to provenance data, in accordance to ¶00120 from applicant specs. Refer to ¶0066 and ¶0085 for more authentication information details.)
generate a metadata file in a form of a script object notation including the provenance data for the physical product; (In ¶0038; Fig. 5 (S4300); Fig. 6 (S5200): teaches that the “storage unit 310 may request generation of authentication information to be packed in an NFT and request issuance of the NFT by transmitting the genuine information to the authentication management company server upon receiving the genuine information from at least one certified authority server 500 to issue a surface fingerprint tag” wherein “the surface fingerprint may be a digitized surface fingerprint processed by magnifying all or some of the physical characteristics of the product image of the product” which is directed to the metadata file generation, in accordance to ¶0072 and ¶00121 from applicant specs. Refer to ¶0048 wherein the “registration unit 320 may pack the authentication information stored in the authentication information database in the NFT and then register the NFT authentication information in the block chain”.)
upload the generated metadata file to a decentralized storage system to generate a unique content identifier (CID) for the metadata file; (In ¶0038; Fig. 5 (S4300 – 4500); Fig. 6 (S5200): teaches that “the storage unit 310 may store, in an authentication information database, authentication information including a unique identification code of a product and a surface fingerprint according to surface characteristics of the product based on genuine information confirmed by at least one certified authority server 500 when authenticity of the product, which is a real asset, is determined”, in accordance to ¶00123 from applicant specs. Refer to ¶0080 wherein after the generated authentication information is stored in the database and packed with the NFT (e.g. directed to a decentralized storage system), it is also registered in the block chain network. Refer to ¶0033 wherein the system’s server uses a “web page, an app page, a program, or an application related to a real asset authentication service using a DID and NFT to store a surface fingerprint, product information, a product image, a unique identification code (serial number), etc., so that authentication information is packed in the NFT and issue the NFT”)
include the unique CID in a smart contract comprising a function configured to mint non-fungible tokens; (In ¶0058; Fig. 5 (S4300 – S4400): teaches that the authentication information that includes “a unique identification code and a surface fingerprint” (directed to the CID) is stored and packed in the NFT when issuing the NFT (see Fig. 5, S4300 – S4400; ¶0080) which is requested by a user when selling the real asset or product and a “smart contract” is created in response (directed to mint the NFTs) and contains these unique identifier in its contents which are related to the “NFT transaction”.)
deploy the smart contract to a blockchain network to call the function; (In ¶0058 Fig. 5 (S4500): teaches that “after creating the smart contract, the smart contract may be distributed on the block chain network to confirm whether the NFT transaction progresses smoothly” based on the “transaction conditions” of the smart contract (e.g. contents) being met.)
generate, via calling the function, a non-fungible token specific to the physical product; (In ¶0080; Fig. 5 (S4400 – 4500); Fig. 6 (S5200 – 5300): teaches that after packing “the generated information in the NFT to issue the NFT (S4400)”, the “NFT issued in this way is transmitted to the authentication service providing server 300, and the authentication service providing server 300 registers the NFT in the block chain network along with the public key (S4500)”. Refer to ¶0058 wherein the system applies a “NFTracer” which is a “proof-of-concept of NFT tracking based on a block chain” that is used during a “transfer of ownership of art, it is implemented as a token to confirm authenticity according to a cryptographic signature and a timestamp, and the transaction thereof is processed as a smart contract” (see ¶0050).)
link the generated metadata to the generated non-fungible token by referencing the generated metadata using the unique CID; (In ¶0080 – 81; Fig. 5 (S4500 – 4700); Fig. 6 (S5300): teaches that “the authentication service providing server 300 registers the NFT in the block chain network along with the public key (S4500)”, wherein the packed NFT stores its authentication information that includes “a unique identification code and a surface fingerprint” (directed to the CID; see ¶0080), directed to the unique content identifier (CID). Then, the system “transmits the NFT to the user terminal 100 (S4510)” which can further request a comparison of “the surface fingerprint in the NFT registered in the block chain with the surface fingerprint in the NFT uploaded in the NFT transaction platform (S4700) and transmits the comparison result to the purchaser terminal 400” to “confirm whether the product is genuine” (see ¶0081) which is directed to referencing the metadata with a UCI.)
provide access to a mobile application via a user device, wherein the mobile application comprises an intelligent feature recognition engine; (In ¶0081; Fig. 5 (S4600 – 4710): teaches that “the authentication service providing server 300 provides the NFT transaction platform by itself” to the “user terminal”. Refer to ¶0052 – 56 for details regarding the “Image Similarity Analysis Model Using Deep Learning” (directed to the intelligent feature recognition engine, in accordance to ¶00126 from Applicant specs) which further uses a CNN that is built and trained for extracting “features of images and analyze similarity” in a real asset versus its NFT and refer to ¶0063 for the provision of “a platform for registering and transacting products based on the NFT after analyzing similarity using the deep learning to block the risk of registering an NFT for a counterfeit or a forgery.”)
based on received infrared data, determine validity of the physical product as compared to the physical product's physical attributes stored in the unique CID linked in the non-fungible token specific to the physical product, (In ¶0081; Fig. 5 (S4700 and S4900 – 4930): teaches that “when the delivery completion event is output from a delivery driver terminal (not illustrated), the purchaser terminal 400 confirms whether the photographing device is interlocked (S4900) and then performs control to photograph the surface fingerprint (S4900)” (e.g. directed to physical product's physical attributes) and then, “in operation S4700, the surface fingerprint photographed in this way may be output from the purchaser terminal 400 through the surface fingerprint comparison process (S4910) according to whether the product is genuine or not (S4930)”. Refer to ¶0087 for more details about authentication of an original product “when the surface fingerprint packed in the NFT registered in the block chain matches the surface fingerprint photographed by the purchaser terminal (S5400)”. See ¶0027 for more details about the “the authentication service providing server” that compares the “surface fingerprint in the NFT” with the product purchased)
wherein determining validity of the physical product as compared to the physical product's physical attributes further comprises utilizing the intelligent feature recognition engine with at least one machine learning algorithm trained to recognize, from the infrared data, specific features of the physical product, including a woven pattern; and (In ¶0052: teaches this claim limitation under the broadest reasonable interpretation (BRI), as the system includes an “image copy detection system based on a convolutional neural network (CNN) model” developed that can also be trained by “automatically extracting shape values of each image” that is further “converted into 44 different formats” (e.g. interpreted as recognizing infrared data with specific features) to detect “duplicate images by rotation, scaling, and other content manipulations” which can be used in addition to the “verifying that a product is genuine in the certified authority server 500” and the analysis of “similarity with a genuine product image for a real asset using deep learning” (i.e. directed to the specific features of a physical product such a woven patterns) using the CNN model (see ¶0054), in accordance to the example given in ¶0013 – 14 for the “intelligent feature recognition” claimed and ¶0082 for pattern forms of scannable region from Applicant disclosure. Moreover in ¶0055 – 56, the CNN models can be modified to detect similar designs during the “similarity test” wherein similarity is analyzed by extracting “features of images” and building the CNN model with a training data set. As for the specific feature of a woven patterns from the infrared data of a physical product, this non-functional descriptive matter does not hold any patentable weight and the reference system, in ¶0038 already stores a “real asset” including the “surface fingerprint” which may be “unique surface characteristics of a real thing itself” that further is “a mark of an artificially generated unique fingerprint pattern” that can be any type of pattern “identified in an a surface fingerprint” by utilizing “one image processing technology of a flat image, a three-dimensional image, a perspective (x-ray) image, and a holographic image” (see ¶0018).)
Suk does not explicitly teach the ability of generating the metadata file in a specific form of a script object notation. However, Damrow teaches:
generating a metadata file in a form of a script object notation… (In ¶0079; Fig. 2 (240): teaches that the “blockchain storage 156” includes the physical object and their corresponding NFT, their “digital twin” or” object file” along with its related metadata (see ¶0078) and their “smart contract” (see ¶0053 for more details) which are “programmable scripts” that “can automate various processes and actions, such as royalty payments, access control, and verification of digital identifiers and credentials”. But also, the smart contract is a “script or code” configured to “combine one or more attributes of the physical or digital asset together to create a single control structure for each metadata object” and can be “written in any form of programming language” (directed to JavaScript language or JSON, in accordance to ¶0072 and ¶00121 from Applicant specs) which is directed to the generation of a metadata file in the specific form of a script object notation (see ¶0098 and ¶0059 for more details).)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Suk to provide the ability of generating the metadata file in a specific form of a script object notation, as taught by Damrow in order to deploy the written smart contract control structure “as a stand-alone program or as a circuit, component, subroutine, object, or other unit suitable for use in a computing environment” (¶0098; Damrow).
Neither Suk or Damrow explicitly teach the abilities of specifically receiving infrared data related to intensity of infrared light emitted by a physical attribute captured by an infrared sensor of the user device and specifically having physical product’s physical attributes that further comprise of materials with heat absorption capacities with specific heat absorptions in order to emit specific pattern of infrared thermal imaging. However, Vosseller teaches:
receive, via the mobile application, infrared data associated with an intensity of infrared light emitted by a physical attribute of the physical attributes captured by an infrared sensor of the user device; and (In ¶0042; Fig. 2 (202); Fig. 5 (504 – 506): teaches that “the fingerprint capture system 130 is depicted obtaining sensor-captured features 202 of physical item 204” wherein the “sensor-captured features 202 may include, but are not limited to, images (e.g., high-resolution images of the physical item 204's features), videos of the physical item 204, data derived from various electromagnetic spectrum features captured by the sensors about the physical item 204, measured temperatures at different locations of the physical item 204 (or a map of them), a LIDAR scan of the physical item 204, or measurements (or estimated values) of one or more elements or compounds at different locations of the physical item 204”. Thus, at least the “electromagnetic spectrum features captured”, “measured temperatures at different locations of the physical item 204” and the “LIDAR scan of the physical item 204, or measurements (or estimated values) of one or more elements or compounds at different locations of the physical item 204” are interpreted as infrared data that includes intensity of infrared light emitted by a physical attribute. The sensors used by the system are “imaging sensors (e.g., one or more high-resolution digital cameras, one or more low-resolution digital cameras), temperature sensors, LIDAR, biochemical sensors”, etc.)
wherein the physical product's physical attributes further comprise materials with different capacities for specific heat absorption configured to cause the physical product to emit a specific pattern of infrared thermal imaging due to boundaries of different materials woven into the physical product. (In ¶0044; Fig. 2 (202); Fig. 5 (504 – 506): this descriptive matter about the physical product’s physical attributes having materials with heat absorption capacities does not hold patentable weight and is not part of the invention scope. However, this prior art satisfies this limitation under the broadest reasonable interpretation (BRI) as “the sensor-captured features 202 may include, but are not limited to, images (e.g., high-resolution images of the physical item 204's features), videos of the physical item 204, data derived from various electromagnetic spectrum features captured by the sensors about the physical item 204, measured temperatures at different locations of the physical item 204 (or a map of them), a LIDAR scan of the physical item 204, or measurements (or estimated values) of one or more elements or compounds at different locations of the physical item 204, to name just a few”. Refer to ¶0047 wherein the system further compares “the digital fingerprint 206 and the distinguishing feature data 138” to identify “those authentic items and/or differentiating them from items that are not authentic (e.g., knockoffs)” based on the “sensory capacity to detect one or more of the same features and/or compare digital fingerprints to the distinguishing feature data 138 at the level required to identify a physical item as authentic” wherein “different types of sensors” can be used such as “imaging sensors, to capture features of the physical item, including temperature sensors, LIDAR, and biochemical sensors” which can detect the specific pattern emission of infrared thermal imaging claimed (see ¶0062).)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Suk and Damrow to provide the abilities of specifically receiving infrared data related to intensity of infrared light emitted by a physical attribute captured by an infrared sensor of the user device and specifically having physical product’s physical attributes that further comprise of materials with heat absorption capacities with specific heat absorptions in order to emit specific pattern of infrared thermal imaging, as taught by Vosseller in order to identify “those authentic items and/or differentiating them from items that are not authentic (e.g., knockoffs)” and achieve the “sensory capacity” that humans don’t have to “detect one or more of the same features and/or compare digital fingerprints to the distinguishing feature data 138 at the level required to identify a physical item as authentic” wherein such level may be in a more granular, precise and accurate level (¶0047; Vosseller).
Regarding claims 2, 9 and 15:
The combination of Suk, Damrow and Vosseller, as shown in the rejection above, discloses the limitations of claims 1, 8 and 14, respectively.
Suk further teaches:
wherein the physical product's physical attributes further comprise a unique code embedded into the physical product during its manufacturing process. (In ¶0066: teaches the “basic information for a surface of a product, for example, a product name, a date of manufacture, a manufacturer, a country of manufacture, a warranty period, a product size, an ingredient or material of a product, external characteristic information of a product such as color, size, and morphological characteristics, and include a subject who generates authentication information for a product, a uniform resource identifier (URI) capable of confirming information on the product or a service access address for original authentication, a QR code including physical characteristic information, the entire image or at least a portion of an image for an authentication area, a digitized surface fingerprint image showing the authentication area, the authentication points, and the physical arrangement relationship in one picture, and at least one of a certificate or a warranty including information for authentication”, in accordance to ¶0072 and ¶00121 from applicant specs.)
Regarding claims 3, 10 and 16:
The combination of Suk, Damrow and Vosseller, as shown in the rejection above, discloses the limitations of claims 1, 8 and 14, respectively.
Suk further teaches:
wherein the physical product's physical attributes further comprise a barcode, QR code, RFID tag, unique set of threads or rivet patterns, or physical marker uniquely associated with the physical product. (In ¶0066: teaches the “basic information for a surface of a product” such as “a QR code including physical characteristic information”. Refer to ¶0018 and ¶0038 wherein the “surface fingerprint” of a physical product include different types of patterns as a “an artificially produced mark, pattern, or the like”.)
Regarding claims 4, 11 and 17:
The combination of Suk, Damrow and Vosseller, as shown in the rejection above, discloses the limitations of claims 1, 8 and 14, respectively.
Suk further teaches:
wherein determining validity of the physical product as compared to the physical product's physical attributes further comprises utilizing the intelligent feature recognition engine with at least one machine learning algorithm trained to recognize specific features of the physical product, including color, shape, texture, or specific design elements. (In ¶0052: teaches that the system includes an “image copy detection system based on a convolutional neural network (CNN) model” developed that can also be trained by “automatically extracting shape values of each image” to detect “duplicate images by rotation, scaling, and other content manipulations” which can be used in addition to the “verifying that a product is genuine in the certified authority server 500” (see ¶0054) and the analysis of “similarity with a genuine product image for a real asset using deep learning” using the CNN model.)
Regarding claims 21 – 23:
The combination of Suk and Damrow, as shown in the rejection above, discloses the limitations of claims 1, 8 and 14, respectively.
Suk teaches the receiving and stored “surface fingerprint” of a “real asset” that may be a “mark of an artificially generated unique fingerprint pattern” (see ¶0021, ¶0038 and ¶0065; Suk). However, neither Suk or Damrow explicitly teach the ability of specifically having physical product’s physical attributes that further comprise of materials with heat absorption capacities in order to emit specific pattern of infrared thermal imaging. However, Vosseller further teaches:
wherein the physical product's physical attributes further comprise materials with different capacities for specific heat absorption configured to cause the physical product to emit a specific pattern of infrared thermal imaging. (In ¶0044; Fig. 2 (202); Fig. 5 (504 – 506): this descriptive matter about the physical product’s physical attributes having materials with heat absorption capacities does not hold patentable weight and is not part of the invention scope. However, this prior art satisfies this limitation under the broadest reasonable interpretation (BRI) as “the sensor-captured features 202 may include, but are not limited to, images (e.g., high-resolution images of the physical item 204's features), videos of the physical item 204, data derived from various electromagnetic spectrum features captured by the sensors about the physical item 204, measured temperatures at different locations of the physical item 204 (or a map of them), a LIDAR scan of the physical item 204, or measurements (or estimated values) of one or more elements or compounds at different locations of the physical item 204, to name just a few”. Refer to ¶0047 wherein the system further compares “the digital fingerprint 206 and the distinguishing feature data 138” to identify “those authentic items and/or differentiating them from items that are not authentic (e.g., knockoffs)” based on the “sensory capacity to detect one or more of the same features and/or compare digital fingerprints to the distinguishing feature data 138 at the level required to identify a physical item as authentic” wherein “different types of sensors” can be used such as “imaging sensors, to capture features of the physical item, including temperature sensors, LIDAR, and biochemical sensors” which can detect the specific pattern emission of infrared thermal imaging claimed (see ¶0062).)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Suk and Damrow to provide the ability of specifically having physical product’s physical attributes that further comprise of materials with heat absorption capacities in order to emit specific pattern of infrared thermal imaging, as taught by Vosseller in order to identify “those authentic items and/or differentiating them from items that are not authentic (e.g., knockoffs)” and achieve the “sensory capacity” that humans don’t have to “detect one or more of the same features and/or compare digital fingerprints to the distinguishing feature data 138 at the level required to identify a physical item as authentic” wherein such level may be in a more granular, precise and accurate level (¶0047; Vosseller).
Claims 5, 7, 12, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Suk (U.S. Pub No. 20230145439 A1) in view of Damrow (U.S. Pub No. 20230351347 A1) in further view of Vosseller (U.S. Pub No. 20230109574 A1) and Chen (U.S. Pub No. 20220215382 A1).
Regarding claims 5, 12 and 18:
The combination of Suk, Damrow and Vosseller, as shown in the rejection above, discloses the limitations of claims 2, 9 and 15, respectively.
Suk further teaches:
determine one or more product features do not match or the unique code has been tampered with or is not recognized; flag the physical product as potentially fake; (In ¶0046: teaches this conditional limitations as the “Genuine Product-Counterfeit Determination” process wherein the user of “a certified authority” (e.g. an employee from the certified authority) can use the process for a “measure for determining the input image as a genuine product or a counterfeit with reference to an original image” with a “first comparative measure” such as “a mean absolute difference (MAD)” (see ¶0040 – 41). Further, this method includes “an image pre-processing process of recognizing some or all of captured images using the camera of the certified authority server 500 and cutting and converting the recognized captured images to a resolution of a certain size” that “when a product is determined as a genuine product or a counterfeit through primary discrimination using the obtained optimal mean absolute difference, according to the determination result, a discrimination message is transmitted to an employee in a certified authority” which is directed to flagging the physical product as potentially fake.)
generate a notification containing a rationale that the physical product is potentially fake; and transmit the notification via the mobile application, (In ¶0047: teaches, under the broadest reasonable interpretation BRI, that “after obtaining a score of the MAD for the unrecognized image, a score is given using the number of large error pixels and the SSIM value” for their distance in coordinates wherein if “the score is 4 or more and the “genuine” message is −4 or less, when the “fake” message has a value between −4 and 4, the re-photograph message may be transmitted to the employee of the certified authority” which is directed to the generation and transmission notification for the physical product is potentially fake rationale to the user.)
Neither Suk, Damrow or Vosseller explicitly teach the ability of further include a link to contact a merchant from which the physical product originated in the notification. However, Chen teaches:
wherein the notification further comprises a link to contact a merchant from which the physical product originated. (In ¶0028 – 29; Fig. 5B and Fig. 7 (730 – 735): teaches that when “the product verification system authenticates the instance of the product, the product verification system may provide the user device access to the data associated with the corresponding token retrieved from the product blockchain” such as “supply chain data added by different entities within the supply chain for producing the instance of the product” and “show the entities involved in the supply chain of manufacturing the instance of the product” which under the broadest reasonable interpretation (BRI) is directed to a link to contact a merchant. Refer to ¶0035 – 36 wherein the “manufacturer and/or the entities in the supply chain” as well as “other entities” can include “records of the maintenance” for future maintenance proof, or other “new content to the blockchain in association with the instances of the product for consumption by the owners or other people who scan the codes” for transmitting promotional data or advertisements.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Suk, Damrow and Vosseller to provide the ability of further include a link to contact a merchant from which the physical product originated in the notification, as taught by Chen in order to provide “an enhanced user experience in association with the instance of the product” and for the manufacturers to “directly communicate with their consumers and others who may be interested in their products (e.g., the people who scan the codes associated with different instances of the product)” (¶0032 and ¶0036; Chen).
Regarding claims 7 and 20:
The combination of Suk, Damrow, Vosseller and Chen, as shown in the rejection above, discloses the limitations of claims 5 and 18, respectively.
Neither Suk, Damrow or Vosseller explicitly teach the ability of further include a link to verified merchant(s) in the notification. However, Chen further teaches:
wherein the notification further comprises a link to one or more verified merchants. (In ¶0036; Fig. 5B and Fig. 7 (730 – 735): teaches under BRI, the notifications with link to verified merchants since “the product verification system authenticates the instance of the product, the product verification system may provide the user device access to the data associated with the corresponding token retrieved from the product blockchain” (which is already authenticated along with the product related content; see ¶0028) and includes content from the “manufacturer and/or the entities in the supply chain” as well as “other entities” that can be accessed via codes (directed to the link) by first consumers or new owners of the product to view their information.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Suk, Damrow and Vosseller to provide the ability of further include a link to verified merchant(s) in the notification, as taught by Chen in order to provide “an enhanced user experience in association with the instance of the product” and for the manufacturers to “directly communicate with their consumers and others who may be interested in their products (e.g., the people who scan the codes associated with different instances of the product)” (¶0032 and ¶0036; Chen).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
BARHUDARIAN (U.S. Pub No. 20230135947 A1) is pertinent because it is “related to a non-fungible physical fabric token (NFPFT) system, including a piece of smart fabric coupled to a physical item.”
Rubinson (U.S. Pub No. 20240346504 A1) is pertinent because it “generally relate to systems, methods, and computer storage media for, among other things, that provide for the minting of one or more non-fungible tokens (NFTs) using one or more location verification devices.”
Norton (U.S. Patent No. 11720888 B2) is pertinent because it “generally relate to the validation and transfer of assets and, more particularly, to a distributed ledger system for recording asset provenance and titling information.”
Rymer (U.S. Pub No. 20240062190 A1) is pertinent because it “relate to digital tokens that correspond to other assets, and more specifically to the generation and maintenance of digital tokens on a blockchain.”
Madhusudhan (U.S. Pub No. 20230070389 A1) is pertinent because it “relates to systems and methods for tracking the authenticity of assets. The present disclosure particularly relates to the system and method for creating Non-Fungible Token (NFT) from physical assets and validating the authenticity of digital assets and physical assets associated with an NFT.”
Gagne-Keats (U.S. Pub No. 20230237483 A1) is pertinent because it “generally relate to digital non-fungible assets in persistent virtual environments linked to physical assets in the real world.”
Testagrossa (U.S. Pub No. 20220345316 A1) is pertinent because it “relates generally to authenticating physical assets.”
Blackburn (U.S. Pub No. 20220114600 A1) is pertinent because it is a “system for verification and management of non-fungible tokens (NFT) that are assets themselves, digital asset, or a digital asset paired to a physical asset.”
Matthews (U.S. Pub No. 20230073859 A1) is pertinent because it is “a system and techniques for listing N[F]Ts associated with physical items.”
Jakobsson (U.S. Pub No. 20220398340 A1) is pertinent because it “relates generally to computer security and more specifically to systems and methods for securely accessing encrypted user profile data automatically accumulated using entries in immutable ledgers.”
Jackson (U.S. Pub No. 20230205849 A1) is pertinent because it is about “techniques that facilitate digital asset tracking of digital media files, using non-fungible tokens (NFTs) stored on a distributed ledger.”
Kalaldeh (U.S. Pub No. 20230045071 A1) is pertinent because it “relates, in general, to methods, tools and systems for Non-Fungible Tokens (NFTs) specifically created for representing physical items.”
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/IVONNEMARY RIVERA GONZALEZ/Examiner, Art Unit 3626
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626