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 .
DETAILED ACTION
This communication is a Non-Final Office Action in response to communications received on 10/8/25.
Claims 1, 6-7, 25, 31 have been amended.
Claims 8-17, 24 have been previously cancelled.
Therefore, Claims 1-7, 18-23, 25-31 are now pending and have been addressed below.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 10/8/25 has been entered.
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 1-7, 18-23, 25-31 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1: Identifying Statutory Categories
In the instant case, Claims 1-7, 18-23, 25-31 are directed to a system.
Step 2A: Prong 1 Identifying a Judicial Exception
Under Step 2A, prong 1, Claims 1-7, 18-23, 25-31 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claim 1 recites system that receive the plurality of item listings from one or more users corresponding to a plurality of items submitted for sale in an electronic marketplace that lists a plurality of sale items for selection and purchase by one or more buyers; store at least photo information of the plurality of items; wherein the photo information is received; prior to listing an item listing among the plurality of item listings, corresponding to an item among the plurality of items, as one of the plurality of sale items for selection and purchase: extract features of the item and to determine a risk measure for the item based on the features including at least the photo information of the item; wherein each of the plurality of entries includes photo information and a label indicating a genuine or counterfeit item; placing the item listing into a first-level queue in a hierarchical plurality of queues for evaluation by an expert and obtaining a validation status from the expert, wherein the validation status indicates a genuine or counterfeit item; deciding, based on the risk measure, whether to place the item listing into a second- level queue for additional evaluation and update of the validation status by a second expert; updating an entry associated with the item with the validation status and approving listing the item in the electronic marketplace in accordance with the validation status
These limitations as drafted, are a process that, under its broadest reasonable interpretation, covers methods of organizing human activity (including commercial interactions such as business relations, managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)), but for the recitation of generic computer components. That is, other than reciting the structural elements (such as a distributed database, an interface component, one or more processors, machine learning engine), the claims are directed to providing validation status for item as being genuine or counterfeit. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of organizing human activity but for the recitation of generic computer components, the claim recites an abstract idea.
Step 2A Prong 2 - This judicial exception is not integrated into a practical application because the claim merely describes how to generally “apply” the concept of receiving item data, analyzing it, and providing verification status. In particular, the claims only recites the additional element – a distributed database, an interface component, one or more processors, machine learning engine. The additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Simply implementing the abstract idea on generic components is not a practical application of the abstract idea. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. a) The additional elements merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). The claims are directed to an abstract idea. Furthermore, such applying of a ML model is no more than putting data into a black box machine learning operation, devoid of technological implementation and application details. Each step requires a generic computer to perform generic computer functions. When considered in combination, the claims do not amount to improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a), applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b), effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c), or applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they does not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
Step 2B: Considering Additional Elements
The claimed invention is directed to an abstract idea without significantly more. The claim does not include additional elements that are sufficient to amount significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claims describe how to generally “apply” to; providing verification status for item as being genuine or counterfeit. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. The claims are not patent eligible. The dependent claim(s) when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail to establish that the claim(s) is/are not directed to an abstract idea. The dependent claims are not significantly more because they are part of the identified judicial exception. See MPEP 2106.05(g). The claims are not patent eligible. With respect to a distributed database, an interface component, one or more processors, machine learning engine, these limitations are described in Applicant’s own specification as generic and conventional elements. See Applicants specification, Paragraph [0057] details “ user interface as API, [0017] distributed computer system 100.[0013] machine learning engine to determine risk” These are basic computer elements applied merely to carry out data processing such as, discussed above, receiving, analyzing, transmitting and displaying data. Furthermore, the use of such generic computers to receive or transmit data over a network has been identified as a well understood, routine and conventional activity by the courts. See Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AVAuto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result-a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added)); Also see MPEP 2106.05(d) discussing elements that the courts have recognized as well-understood, routine and conventional activities in particular fields. Lastly, the computing device provides only a result-oriented solution which lacks details as to how the computer performs the claimed abstract idea. Therefore the processor/device amounts to mere instructions to apply the exception. See MPEP 2106.05(f).
Furthermore, these steps/components are not explicitly recited and therefore must be construed at the highest level of generality and are well-understood, routine and conventional limitations that amount to mere instructions to implement the abstract idea on a computer. Therefore, the claimed invention does not demonstrate a technologically rooted solution to a computer-centric problem or recite an improvement to another technology or technical field, an improvement to the function of any computer itself, applying the exception with, or by use of, a particular machine, effect a transformation or reduction of a particular article to a different state or thing, add a specific limitation other than what is well-understood, routine and conventional in the field, add unconventional steps that confine the claim to a particular useful application, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment such as computing. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. Taking the additional claimed elements individually and in combination, the computer components at each step of the process perform purely generic computer functions. Viewed as a whole, the claims do not purport to improve the functioning of the computer itself, or to improve any other technology or technical field. Use of an unspecified, generic computer does not transform an abstract idea into a patent-eligible invention. Thus, the claim does not amount to significantly more than the abstract idea itself. Further, claims to a system and computer-readable storage medium are held ineligible for the same reason, e.g., the generically-recited computers add nothing of substance to the underlying abstract idea.
Dependent claims 2-7, 18-23, 25-31 add additional limitations, for example but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as representative claim 1.
Claims 2-5 recites the expert is permitted to select the item submitted by the respective user from the queue of the submitted items for sale; assign a review status of the item to the expert and remove the item from the queue; determine a matching of at least one of the submitted items for sale to the expert; determine a matching of the item to be verified with a second one of the plurality of experts, and conducting a second review of the item to be verified. These limitations of merely adds the words apply it (or an equivalent) with the judicial exception , or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea as discussed in MPEP 2106.05(f). Claims 6-7 recite database of items for genuine and counterfeit. Claim 18 recite determining a match score and determine a risk measure/risk score based on matching. These limitations are directed to abstract idea of mathematical calculations. Claims 19-23, 29-31 recites user criteria for counterfeit items; user interface to submit photos; determine category. Claims 25-28 recites a machine learning model and trained machine learning engine using dataset, designer identification, category of item and user criteria. These limitations merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Further, the limitation of “train the machine learning system” is simply application of a computer model, itself an abstract idea. Furthermore, such training and applying of a model is no more than putting data into a black box machine learning operation. The machine learning model is a functional label, devoid of technological implementation and application details. Each step requires a generic computer to perform generic computer functions.
The dependent claims do not integrate into a practical application. As such, the additional elements individually or in combination do not integrate the exception into a practical application, but rather, the recitation of any additional element amounts to merely reciting the words “apply it” (or equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (See MPEP 2106.05(f)). The dependent claims also do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a computing system is merely being used to apply the abstract idea to a technological environment. These limitations do not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. See MPEP 2106.05d. Thus, the claims do not add significantly more to an abstract idea. The claims are ineligible. Therefore, since there are no limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself, the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter. See (Alice Corporation Pty. Ltd. v. CLS Bank International, et al.).
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, 18-23, 25-31 are rejected under 35 U.S.C. 103 as being unpatentable over Hedges (US 2010/0241528 A1) in view of Burgin et al. (US 2019/0236614A1), further in view of McKenzie (US 2022/0051040A1)
Regarding Claim 1. Hedges discloses the system for permitting an expert to gather information on a plurality of listing items and select one or more of the plurality of items listings for publication ([0047] an analysis of the listings of the items for sale may be performed by any entity, person, or computing system to verify the authenticity of the item, [0060] The human being may perform the verification process by manually analyzing each listing or a selected group of listings.) comprising:
Hedges discloses an interface component configured to receive a plurality of item listings from one or more users corresponding to a plurality of items, submitted for sale in an electronic marketplace that lists a plurality of sale items for selection and purchase by one or more buyers (Fig 5 # 500 access online auction product listings (electronic marketplace), #502 search product listing by category, Fig 6 shows product listing (interface component), [0022] The auctioneer's server computer may permit a seller to create a listing of an item for sale. It may also allow a buyer to search among various posted listings for a particular item and to place a bid to purchase an item in an auction-style sale., [0036] the user may access the web page of the online auction by sending a request over the Internet to the web-based server to view a web page that permits the user to upload (electronically transfer the data file) the image of the item for sale to a web page that includes a listing of the item. The listing may become the webpage that other users may access during the auction process to view the item that is for sale., [0050]);
Hedges discloses wherein the photo information is received by the interface component ([0045] the seller may be required to or optionally choose to enter descriptive text about the item 202, upload photographs or other images of the item 204, and enter the product's unique identifier 206., [0050] the seller may enter descriptive text about the item 202, be permitted to upload photographs or other images of the item 204, and enter the product's unique identifier 206, Fig 8 shows image/photo of item on listing, [0022] Both buyers and sellers may have user profiles that include general personal and contact information, user preferences, payment information, and the like. Any suitable information may be stored on the computing system for access over the Internet by its users (i.e., buyers, sellers, browsers, etc.).): and
Hedges discloses one or more processors configured to implement, prior to listing an item listing among the plurality of item listings, corresponding to an item among the plurality of items, as one of the plurality of sale items for selection and purchase ([0044] FIG. 1, in order for a user to buy and/or sell an item, an online auctioneer may require the user to create a user profile 100. The user profile may include such user information as the user's name, date of birth, physical address, electronic mail address, telephone number, and the like. The user may select a username and password that is to be associated with the user's profile. [0045] Once the user has decided to sell an item, the online auction website may require that the user proceed through a plurality of steps that solicit information from the seller about the item to be sold to create a listing for the item 104 Fig 1 # 100-108 analyze the item listing for authenticity of the item, [0060] the auctioneer, the manufacturer of the item, a buyer, a monitoring agency, the government, or any combination thereof may analyze a listing to verify the authenticity of the item being sold. Fig 6 create a listing ): obtaining a validation status from the expert, wherein the validation status indicates a genuine or counterfeit item ([0060] The human being (expert) may perform the verification process by manually analyzing each listing or a selected group of listings. The computing system may perform the verification process by implementing software that identifies possible counterfeit items based on the contents of the listings. The human and the computing system may perform these methods either manually or automatically and the criteria may be based on any characteristics of the listing, including but not limited to the brand and the product code of the item for sale. The computing system may also automatically provide a report of the analysis that includes a listing of the auctions containing counterfeit (and/or potentially counterfeit/suspicious) items.)
Hedges discloses listing the item in the electronic ([Fig 5 # 510 identify counterfeit foot ware, [0059] An analysis of the authenticity of the footwear may be performed by performing a search for the footwear's brand and/or product code and determining whether the footwear is authentic based on the information contained in the listing. When the footwear description does not match the brand and/or the product code, the footwear may be identified as counterfeit footwear. In another example, if the product code is either invalid (does not match a list of known product codes for a manufacturer or brand) or the product code provided by the seller does not match the item being sold, then the footwear may be identified as a counterfeit item., [0071] Once a listing or group of listings is found in a particular search, the manufacturer may analyze each listing to determine whether the items being sold are counterfeit items. For example, the manufacturer may first verify that the product code is authentic, e.g., that the entered code is a code actually used by the manufacturer or that the code is indeed associated with the particular kind of item being offered for sale. If the code is authentic, further analysis of the listing may be performed, such as verifying that the photographs or images of the footwear match the style number (and/or the product code) for that article of footwear. Additionally, the color code may also be matched to the seller's image or photograph of the footwear and/or the descriptive text of the color entered by the seller. [0047] an analysis of the listings of the items for sale may be performed by any entity, person, or computing system to verify the authenticity of the item 108. If the item is verified to be an authentic item, no further action is taken and the transaction proceeds to completion 110. Sometimes items for sale in an online auction are identified as counterfeit items or a may be identified as potentially counterfeit items 112. When a counterfeit item is identified or an item is identified as a potentially counterfeit item, the item listing may be removed from the online auction website, [0010] If and when a counterfeit item is discovered on an online auctioneer's website, it may be removed and the seller may be reprimanded or banned from using the auction services. Significant efforts are necessary to monitor the listing on the online auctioneer's websites for the sale of counterfeit goods. Oftentimes, this monitoring process includes meticulous analysis of the listings on an individual basis by a person (i.e., a person is tasked to check individual products' listings and perform an analysis to determine whether the product is deemed counterfeit or fraudulent)., Fig 4 # 408 post listing with brand and product code and [0057] The auctioneer may query the user for the product information described above relating to the footwear that is being sold 402. In some examples, the auctioneer may require that the seller enter the brand of the footwear and/or the footwear's product code (unique identifier) 406. The listing of the footwear may be posted with the footwear's brand and product code 408., Fig 5 # 510,512 resolve sale of counterfeit footwear. [0065] the online auction website could combat this problem by requiring that a known brand is selected prior to permitting the posting of a listing that includes a sub-brand (e.g., if only a sub-brand is included in the listing, the online auctioneer will not post the listing on the auction website, but rather will require additional information from the seller prior to posting the listing)); updating an entry, in the distributed database, associated with the item with the validation status ([0064] Such a listing may then be posted for online auction without any reference to the brand or the product code associated with the footwear. When such a listing is discovered, the auctioneer may place the listing on hold until the seller enters the necessary information. If and when the seller is able to provide the necessary authenticating information, then the listing may become active again in the online auction)
Hedge does not specifically teach a distributed database configured to store at least photo information of the plurality of items; executing a machine learning engine to extract features of the item and to determine a risk measure for the item based on the features including at least the photo information of the item, wherein the machine learning engine is trained on a plurality of entries on the distributed database, wherein each of the plurality of entries include photo information and a label indicating a genuine or counterfeit item; placing the item listing, based on the risk measure, into a first-level queue in a hierarchical plurality of queues for evaluation by an expert and obtaining a validation status from the expert,
Burgin teaches a distributed database configured to store at least photo information of the plurality of items ([0035] supervised learning is done through a database maintained containing item information such as description and unique identifier information. This is linked to photographs and text examples and explanations of counterfeiting of that item. This is in addition to images and text descriptions of the genuine item.) wherein the photo information is received by the interface component ([0039] The client application environment 150 may include a camera 151 to capture an image of an object 170 to determine whether the object 170 is counterfeit. The mobile application 152 may be launched to facilitate taking a picture of the object 170.); executing a machine learning engine to extract features of the item and to determine a risk measure for the item based on the features including at least the photo information of the item ([0029] For the supervised component, a combination of optical character recognition (OCR) and deep learning for determining features of an item, such as distance of text from a boarder of packaging, etc., may be used. For example, the supervised counterfeit detection may be performed using a database containing item information such as description and unique identifier information. This is linked to photographs and text examples and explanations of counterfeiting of that item., [0030] Also, the unsupervised counterfeit detection may be performed through text analysis extracted from object images through OCR, and the text may then be checked for grammatical correctness and spelling. This would then influence an AI classifier to provide a probability that indicates whether the item is counterfeit or genuine.), wherein the machine learning engine is trained on a plurality of entries on the distributed database, wherein each of the plurality of entries include photo information and a label indicating a genuine or counterfeit item ([0041] The training set 120 may include an image database with labels for images to support supervised machine learning. At 202, the training set 120 is processed by the pre-processing system 112. For example, texture anti-counterfeiting techniques use the change of thickness and color to differentiate a fake from a genuine. FIGS. 3A-G show a series of images of the pre-processing to emphasize texture and morphology difference. Pre-processing techniques resulting in the example images shown in FIGS. 3A-G may highlight image properties that are not otherwise visible in the original image such as edge, contour, texture, and/or other features (extract features) that may improve accuracy of the machine learning model, [0064] the CAM 510 may discover the features and relative importance of those features that the discriminator 506 used to make its prediction of whether the input image is a counterfeit image. [0016] a counterfeit detection system employs artificial intelligence (AI) techniques to detect fake or counterfeit goods, [0041] The training set 120 may include an image database with labels for images to support supervised machine learning. At 202, the training set 120 is processed by the pre-processing system 112. For example, texture anti-counterfeiting techniques use the change of thickness and color to differentiate a fake from a genuine., [0043] images of the object focusing on particular elements may be compared to counterfeit examples, and a probability of each element being counterfeit is generated through the CNN, [0046] a situation might arise where a fashion garment is being analyzed. A close-up image of stitching (photo), overall garment shape (type of item) from multiple angles are other features, a close-up of any graphics, e.g., printed or stitched graphics, etc., may be used to generate another feature probability (risk measure). To determine a probability (risk measure based on feature) that a garment is counterfeit, the CNN may analyze a close-up image of stitching (feature) in the garment, [0022] CNN that uses convolutional layers that each analyses a different region of an image being classified by the generator. It should be noted that “regions of an image” corresponds to an actual region of an item being imaged in the image.); placing based on the risk measure the item listing into a first-level queue in a hierarchical plurality of queues for evaluation by an expert ([0024] The heatmap may be presented to a subject matter expert (SME). The SME may include a human user that has expertise in identifying faked items. [0064] At 804, the CAM 510 may provide an indication of the regions of the input image to a subject matter expert (such as a SME 513).) and obtaining a validation status from the expert ([0024] the system may provide the heatmap to SMEs to refine the classification, which is provided as feedback to the discriminator. Thus, the expertise of the SME may further train the discriminator, which in turn further trains the generator. To refine the classification and further train the discriminator, the system may receive annotations from the SME (validation). The annotations may indicate an SME's assessment of whether the discriminator used appropriate regions, weights, or otherwise made proper decisions in classifying an image., [0038] The GAN subsystem 130 may further integrate an explainable AI feature in which the reasons for the classification may be interrogated by a classification activation module and used to solicit feedback from subject matter experts. The feedback may indicate whether or not various weighted decisions were appropriate. The GAN subsystem 130 may retrain the discriminator and/or the CAM based on the annotation., [0048] The heatmaps 511 may be provided to SMEs 513, who may be human operators that are skilled at counterfeit detection for the type of item being analyzed. For instance, the heatmaps 511 may be provided to the SMEs 513 via interactive user interfaces. The SMEs 513 may assess the heatmaps 511 and/or other data from the CAM 510 and provide annotations to the CAM 510. [0066] At 805, the CAM 510 may receive an annotation from the SME 513. The annotation may provide input relating to the features. For example, the annotation may include an indication that a feature deemed important to the prediction is confirmed to be important or should not have been deemed to be important. In some instances, the annotation includes a binary (yes/no) indication of the importance of the feature, or may include a scale such that the weight for that feature may be adjusted accordingly, [0067] the discriminator 506 may re-adjust the weights used by the convolutional layers, thereby retraining the predictive model that classifies whether an input image is of a counterfeit or genuine item.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included a distributed database configured to store at least photo information of the plurality of items; executing a machine learning engine to extract features of the item and to determine a risk measure for the item based on the features including at least the photo information of the item, wherein the machine learning engine is trained on a plurality of entries on the distributed database, wherein each of the plurality of entries include photo information and a label indicating a genuine or counterfeit item; placing the item listing, based on the risk measure, into a first-level queue in a hierarchical plurality of queues for evaluation by an expert and obtaining a validation status from the expert, as disclosed by Burgin in the system disclosed by Hedges, for the motivation of providing a method of training a machine learning model to identify counterfeit goods based on image properties that are not otherwise visible in the original image such as edge, contour, texture, and/or other features that may improve accuracy of the machine learning model. ([0041] Burgin).
Hedges/Burgin do not teach wherein the validation status indicates a genuine or counterfeit item; deciding, based on the risk measure and/or the validation status, whether to place the item listing into a second- level queue for additional evaluation and update of the validation status by a second expert, wherein the second expert is more experienced that the expert; updating an entry, in the distributed database, associated with the item with the validation status and approving the item listing of the item in the electronic marketplace in accordance with the validation status
McKenzie teaches wherein the validation status indicates a genuine or counterfeit item ([0033] Each of columns 331 store the date on which the corresponding authentication was performed. Each of columns 332 store a unique identifier for the authenticator that performed the corresponding authentication. Columns 331 and 332 represent various authentications for a product., [0032] In block 210, the product is sent to an authenticator for authentication. In block 220, the authenticator attempts to authenticate the product. For example, the authenticator may determine that the product was manufactured by a particular manufacturer, is composed of genuine materials (e.g., genuine leather), is certified by a particular certification authority (e.g., a sustainability certification), and so on. In decision block 230, authenticator verifies that the product is authentic); deciding, based on the risk measure and/or the validation status, whether to place the item listing into a second- level queue for additional evaluation and update of the validation status by a second expert ([0033] For example, the top row indicates that the product with the product ID AF214 and tag ID C213D was authenticated in one category on Jan. 2, 2020 by the manufacturer and in a second category on Mar. 2, 2020 by a third party (e.g., a product or certification expert) (second level). [0032]In block 210, the product is sent to an authenticator for authentication. In block 220, the authenticator attempts to authenticate the product. For example, the authenticator may determine that the product was manufactured by a particular manufacturer, is composed of genuine materials (e.g., genuine leather), is certified by a particular certification authority (e.g., a sustainability certification), and so on. In decision block 230, authenticator verifies that the product is authentic), wherein the second expert is more experienced that the expert ([0033] product authenticated in a second category on Mar. 2, 2020 by a third party (e.g., a product or certification expert) (second level).; updating an entry, in the distributed database, associated with the item with the validation status (Fig 3 # 310, 330 status 331, 332 authenticator/expert, [0024] Furthermore, information about the product and tag is issued on a blockchain, such as a tag id, current owner, date and time authenticated, authenticator, and so on using a transaction signed using a private key (of a public/private key pair) of the authenticator. Furthermore, an authentication flag in the product authentication data store can be updated by the seller for the tag to identify an authentic original product for a corresponding brand.) and approving the item listing of the item in the electronic marketplace in accordance with the validation status ([0035] In block 524, the seller logs in to the scanner application to prove their identity (if they have not done so already) and scans the tag attached to the product to, for example, provide proof of ownership (verifiable via a secure, trusted tracking system, such as a blockchain, and/or the product authentication store) and proof of presence. In decision block 525, if the seller's scanner application confirms that the product's authentication is valid (by, for example, checking transactions in a blockchain or a product authentication data store) and that the product is owned by the seller, processing continues at block 526, else the component returns false.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included deciding, based on the risk measure, whether to place the item listing into a second- level queue for additional evaluation and update of the validation status by a second expert; and listing the item in the electronic marketplace in accordance with the validation status, as disclosed by McKenzie in the system disclosed by Hedges/Burgin, for the motivation of providing a method of authenticating products, such as products from original manufacturers and/or resellers, that provide a trusted and reliable mechanism for buyers and sellers to prove the authenticity of a product and for authenticators to establish an authentication that can be relied on during downstream transactions (Abstract McKenzie)
Regarding Claim 2. Hedges as modified by Burgin/McKenzie teaches the system according to claim 1,
Hedges discloses wherein the expert is permitted to select the item from the first-level queue. ([0047] an analysis of the listings of the items for sale may be performed by any entity, person, or computing system to verify the authenticity of the item 108. If the item is verified to be an authentic item, no further action is taken and the transaction proceeds to completion 110. Sometimes items for sale in an online auction are identified as counterfeit items or a may be identified as potentially counterfeit items 112).
Regarding Claim 3. Hedges as modified by Burgin/McKenzie teaches the system according to claim 2,
Hedges discloses wherein the system is configured to assign a review status of the item to the expert and remove the item listing from the first-level queue. ([0047] an analysis of the listings of the items for sale may be performed by any entity, person, or computing system to verify the authenticity of the item 108. If the item is verified (review status) to be an authentic item, no further action is taken and the transaction proceeds to completion 110. Sometimes items for sale in an online auction are identified as counterfeit items or a may be identified as potentially counterfeit items 112, [0059] An analysis of the authenticity of the footwear may be performed by performing a search for the footwear's brand and/or product code and determining whether the footwear is authentic based on the information contained in the listing. When the footwear description does not match the brand and/or the product code, the footwear may be identified as counterfeit footwear. The resolution may be in various forms, such as, but not limited to, submitting a request to the auctioneer that the listing is removed from the online auction. , [0060] Various people and entities may be responsible for verifying the authenticity of the footwear or other items that a seller wishes to sell on the online auction's website. For example, the auctioneer, the manufacturer of the item, a buyer, a monitoring agency, the government, or any combination thereof may analyze a listing to verify the authenticity of the item being sold. This analysis may be performed by a human being or a computing system. The human being may perform the verification process by manually analyzing each listing or a selected group of listings. )
Regarding Claim 4, Hedges as modified by Burgin/McKenzie teaches the system according to claim 1,
Hedges discloses wherein the one or more processors are further configured to match the expert with the item listing in the first-level queue. ([0059] An analysis of the authenticity of the footwear may be performed by performing a search for the footwear's brand and/or product code and determining whether the footwear is authentic based on the information contained in the listing. When the footwear description does not match the brand and/or the product code, the footwear may be identified as counterfeit footwear. In another example, if the product code is either invalid (does not match a list of known product codes for a manufacturer or brand) or the product code provided by the seller does not match the item being sold, then the footwear may be identified as a counterfeit item. [0060] Various people and entities may be responsible for verifying the authenticity of the footwear or other items that a seller wishes to sell on the online auction's website. For example, the auctioneer, the manufacturer of the item, a buyer, a monitoring agency, the government, or any combination thereof may analyze a listing to verify the authenticity of the item being sold. This analysis may be performed by a human being (match) or a computing system. The human being may perform the verification process by manually analyzing each listing or a selected group of listings. )
Regarding Claim 5. Hedges as modified by Burgin/McKenzie teaches the system according to claim 4,
Hedges discloses a component that is configured to match the expert with the item listing in the queue. ([0060] Various people and entities may be responsible for verifying the authenticity of the footwear or other items that a seller wishes to sell on the online auction's website. For example, the auctioneer, the manufacturer of the item, a buyer, a monitoring agency, the government, or any combination thereof may analyze a listing to verify the authenticity of the item being sold. This analysis may be performed by a human being or a computing system. The human being may perform the verification process by manually analyzing each listing or a selected group of listings. ). However, Hedges do not teach match the second expert with the item listing in the second level queue
McKenzie teaches match the second expert with the item listing in the second level queue ([0033] For example, the top row indicates that the product with the product ID AF214 and tag ID C213D was authenticated in one category on Jan. 2, 2020 by the manufacturer and in a second category on Mar. 2, 2020 by a third party (e.g., a product or certification expert) (second level). [0032]In block 210, the product is sent to an authenticator for authentication. In block 220, the authenticator attempts to authenticate the product. For example, the authenticator may determine that the product was manufactured by a particular manufacturer, is composed of genuine materials (e.g., genuine leather), is certified by a particular certification authority (e.g., a sustainability certification), and so on. In decision block 230, authenticator verifies that the product is authentic);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included match the second expert with the item listing in the second level queue, as disclosed by McKenzie in the system disclosed by Hedges/Burgin, for the motivation of providing a method of authenticating products, such as products from original manufacturers and/or resellers, that provide a trusted and reliable mechanism for buyers and sellers to prove the authenticity of a product and for authenticators to establish an authentication that can be relied on during downstream transactions (Abstract McKenzie)
Regarding Claim 6. Hedges as modified by Burgin/McKenzie teaches the system according to claim 1, further comprising
Hedges discloses a database of items that have been previously identified as being genuine. ([0052]The product's unique identifier may be authenticated. This may include comparing the entered product identifier to one or more valid product identifier's for the manufacturer of the item being sold. For example, if Company A allegedly manufactures the product being sold, then the unique product identifier input by the seller would be compared to a listing of unique product identifiers provided by Company A. [0053] the online auctioneer may have a software program that tracks and stores data relating to authentic product code identifiers for one or more manufacturers and/or sellers. The entity may use this list to authenticate new listing for products being sold., [0069])
Hedges does not specifically teach the distributed database includes items that have been previously identified as being genuine
Burgin teaches the distributed database includes items that have been previously identified as being genuine ([0018] The goal of the generator is to generate images that the discriminator classifies as an image of a genuine item. Images of a genuine item will also be referred to as “genuine images” . Fig 3A original/genuine product picture, [0028] The system receives images of genuine medicine products. [0041] The training set 120 may include an image database with labels for images to support supervised machine learning. At 202, the training set 120 is processed by the pre-processing system 112. For example, texture anti-counterfeiting techniques use the change of thickness and color to differentiate a fake from a genuine., [0043] images of the object focusing on particular elements may be compared to counterfeit examples, and a probability of each element being counterfeit is generated through the CNN, [0046] a situation might arise where a fashion garment is being analyzed. A close-up image of stitching (photo), overall garment shape (type of item) from multiple angles are other features, a close-up of any graphics, e.g., printed or stitched graphics, etc., may be used to generate another feature probability (risk measure). To determine a probability (risk measure based on feature) that a garment is counterfeit, the CNN may analyze a close-up image of stitching (feature) in the garment, [0022] CNN that uses convolutional layers that each analyses a different region of an image being classified by the generator. It should be noted that “regions of an image” corresponds to an actual region of an item being imaged in the image.);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the distributed database includes items that have been previously identified as being genuine, as disclosed by Burgin in the system disclosed by Hedges, for the motivation of providing a method of training a machine learning model to identify counterfeit goods based on image properties that are not otherwise visible in the original image such as edge, contour, texture, and/or other features that may improve accuracy of the machine learning model. ([0041] Burgin).
Regarding Claim 7. Hedges as modified by Burgin/McKenzie teaches the system according to claim 1, further comprising
Hedges discloses a database of items that have been previously identified as being counterfeit.([0052] The product's unique identifier may be authenticated. This may include comparing the entered product identifier to one or more valid product identifier's for the manufacturer of the item being sold. For example, if Company A allegedly manufactures the product being sold, then the unique product identifier input by the seller would be compared to a listing of unique product identifiers provided by Company A. [0053] the online auctioneer may have