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 .
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 3/11/2026 and 11/06/2025 is(are) in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
The amendment filed March 11, 2026 has been entered. Claims 1 and 3-8 remain pending in the application. Applicant’s amendments to the Claims have overcome each and every objections previously set forth in the Non-Final Office Action mailed December 18, 2025.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1 and 3-8 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The newly-added limitation “data augmentation” in claims 1 and 6 are not supported by the original disclosure. Claims 3-5 and 7-8 are rejected further due to their dependency.
Appropriate correction/clarification is required.
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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under the Step 1 of the Section 101 analysis, Claims 1-5 are drawn to a system which is within the four statutory categories (i.e. a machine), and Claims 6-8 are drawn to a method which is within the four statutory categories (i.e., a process).
Since the claims are directed toward statutory categories, it must be determined if the claims are directed towards a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea). Based on consideration of all of the relevant factors with respect to the claim as a whole, claims 1-8 are determined to be directed to an abstract idea. The rationale for this determination is explained below:
Regarding Claims 1 and 6:
Claims 1 and 6 are drawn to an abstract idea without significantly more. The claims recite “a GAT artificial intelligence (AI) engine server configured to train a GAT-based AI model using an index of a full node for each virtual asset pre-stored in an index database server, common transaction item information pre-stored in a virtual asset transaction analysis database server, and a main blacklist including virtual asset wallet addresses pre-stored in a main blacklist server, to calculate GAT scores based on the trained GAT-based AI model, to estimate the virtual asset wallet addresses using the calculated GAT scores, and to generate a GAT blacklist database including the estimated virtual asset wallet addresses; and a GAT blacklist database server configured to store the GAT blacklist database generated by the GAT AI engine server, wherein the GAT AI engine server includes: a first data preprocessing module configured to preprocess the common transaction item information, label each of transactions based on the preprocessed common transaction item information according to each virtual asset, and query the transactions corresponding to a predetermined virtual asset wallet address; an AI learning module configured to perform GAT learning using the transactions queried by the first data preprocessing module; a teacher module configured to pseudo-label transactions that are unlabeled by the first data preprocessing module, and feed the pseudo-labeled transactions to the AI learning module to re-learn the pseudo-labeled transactions by data augmentation; and a first risk calculation module configured to calculate a risk level representing the GAT scores of the virtual asset wallet addresses based on results of the GAT learning performed by the AI learning module, wherein the AI learning module extracts a GAT graph structure for the transactions based on the GAT using the index of the full node for each virtual asset and the common transaction item information, wherein the AI learning module generates a feature matrix by assigning a weight to each online source site during crawl collection for data matrixed by risk category using the main blacklist, wherein the AI learning module passes the feature matrix and an adjacency matrix through an attention layer to define nodes and edges of the GAT graph structure, such that a pre-trained model for labeled training data associated with the virtual asset wallet addresses is generated, wherein the AI learning module, using the GAT graph structure, predicts unlabeled data for the virtual asset wallet addresses and performs the GAT learning based on semi-supervised learning, such that the GAT scores of the virtual asset wallet addresses are generated, and wherein the AI learning module automatically generates and updates the GAT blacklist database by repeatedly executing the GAT graph structure, the GAT blacklist database including the GAT scores.”
Under the Step 2A Prong One, the limitations, as underlined above, are processes that, under its broadest reasonable interpretation, cover Certain Methods Of Organizing Human Activity such as Mental Processes such as concepts performed in the human mind (including an observation, evaluation, judgment, opinion). For example, but for the “GAT artificial intelligence (AI) engine server”, “GAT-based AI model”, “node”, “virtual asset”, “index database server”, “virtual asset transaction analysis database server”, “virtual asset wallet addresses”, “blacklist server”, “GAT scores”, “GAT blacklist”, “GAT blacklist database”, “GAT blacklist database server configured to store the GAT blacklist database generated by the GAT AI engine server”, “AI learning module”, “GAT learning”, “GAT graph structure”, “GAT”, “online source site”, “crawl collection”, “attention layer”, and “semi-supervised learning” language, the underlined limitations in the context of this claim encompass the human activity or mental processes. The series of steps belong to a typical observation, evaluation, judgment, or opinion, because training, calculating, estimating, and generating can be performed by human mind.
Under the Step 2A Prong Two, this judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – “A system for generating a virtual asset wallet address blacklist database based on a graph attention network (GAT), the system comprising:”, “A method for generating a virtual asset wallet address blacklist database based on a graph attention network (GAT), the method comprising:”, “GAT artificial intelligence (AI) engine server”, “GAT-based AI model”, “node”, “virtual asset”, “index database server”, “virtual asset transaction analysis database server”, “virtual asset wallet addresses”, “blacklist server”, “GAT scores”, “GAT blacklist”, “GAT blacklist database”, “GAT blacklist database server configured to store the GAT blacklist database generated by the GAT AI engine server”, “AI learning module”, “GAT learning”, “GAT graph structure”, “GAT”, “online source site”, “crawl collection”, “attention layer”, and “semi-supervised learning”. The additional elements are recited at a high-level of generality (i.e., performing generic functions of an interaction) such that it amounts no more than mere instructions to apply the exception using a generic computer component, merely implementing an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea. Additionally, regarding the specification and claims, there is no improvement in the functioning of a computer or an improvement to other technology or technical field present, there is no applying or using the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition present, there is no implementing the judicial exception with or using the judicial exception in conjunction with a particular machine or manufacture that is integral to the claim present, there is no effecting a transformation or reduction of a particular article to a different state or thing present, and there is no 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 present such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Accordingly, these additional elements, individually or in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Under the Step 2B, the claims 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 in the process amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Regarding Claims 3-5 and 7-8:
Dependent claims 3-5 and 7-8 include additional limitations, for example, “wallet address service server”, “virtual asset wallet address”, and “virtual asset exchange server” (Claims 3 and 7); “wallet address service server”, “data preprocessing module”, “virtual asset wallet address”, “virtual asset exchange server”, “transaction inquiry module”, “virtual asset transaction analysis database server”, “risk calculation module”, “GAT score”, “virtual asset wallet address”, “learning update module”, “AI learning module”, and “artificial intelligence model” (Claim 4); and “wallet address management server”, “virtual asset wallet address”, and “administrator terminal” (Claims 5 and 8), but none of these limitations are deemed significantly more than the abstract idea because, as stated above, they require no more than generic computer structures or signals to be executed, and do not recite any Improvements to the functioning of a computer, or Improvements to any other technology or technical field.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation or implementing the judicial exception on a generic computer.
Therefore, whether taken individually or as an ordered combination, claims 3-5 and 7-8 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
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.
Claim(s) 1 and 3-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim (US 20220101326 A1) in view of Wadhwa (US 20230126708 A1).
Regarding Claims 1 and 6, Kim teaches A system for generating a virtual asset wallet address blacklist database based on …, the system comprising (Kim: Paragraph(s) 0152-0153, 0119): A method for generating a virtual asset wallet address blacklist database based on a …, the method comprising (Kim: Paragraph(s) 0152-0153, 0119):
a … artificial intelligence (AI) engine server configured to train a …-based AI model using an index of a full node for each virtual asset pre-stored in an index database server, common transaction item information pre-stored in a virtual asset transaction analysis database server, and a main blacklist including virtual asset wallet addresses pre-stored in a main blacklist server (Kim: Paragraph(s) 0330, 0320, 0323, 0350, 0152-0153 teach(es) they are obtained based on machine learning from extended paths of known malicious and normal wallet instances that represented a good degree of distinction between malicious and normal wallets. The transactional information of this set of wallets form the training set used to derive the threshold values for each characteristic during the machine learning stage), to calculate … scores based on the trained GAT-based AI model (Kim: Abstract; Paragraph(s) 0310, 0353 teach(es) The CARA algorithm has 3 functions to compute a risk score indicative of a probability that the inspected wallet is a malicious wallet. In the present example, higher risk score refers to higher probability that the inspected wallet is malicious), to estimate the virtual asset wallet addresses using the calculated … scores (Kim: Paragraph(s) 0359-0360 teach(es) The reason why the 3 metrics above are chosen is to make sure that only wallets that behave maliciously through a majority of its lifetime in existence (number of bad days is almost equal to lifetime), and transact almost entirely with bad tasks (number of bad tasks is almost equal to number of total tasks) that involve large amounts of bad tokens would have a high risk score of greater than 60 out of 100. Malicious wallets belonging to malicious actors that engage in malicious tasks for majority of its lifetime in existence justifies having a high risk score), and to generate a … blacklist database including the estimated virtual asset wallet addresses (Kim: Paragraph(s) 0164, 0212 teach(es) The updated threat information may comprise an indicator for adding to a blacklist of the TRDB and/or an indicator for adding to a whitelist of the TRDB); and a … blacklist database server configured to store the … blacklist database generated by the … AI engine server (Kim: Paragraph(s) 0152-0153, 0164),
wherein the … AI engine server includes: a first data preprocessing module configured to preprocess the common transaction item information, label each of transactions based on the preprocessed common transaction item information according to each virtual asset, and query the transactions corresponding to a predetermined virtual asset wallet address (Kim: Paragraph(s) 0330, 0332, 0314, 0461 teach(es) The transactional information of this set of wallets form the training set used to derive the threshold values for each characteristic during the machine learning stage; immediate neighbour wallets are labelled as “malicious” if, for example, they are found in the blacklist of the TRDB, “suspicious” if determined as such based on the characteristics defined by 1 to 5 above, or “normal (i.e. non-malicious)”);
an AI learning module configured to perform … learning using the transactions queried by the first data preprocessing module (Kim: Paragraph(s) 0323, 0330 teach(es) Such suspicious paths are identified by the CARA algorithm based on characteristics learnt from machine learning. Applying principal component analysis, the characteristics that resemble different obfuscation techniques employed by malicious actors are learnt from a training set during the machine learning stage);
a teacher module configured to pseudo-label transactions that are unlabeled by the first data preprocessing module, and feed the pseudo-labeled transactions to the … learning module to re-learn the pseudo-labeled transactions by data augmentation (Kim: Paragraph(s) 0332, 0385-0397, 0164, 0625 teach(es) the Function 1 is processed such that immediate neighbour wallets are labelled as “malicious” if, for example, they are found in the blacklist of the TRDB, “suspicious” if determined as such based on the characteristics defined by 1 to 5 above, or “normal (i.e. non-malicious)”. Those wallets labelled as normal, malicious or suspicious are passed on to the next Function 2; After verification of the scam information by the Sentinels, updated threat information is sent to the Threat DB. The Sentinels may analyze the unknown threat reported by the user using a sandbox or a distributed sandbox or additional tools. The updated threat information may comprise an indicator for adding to a blacklist of the TRDB and/or an indicator for adding to a whitelist of the TRD; The suspicious transaction behaviour of one or more address upstream and/or downstream of the subject address may be determined using a trained artificial intelligence system that had undergone machine learning. For instance, principal component analysis may be applied to obtain characteristics that resemble different obfuscation techniques employed by malicious actors from a training set during the machine learning stage); and
a first risk calculation module configured to calculate a risk level representing the … scores of the virtual asset wallet addresses based on results of the … learning performed by the AI learning module (Kim: Paragraph(s) 0310, 0353, 0359-0360), …,
wherein the AI learning module generates a … matrix by assigning a weight to each online source site during crawl collection for data matrixed by risk category using the main blacklist (Kim: Paragraph(s) 0492, 0152-0153, 0353 teach(es) Crypto Analysis Transaction Visualisation CATV application, and are stored in the threat reputation database (TRDB). The same annotations are also saved in a separate database local to the wallet crawler system (hereinafter wallet crawler DB), Note that unidentified wallets in the wallet crawler DB) is a node with no information in CATV. Information of such unidentified wallets will not be saved in the TRDB. Information of such unidentified wallets can be stored in the separate database local to the wallet crawler system. The unidentified wallets can be recorded in the TRDB once they are identified; If the queried address is identified as scam/harm (i.e. a blacklist in TRDB), it will be blocked according to the Sentinel Protocol; each task is classified as normal, suspicious, highly suspicious or malicious based on the following criteria and a weight is associated with each task to represent the severity of maliciousness of the task in the range of 0 to 1), …, and
wherein the AI learning module automatically generates and updates the … blacklist database by repeatedly executing the … graph structure, the … blacklist database including the … scores (Kim: Paragraph(s) 0164, 0212 teach(es) The updated threat information may comprise an indicator for adding to a blacklist of the TRDB and/or an indicator for adding to a whitelist of the TRDB).
However, Kim does not explicitly teach a graph attention network (GAT), wherein the AI learning module extracts a GAT graph structure for the transactions based on the GAT using the index of the full node for each virtual asset and the common transaction item information, wherein the AI learning module generates a feature matrix, wherein the AI learning module passes the feature matrix and an adjacency matrix through an attention layer to define nodes and edges of the GAT graph structure, such that a pre-trained model for labeled training data associated with the virtual asset wallet addresses is generated, wherein the AI learning module, using the GAT graph structure, predicts unlabeled data for the virtual asset wallet addresses and performs the GAT learning based on semi-supervised learning, such that the GAT scores of the virtual asset wallet addresses are generated.
Wadhwa from same or similar field of endeavor teaches a graph attention network (GAT) (Wadhwa: Paragraph(s) 0096, 0130),
wherein the AI learning module extracts a … graph structure for the transactions based on the GAT using the index of the full node for each virtual asset and the common transaction item information (Wadhwa: Paragraph(s) 0079 teach(es) the detection model may be a graph neural network model that is capable of directly operating on the transaction graph structure; a typical application of the GNN model is node classification. In one example, the GNN model may be based on a Deepwalk model, a GraphS age model, a node2vec, etc., among other suitable models),
wherein the AI learning module generates a feature matrix … (Wadhwa: Paragraph(s) 0094-0095),
wherein the AI learning module passes the feature matrix and an adjacency matrix through an attention layer to define nodes and edges of the GAT graph structure, such that a pre-trained model for labeled training data associated with the virtual asset wallet addresses is generated, wherein the AI learning module, using the GAT graph structure, predicts unlabeled data for the virtual asset wallet addresses and performs the GAT learning based on semi-supervised learning, such that the GAT scores of the virtual asset wallet addresses are generated (Wadhwa: Paragraph(s) 0096-0097).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Kim to incorporate the teachings of Wadhwa for a graph attention network (GAT), wherein the AI learning module extracts a GAT graph structure for the transactions based on the GAT using the index of the full node for each virtual asset and the common transaction item information, wherein the AI learning module generates a feature matrix, wherein the AI learning module passes the feature matrix and an adjacency matrix through an attention layer to define nodes and edges of the GAT graph structure, such that a pre-trained model for labeled training data associated with the virtual asset wallet addresses is generated, wherein the AI learning module, using the GAT graph structure, predicts unlabeled data for the virtual asset wallet addresses and performs the GAT learning based on semi-supervised learning, such that the GAT scores of the virtual asset wallet addresses are generated.
There is motivation to combine Wadhwa into Kim because Wadhwa’s teachings of a graph attention network (GAT) would facilitate to generate the blacklist of virtual asset wallet addresses (Wadhwa: Paragraph(s) 0094-0097, 0130).
Regarding Claims 3 and 7, the combination of Kim and Wadhwa teaches all the limitations of claims 1 and 6 above; and Kim further teaches further comprising a wallet address service server configured to receive a virtual asset wallet address from a virtual asset exchange server, to calculate the risk level for the received virtual asset wallet address, and to respond to the virtual asset exchange server with the calculated risk level (Kim: Paragraph(s) 0312, 0310, 0353, 0359-0360).
Regarding Claim 4, the combination of Kim and Wadhwa teaches all the limitations of claim 3 and GAT above; and Kim further teaches wherein the wallet address service server comprises: a second data preprocessing module configured to receive a virtual asset wallet address from the virtual asset exchange server and perform preprocessing; a transaction query module configured to query transactions of the virtual asset wallet address, which has undergone preprocessing by the second data preprocessing module, from the virtual asset transaction analysis database server; a second risk calculation module configured to calculate the risk level based on the … score using the transactions queried by the transaction query module (Kim: Paragraph(s) 0303, 0313, 0315, 0310, 0353, 0359-0360); and a learning update module configured to request the AI learning module to train an artificial intelligence model based on the risk level calculated by the second risk calculation module (Kim: Paragraph(s) 0320, 0323, 0330, 0350, 0409, as stated above with respect to claim 1).
Regarding Claims 5 and 8, the combination of Kim and Wadhwa teaches all the limitations of claims 4 and 7 above; and Kim further teaches further comprising a wallet address management server configured to receive a virtual asset wallet address from an administrator terminal, to calculate the risk level for the received virtual asset wallet address, and to respond to the administrator terminal with the calculated risk level (Kim: Paragraph(s) 0310, 0312, 0353, 0359-0360, 0426).
Response to Arguments
Applicant's arguments filed March 11, 2026 have been fully considered but they are not persuasive.
Regarding applicant’s argument under Claim Rejections - 35 USC § 101 that “Automatically generating and updating the GAT blacklist database by executing the GAT graph structure cannot be performed by human minds,” examiner respectfully argues that generating and updating the blacklist by executing the graph structure can be performed by human minds, and the additional elements including GAT blacklist database, GAT graph structure, etc. are recited without technical details and contexts enough to provide any improvements of the functioning of computer or other technology or technical fields. For example, the features such as feature matrix, adjacency matrix, GAT, full node, online source site, crawl collection, attention layer, unlabeled data, virtual asset wallet address, etc., and their mutual interaction and usage are recited without technical details and contexts. Therefore, monitoring suspicious activities through using addresses for preventing fraudulent or illegal uses of them can be done manually by people, especially when the additional elements are recited without technical details and contexts.
Regarding applicant’s argument under Claim Rejections - 35 USC § 103 that “Kim's deterministic heuristic process fails to teach or suggest the re-learning or data augmentation for labeling,” examiner respectfully argues that the claims recite “re-learning or data augmentation for labeling” without technical details and contexts. For example, feature/adjacency, crawling of online source site, attention layer, labeled/unlabeled/pseudo-labeled, semi-supervised learning, etc. are not recited with sufficient technical details and contexts enough to overcome the cited references. It is recommend for the applicant to amend the claims further with more technical details and contexts.
Regarding applicant’s argument that “While Kim teaches or suggests assigning weights according to the nature of the virtual asset transactions, it fails to teach or suggest assigning a weight according to an online source site, and it is also silent as to generating a feature matrix based on the weight assigned based on the online source site,” examiner respectfully argues that the combination of Kim and Wadhwa teaches the features (Kim: Paragraph(s) 0492, 0152-0153, 0353; and Wadhwa: Paragraph(s) 0094-0095). As stated above, it is recommend for the applicant to amend the claims further with more technical details and contexts.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Fang (US 20220067738 A1) teaches System And Method For Blockchain Automatic Tracing Of Money Flow Using Artificial Intelligence, including blacklist, risk score, and labeling.
Rose (US 20210174347 A1) teaches User Routing Application And Recommendation Engine For Distributed Terminal Network, including risk score.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/CLAY C LEE/Primary Examiner, Art Unit 3699
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