DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. 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
2. The information disclosure statements (IDS) submitted on 06/19/2024, 08/24/2024, and 10/09/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Status of the Claims
3. Applicant's election with traverse of 1-6 and 14 in the reply filed on 05/04/2026 is acknowledged. The traversal is on the ground(s) that “claim 7 is not limited to transactions that have already been recorded in a blockchain, but instead recites predicting whether a transaction of a digital currency is fraudulent and does not necessitate the transaction already being recorded in the blockchain”. This is not found persuasive because claim 7 and dependent claims 8-13 are directed to a method for predicting whether a transaction is fraudulent using a machine learning model and classified in G06Q20/4016, that is different from claims 1-6 and 14 that directed to a method and node that for providing a trained machine learning model based on obtained transaction data and classified in G06F30/27.
The requirement is still deemed proper and is therefore made FINAL.
Claims 1-6 and 14 are amended. Claims 15-20 are newly added. Claims 1-6 and 14-20 are pending.
Claim Rejections - 35 USC §101
4. 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.
5. Claims 1-6 and 14-20 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.
6. In the instant case, independent claims 1 and 14 are directed to “a method and a node for training a model using a machine learning process”.
7. Claim 1 recites “predicting whether a stored transaction is fraudulent”. Specifically, claim 1 recites [abstract ideas emphasized in bold] “unpacking a block in the blockchain into a table comprising rows of input and output data for a previous transaction stored in the block; aggregating the rows of input and output data to form an aggregated row of transaction data for the previous transaction; labelling the aggregated row of transaction data for the previous transaction according to whether the previous transaction was fraudulent; and using the aggregated row of transaction data and the label as training data with which to train the model”. Subject matter grouped under “Certain methods of organizing human activity” (e.g., fundamental economic principles and practices) and an abstract idea in prong one of step 2A (MPEP 2106.04(a)).
8. This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A (MPEP 2106.04 II), the additional elements of claim 1 such as “a model”, “a machine learning process”, “a digital currency”, “a blockchain”, and “a block in the blockchain” represent the use of a computer as a tool to perform an abstract idea and/or does no more than generally link the abstract idea to a particular field of use. Therefore, the additional elements do not integrate the abstract idea into a practical application as they do no more than represent a computer performing functions that correspond to (i.e., automate) the acts of predicting whether a stored transaction is fraudulent.
9. When analyzed under step 2B (MPEP 2106.04 II), the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception itself. Viewed as a whole, the combination of elements recited in the claim merely describes the concept of predicting whether a stored transaction is fraudulent using computer technology. Therefore, as the use of these additional elements do no more than employ a computer as a tool to automate and/or implement the abstract idea, they cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)).
10. Hence, claim 1 is not patent eligible.
11. Claim 14 also recites “predicting whether a stored transaction is fraudulent”. Specifically, claim recites [abstract ideas emphasized in bold] “unpack a block in the blockchain into a table comprising rows of input and output data for a previous transaction stored in the block; aggregate the rows of input and output data to form an aggregated row of transaction data for the previous transaction; label the aggregated row of transaction data for the previous transaction according to whether the previous transaction was fraudulent; and training the model using the aggregated row of transaction data and the label as training data”. Subject matter grouped under “Certain methods of organizing human activity” (e.g., fundamental economic principles and practices) and an abstract idea in prong one of step 2A (MPEP 2106.04(a)).
12. This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A (MPEP 2106.04 II), the additional elements of claim 14 such as “a node in a computing network”, “a model”, “a machine learning process”, “a digital currency”, “a blockchain”, “a memory”, “a processor”, and “a block in the blockchain” represent the use of a computer as a tool to perform an abstract idea and/or does no more than generally link the abstract idea to a particular field of use. Therefore, the additional elements do not integrate the abstract idea into a practical application as they do no more than represent a computer performing functions that correspond to (i.e., automate) the acts of predicting whether a stored transaction is fraudulent.
13. When analyzed under step 2B (MPEP 2106.04 II), the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception itself. Viewed as a whole, the combination of elements recited in the claim merely describes the concept of predicting whether a stored transaction is fraudulent using computer technology. Therefore, as the use of these additional elements do no more than employ a computer as a tool to automate and/or implement the abstract idea, they cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)).
14. Hence, claim 14 is not patent eligible.
15. The following dependent claims recent additional elements not addressed above:
claims 2 and 15 recite “a tree-like structure”; and
claims 4 and 16 recite “NoSQL format”.
When considered individually, and as a whole, each of these additional elements amount to merely "apply it", as they are merely applying the abstract idea to the technical environment of the tree-like structure, and NoSQL format.
Dependent claims 2-6 and 15-20 merely expand upon the abstract ideas of the independent claims, and are therefore rejected under the same rationale as claims 1 and 14 respectively.
Conclusion of 35 USC §101
16. The claims as a whole do not amount to significantly more than the abstract idea itself. This is because the claims do not effect an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of a computer system itself; and the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment.
17. Accordingly, there are no meaningful limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself.
Claim Rejections - 35 USC § 103
18. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
19. 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.
20. 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.
21. Claims 1-6 and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over US11436615B2 to Fang et al. in view of US12481998B2 to Cheong.
22. As per claim 1:
Fang et al. discloses the following limitations:
A computer implemented method of training a model, using a machine learning process, to predict whether a transaction of a digital currency stored in a blockchain is fraudulent, the method comprising (Col.1, lines 28-31 “A method of operating a risk management system for blockchain digital assets involves receiving digital on blockchain information and digital off blockchain information, wherein the receiving includes a digital asset intake engine”, col.7, lines 46-48 “FIG. 4 illustrates a method 400 for training the machine learning classification model in accordance with on embodiment”, col.11, lines 51-55 “the risk scoring regression engine may be trained with a dataset of Risky transactions (score of 100), and Safe transactions (score of 0). From this dataset, a regression model can be trained to interpolate scores from 0 to 100 based on input data”)
aggregating the rows of input and output data to form an aggregated row of transaction data for the previous transaction (Col.9, lines 35-38 “The statistics feature category may include items such as counts of inbound and outbound transactions, a sum of inbound and outbound amounts, maximum transaction amounts, etc.”, col.9, lines 39-40 “The topology feature category may include items such as the number of input addresses, graph centrality, etc.”, col.12, lines 1-3 “X.sub.r: Risk score features such as: number of outgoing blockchain transactions, number of inbound addresses, geolocation, device type, etc.”)
using the aggregated row of transaction data and the label as training data with which to train the model (Col.7, lines 46-55 “FIG. 4 illustrates a method 400 for training the machine learning classification model in accordance with on embodiment. The method 400 involves preparing a labeled dataset (block 402). The in block 404, the method 400 pre-process the labeled dataset. In block 406, the method 400 configures the AutoML Parallel Training System 418 with parameters and success criteria settings. The method 400 then moves to block 408 where the AutoML features are extracted and transformed. In block 410, the method 400 trains the AutoML model.”, col.11, lines 51-55 “the risk scoring regression engine may be trained with a dataset of Risky transactions (score of 100), and Safe transactions (score of 0). From this dataset, a regression model can be trained to interpolate scores from 0 to 100 based on input data”, col.8, lines 43-47 “The AutoML system takes the labeled data as input, runs a parallel competition to select the best machine learning model that meets the success criteria, and eventually emits a serialized machine learning model that can be deployed in the prediction pipeline.”)
Fang et al. does not disclose, however, Cheong, as shown, disclosed the following limitations:
unpacking a block in the blockchain into a table comprising rows of input and output data for a previous transaction stored in the block (Col/line 8/63-9/3 “The fraud detection server 1000 of the present disclosure may detect whether there is a user's fraudulent transaction based on a blockchain transaction (hereinafter referred to as a transaction) related to a wallet address held by the user, an input code that is included in each transaction and serves as a factor for performing a smart contract, and a contract bytecode (hereinafter referred to as a bytecode) used in the distribution of the smart contract.”, col.11, lines 15-20 “16 kinds of statistical features are calculated based on the transaction data received at the specific wallet address, and 16 statistical features are calculated based on the transaction data transmitted from the specific wallet address, so that total 32 statistical features may be calculated at the specific wallet address”, col.13, lines 10-12 “the statistical feature preprocessor 110 may be grouped into one record in association with the corresponding wallet address as illustrated in the table T1 of FIG. 7.”)
labelling the aggregated row of transaction data for the previous transaction according to whether the previous transaction was fraudulent (Col.9, lines 13-20 “The fraud detection server 1000 may learn the fraud detection model using a supervised data set (that is, a data set to which a label value for abnormality of each data is assigned). In this case, the supervised data set for the purpose of detecting whether there is a fraudulent transaction may be configured as follows. In a label column in [Table 1] below, 1 may mean a fraudulent transaction, and 0 may mean a normal transaction.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a fraud detection method performed in a fraud detection server that collects transaction data of cryptocurrency, includes generating a first data set by deriving a plurality of statistical features based on the transaction data for a specific wallet address of Cheong (‘998, col.1, lines 45-49) with teaching of Fang et al. for operating a risk management system for blockchain digital assets involves receiving digital on blockchain information and digital off blockchain information (‘615, col.1, lines 28-30) for parses on-blockchain transaction data into a Table T1 whose rows hold received (input) and transmitted (output) data of the wallet's previous transactions and attaching a per row label of fraudulent and normal for to each aggregated row (‘998, Col/line 8/63-9/3, col.9, lines 13-20).
23. As per claim 2:
Fang et al. discloses the following limitations:
the transaction data for the previous transaction is stored in a tree-like structure (Col.24, lines 33-36 “Blocks hold batches of valid transactions that are hashed and encoded, for example into a Merkle tree. Each block includes the cryptographic hash of the prior block in the blockchain formation 1500, linking the two.”)
the step of unpacking comprises: unpacking the block into a plurality of stages (Col.5, lines 53-58 “FIG. 1 illustrates a system 100 for blockchain transaction risk management. The system 100 comprises a digital asset intake engine 108, a risk classification engine 102, a risk scoring regression engine 104, a risk policy engine 148, a security control system 106, and an entity knowledge base engine 110.” col/line 5/65-6/2 “The digital asset intake engine 108 is configured to receive digital on blockchain information and digital off blockchain information, and extract digital data from the digital on blockchain information and the digital off blockchain information.”)
performing outer joins between the plurality of stages to obtain a table comprising the rows of input and output data for the previous transaction (Col.6, lines 33-40 “The extracted digital data and the digital off blockchain information and the digital on blockchain information is then contextualized by a risk classification engine 102, which leverages information stored in entity knowledge bases that include black list intelligence database 112, a device intelligence database 114, a computer network intelligence database 116, and a blockchain ledger 118.”)
Claim 15 is rejected using the same rationale that was used for the rejection of claim 2.
24. As per claim 3:
Fang et al. discloses the following limitations:
A method as in claim 2 wherein the step of performing outer joins comprises performing outer joins to the stages in the plurality of stages to extract unnested information from the block into the table (Col.6, lines 22-30 “The digital asset intake engine 108 extracts data (extracted data 132), which pulls out the digital data and the digital off blockchain information and the digital on blockchain information that includes a blockchain address 134, a transaction identification 136, a user information 138, a device information 140, a business type 146, and a device IP address 142, as well as the exchange or custodian information 144, associated with the user 120's actions.”)
25. As per claim 4:
Fang et al. discloses the following limitations:
A method as in claim 1, wherein the block is stored in NoSQL format (Col.24, lines 33-36 “Blocks hold batches of valid transactions that are hashed and encoded, for example into a Merkle tree. Each block includes the cryptographic hash of the prior block in the blockchain formation 1500, linking the two.”)
Claim 16 is rejected using the same rationale that was used for the rejection of claim 4.
26. As per claim 5:
Fang et al. does not disclose, however, Cheong, as shown, disclosed the following limitations:
A method as in claim 1 wherein the step of aggregating the rows of input and output data comprises computing a statistical aggregation of values of respective fields in the rows of input and output data (“16 kinds of statistical features are calculated based on the transaction data received at the specific wallet address, and 16 statistical features are calculated based on the transaction data transmitted from the specific wallet address, so that total 32 statistical features may be calculated at the specific wallet address.”, “(4) Total ETH transmission amount: since the first user USER1 sent 0.2 ETH, 0.3 ETH, 0.1 ETH, and 0.2 ETH for each transaction, the total ETH transmission amount is 0.8.”“(7) Average ETH transmission amount: since the first user USER1 sent 0.2 ETH, 0.3 ETH, 0.1 ETH, and 0.2 ETH for each transaction, and the average value of these values is 0.2, the average ETH transmission amount is 0.2.”,“(8) ETH transmission amount standard deviation: since the first user USER1 sent 0.2 ETH, 0.3 ETH, 0.1 ETH, and 0.2 ETH for each transaction, and the standard deviation of these values is about 0.071, the standard deviation of the Ether transmission amount is 0.071.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a fraud detection method performed in a fraud detection server that collects transaction data of cryptocurrency, includes generating a first data set by deriving a plurality of statistical features based on the transaction data for a specific wallet address of Cheong (‘998, col.1, lines 45-49) with teaching of Fang et al. for operating a risk management system for blockchain digital assets involves receiving digital on blockchain information and digital off blockchain information (‘615, col.1, lines 28-30) for parses on-blockchain transaction data into a Table T1 whose rows hold received (input) and transmitted (output) data of the wallet's previous transactions and attaching a per row label of fraudulent and normal for to each aggregated row (‘998, Col/line 8/63-9/3, col.9, lines 13-20).
Claim 17 is rejected using the same rationale that was used for the rejection of claim 5.
27. As per claim 6:
Fang et al. discloses the following limitations:
A method as in claim 1 wherein the step of labelling is based in part on whether an addressee listed in the rows of input or output data for the transaction is known to be involved in fraudulent activity (Col.3, lines 15-17 “Existing applications such as blacklisted blockchain addresses or sanctioned terrorists' personal information lack of adaptability to new emerging risks.”, col/line 6/61-7/1 “In the process 200, blockchain address and device information/IP 218 is received following user action on the blockchain or associated with the blockchain on a cryptocurrency exchange. At decision block 202, the blockchain address and device information/IP 218 is analyzed against a blacklist database 210 and a determination is made whether the blockchain address and device information/IP 218 have been blacklisted from operating on the exchange.”, col.11, lines 40-46 “The machine learning model utilizing the graph connectivity characteristics may assign a high risk score based on proximity to known bad actors, such as fraud, scammers, ransomware hackers, terrorists on sanction lists, inbound/outbound fund flow, and recent time events such as recent transactions can play a bigger role than older one.”, col.7, lines 61-64 “An example of the prepared labeled dataset may be a dataset that includes information such as: A is a hacker address with 100 transactions; B is an exchange wallet address with 2000 transactions.”)
Claim 18 is rejected using the same rationale that was used for the rejection of claim 6.
28. As per claim 14:
Fang et al. discloses the following limitations:
A node in a computing network for training a model, the node using a machine learning process to predict whether a transaction of a digital currency stored in a blockchain is fraudulent, the node comprising (Col.1, lines 28-31 “A method of operating a risk management system for blockchain digital assets involves receiving digital on blockchain information and digital off blockchain information, wherein the receiving includes a digital asset intake engine”, col.7, lines 46-48 “FIG. 4 illustrates a method 400 for training the machine learning classification model in accordance with on embodiment”)
a memory comprising instruction data representing a set of instructions (Col.26, lines 15-20, 23-26 “Data server 1710, e.g., may include a processor 1712 controlling overall operation of the data server 1710. Data server 1710 may further include RAM 1716, ROM 1718, network interface 1714, input/output interfaces 1720 (e.g., keyboard, mouse, display, printer, etc.), and memory 1722 …Memory 1722 may further store operating system software 1724 for controlling overall operation of the data server 1710, control logic 1726 for instructing data server 1710 to perform aspects described herein”)
a processor configured to communicate with the memory and to execute the set of instructions, wherein the set of instructions, when executed by the processor, cause the processor to (Col.26, 15-17 “Data server 1710, e.g., may include a processor 1712 controlling overall operation of the data server 1710.”)
aggregate the rows of input and output data to form an aggregated row of transaction data for the previous transaction (Col.9, lines 35-38 “The statistics feature category may include items such as counts of inbound and outbound transactions, a sum of inbound and outbound amounts, maximum transaction amounts, etc.”, col.9, lines 39-40 “The topology feature category may include items such as the number of input addresses, graph centrality, etc.”, col.12, lines 1-3 “X.sub.r: Risk score features such as: number of outgoing blockchain transactions, number of inbound addresses, geolocation, device type, etc.”);
training the model using the aggregated row of transaction data and the label as training data (Col.7, lines 46-55 “FIG. 4 illustrates a method 400 for training the machine learning classification model in accordance with on embodiment. The method 400 involves preparing a labeled dataset (block 402). The in block 404, the method 400 pre-process the labeled dataset. In block 406, the method 400 configures the AutoML Parallel Training System 418 with parameters and success criteria settings. The method 400 then moves to block 408 where the AutoML features are extracted and transformed. In block 410, the method 400 trains the AutoML model.”, col.11, lines 51-55 “the risk scoring regression engine may be trained with a dataset of Risky transactions (score of 100), and Safe transactions (score of 0). From this dataset, a regression model can be trained to interpolate scores from 0 to 100 based on input data”)
Fang et al. does not disclose, however, Cheong, as shown, disclosed the following limitations:
unpack a block in the blockchain into a table comprising rows of input and output data for a previous transaction stored in the block (Col/line 8/63-9/3 “The fraud detection server 1000 of the present disclosure may detect whether there is a user's fraudulent transaction based on a blockchain transaction (hereinafter referred to as a transaction) related to a wallet address held by the user, an input code that is included in each transaction and serves as a factor for performing a smart contract, and a contract bytecode (hereinafter referred to as a bytecode) used in the distribution of the smart contract.”, col.11, lines 15-20 “16 kinds of statistical features are calculated based on the transaction data received at the specific wallet address, and 16 statistical features are calculated based on the transaction data transmitted from the specific wallet address, so that total 32 statistical features may be calculated at the specific wallet address”, col.13, lines 10-12 “the statistical feature preprocessor 110 may be grouped into one record in association with the corresponding wallet address as illustrated in the table T1 of FIG. 7.”)
label the aggregated row of transaction data for the previous transaction according to whether the previous transaction was fraudulent (Col.9, lines 13-20 “The fraud detection server 1000 may learn the fraud detection model using a supervised data set (that is, a data set to which a label value for abnormality of each data is assigned). In this case, the supervised data set for the purpose of detecting whether there is a fraudulent transaction may be configured as follows. In a label column in [Table 1] below, 1 may mean a fraudulent transaction, and 0 may mean a normal transaction.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate a fraud detection method performed in a fraud detection server that collects transaction data of cryptocurrency, includes generating a first data set by deriving a plurality of statistical features based on the transaction data for a specific wallet address of Cheong (‘998, col.1, lines 45-49) with teaching of Fang et al. for operating a risk management system for blockchain digital assets involves receiving digital on blockchain information and digital off blockchain information (‘615, col.1, lines 28-30) for parses on-blockchain transaction data into a Table T1 whose rows hold received (input) and transmitted (output) data of the wallet's previous transactions and attaching a per row label of fraudulent and normal for to each aggregated row (‘998, Col/line 8/63-9/3, col.9, lines 13-20).
29. As per claim 19:
Fang et al. discloses the following limitations:
The node of claim 14, wherein the aggregated row is labeled using a heuristic (Col.6, lines 46-48 “The classified risk data is then communicated to the risk policy engine 148, which is a rules based engine”, col/line 6/64-7/1 “the blockchain address and device information/IP 218 is analyzed against a blacklist database 210 and a determination is made whether the blockchain address and device information/IP 218 have been blacklisted from operating on the exchange.”)
30. As per claim 20:
Fang et al. discloses the following limitations:
The node of claim 19, wherein the using of the heuristic comprises (Col.6, lines 46-48 “The classified risk data is then communicated to the risk policy engine 148, which is a rules based engine”)
using a tool to generate flags (Col/line 6/64-7/1 “the blockchain address and device information/IP 218 is analyzed against a blacklist database 210 and a determination is made whether the blockchain address and device information/IP 218 have been blacklisted from operating on the exchange.”, col.6, lines 43-46 “The classified risk data is then communicated to a risk scoring regression engine 104 that analyzes the classified risk data and assigns a risk score to each classified risk data.”)
combining the flags into a single flag to indicate whether the previous transaction is fraudulent (Col.6, lines 43-46 “The classified risk data is then communicated to a risk scoring regression engine 104 that analyzes the classified risk data and assigns a risk score to each classified risk data.”, col.1, lines 46-49 “A security control system takes an action on the digital on blockchain information and digital off blockchain information based on the assigned risk score and any deviations from rules or standards.”)
Conclusion
31. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US12524723B2 – Zhi et al. – Discloses systems and methods for risk diagnosis of cryptocurrency addresses on blockchains using anonymous and public information, wherein a method may include a risk diagnosis computer program executed by a server receiving data with labels and data without labels from public data databases and/or anonymous data databases.
US12198139B2 – Liu et al. – Discloses novel technical ways of analyzing a blockchain system using machine learning are presented, including structures and techniques that can facilitate blockchain address risk assessment via graph analysis, wherein a system can access a blockchain.
US11790459B1 – Strauss – Discloses apparatuses and methods for AI-based ledger prediction, wherein a processor is configured by instructions on a memory to receive and categorize a ledger file to a ledger type based on the data contained within the ledger file and the ledger data may contain information related to an insurance policy and investments made based on the payments into the policy over a period of time.
32. Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMANULLA ABDULLAEV whose telephone number is (571)272-4367. The examiner can normally be reached Monday-Friday 9:30AM -4:30PM ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ryan D Donlon can be reached at 571-270-3602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/AMANULLA ABDULLAEV/Examiner, Art Unit 3692
/RYAN D DONLON/Supervisory Patent Examiner, Art Unit 3692
August 26, 2026
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