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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 8-9, 11-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 8-9, the claims recite, “wherein the GNN model is implemented in one or more nodes of the blockchain,” and “wherein the GNN model is hosted remote from and communicatively coupled to one or more nodes of the blockchain,” respectively. However, claim 1, from which claims 8-9 depend, is explicitly directed to “an artificial intelligence (AI) tool comprising a processor, a graphics processing unit (GPU), and a graph neural network (GNN) model.” Accordingly, it is unclear whether the GNN model is no longer a part of the AI tool or whether the AI tool as a whole is implemented in one or more nodes or hosted remotely. Therefore, the scope of the claims is unclear.
Claims 11 and 18 recite the limitations "the GPU" and “the GNN” in “via the GPU, generating an address graph based on the block exchange data to display one or more addresses associated with an anomaly by causing the GNN to perform...” There is insufficient antecedent basis for these limitations in the claims.
Claims 12-17 and 19-20 are also rejected due to their dependence on at least claim 11 or 18.
Regarding claim 18, the claim recites, “A non-transitory storage medium including instructions stored thereon, the instructions, when executed by a computer system, cause the computer system to perform...” It is unclear whether the claimed computer system includes the necessary components to carry out the claimed operations (e.g. processor, memory, GPU, and GNN) or whether it is merely a remote system delegating such operations to a different computing device. Therefore, the scope is unclear.
Claims 19-20 are also rejected due to their dependence on at least claim 18.
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.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Choudhary et al. (US 20240062041 "Choudhary") in view of Kursun (US 20200167786 "Kursun") and further in view of Chen et al. (US 20210067527 "Chen").
Regarding claims 1, 11, and 18, Choudhary discloses: A system, method, and non-transitory storage medium to identify blockchain anomalies, comprising:
an artificial intelligence (AI) tool comprising a processor...and a graph neural network (GNN) model (Fig. 2, 0079);
a memory communicatively coupled to the processor; and
machine-readable instructions stored in the memory that, upon execution by the processor, cause the processor to:
receive block exchange data generated from exchanges between a plurality of addresses (Fig. 8, 0143-0144);
track the block exchange data over a predetermined time, the block exchange data including data indicating directions for block exchanges between addresses (0065-0066, 0080-0081, 0162, 0170).
Choudhary does not disclose: determine behavioral patterns from the block exchange data, including at least one of: a number of unidirectional block exchanges above a predetermined threshold for the one or more addresses; addresses with interaction history ending within the predetermined time period;
based on determining that at least one of the behavioral patterns indicates anomalous activity, identify the one or more addresses as associated with the anomaly; and
generate an alert when the anomaly is identified.
However, in the same field of endeavor, Kursun discloses: track the block exchange data over a predetermined time, the block exchange data including data indicating directions for block exchanges between addresses (Fig. 8-10, 0090, 0120-0123, 0146-0150);
determine behavioral patterns from the block exchange data, including at least one of: a number of unidirectional block exchanges above a predetermined threshold for the one or more addresses; addresses with interaction history ending within the predetermined time period (Fig. 8-10, 0146-0150);
based on determining that at least one of the behavioral patterns indicates anomalous activity, identify the one or more addresses as associated with the anomaly (Fig. 8-10, 0159, 0161); and
generate an alert when the anomaly is identified (Fig. 10, 0165-0166).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify claims 1, 11, and 18 disclosed by Choudhary by including determining behavioral patterns based on directional transaction graphs over a time period as disclosed by Kursun. One of ordinary skill in the art would have been motivated to make this modification to detect sink regions associated with malfeasance through dynamic directed transaction graphs (Kursun 0038).
Choudhary in view of Kursun does not disclose: a graphics processing unit (GPU);
via the GPU, generate an address graph based on the block exchange data to display one or more addresses associated with an anomaly by causing the GNN...
However, in the same field of endeavor, Chen discloses: an artificial intelligence (AI) tool comprising a processor, a graphics processing unit (GPU), and a graph neural network (GNN) model (Fig. 1, 0025, 0027, 0030);
a memory communicatively coupled to the processor; and
machine-readable instructions stored in the memory that, upon execution by the processor, cause the processor to:
via the GPU, generate an address graph based on the block exchange data to display one or more addresses associated with an anomaly by causing the GNN (Fig. 5-7, 0038, 0081-0082)...
and generate an alert when the anomaly is identified (Fig. 4, Fig. 7, 0052, 0083-0084).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify claims 1, 11, and 18 disclosed by Choudhary in view of Kursun by including a GPU and generating and displaying an alert as disclosed by Chen. One of ordinary skill in the art would have been motivated to make this modification as a simple substitution of one known element (GNN system of Chen) for another (GNN system of Choudhary) to obtain predictable results (KSR International Co. v. Teleflex Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007)).
Regarding claims 2 and 12, Choudhary in view of Kursun and Chen disclose all limitations of claims 1 and 11. Choudhary further discloses: extract one or more graph parameters from a block transactions graph of a block of a blockchain (Fig. 5, Fig. 8, 0143-0144, 0179-0181);
detect an irregular graph pattern in the block transactions graph (Fig. 5, Fig. 8, 0102, 0186);
and via the GNN model, identify an anomaly within the block transactions graph based on the irregular graph pattern in the block transactions graph (Fig. 5, Fig. 8, 0102, 0186). Chen also further discloses: extract one or more graph parameters from a block transactions graph of a block of a blockchain (Fig. 5-6, 0037, 0063, 0079-0080);
detect an irregular graph pattern in the block transactions graph (Fig. 5-7, 0038, 0081-0082);
and via the GNN model, identify an anomaly within the block transactions graph based on the irregular graph pattern in the block transactions graph (Fig. 5-7, 0038, 0081-0082).
Regarding claims 4 and 14, Choudhary in view of Kursun and Chen disclose all limitations of claims 2 and 11. Choudhary further discloses: extract block data from the block of the blockchain over time (0080-0081, 0144-0145);
and generate, via the GPU, the block transactions graph of the block based on the block data to summarize exchanges between individual transaction addresses (0086-0088, 0144-0145).
Regarding claims 5, 15, and 19, Choudhary in view of Kursun and Chen disclose all limitations of claims 1, 11, and 18. Choudhary further discloses: wherein the one or more addresses associated with the anomaly comprise one or more addresses involved in the anomaly, causing the anomaly, or combinations thereof (Fig. 4, 0059, 0070, 0112-0114, 0144-0145).
Regarding claims 6, 16, and 20, Choudhary in view of Kursun and Chen disclose all limitations of claims 1, 11, and 18. Chen further discloses: train the GNN model based on a training set to generate one or more classifiers of types of anomalies (0084, 0091-0094);
and identify the anomaly based on one of the one or more classifiers (0084, 0091-0094).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify claims 6, 16, and 20 disclosed by Choudhary in view of Kursun and Chen by including training the GNN model to identify anomalies with classifiers as disclosed by Chen. One of ordinary skill in the art would have been motivated to make this modification as a simple substitution of one known element (GNN system of Chen) for another (GNN system of Choudhary) to obtain predictable results (KSR International Co. v. Teleflex Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007)).
Regarding claim 7, Choudhary in view of Kursun and Chen disclose all limitations of claim 6. Choudhary further discloses: wherein the one or more classifiers comprise a classification of a phishing anomaly, a fraud anomaly, a financial fraud anomaly, or combinations thereof, and wherein the financial fraud anomaly is based on a fluctuation over a transaction pattern threshold of gas price transaction pattern, a sell/buy transaction pattern, or combinations thereof (0034, 0102-0104, 0144-0145).
Regarding claims 8 and 17, Choudhary in view of Kursun and Chen disclose all limitations of claims 1 and 11. Choudhary further discloses: wherein the GNN model is implemented in one or more nodes of the blockchain (Fig. 1-2, 0144-0145, 0194).
Regarding claim 9, Choudhary in view of Kursun and Chen disclose all limitations of claim 1. Choudhary further discloses: wherein the GNN model is hosted remote from and communicatively coupled to one or more nodes of the blockchain (Fig. 1-2, 0144-0145, 0194).
Regarding claim 10, Choudhary in view of Kursun and Chen disclose all limitations of claim 1. Kursun further discloses: wherein determining behavioral patterns includes analyzing temporal interaction patterns of addresses over configurable time periods (Fig. 8-10, 0090, 0120-0123, 0146-0150).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify claim 10 disclosed by Choudhary in view of Kursun and Chen by including analyzing temporal interaction patterns over a time period as disclosed by Kursun. One of ordinary skill in the art would have been motivated to make this modification to detect sink regions associated with malfeasance through dynamic directed transaction graphs (Kursun 0038).
Allowable Subject Matter
Claims 3 and 13 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
The prior art does not disclose, neither singly nor in combination, for claims 3 and 13: (i) generating one or more statistical approximations of the block transactions graph based on the one or more graph parameters, comparing the one or more statistical approximations of the block transactions graph to at least one anomaly threshold, and determining the one or more statistical approximations exceed the at least one anomaly threshold; and(ii) determining, based on the one or more graph parameters extracted from the block transactions graph, a topology of transaction clusters within the block transactions graph and determining that an overlapping amount of clusters of block transactions exceed a predetermined allowable threshold; wherein each of (i) and (ii) independently identifies the irregular graph pattern.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chari et al. (US 20170140382) generally discloses methods and systems for identifying fraudulent transaction using transaction payment relationship graphs.
Kundu et al. (US 20200364366) generally discloses a method for providing security actions based on identity profile graphs.
Chen et al. (US 20210256355) generally discloses a system for evolving graph convolutional networks for given time steps.
Tian et al. (US 20240086926) generally discloses a method for generating synthetic graphs representing a probability that a real-time-payment transaction may be conducted and inserting different adversarial activity into the dynamic graphs to create data sets for machine learning.
R. Tan et al. ("Graph Neural Network for Ethereum Fraud Detection") generally discloses a system for using graph convolutional networks to classify Ethereum address into legal and fraudulent addresses based on extracted node features.
A. Sardana et al. ("Optimizing Fraud Detection in Financial Services with Graph Neural Networks and NVIDIA GPUs") generally discloses methods for training and deploying GNNs utilizing GPUs for use in fraud detection.
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/T.R./Examiner, Art Unit 3697
/JOHN W HAYES/Supervisory Patent Examiner, Art Unit 3697