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 .
1. This is in response to the communications filed on 24 April 2026.
2. Claims 1-20 are pending in the application.
3. Claims 1-20 have been rejected.
Information Disclosure Statement
4. The examiner has considered the information disclosure statement (IDS) filed on 24 April 2026.
Claim Objections
5. Claim 10 is objected to because of the following informalities: typographical error. Claim 10 recites “aone” which should be “a one”. Appropriate correction is required.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
6. Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,316,663 B2 (hereinafter the ‘663 patent) in view of Kar et al US 2019/0058719 A1 (hereinafter Kar).
As to claims 1, 10 and 19 the ‘663 patent discloses:
receiving transaction information associated with an autonomous program protocol that is recorded on a blockchain (i.e. receiving state information) [column 34, lines 23-24];
extracting a trace log associated with the transaction information, the trace log comprising machine events executable by the blockchain, the machine events associated with one or more transactions (i.e. generating trace log) [column 34, lines 25-31];
extracting a set of features from the trace log, wherein a feature in the set comprises a summary of a machine event executable by the blockchain [column 34, lines 32-34].
The ‘663 patent does not teach clustering the one or more transactions with known transactions executed by the blockchain, wherein the clustering is based on the set of features extracted from the trace log. The ‘663 patent does not teach identifying an abnormal transaction from the one or more transactions based on the clustering.
Kar teaches clustering the one or more transactions with known transactions executed by the blockchain (i.e. similar transactional behavior are clustered) [0048], wherein the clustering is based on the set of features extracted from the trace log (i.e. based on transactional history) [0048]. Kar teaches identifying an abnormal transaction from the one or more transactions based on the clustering (i.e. detecting anomalies) [0036].
Therefore, it 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 to have modified the ‘663 patent so that the one or more transactions would have been clustered with known transactions executed by the blockchain, wherein the clustering would have been based on the set of features extracted from the trace log. An abnormal transaction would have been identified from the one or more transactions based on the clustering.
It 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 to have modified the ‘663 patent by the teaching of Kar because it effectively detects anomalous activities in a distributed and decentralized network which offers user anonymity [0001].
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.
7. Claims 1-20 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.
Independent claims 1, 10 and 19 include the limitations of “clustering the one or more transactions with known transactions executed by the blockchain, wherein the clustering is based on the set of features extracted from the trace log” and “identifying an abnormal transaction from the one or more transactions based on the clustering”. Dependent claims 3 and 12 recite “generating a vector including an opcode identifier, gas consumed, and a stack-state snapshot”. Dependent claims 4 and 13 recite “clustering is performed by an unsupervised machine-learning algorithm selected from k-means, density-based spatial clustering of applications with noise, or hierarchical agglomerative clustering”. Dependent claims 5 and 14 recite “the unsupervised machine-learning algorithm operates in an embedding space produced by a transformer encoder trained on historical trace logs”. Dependent claims 6 and 15 recite “identifying the abnormal transaction comprises determining that a distance between the transaction and a cluster that is associated with historical abnormal transactions in training data”. Dependent claims 7 and 16 recite “generating an alert that includes a transaction hash, an abnormality score, and a ranked list of contributing features”. Dependent claims 8 and 17 recite “generating a label corresponding to the abnormal transaction to a threat-intelligence database and retraining a supervised fraud-detection model using labeled data”. Dependent claims 9, 18 and 20 recite “the abnormal transaction is identified as part of a series of transactions and the clustering further accounts for temporal correlations across blocks”. However, after a review of the applicant’s specification the examiner has not found support for the limitations . The specification, at the time the application was filed, would not have taught one of ordinary skill in the art how to make and/or use the full scope of the claimed invention without undue experimentation.
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.
8. Claim(s) 1, 10 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chu et al US 2024/0169006 A1 (hereinafter Chu) in view of Ross et al US 2021/0342441 A1 (hereinafter Ross) and Kar et al US 2019/0058719 A1 (hereinafter Kar).
As to claim 1, Chu discloses a computer-implemented method comprising:
receiving transaction information associated with an autonomous program protocol that is recorded on a blockchain (i.e. an execution condition associated with the smart contract stored on the blockchain, the smart contract is executable codes stored in the blockchain) [0030];
extracting a trace log associated with the transaction information, the trace log comprising machine events executable by the blockchain, the machine events associated with one or more transactions (i.e. the execution of the smart contract is recorded as a transaction on the blockchain) [0030].
Chu does not teach extracting a set of features from the trace log, wherein a feature in the set comprises a summary of a machine event executable by the blockchain. Chu does not teach clustering the one or more transactions with known transactions executed by the blockchain, wherein the clustering is based on the set of features extracted from the trace log. Chu does not teach identifying an abnormal transaction from the one or more transactions based on the clustering.
Ross teaches extracting a set of features from the trace log, wherein a feature in the set comprises a summary of a machine event executable by the blockchain (i.e. summary data from the aggregation of log data) [0036].
Therefore, it 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 to have modified Chu so that a set of features would have been extracted from the trace log, wherein a feature in the set would have comprised a summary of a machine event executable by the blockchain.
It 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 to have modified Chu by the teaching of Ross because it helps distinguish legitimate behavior from malicious behavior [0004].
The Chu-Ross combination does not teach clustering the one or more transactions with known transactions executed by the blockchain, wherein the clustering is based on the set of features extracted from the trace log. The Chu-Ross combination does not teach identifying an abnormal transaction from the one or more transactions based on the clustering.
Kar teaches clustering the one or more transactions with known transactions executed by the blockchain (i.e. similar transactional behavior are clustered) [0048], wherein the clustering is based on the set of features extracted from the trace log (i.e. based on transactional history) [0048]. Kar teaches identifying an abnormal transaction from the one or more transactions based on the clustering (i.e. detecting anomalies) [0036].
Therefore, it 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 to have modified the Chu-Ross combination so that the one or more transactions would have been clustered with known transactions executed by the blockchain, wherein the clustering would have been based on the set of features extracted from the trace log. An abnormal transaction would have been identified from the one or more transactions based on the clustering.
It 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 to have modified the Chu-Ross combination by the teaching of Kar because it effectively detects anomalous activities in a distributed and decentralized network which offers user anonymity [0001].
As to claim 10, Chu discloses a system comprising:
one or more processors [0384]; and
non-transitory computer readable medium storing code comprising instructions, wherein the instructions, when executed by the one or more processors [0384], cause the one or more processors to:
receive transaction information associated with an autonomous program protocol that is recorded on a blockchain (i.e. an execution condition associated with the smart contract stored on the blockchain, the smart contract is executable codes stored in the blockchain) [0030];
extract a trace log associated with the transaction information, the trace log comprising machine events executable by the blockchain, the machine events associated with a one or more transactions (i.e. the execution of the smart contract is recorded as a transaction on the blockchain) [0030].
Chu does not teach extracting a set of features from the trace log, wherein a feature in the set comprises a summary of a machine event executable by the blockchain. Chu does not teach clustering the one or more transactions with known transactions executed by the blockchain, wherein the clustering is based on the set of features extracted from the trace log. Chu does not teach identifying an abnormal transaction from the one or more transactions based on the clustering.
Ross teaches extracting a set of features from the trace log, wherein a feature in the set comprises a summary of a machine event executable by the blockchain (i.e. summary data from the aggregation of log data) [0036].
Therefore, it 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 to have modified Chu so that a set of features would have been extracted from the trace log, wherein a feature in the set would have comprised a summary of a machine event executable by the blockchain.
It 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 to have modified Chu by the teaching of Ross because it helps distinguish legitimate behavior from malicious behavior [0004].
The Chu-Ross combination does not teach clustering the one or more transactions with known transactions executed by the blockchain, wherein the clustering is based on the set of features extracted from the trace log. The Chu-Ross combination does not teach identifying an abnormal transaction from the one or more transactions based on the clustering.
Kar teaches clustering the one or more transactions with known transactions executed by the blockchain (i.e. similar transactional behavior are clustered) [0048], wherein the clustering is based on the set of features extracted from the trace log (i.e. based on transactional history) [0048]. Kar teaches identifying an abnormal transaction from the one or more transactions based on the clustering (i.e. detecting anomalies) [0036].
Therefore, it 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 to have modified the Chu-Ross combination so that the one or more transactions would have been clustered with known transactions executed by the blockchain, wherein the clustering would have been based on the set of features extracted from the trace log. An abnormal transaction would have been identified from the one or more transactions based on the clustering.
It 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 to have modified the Chu-Ross combination by the teaching of Kar because it effectively detects anomalous activities in a distributed and decentralized network which offers user anonymity [0001].
As to claim 19, Chu discloses a non-transitory computer-readable medium configured to store code comprising instructions, wherein the instructions, when executed by one or more processors, cause the one or more processors to:
receive transaction information associated with an autonomous program protocol that is recorded on a blockchain (i.e. an execution condition associated with the smart contract stored on the blockchain, the smart contract is executable codes stored in the blockchain) [0030];
extract a trace log associated with the transaction information, the trace log comprising machine events executable by the blockchain, the machine events associated with one or more transactions (i.e. the execution of the smart contract is recorded as a transaction on the blockchain) [0030].
Chu does not teach extracting a set of features from the trace log, wherein a feature in the set comprises a summary of a machine event executable by the blockchain. Chu does not teach clustering the one or more transactions with known transactions executed by the blockchain, wherein the clustering is based on the set of features extracted from the trace log. Chu does not teach identifying an abnormal transaction from the one or more transactions based on the clustering.
Ross teaches extracting a set of features from the trace log, wherein a feature in the set comprises a summary of a machine event executable by the blockchain (i.e. summary data from the aggregation of log data) [0036].
Therefore, it 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 to have modified Chu so that a set of features would have been extracted from the trace log, wherein a feature in the set would have comprised a summary of a machine event executable by the blockchain.
It 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 to have modified Chu by the teaching of Ross because it helps distinguish legitimate behavior from malicious behavior [0004].
The Chu-Ross combination does not teach clustering the one or more transactions with known transactions executed by the blockchain, wherein the clustering is based on the set of features extracted from the trace log. The Chu-Ross combination does not teach identifying an abnormal transaction from the one or more transactions based on the clustering.
Kar teaches clustering the one or more transactions with known transactions executed by the blockchain (i.e. similar transactional behavior are clustered) [0048], wherein the clustering is based on the set of features extracted from the trace log (i.e. based on transactional history) [0048]. Kar teaches identifying an abnormal transaction from the one or more transactions based on the clustering (i.e. detecting anomalies) [0036].
Therefore, it 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 to have modified the Chu-Ross combination so that the one or more transactions would have been clustered with known transactions executed by the blockchain, wherein the clustering would have been based on the set of features extracted from the trace log. An abnormal transaction would have been identified from the one or more transactions based on the clustering.
It 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 to have modified the Chu-Ross combination by the teaching of Kar because it effectively detects anomalous activities in a distributed and decentralized network which offers user anonymity [0001].
9. Claim(s) 2 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chu et al US 2024/0169006 A1 (hereinafter Chu), Ross et al US 2021/0342441 A1 (hereinafter Ross) and Kar et al US 2019/0058719 A1 (hereinafter Kar) as applied to claims 1 and 10 above, and further in view of Singh et al US 2024/0163095 A1.
As to claim 2, the Chu-Ross-Kar combination does not teach receiving a transaction hash to be broadcasted to the blockchain. The Chu-Ross-Kar combination does not teach retrieving metadata describing the transaction.
Singh teaches receiving a transaction hash to be broadcasted to the blockchain (i.e. hash and select which transactions are broadcasted to the blockchain) [0088]. Singh teaches retrieving metadata describing the transaction (i.e. collection of transaction metadata) [0027].
Therefore, it 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 to have modified the Chu-Ross-Kar combination so that a transaction hash to be broadcasted to the blockchain would have been received. Metadata describing the transaction would have been retrieved.
It 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 to have modified the Chu-Ross-Kar combination by the teaching of Singh because it helps to generate a centralized collection of data [0001].
As to claim 11, the Chu-Ross-Kar combination does not teach receive a transaction hash to be broadcasted to the blockchain. The Chu-Ross-Kar combination does not teach retrieve metadata describing the transaction.
Singh teaches receiving a transaction hash to be broadcasted to the blockchain (i.e. hash and select which transactions are broadcasted to the blockchain) [0088]. Singh teaches retrieving metadata describing the transaction (i.e. collection of transaction metadata) [0027].
Therefore, it 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 to have modified the Chu-Ross-Kar combination so that a transaction hash to be broadcasted to the blockchain would have been received. Metadata describing the transaction would have been retrieved.
It 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 to have modified the Chu-Ross-Kar combination by the teaching of Singh because it helps to generate a centralized collection of data [0001].
10. Claim(s) 4 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chu et al US 2024/0169006 A1 (hereinafter Chu), Ross et al US 2021/0342441 A1 (hereinafter Ross) and Kar et al US 2019/0058719 A1 (hereinafter Kar) as applied to claims 1 and 10 above, and further in view of Leibman et al US 2020/0007563 A1 (hereinafter Leibman).
As to claim 4, the Chu-Ross-Kar combination does not teach the computer-implemented method of claim 1, wherein clustering is performed by an unsupervised machine-learning algorithm selected from k-means, density-based spatial clustering of applications with noise, or hierarchical agglomerative clustering.
Leibman teaches that clustering is performed by an unsupervised machine-learning algorithm selected from k-means, density-based spatial clustering of applications with noise, or hierarchical agglomerative clustering [0011].
Therefore, it 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 to have modified the Chu-Ross-Kar combination so that clustering would have been performed by an unsupervised machine-learning algorithm selected from k-means, density-based spatial clustering of applications with noise, or hierarchical agglomerative clustering.
It 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 to have modified the Chu-Ross-Kar combination by the teaching of Leibman because it helps detect if a device is behaving anomalously [0003].
As to claim 13, the Chu-Ross-Kar combination does not teach the system of claim 10, wherein clustering is performed by an unsupervised machine-learning algorithm selected from k-means, density-based spatial clustering of applications with noise, or hierarchical agglomerative clustering.
Leibman teaches that clustering is performed by an unsupervised machine-learning algorithm selected from k-means, density-based spatial clustering of applications with noise, or hierarchical agglomerative clustering [0011].
Therefore, it 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 to have modified the Chu-Ross-Kar combination so that clustering would have been performed by an unsupervised machine-learning algorithm selected from k-means, density-based spatial clustering of applications with noise, or hierarchical agglomerative clustering.
It 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 to have modified the Chu-Ross-Kar combination by the teaching of Leibman because it helps detect if a device is behaving anomalously [0003].
Allowable Subject Matter
11. Claims 3, 5, 6-9, 12, 14-18 and 20 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.
As to claim 3, the prior art does not disclose, teach or fairly suggest the computer-implemented method of claim 1, wherein extracting the set of features comprises, for each machine event, generating a vector including an opcode identifier, gas consumed, and a stack-state snapshot.
As to claim 5, the prior art does not disclose, teach or fairly suggest the computer-implemented method of claim 4, wherein the unsupervised machine-learning algorithm operates in an embedding space produced by a transformer encoder trained on historical trace logs.
As to claim 6, the prior art does not disclose, teach or fairly suggest the computer-implemented method of claim 1, wherein identifying the abnormal transaction comprises determining that a distance between the transaction and a cluster that is associated with historical abnormal transactions in training data.
As to claim 7, the prior art does not disclose, teach or fairly suggest the computer-implemented method of claim 1, further comprising generating an alert that includes a transaction hash, an abnormality score, and a ranked list of contributing features.
As to claim 8, the prior art does not disclose, teach or fairly suggest the computer-implemented method of claim 1, further comprising generating a label corresponding to the abnormal transaction to a threat-intelligence database and retraining a supervised fraud-detection model using labeled data.
As to claim 9, the prior art does not disclose, teach or fairly suggest the computer-implemented method of claim 1, wherein the abnormal transaction is identified as part of a series of transactions and the clustering further accounts for temporal correlations across blocks.
As to claim 12, the prior art does not disclose, teach or fairly suggest the system of claim 10, wherein the instruction to extract the set of features comprises instructions to, for each machine event, generating a vector including an opcode identifier, gas consumed, and a stack-state snapshot.
As to claim 14, the prior art does not disclose, teach or fairly suggest the system of claim 13, wherein the unsupervised machine-learning algorithm operates in an embedding space produced by a transformer encoder trained on historical trace logs.
As to claim 15, the prior art does not disclose, teach or fairly suggest the system of claim 10, wherein the instruction to identify the abnormal transaction comprises the instruction to determine that a distance between the transaction and a cluster that is associated with historical abnormal transactions in training data.
As to claim 16, the prior art does not disclose, teach or fairly suggest the system of claim 10, wherein the instructions, when executed, further cause the one or more processors to generate an alert that includes a transaction hash, an abnormality score, and a ranked list of contributing features.
As to claim 17, the prior art does not disclose, teach or fairly suggest the system of claim 10, wherein the instructions, when executed, further cause the one or more processors to generate a label corresponding to the abnormal transaction to a threat-intelligence database and retraining a supervised fraud-detection model using labeled data.
As to claim 18, the prior art does not disclose, teach or fairly suggest the system of claim 10, wherein the abnormal transaction is identified as part of a series of transactions and the clustering further accounts for temporal correlations across blocks.
As to claim 20, the prior art does not disclose, teach or fairly suggest the non-transitory computer-readable medium of claim 19, wherein the abnormal transaction is identified as part of a series of transactions and the clustering further accounts for temporal correlations across blocks.
Relevant Prior Art
12. The following references have been considered relevant by the examiner:
A. Dutta et al US 2023/0186075 A1 directed to computing devices and, more specifically, to anomaly detection with model hyperparameter selection [0002].
B. Ikeda et al US 2020/0334578 A1 directed to a technique for monitoring data collected from a system in real time and continuously performing anomaly detecting using a detector based on a learning model [0001].
C. Burnett et al US 2024/0095736 A1 directed to a transaction exchange platform using a streaming data platform and microservices to process transactions in accordance with corresponding workflows [abstract].
Conclusion
13. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ARAVIND K MOORTHY whose telephone number is (571)272-3793. The examiner can normally be reached M-F 4:30-3:00.
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/ARAVIND K MOORTHY/ Primary Examiner, Art Unit 2407
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