Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This action is responsive to claims filed on June 18, 2025. Claims 1-20 are pending and presented for examination.
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Allowable Subject Matter
No rejection under 35 USC §§ 102, 103, 112 or 101 has been applied to claims 1-20 with the exception to the double patenting rejection below.
Prior art Cage et al “Cage”, US-PGPub. No. 20220255969 discloses generating, by at least one computing node in the reconciliation network, a network token, the network token associated with a physical asset, the physical asset being stored in a physical entity, the network token being transferable in the enterprise network; generating, by the at least one computing node in the reconciliation network, a digital contract associated with the network token or the generation of the network token, the digital contract enabling a transaction between a first computing node in the enterprise network and a second computing node in the enterprise network, the transaction comprising a transfer of the network token between the first computing node and the second computing node; and reconciling, by the at least one computing node in the reconciliation network, and in the distributed ledger, the transfer of the network token between the first computing node in the enterprise network and the second computing node in the enterprise network (Paragraph(s) [0004], [0023]). In addition, Cage discloses in some embodiments, a digital facilitator or digital contract may have an associated life cycle. A digital facilitator or digital contact may go through this life cycle as part of an association with an enterprise ledger. The steps of the life cycle may be performed in any order, and not all steps may need to be executed. The life cycle steps for a digital facilitator or digital contract may include: identifying an underlying distributed ledger, onboarding partners, creating ledger accounts, managing private keys, connecting existing applications, establishing contract workflows, building contract templates, maintaining test environments, translating enterprise resource planning software, maintaining ledger notes, compiling contracts, deploying contracts, managing contract amendments, securely signing transactions, tracking execution of contract terms, reconciling anomalies, mediating and resolving disputes, and processing payments. Contracts may include digital facilitators or digital contracts, or may include digital interpretations or scans of physical agreements (Paragraph(s) [0446]). Furthermore, Cage discloses the computing environment may include a ledger which may be a distributed ledger, a distributed database, a data collection ledger, a centralized ledger, an asynchronous ledger, a synchronous ledger, a blockchain network, etc. (Paragraph(s) [0375]). Additionally, Cage discloses the enterprise network and/or the reconciliation network may be associated with, communicate with, and/or comprise one or more distributed ledgers (e.g., decentralized distributed ledgers) and/or blockchain systems (Paragraph(s) [0088]).
Prior art Levine et al "Levine", US-PGPub. No. 20230196370 discloses an AI-based transaction data processing and reconciliation system employing various AI-based techniques to identify different errors in the transactions such as but not limited to anomalous transactions, transactions with Required Adjustments tag (R-tag), and/or aging transactions that are not resolved or reconciled for a longer time. In an example, a plurality of anomaly detection models are used to determine anomalous transactions from transaction data that is periodically generated. The plurality of anomaly detection models score transactions from the transaction data and transactions with higher scores can be automatically classified as anomalous transactions which are flagged for further review. A plurality of tag prediction models are used to score transactions to generate R-tag risk scores that predict the transactions that are likely to be associated with R-tags. Similarly, a plurality of aging prediction models (for different regions) are used to predict that transactions with a high risk of aging i.e., transactions that cannot be reconciled for longer periods. Features such as but not limited to, risk tables, amount anomalies, and transaction text, etc., can be extracted from the transaction data and provided to the plurality of tag prediction models and the plurality of aging prediction models for generating the risk scores. In an example, each tag prediction model can include a random forest ensemble of decision trees. Similarly, each of the plurality of aging prediction models can also include a random forest ensemble of decision trees. The anomalous transactions, R-tagged transactions, and aging transactions thus identified are flagged for review. In an example, the transactions can be manually reviewed. The feedback from the reviews can be stored and used to further train the various AI models. In an example, the feedback can include changes to actions executed during the automatic reconciliation of the transactions (Paragraph(s) [0016], [0020], [0026]).
While the cited prior art discloses some of the claimed features as explained above, however, the cited prior art fails to disclose or suggest each and every limitation together as claimed in the context of claims 1, 11 and 20. Furthermore, the Examiner cannot determine a reasonable motivation, either from the cited prior art or the existing case law, to combine the known references to render the claimed invention.
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).
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over Claims 1-20 of U.S. Patent No. 12,346,966. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant application claims are anticipated by the claims recited in the U.S. Patent.
Said claims in both applications are directed to dynamically performing centralized reconciliations of trades at a plurality of real-time trade processing and centralized reconciliation engines, in real-time, based on trade metadata and a decentralized reconciliation of trades at an anomaly detection and reconciliation mesh analysis engine based on a system-generated hashed tokenized trade digital DNA.
Taking claims 1, 11 and 20 in the instant application, as an exemplary to show the similarity in features being claimed in both applications, to compare to claims 1, 11 and 20 in the U.S. Patent (see comparison table below). Therefore, it would have been obvious to one of the ordinary skill in the art, having the same claim elements of the parent application, to construct a system as claimed in the instant application capable of performing identical functions as the parent application. The rest of the instant application claims recite similar features as those cited in the U.S. Patent.
An exemplary table to show similarity among the conflicting claims.
Instant Application
U.S. Patent No. 12,346,966
1. A computing system comprising:
a real-time trade processing and centralized reconciliation engine comprising:
at least a first processor;
a first communication interface communicatively coupled to the at least first processor; and
first memory storing computer-readable instructions that, when executed by the at least first processor, causes the real-time trade processing and centralized reconciliation engine to:
process a first trade by retrieving first trade metadata associated with the first trade from a first trade system associated with the first trade;
determine whether there are any anomalies in the first trade metadata; and
responsive to determining that there are no anomalies in the first trade metadata, send the first trade metadata to an anomaly detection and reconciliation mesh analysis engine; and
the anomaly detection and reconciliation mesh analysis engine, wherein the anomaly detection and reconciliation mesh analysis engine comprises:
at least a second processor;
a second communication interface communicatively coupled to the at least first processor; and
second memory storing computer-readable instructions that, when executed by the at least second processor, causes the anomaly detection and reconciliation mesh analysis engine to:
receive the first trade metadata from the real-time trade processing and centralized reconciliation engine;
generate, from the first trade metadata, first tokenized trade digital DNA;
perform hashing on the first tokenized trade digital DNA to generate hashed data;
compare the hashed data to determine whether there are any anomalies within the hashed data; and
responsive to determining that the hashed data comprises at least one anomaly, input the hashed data into a decentralized reconciliation mesh.
1. A computing system comprising:
a real-time trade processing and centralized reconciliation engine comprising:
at least a first processor;
a first communication interface communicatively coupled to the at least first processor; and
first memory storing computer-readable instructions that, when executed by the at least first processor, causes the real-time trade processing and centralized reconciliation engine to:
process a first trade by retrieving first trade metadata associated with the first trade from a first trade system associated with the first trade;
determine whether there are any anomalies in the first trade metadata by performing a centralized reconciliation on the first trade metadata using historical trade data of the first trade system associated with the first trade and an anomaly history of the first trade system associated with the first trade; and
responsive to determining that there are no anomalies in the first trade metadata, send the first trade metadata to an anomaly detection and reconciliation mesh analysis engine; and
the anomaly detection and reconciliation mesh analysis engine, wherein the anomaly detection and reconciliation mesh analysis engine comprises:
at least a second processor;
a second communication interface communicatively coupled to the at least first processor; and
second memory storing computer-readable instructions that, when executed by the at least second processor, causes the anomaly detection and reconciliation mesh analysis engine to:
receive the first trade metadata from the real-time trade processing and centralized reconciliation engine;
tokenize the first trade metadata to generate first tokenized trade metadata;
generate, from at least the first tokenized trade metadata, first tokenized trade digital DNA;
perform hashing on the first tokenized trade digital DNA to generate hashed data;
compare the hashed data to determine whether there are any anomalies within the hashed data; and
responsive to determining that the hashed data comprises at least one anomaly, input the hashed data into a decentralized reconciliation mesh.
11. A method comprising:
at a real-time trade processing and centralized reconciliation engine comprising at least a first processor, a first communication interface, and first memory:
processing a first trade by retrieving first trade metadata associated with the first trade from a first trade system associated with the first trade;
determining whether there are any anomalies in the first trade metadata; and
responsive to determining that there are no anomalies in the first trade metadata, sending the first trade metadata to an anomaly detection and reconciliation mesh analysis engine; and
at the anomaly detection and reconciliation mesh analysis engine comprising at least a second processor, a second communication interface, and a second memory:
receiving the first trade metadata from the real-time trade processing and centralized reconciliation engine;
generating, from at least the first trade metadata, first tokenized trade digital DNA;
performing hashing on the first tokenized trade digital DNA to generate hashed data;
comparing the hashed data to determine whether there are any anomalies within the hashed data; and
responsive to determining that the hashed data comprises at least one anomaly, input the hashed data into a decentralized reconciliation mesh.
11. A method comprising:
at a real-time trade processing and centralized reconciliation engine comprising at least a first processor, a first communication interface, and first memory:
processing a first trade by retrieving first trade metadata associated with the first trade from a first trade system associated with the first trade;
determining whether there are any anomalies in the first trade metadata by performing a centralized reconciliation on the first trade metadata using historical trade data of the first trade system associated with the first trade and an anomaly history of the first trade system associated with the first trade; and
responsive to determining that there are no anomalies in the first trade metadata, sending the first trade metadata to an anomaly detection and reconciliation mesh analysis engine; and
at the anomaly detection and reconciliation mesh analysis engine comprising at least a second processor, a second communication interface, and a second memory:
receiving the first trade metadata from the real-time trade processing and centralized reconciliation engine;
tokenizing the first trade metadata to generate first tokenized trade metadata;
generating, from at least the first tokenized trade metadata, first tokenized trade digital DNA;
performing hashing on the first tokenized trade digital DNA to generate hashed data;
comparing the hashed data to determine whether there are any anomalies within the hashed data; and
responsive to determining that the hashed data comprises at least one anomaly, input the hashed data into a decentralized reconciliation mesh.
20. A plurality of non-transitory computer-readable media comprising:
first non-transitory computer-readable media storing instructions that, when executed by a real-time trade processing and centralized reconciliation engine comprising at least a first processor, a first communication interface, and first memory, cause the real-time trade processing and centralized reconciliation engine to:
process a first trade by retrieving first trade metadata associated with the first trade from a first trade system associated with the first trade;
determine whether there are any anomalies in the first trade metadata; and
responsive to determining that there are no anomalies in the first trade metadata, send the first trade metadata to an anomaly detection and reconciliation mesh analysis engine; and
second non-transitory computer-readable media storing instructions that, when executed by the anomaly detection and reconciliation mesh analysis engine comprising at least a second processor, a second communication interface, and a second memory, cause the anomaly detection and reconciliation mesh analysis engine to:
receive the first trade metadata from the real-time trade processing and centralized reconciliation engine;
generate, from at least the first trade metadata, first tokenized trade digital DNA;
perform hashing on the first tokenized trade digital DNA to generate hashed data;
compare the hashed data to determine whether there are any anomalies within the hashed data; and
responsive to determining that the hashed data comprises at least one anomaly, input the hashed data into a decentralize.
20. A plurality of non-transitory computer-readable media comprising:
first non-transitory computer-readable media storing instructions that, when executed by a real-time trade processing and centralized reconciliation engine comprising at least a first processor, a first communication interface, and first memory, cause the real-time trade processing and centralized reconciliation engine to:
process a first trade by retrieving first trade metadata associated with the first trade from a first trade system associated with the first trade;
determine whether there are any anomalies in the first trade metadata by performing a centralized reconciliation on the first trade metadata using historical trade data of the first trade system associated with the first trade and an anomaly history of the first trade system associated with the first trade; and
responsive to determining that there are no anomalies in the first trade metadata, send the first trade metadata to an anomaly detection and reconciliation mesh analysis engine; and
second non-transitory computer-readable media storing instructions that, when executed by the anomaly detection and reconciliation mesh analysis engine comprising at least a second processor, a second communication interface, and a second memory, cause the anomaly detection and reconciliation mesh analysis engine to:
receive the first trade metadata from the real-time trade processing and centralized reconciliation engine;
tokenize the first trade metadata to generate first tokenized trade metadata;
generate, from at least the first tokenized trade metadata, first tokenized trade digital DNA;
perform hashing on the first tokenized trade digital DNA to generate hashed data;
compare the hashed data to determine whether there are any anomalies within the hashed data; and
responsive to determining that the hashed data comprises at least one anomaly, input the hashed data into a decentralized reconciliation mesh.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please refer to form PTO-892 (Notice of Reference Cited) for a list of relevant prior art.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED A WASEL whose telephone number is (571) 272-2669. The examiner can normally be reached Mon-Fri (8:00 am – 4:30 pm).
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Glenton Burgess can be reached on (571)272-3949. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOHAMED A. WASEL/Primary Examiner, Art Unit 2454
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