DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This action is responsive to pending claims 1-20 filed 9/22/2023.
Claim Objections
The following claim(s) are objected to for formality issues:
Claim 11, 18 depends on claim 8, 15 but references “the microservices system”, which finds antecedent in claims 9, 16 and not claim 8, 15.
Appropriate correction(s) are required.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 6-8, 10, 13-15, 17, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Lopes (US 20210350382 A1) in view of Ganesh (US 20220092035 A1).
For claim 1, Lopes discloses: a computing platform comprising:
at least one processor (fig.6:620);
a communication interface communicatively coupled to the at least one processor (fig.6:670); and
memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform (fig.6:630-640) to:
retrieve, from an event processing system, a plurality of historical event processing requests and a plurality of historical change instructions (fig.1, 0016 gives an overview of the process, with fig.1:100-110, 0017-19 disclosing ingestion / retrieval system of historical (see 0019, fig.3 in particular) stored records of processing requests from various sources (e.g., requests for processing purchase, trades, transactions, etc., see 0017) and change instructions, see 0022: associating or matching groups of transactions, including later additions / unmatched modifications to a transaction; hence, latter change instructions creating changes to the transaction);
process, using natural language processing, the plurality of historical change instructions (0025: extracting keywords, 0066: using NLP to extract keywords and other features);
train a change prediction model based on the encoded plurality of historical event processing requests and based on the processed plurality of historical change instructions (fig.3. 0065: training a matching model for predicting which change information associate with which transaction or events, hence, change prediction model; 0068: predicting a target value, such as a prediction of whether a change and an event are associated, such as via supervised learning; fig.2D, 0054-56: generating predictions between transactions to associate event processing requests with change instructions and predicting missing instructions), wherein training the change prediction model configures the change prediction model to output probability scores and sets of predicted change instructions for event processing requests (0068, fig.2D, 0054-56: probability scores are output for the associated predicted sets of change instructions for each event, or prediction of missing change instructions for prior transaction events; 0082: output recommendation of association between change and event transaction), and wherein a given probability score indicates a likelihood that a change instruction should be applied to a given event processing request (ibid);
receive a first event processing request from a user device (fig.4, 0080, 0082: trained model of fig.3 is used to process inference tasks);
generate, by inputting the encoded first event processing request into the change prediction model, a probability score for the first event processing request, wherein the probability score indicates a likelihood that one or more change instructions corresponding to the first event processing request are awaiting identification (0068, 0082, fig.2E, 0059);
compare the probability score to a threshold score (0082, 0059);
determine, based on comparing the probability score to the threshold score, whether the probability score satisfies the threshold score (0082, 0059);
generate, based on determining that the probability score satisfies the threshold score, a set of predicted change instructions for the first event processing request, wherein the set of predicted change instructions identifies the one or more change instructions corresponding the first event processing request (0082, fig.2E, 0059: generating predictions based on thresholds and performing automated actions for the change instructions and the event processing requests);
update, based on the set of predicted change instructions, the quantum change prediction model (fig.3, fig.1:140, 0024, 0046, 0054); and
cause, based on the set of predicted change instructions, the event processing system to process the first event processing request (0082, fig.1:160, 0032, 0059).
Lopes does not disclose: encode the plurality of historical event processing requests using quantum encoding; wherein the change prediction model is quantum; encode the first event processing request using quantum encoding.
Ganesh discloses: encode the plurality of historical event processing requests using quantum encoding (fig.1:102-104, 108, 0031-34: encoding feature sets, such as based on text data (0020), for processing by quantum neural networks, see 0031-34, hence, combination with the event processing of Lopes yielding encoding of historical event processing data via quantum encoding for processing via quantum circuits); wherein the change prediction model is quantum (fig.1:108, 0033-34); encode the first event processing request using quantum encoding (fig.1:108, 0032).
It would have been obvious before the effective filing date to a person of ordinary skill in the art to modify the platform of Lopes by incorporating the quantum processing technique of Ganesh. Both concern the art of machine learning and neural networks, and the incorporation would have, according to Ganesh, optimize information processing and training via a quantum computing platform adapted for traditional machine learning tasks (0005, 0025).
For claim 6, Lopes modified by Ganesh discloses the platform of claim 1, as described above. Lopes further discloses: wherein the set of predicted change instructions comprises one or more instructions to:
modify a destination of the first event processing request,
modify a timeframe associated with processing the first event processing request,
modify one or more indications of applications associated with processing the first event processing request, or
modify one or more numerical values associated with the event processing request (0061: modify numerical amounts associated with transaction destinations).
For claim 7, Lopes modified by Ganesh discloses the platform of claim 1, as described above. Ganesh further discloses: wherein encoding the first event processing request comprises encoding event processing information of the first event processing request into one or more amplitudes of a quantum state (0031-32).
Claim(s) 8 disclose analogous methods and computer readable media and are hence likewise rejected.
For claim 10, Lopes modified by Ganesh discloses the platform of claim 8, as described above. Lopes further discloses: wherein the change schema comprises one or more of:
a timeframe for processing the first event processing request,
a final destination for routing the first event processing request (fig.2E, 0059, 0061 contemplates predicting missing data for unreconciled transactions in order to reconcile the transaction, hence, additional change instructions are made for the final destination of the first transaction in order to balance the transaction for reconciliation),
an intermediary destination for routing the first event processing request, or
an application for processing the first event processing request.
Claims 13-15, 17, 20 disclose analogous methods and computer readable media and are hence likewise rejected.
Claim(s) 2-5, 9, 11-12, 16, 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Lopes (US 20210350382 A1) in view of Ganesh (US 20220092035 A1) in view of Sabri (US 20220019950 A1).
For claim 2, Lopes modified by Ganesh discloses the platform of claim 1, as described above. Lopes further discloses: input the first event processing request and the set of predicted change instructions into a quantum change schema model, wherein the quantum change schema model is configured to output change schema identifying one or more parameters for processing event processing requests (fig.1:160, 0032: updating data structure fields based on model predictions, hence, updating structured data fields based on a change schema identifying appropriate fields based on prediction parameters for associated transaction event requests; fig.2E, 0059-61: structured missing transaction data is generated based on predilected parameters from processing event processing requests and predicted change instructions); and
generate, based on inputting the encoded first event processing request and the set of predicted change instructions into the quantum change schema model, a change schema for the first event processing request (ibid: the change schema associating the first event processing request with change requests via updating of data fields is generated; change schema associated with missing transaction entry is generated),
wherein causing the event processing system to process the first event processing request comprises causing a system to implement one or more changes to the first event processing request based on the change schema (ibid: the changes in fields and the association are implemented; missing transaction data is instantiated and associated with the first transaction request, hence, implementing changes based on change schema).
Lopes modified by Ganesh does not disclose: wherein the system is a microservice system.
Sabri discloses: wherein the system is a microservice system (fig.3B, 0039).
It would have been obvious before the effective filing date to a person of ordinary skill in the art to modify the platform of Lopes modified by Ganesh by incorporating the microservices technique of Sabri. Both concern the art of financial reconciliation processing, and the incorporation would have, according to Sabri, provided various implementation advantages such as platform independence, efficiency, security (0039).
For claim 3, Lopes modified by Ganesh modified by Sabri discloses the platform of claim 2, as described above. Lopes further discloses: wherein the change schema comprises one or more of:
a timeframe for processing the first event processing request,
a final destination for routing the first event processing request (fig.2E, 0059, 0061 contemplates predicting missing data for unreconciled transactions in order to reconcile the transaction, hence, additional change instructions are made for the final destination of the first transaction in order to balance the transaction for reconciliation),
an intermediary destination for routing the first event processing request, or
an application for processing the first event processing request.
For claim 4, Lopes modified by Ganesh modified by Sabri discloses the platform of claim 2, as described above. Lopes modified by Sabri further discloses: wherein causing the event processing system to process the first event processing request further comprises causing the microservice system to broadcast, to one or more devices associated with the enterprise system, one or more notifications indicating the one or more change instructions (Lopez fig.9:950, 0147, 0153, with Sabri fig.3B 0039 disclosing microservices implementation).
For claim 5, Lopes modified by Ganesh discloses the platform of claim 1, as described above. Lopes modified by Ganesh further discloses: identify, via a system, a second user device, wherein the second user device is configured to receive a change event alert (fig.9:950, 0147, 0153: identifying devices for confirmation notification for predicted change event); and
cause, based on determining the probability score satisfies the threshold score, display of the change event alert via the second user device (ibid, with the notifications being based on predictions formed from thresholds of 0082, 0059 as described above, with 0033, 0100 disclosing display on user device).
Lopes modified by Ganesh does not disclose: wherein the system is a microservice system.
Sabri discloses: wherein the system is a microservice system (fig.3B, 0039).
It would have been obvious before the effective filing date to a person of ordinary skill in the art to modify the platform of Lopes modified by Ganesh by incorporating the microservices technique of Sabri. Both concern the art of financial reconciliation processing, and the incorporation would have, according to Sabri, provided various implementation advantages such as platform independence, efficiency, security (0039).
Claims 9, 11-12, 16, 18-19 disclose analogous methods and computer readable media and are hence likewise rejected.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Srivastava (US 20240012676 A1) discloses a microservice-based machine learning platform for transaction clearing.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIANG LI whose telephone number is (303)297-4263. The examiner can normally be reached Mon-Fri 9-12p, 3-11p MT (11-2p, 5-1a ET).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Jennifer Welch can be reached on (571)272-7212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LIANG LI/
Primary examiner AU 2143
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