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 .
Status of Claims
This action is in reply to the response filed on 11/6/2025. Claims 1, 6-15, 17, 18 and 20 have been amended. Claims 2-5, 16 and 19 have been cancelled. New claims 21-23 have been added. Claims 1, 6-15, 17, 18 and 20-23 are currently pending and have been examined. Claims 8-10, 12 and 14 include the status identifier “(Currently Amended)”, but these claims have not been amended. Appropriate correction is requested.
Claim Objection
Claim 21 is objected to because of the following informality: it appears that “the the” should be –the—in line 1 of claim 21. Appropriate correction is required.
Claim Rejections - 35 USC § 101
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.
Claims 1, 6-15, 17, 18 and 20-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. A Section 101 analysis is below.
Step 1 – are the claims directed to a process, machine, manufacture or composition of matter. The apparatus of claim 1, method of claim 15 and CRM of claim 18 are within the statutory categories of invention. For the purposes of this analysis, representative claim 1 is addressed.
Step 2A, prong one – do the claims recite a judicial exception, which is an abstract idea enumerated in MPEP 2106, a law of nature, or a natural phenomenon. Abstract ideas are in bold below, and represent the abstract idea of the mental process of generating messages for use in other systems, the generated messages being configured in accordance with the data structures. Please see MPEP 2106.04(a)(2)(III)(A) which notes claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include: claims to "comparing BRCA sequences and determining the existence of alterations," where the claims cover any way of comparing BRCA sequences such that the comparison steps can practically be performed in the human mind, University of Utah Research Foundation v. Ambry Genetics, 774 F.3d 755, 763, 113 USPQ2d 1241, 1246 (Fed. Cir. 2014). Referring to Applicant’s specification, [0002]-[0004], the Applicant notes “Creating valid messages under the ISO 20022 approach for financial transactions and activity is challenging. A human individual must manually create a massive ISO 20022-compliant extensible markup language (XML) message file (sometimes referred to as an XM message) from a flat text file. This endeavor is complex and may require receiving and validating large amounts of data from upstream systems for inclusion in an XM/ISO 20022 message. Up to approximately 3.5 thousand mandatory and optional tags will need to be checked for conformance to validity constraints on data elements. These tags, for example, may include the length of a data element, its format, the logic employed by a portion of a message, when and how the message element is used, and restrictions on who can populate certain aspects of a given message. There is a need for a process to make the creation of XM/ISO 20022 messages with less effort, for example, for testing in various contexts such as in downstream ISO 20022 systems. An objective of the disclosure is to automate at least portions of the process of creating ISO 20022 test messages. Another objective of the disclosure is to automate at least portions of the process of creating ISO 20022 messages for use in actual operation or (in the form of test messages) in a test environment, for example, in different use cases.”
1. Apparatus comprising:
computer memory configured to non-transiently hold messages including data structures and configured data configured in accordance with the data structures; and
a machine learning processing circuit comprising a prediction data input and configured to receive various messages at the prediction data input and to hold the various messages in the computer memory, the machine learning processing circuit comprising a message generator configured to generate messages for use in other systems, the generated messages being configured in accordance with the data structures, the various messages including sample payment messages obtained from production data from financial transactions, and the generated messages including both associated error indications and associated use cases, the generated messages thereby comprising test messages configured for use with payments flow simulations in accordance with the associated use cases;
wherein the data structures include a markup language and file format organized in accordance with a tree structure, and wherein the file format comprises an MX/ISO (international organization for standardization) 20022 messaging format representing a payment file for a payment;
wherein the machine learning processing circuit further comprises a training input and is configured to receive training messages at the training input and to hold the training messages in the computer memory; and
wherein the training input is further configured to receive accompanying features with the training messages, the accompanying features providing error occurrence data.
Step 2A, prong two – do the claims recite additional elements that integrate the judicial exception into a practical application. Integration of the judicial exception into a practical application requires an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. The additional elements are considered as follows:
The “computer memory”, “machine learning processing circuit”, “prediction data input”, “message generator” and “training input”. Referring to MPEP 2106.05(f), the preceding recited additional elements are no more than mere instructions to implement an abstract idea or other exception on a computer. The computer components are recited at a high-level of generality (e.g., to receive, store, or transmit data) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Please see MPEP 2106.05(f)(1) discussing when the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished this does not show integration into a practical application. Please see MPEP 2106.05(f)(2) discussing when the claim invokes computers or other machinery merely as a tool to perform an existing process including use of a computer or other machinery for economic tasks this does not show integration into a practical application.
Step 2B – do the claims recited additional elements that amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The computer components implementing the abstract idea appear to be generic in view of at least Applicant’s specification, [0052].
In view of the above analysis, independent claims 1, 15 and 18 are not patent eligible. Dependent claims 6-14, 17 and 20-23 do not cure the deficiencies in their respective base claims, and are also not patent eligible. Specifically, claims 6-14, 17 and 20-23 merely refine the abstract idea (2A1) by invoking a computer as a tool to perform an existing process (2A2, 2B). Regarding the further additional elements in the dependent claims including a feature store (claim 7); machine learning model comprising a decision tree (claim 11); random forest algorithm (claim 12); machine learning model comprising a Naive Bayes classifier (claim 13); one or more decision trees (claim 14); and message-validation portal (claims 21, 22, 23), please see MPEP 2106.05(f)(2) discussing when the claim invokes computers or other machinery merely as a tool to perform an existing process including use of a computer or other machinery for economic tasks this does not show integration into a practical application or provide significantly more. With respect to the machine learning additional elements, please also see Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 (Fed. Cir. Apr. 18, 2025), which affirmed the District of Delaware’s dismissal of Recentive’s infringement suit on the ground that the asserted patents were directed to ineligible subject matter under 35 U.S.C. § 101. The decision reinforces the courts’ view that applying generic machine learning techniques to known problems — without technical innovation in the machine learning methods themselves — is insufficient for patent eligibility.
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.
Claims 1, 6-8, 11-15, 17, 18 and 20-23 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou (US 2025/0045758) in view of Abdelaal (US 2025/0348470).
Claim 1 recites:
Apparatus comprising: (Zhou, Fig. 4, [0093], ML subsystem 400)
computer memory configured to non-transiently hold messages including data structures and configured data configured in accordance with the data structures; and (Zhou, Fig. 4, [0095], data ingestion engine 410 moves data to a destination for storage; [0094], historical standard-compliant messages; [0096], data pre-processing engine 416; see also [0074], [0075], configuration files for messaging standards; [0052], memory, non-transitory)
a machine learning processing circuit comprising a prediction data input and configured to receive various messages at the prediction data input and to hold the various messages in the computer memory, (Zhou, Fig. 4, [0093], ML subsystem 400 includes data ingestion engine 410; [0095] data ingestion engine 410 moves data to a destination for storage; [0065], circuitry)
the machine learning processing circuit comprising a message generator configured to generate messages for use in other systems, the generated messages being configured in accordance with the data structures, (Zhou, [0102], “data engine may use the one or more machine learning models and/or the one or more inference engines to generate messages using a messaging standard (e.g., standard-compliant messages) for initiating and/or conducting transactions”)
the various messages including sample payment messages obtained from production data from financial transactions, and (Zhou, Fig. 4, [0095]-[0097], data ingestion 410, data pre-processing 416, generate training data 418)
the generated messages including both associated error indications and associated use cases, (Zhou, Fig. 4, [0097], generate training data 418, enriched using labels to provide context. Zhou does not specifically disclose an error indication. Abdelaal, [0052], discusses proactive error management by tagging data samples known to be corrupted. It would have been obvious to a person of ordinary skill in the art before the time of effective filing to modify the labelling of Zhou to include labelling errors as in Abdelaal in order to improve accuracy as discussed in Abdelaal, [0007], and Zhou, [0044].)
the generated messages thereby comprising test messages configured for use with payments flow simulations in accordance with the associated use cases; (Zhou, Fig. 4, [0098], training machine learning model 424 using training data 418)
wherein the data structures include a markup language and file format organized in accordance with a tree structure, and (Zhou, [0074], XML Schema)
wherein the file format comprises an MX/ISO (international organization for standardization) 20022 messaging format representing a payment file for a payment; (Zhou, [0074], ISO 20022 standard)
wherein the machine learning processing circuit further comprises a training input and is configured to receive training messages at the training input and to hold the training messages in the computer memory; and (Zhou, Fig. 4, [0097], [0098], training data 418; [0095], storage)
wherein the training input is further configured to receive accompanying features with the training messages, the accompanying features providing error occurrence data. (Zhou, Fig. 4, [0097], generate training data 418, enriched using labels to provide context. Zhou does not specifically disclose error occurrence data. Abdelaal, [0052], discusses proactive error management by tagging data samples known to be corrupted, and Abdelaal, [0062], discusses data quality includes error rate. It would have been obvious to a person of ordinary skill in the art before the time of effective filing to modify the labelling of Zhou to include data such as error rate as in Abdelaal in order to improve accuracy as discussed in Abdelaal, [0007], and Zhou, [0044].)
Claims 15 and 18 correspond to claim 1 and are rejected on the same grounds. Regarding method claim 15, Zhou, Fig. 3, [0081], process flow 300. Regarding CRM claim 18, Zhou, [0060], CRM.
Claim 6 recites:
The apparatus according to claim 1, wherein the machine learning processing circuit is further configured to map at least one of the test messages to use cases. (Zhou, [0074], mapping data from a funding schedule to ISO 20022 data categories; see also [0099], mapping)
Claims 17 and 20 correspond to claim 6 and are rejected on the same grounds.
Claim 7 recites:
The apparatus according to claim 1, wherein the computer memory comprises a feature store comprising feature reference data comprising features and feature values used by the machine learning processing circuit for inferencing. (Zhou, Fig. 4, [0101], inference engine 436; see also [0093], [0097], [0102], inferencing)
Claim 8 recites:
The apparatus according to claim 7, wherein the feature reference data is also used by the machine learning processing circuit for training. (Zhou, Fig. 4, [0097], training data 418 includes features, inferences; [0100], [0101], ML model tuning engine 422 repeatedly executes cycles of initialization 426, testing 428 and calibration 430)
Claim 11 recites:
The apparatus according to claim 1, wherein the machine learning processing circuit is configured to implement a machine learning model comprising a decision tree. (Zhou, Fig. 4, [0099], decision trees)
Claim 12 recites:
The apparatus according to claim 11, wherein the machine learning model further comprises a random forest algorithm. (Zhou, Fig. 4, [0099], random forest)
Claim 13 recites:
The apparatus according to claim 1, wherein the machine learning processing circuit is configured to implement a machine learning model comprising a Naive Bayes classifier. (Zhou, Fig. 4, [0099], Naïve Bayes)
Claim 14 recites:
The apparatus according to claim 13, wherein the machine learning model further comprises one or more decision trees. (Zhou, Fig. 4, [0099], decision trees)
Claim 21 recites:
The apparatus according to claim 1, wherein the the machine-learning processing circuit is further configured to receive message-validation information including valid, warning-invalid, and invalid indications from a message-validation portal performing schema validations, (Zhou, Fig. 2, [0073], [0074], process flow 200 includes determining whether record 202 includes required information for messaging standard and provide notification)
the message-validation information being used by the machine-learning processing circuit to improve generation accuracy. (Zhou, Fig. 4, [0100], tuning machine learning model using validation set)
Claims 22 and 23 correspond to claim 21 and are rejected on the same grounds.
Claims 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou (US 2025/0045758) in view of Abdelaal (US 2025/0348470) and further in view of Harris (US 2121/0027182).
Claim 9 recites:
The apparatus according to claim 8, wherein the feature store further comprises biasing prior knowledge data. (Zhou, Fig. 4, [0097], [0100], [0101], training data 418 includes features, inferences. Abdelaal, [0010], discusses how different imputation methods bias data. Zhou and Abdelaal do not specifically disclose biasing prior knowledge data. Harris, [0063], [0072], discusses setting bias. It would have been obvious to modify the training of Zhou as modified by Abdelaal to include the setting of bias as in Harris in order to optimize a model as discussed in Zhou, [0100], Abdelaal, [0007], and Harris, [0072].
Claim 10 recites:
The apparatus according to claim 9, wherein the feature store further comprises use case contextual data. (Zhou, Fig. 4, [0097], labels to provide context)
Response to Arguments
Applicant's arguments filed 11/6/2025 have been fully considered and are addressed below.
Regarding the rejection under 35 U.S.C. 101, Applicant’s arguments have been fully considered but they are not persuasive. Regarding the arguments concerning Step 2A, prong one, the Applicant argues “The examiner asserted that the claims recite a mental process. The recited machine learning and related limitations are clearly claimed — that is, they are not recited in a way to broadly describe machine learning and related limitations in any way that can be practically performed in the human mind. Accordingly, the claims do not recite a mental process. See, MPEP 2106.04(a)(2) subsection III, point A.” The Examiner respectfully disagrees. MPEP 2106.04(a)(2)(III)(A) notes claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include: claims to "comparing BRCA sequences and determining the existence of alterations," where the claims cover any way of comparing BRCA sequences such that the comparison steps can practically be performed in the human mind, University of Utah Research Foundation v. Ambry Genetics, 774 F.3d 755, 763, 113 USPQ2d 1241, 1246 (Fed. Cir. 2014). Referring to Applicant’s specification, [0002]-[0004], the Applicant notes “A human individual must manually create a massive ISO 20022-compliant extensible markup language (XML) message file (sometimes referred to as an XM message) from a flat text file.”. Briefly, it appears the Applicant’s specification admits the claimed process could be done manually. Regarding the “machine learning circuit”, it is respectfully submitted that this feature merely implements the abstract idea using generic component components, which does not preclude a claim from reciting an abstract idea.
Regarding Applicant’s arguments regarding Step 2A, prong two, integration into a practical application requires an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. Limitations that are indicative of integration into a practical application include improvements to the functioning of a computer, applying the judicial exception with a particular machine, effecting transformation of a particular article to a different state or thing or applying the judicial exception in some other meaningful was beyond generally linking the use of the judicial exception to a particular technological environment. The Applicant argues “While on the one hand, moving a given known set of manual steps to an automatic setting using a generic computer may fall to an eligibility challenge, MPEP at 2011-66 ("Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential)"), when the new automated steps are different allowing automation to be possible or the claim recites additional details concerning "how" automation is made possible, automation can support eligibility. For example, in McRO, the court relied on the specification's explanation of how the particular rules recited in the claim enabled the automation of specific animation tasks that previously could only be performed subjectively by humans, when determining that the claims were directed to improvements in computer animation instead of an abstract idea. MPEP at 2100-64, citing McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d at 1313-14, 120 USPQ2d at 1100-01.” The Examiner respectfully disagrees. The Applicant admits automating a manual task does not show an improvement to computer functionality. Please see MPEP 2106.05(a). Regarding McRO, animation tasks are not claimed. It is respectfully submitted that the Applicant is not quoting any recited claim language demonstrating integration into a practical application. Please see MPEP 2106.05(f)(1) discussing when the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished this does not show integration into a practical application. Please see MPEP 2106.05(f)(2) discussing when the claim invokes computers or other machinery merely as a tool to perform an existing process including use of a computer or other machinery for economic tasks this does not show integration into a practical application.
Regarding Step 2B, no arguments were presented regarding Step 2B
Regarding the rejections under 35 U.S.C. 103, Applicant’s arguments have been fully considered and the amended claims are addressed in detail above. Applicant argues “Zhou and Harris et al., alone or in any proper combination, fail to teach (among other missing limitations) test messages with error indications. More specifically, for example, the references fail to teach generated messages including both associated error indications and associated use cases, the generated messages thereby comprising test messages configured for use with payments flow simulations in accordance with the associated use cases. Zhou obtains input data (not in the form of a message compliant with any standard) (see, e.g., record 202) as described at paras. [0066]-[0073]. If any information is missing from the input, the process obtains the missing information as described at para. [0073].” As shown above, Harris has been replaced by Abdelaal (US 2025/0348470), which was added to show the previously unexamined feature of test messages including error indications added to the claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure includes: US 12271831; US 12067576; US 20220391720; US 11294926; US 20200073735; and US 20170262847.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Gregory Harper whose telephone number is (571)272-5481. The examiner can normally be reached on M-Th 7am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ryan Donlon can be reached on (571) 270-3602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GREGORY HARPER/Examiner, Art Unit 3692
/DAVID P SHARVIN/Primary Examiner, Art Unit 3692
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