Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination under 37 CFR §1.114
2. A request for continued examination under 37 CFR §1.114, including the fee set forth in 37 CFR §1.17(e), was filed on January 7, 2026 in this application after final rejection. Since this application is eligible for continued examination under 37 CFR §1.114 and the fee set forth in 37 CFR §1.17(e) has been timely paid, the finality of the previous Office action dated October 7, 2025 has been withdrawn pursuant to 37 CFR §1.114 and the submission filed on December 8, 2025 has been entered. Claims 21-23, 25-33, and 35-42 are pending and are rejected for the reasons set forth below.
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 21-23, 25-33, and 35-42 are rejected under 35 U.S.C. §101 because the claimed invention recites and is directed to a judicial exception to patentability (i.e., a law of nature, a natural phenomenon, or an abstract idea) and does not include an inventive concept that is “significantly more” than the judicial exception under the January 2019 and October 2019 patentable subject matter eligibility guidance (2019 PEG) analysis which follows.
Step 1
5. Under the 2019 PEG step 1 analysis, it must first be determined whether the claims are directed to one of the four statutory categories of invention (i.e., process, machine, manufacture, or composition of matter). Applying step 1 of the analysis for patentable subject matter to the claims, it is determined that the claims are directed to the statutory category of a process (claims 21-23, 25-30, and 41-42), a machine (claims 31-33 and 35-37) and a manufacture (claims 38-40); where the machine and the manufacture are substantially directed to the subject matter of the process (See e.g., MPEP §2106.03). Therefore, we proceed to step 2A, Prong 1.
Step 2A, Prong 1
6. Under the 2019 PEG step 2A, Prong 1 analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories of patent ineligible subject matter (i.e., organizing human activity, mathematical concepts, and mental processes) that amount to a judicial exception to patentability.
Claim 21 recites the abstract idea of:
A computer-implemented method for optimizing authorization transaction conversion rates, comprising:
receiving a request for authorization of a transaction from [[a user device]];
identifying one or more missing parameters in the request and supplementing the request by retrieving any missing parameters from a dataset of processing results for corresponding transactions;
determining patterns of acceptance or denial of the request based, at least in part, on processing of transaction parameters and authorization results for a plurality of past transactions from the dataset;
applying, [[by the trained machine learning model]], the [[transaction success model]] to the request and dynamically re-formatting one or more parameters associated with the request based on likelihood of improving the authorization; and
transmitting the re-formatted request to a service provider for authorization of the transaction.
Here, the recited abstract idea falls within one or more of the three enumerated 2019 PEG categories of patent ineligible subject matter, to wit: certain methods of organizing human activity, which includes fundamental economic practices or principles and/or commercial interactions (e.g., here, facilitating the authorization of a transaction).
Step 2A, Prong 2
7. Under the 2019 PEG step 2A, Prong 2 analysis, the identified abstract idea to which claim 21 is directed does not include limitations or additional elements that integrate the abstract idea into a practical application.
Besides reciting the abstract idea, the limitations of claim 1 also recite generic computer components (e.g., a user device, a machine learning model, and a transaction success model). In particular, the recited features of the abstract idea are merely being applied on a computer or computing device or via software programming that is simply being used as a tool (“apply it”) to implement the abstract idea. (See e.g., MPEP §2106.05(f)).
Additionally, claim 21 recites the following limitations:
inputting the determined patterns into a machine learning model to generate a transaction success model, wherein the transaction success model includes weighted authorization success factors based on success data aggregated from past transactions;
training the machine learning model during a training phase using the dataset of processing results, wherein the training includes tuning the transaction success model based on historical data; and
automatically calibrating, by the trained machine learning model, one or more optimization factors of the transaction success model based on one or more authorization success factors, one or more transaction scenarios, and one or more negative results.
This limitation simply recites limitations for inputting the determined patterns into a machine learning model to generate a transaction success model, training the machine learning model using a dataset, and calibrating the transaction model based on various data. However, the claim does not provide significant detail regarding how the transaction success model is generated/trained, or how it is applied to reformat the transaction authorization request. Rather, the claim simply broadly states that these processes are performed by the machine learning model and the transaction success model. Similarly, claim 21 does not provide significant detail regarding how the transaction success model is calibrated. Rather, the claims simply describe the type of data that is used to calibrate the transaction success model (e.g., on one or more authorization success factors, one or more transaction scenarios, and one or more negative results). Such detail does not provide an indication of an improvement to machine learning and/or transaction processing technology. Therefore, these limitations amount to no more than simply applying generic machine learning technology to implement the abstract idea on a computer.
Therefore, these additional elements are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components. In other words, the additional elements are simply used as tools to perform the abstract idea.
Thus, claim 21 does not include any limitations or additional elements that integrate the abstract idea into a practical application. As a result, claim 21 is directed to an abstract idea.
Step 2B
8. Under the 2019 PEG step 2B analysis, the additional elements of claim 21 are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea. (i.e., an innovative concept). Here, the recited additional elements (e.g., a user device, a machine learning model, and a transaction success model), do not amount to an innovative concept since, as stated above in the Step 2A, Prong 2 analysis, the claims are simply using the additional elements as a tool to carry out the abstract idea (i.e., “apply it”) on a computer or computing device and/or via software programming (See e.g., MPEP §2106.05(f)). The additional elements are specified at a high level of generality such that they are being used in the claims to simply implement the abstract idea and are not themselves being technologically improved (See e.g., MPEP §2106.05 I.A.); (See also e.g., applicant’s Specification at least Paragraphs 44-50).
Thus, claim 21 does not recite any additional elements that amount to “significantly more” than the abstract idea.
Additional Independent Claims
9. Independent claims 31 and 38 are similarly rejected under 35 U.S.C. 101 for the reasons described below:
Claim 31 recites limitations that are substantially similar to those recited in claim 21. However, the primary difference between claims 31 and 21 is that claim 31 is drafted as a system rather than as a method. Similarly, as described above regarding claim 21, claim 31 recites generic computer components (e.g., one or more processors, at least one non-transitory computer readable medium storing instructions, a user device, a machine learning model, and a transaction success model) that are simply being used as a tool (“apply it”) to implement the abstract idea. Therefore, since the same analysis should be used for claims 21 and 31, claim 31 is not patent eligible (See Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 2354 (2014)).
Claim 38 recites limitations that are substantially similar to those recited in claim 21. However, the primary difference between claims 38 and 21 is that claim 38 is drafted as a computer readable medium rather than as a method. Similarly, as described above regarding claim 21, claim 38 recites generic computer components (e.g., A non-transitory computer readable medium, one or more processors, a user device, a machine learning model, and a transaction success model) that are simply being used as a tool (“apply it”) to implement the abstract idea. Therefore, since the same analysis should be used for claims 21 and 38, claim 38 is not patent eligible (See Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 2354 (2014)).
Dependent Claims
10. Dependent claims 22-23, 25-30, 32-33, 35-37, and 39-42 are also rejected under 35 U.S.C. 101 for the reasons described below:
Claims 22, 32, and 39 recite the limitations, “wherein automatically calibrating the one or more optimization factors of the transaction success model, further comprises: determining, by the machine learning model, the transaction success model provides an improvement in the transaction conversion rates; and applying, by the machine learning model, the transaction success model to the one or more parameters of the request based, at least in part, on the determination.” These limitations simply refine the abstract idea because they recite process steps (e.g., determining that the transaction success model improves transaction conversion rates, and applying the transaction success model based on the determination) that fall under the category of organizing human activity, as described above regarding claim 21. Additionally, merely stating that this process is performed by the machine learning model and the transaction success model amounts to no more than merely applying generic computer components to implement the abstract idea on a computer.
Claims 23, 33, and 40 simply refine the abstract idea because they recite a process step (e.g., determining whether to include, exclude, or alter a parameter associated with the request) that falls under the category of organizing human activity, as described above regarding claim 21.
Claims 25 and 35 simply refine the abstract idea because they recite a process step (e.g., determining whether the inclusion of a token improves the probability of the transaction request being authorized, and reformatting the request to include the token) that falls under the category of organizing human activity as described above regarding claim 21. The claims do not provide any indication of an improvement to the tokenization process itself. Therefore, this amounts to no more than simply applying generic tokenization technology to implement the abstract idea.
Claims 26 and 36 simply refine the abstract idea because they recite a process step (e.g., determining whether using a particular network improves the probability of the transaction request being authorized, and using the particular network to transmit the request) that falls under the category of organizing human activity as described above regarding claim 21.
Claims 27 and 37 simply refine the abstract idea because they recite a process step (e.g., adding a result of the transaction authorization process to a dataset for use in subsequent transaction) that falls under the category of organizing human activity as described above regarding claim 21. The claims do not provide any technical detail regarding how the authorization result is used in subsequent transactions.
Claim 28 merely provides further definition to the process of reformatting the parameters of the request recited in claim 21. Simply stating that the corresponding transactions include previously processed payment transactions that share at least one parameter with the request, and that the parameters are reformatted in batches, in a que, in real-time, or asynchronously, does not provide any indication of an improvement to any technology or technological field. Rather, this merely defines the type of transaction data used to identify the parameters, and when the parameters are reformatted.
Claim 29 merely provides further definition to the “dataset” recited in claim 21. Simply stating that the dataset includes specific information does not provide any indication of an improvement to any technology or technological field. Rather, this merely defines the type of data within the dataset.
Claim 30 merely provides further definition to the “parameters” recited in claim 21. Simply stating that the parameters include various information does not provide any indication of an improvement to any technology or technological field. Rather, this merely defines the type of information included in the parameters.
Claim 41 simply refines the abstract idea because it recites a process step (e.g., validating the accuracy of the transaction success model by comparing data) that falls under the category of organizing human activity as described above regarding claim 21. The claims do not provide any technical detail regarding how the accuracy of the model is validated. Simply stating that the model is validated based on comparing data regarding the results produced by the model does not amount to a technical improvement to machine learning technology.
Claim 42 simply states that the model is adjusted based on the validation performed in claim 41. However, as described above regarding claim 21, the claims do not provide any technical detail regarding how the parameters are adjusted. Rather, the claims simply state that the parameters are adjusted based on the validation. Therefore, this amounts to no more than simply applying generic machine learning technology to implement the abstract idea on a computer.
Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claims being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Response to Arguments
11. Applicant’s arguments filed December 8, 2025 have been fully considered.
Arguments Regarding 35 U.S.C. 101
12. Applicant’s arguments (Amendment, Pgs. 11-13) concerning the prior rejection of the claims under 35 USC §101, including supposed deficiencies in the rejection, are not persuasive for the following reasons. Under the prior and current 101 analysis under 2019 PEG, the amended claims recite and are directed to a patent ineligible abstract idea, without something significantly more, for the reasons given above after consideration of the claimed features and elements. The abstract idea has been restated herein in line with the 2019 PEG guidance and the amended claims. Applicant is directed to the above full Alice/Mayo analysis in the 101 rejection.
Additionally, on pages 11-13 or their remarks, the applicant argues, "By analogy, independent claims 21, 31, and 38 relate closely to the principles of Desjardins. Such automated calibration involve adaptively adjusting one or more optimization factors of the transaction success model to refine the formatting and handling of future transaction requests to enhance the overall efficiency of the transaction processing system." The examiner respectfully disagrees. Specifically, the examiner notes that the claims do not provide significant technical detail regarding how the machine learning model is trained and implemented. Simply stating that the machine learning model is trained using historical data, applied to the transaction request, and automatically calibrated based on various factors, does not amount to an improvement to machine learning technology itself. As noted above, the claims do not provide significant technical detail regarding how these processes are performed. In other words, the claims are not primarily directed to improvements in the functionality of the machine learning models. Rather, the limitations of the independent claims, when viewed as a whole, are primarily directed to a system/method for improving the rate of transaction authorizations. The claims simply apply generic machine learning techniques to perform the abstract idea.
Therefore, for these reasons and the reasons given above, the rejection of these claims under 35 U.S.C. 101 is maintained.
Citation of Pertinent Prior Art
13. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Sukhija (U.S. Patent No. 10901818): Describes systems and methods for common request processing by a request formatting platform. Embodiments of the invention may be used to communicate and process transaction-related requests, such as payments, captures, credits/refunds, authorization reversals, transaction status checks, chargebacks, notifications, reports and the like.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM D NEWLON whose telephone number is (571)272-4407. The examiner can normally be reached Mon - Fri 8:30 - 4:30.
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/WILLIAM D NEWLON/Examiner, Art Unit 3696
/MATTHEW S GART/Supervisory Patent Examiner, Art Unit 3696