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 .
Response to Amendment
2. The Amendment filed May 22, 2026 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 by measuring a sensitivity of each of the one or more optimization factor at a level of a specific issuer and account range and modifying one or more prior rules based on a continuing assessment of results from transactions sent to the issuer and account range.
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). Similarly, claim 21 does not provide any technical detail regarding how the “sensitivity” of the optimization factors is determined and/or applied to further refine the model. Therefore, simply stating that the sensitivity of the optimization factors is “measured” does not provide an indication of a technical improvement to how the machine learning model is optimized. In other words, 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 May 22, 2026 have been fully considered.
Arguments Regarding 35 U.S.C. 101
12. Applicant’s arguments (Amendment, Pgs. 12-19) 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 page 13 of their remarks, the applicant argues, "As discussed, Applicant incorporated limitations relating to a technical solution that automatically calibrates optimization factors through issuer-specific sensitivity measurements and rule modifications based on continuing assessment of transaction results. This calibration mechanism specifies how the machine learning model measures the sensitivity of each optimization factor at the granular level of specific issuers and account ranges, and how the system modifies prior rules based on ongoing transaction outcomes sent to those issuers and account ranges. This is a specific technical implementation that adaptively refines the transaction success model based on real-world feedback at the issuer level." Similar arguments are made by the applicant on pages 16-18 of their remarks regarding the integration of the abstract idea into a practical application. The examiner respectfully disagrees. As discussed in the 101 rejection above, the newly added claim amendments do not provide significant technical detail regarding how the machine learning model is calibrated. Simply stating that the sensitivity of the optimization factors is “measured” does not provide sufficient clarification regarding how the model is calibrated. In other words, the claim does not provide any technical detail regarding how the sensitivity of the factors is utilized to refine the model and/or how the sensitivity of the factors is determined. Rather, the claim simply states that the sensitivity of the factors is “measured.” Similarly, simply stating that the rules are modified based on a continuing assessment of results from transactions does not provide any technical detail regarding how the model is calibrated. Such limitations amount to no more than broadly-defined training processes and do not amount to an improvement to the functioning of the model itself. Additionally, the examiner notes that the claims of the instant application are not analogous to the claims at issued in Desjardins. Specifically, the claims in Desjardins provide a technical solution to an issue that is specific to machine learning (e.g., methods that allow the model to “effectively learn new tasks in succession whilst
protecting knowledge about previous tasks”). The claims of the instant application are not directed to any such improvement to machine learning technology. Rather, the claims simply utilize generic machine learning techniques to perform the abstract idea.
Additionally, on pages 15 and 16 of their remarks, the applicant argues, "None of the steps recited in the independent claims can be reasonably interpreted to include concepts relating to fundamental economic principles or practices (including hedging, insurance, mitigating risk), commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations), or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Therefore, the claims do not recite a method of organizing human activity." The examiner respectfully disagrees. As noted in the 101 rejection above, the claims clearly recite limitations corresponding to the authorization of financial transactions. Such limitations fall under the category of certain methods of organizing human activity. The mere fact that the claims recite limitations that fall outside of the abstract idea (i.e., additional elements) does not prevent the claims from reciting an abstract idea.
Additionally, on page 19 of their remarks, the applicant argues, "The additional elements in the current claims amount to significantly more than the alleged abstract idea because the steps related to automatically calibrating optimization factors by measuring the sensitivity of each optimization factor at the level of the specific issuer and account range, and modifying prior rules based on a continuing assessment of results from transactions sent to that issuer and account range, are i) not well-known and ii) arranged in a non-conventional manner. This rationale is further supported by the fact that the Office could not provide prior art that sufficiently teaches or suggests the claimed invention." The examiner respectfully disagrees. As discussed above, the claims do not provide any indication of a technical improvement to any technology or technological field. Additionally, the examiner notes that the lack of a prior art rejection does not necessarily indicate that the claims are eligible under 35 U.S.C. 101. As stated in MPEP2106.05(I), “Although the courts often evaluate considerations such as the conventionality of an additional element in the eligibility analysis, the search for an inventive concept should not be confused with a novelty or non-obviousness determination.” In other words, even newly discovered or novel (i.e., unknown) judicial exceptions are still exceptions.
Additionally, on page 19 of their remarks, the applicant argues, "In short, an examiner cannot simply assume that elements or a combination of elements are "well understood, routine or conventional." Rather, the examiner is required to cite to an admission by the applicant in the specification or during prosecution, court cases holding elements conventional, or a written publication establishing that the elements are well understood, routine or conventional. The Office Action does not contain such analyses or citations." The examiner respectfully disagrees. Specifically, evidence that a claim limitation is “well-understood, routine, and conventional” is only needed when the examiner identifies a claim limitation as well-understood, routine and conventional. It is noted that the examiner has not identified any claim limitation as well-understood, routine, and conventional activity. Rather, the examiner has identified the additional elements as being 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. These are separate analyses (See MPEP 2106.05(I)(A); MPEP 2106.05(d); and MPEP 2106.05(f)).
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:
Acuña-Rohter (U.S. Pre-Grant Publication No. 20220101323): Describes a system and method for authorizing a transaction using user data collected from third-party websites/applications/sources, such as social media networks.
Kurian (U.S. Pre-Grant Publication No. 20180314952): Describes systems and methods that generally relate to data processing utilizing artificial intelligence to analyze historical models and rank computer transactions for statistical learning.
Conclusion
THIS ACTION IS MADE FINAL. 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 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
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