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 claims filed 02/25/2026 and Applicant’s communication regarding application 06/18/2025 filed 02/25/2026.
Claims 1, 2, 4-12, and 15-23 have been examined with this office action.
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, 2, 4-12, and 15-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of computing and assigning a variable fee for cross-border transactions without significantly more.
Subject Matter Eligibility Standard
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include fundamental economic practices; certain methods of organizing human activities; an idea itself; and mathematical relationships/formulas. Alice Corporation Pty. Ltd. v.CLS Bank International, et al., 573 U.S. _ (2014) as provided by the interim guidelines FR 12/16/2014 Vol. 79 No. 241.
Analysis
Step 1, the claimed invention must be to one of the four statutory categories. 35 U.S.C. 101 defines the four categories of invention that Congress deemed to be the appropriate subject matter of a patent: processes, machines, manufactures and compositions of matter. In this case independent claim 1 and all claims which depend from it are directed toward a method, and independent claim 16 and all claims which depend from it are directed toward a system and independent claim 20 all claims which depend from it are directed toward a computer readable medium storing instruction to perform functions/steps. As such, all claims fall within one of the four categories of invention deemed to be the appropriate subject matter.
Step 2A Prong 1, Under Step 2 A, Prong 1 of the 2019 Revised § 101 Guidance, it is determined whether the claims are directed to a judicial exception such as a law of nature, a natural phenomenon, or an abstract idea (See Alice, 134 S. Ct. at 2355) by identify the specific limitation(s) in the claim that recites abstract idea(s); and then determine whether the identified limitation(s) falls within at least one of the groupings of abstract ideas enumerated in the 2019 PEG.
Specifically, claim 1 comprises inter alia the functions or steps of “A computer-implemented method, comprising: maintaining, by a computing device, a variable fee manager and simultaneous availability of a first scenario-specific machine learning model, a second scenario-specific machine learning model, and a third scenario-specific machine learning model; receiving an authorization request and transaction data for a pending transaction; determining, from the transaction data, that the pending transaction is a cross-border or cross-currency transaction; based on the determination, prior to forwarding the authorization request to an issuer, performing, by the variable fee manager, the following operations to determine a variable fee to be applied to the pending transaction: obtaining, for the pending transaction, raw data associated with a plurality of transactions including the pending transaction; preparing the raw data for analysis by a scenario-specific model a selected scenario-specific machine learning model, selected from among the first, second, and third scenario-specific machine learning models, the preparing comprising: pre-processing the raw data to produce cleaned data by standardizing formats and removing outliers from the raw data; and extracting transaction dimensions from the cleaned data, the transaction dimensions comprising issuer country, dynamic currency conversion (DCC), merchant country, merchant category code (MCC), card portfolio, card-present code, approved amount, and fraud rate; ranking each of the plurality of transactions based on a ratio of maximum approved transaction amounts to fraud likelihood; based on the ranking, forming a first tier of transactions associated with a first scenario, a second tier of transactions associated with a second scenario, and a third tier of transactions associated with a third scenario, wherein the first scenario corresponds to the first scenario-specific machine learning model, the second scenario corresponds to the second scenario-specific machine learning model, and a third scenario corresponds to the third scenario-specific machine learning model; and assigning a scenario to the pending transaction, the assigned scenario corresponding to one of the following: the first tier of transactions associated with the first scenario, the second tier of transactions associated with the second scenario, or the third tier of transactions associated with the third scenario; determining the selected scenario-specific machine learning model based on the assigned scenario, and applying the pending transaction to selected scenario-specific machine learning model, without executing the first, second, and third scenario-specific machine learning models other than the selected scenario-specific machine learning model, to generate a recommended variable fee for the pending transaction based on the prepared raw data; and causing the recommended variable fee to be applied to the pending transaction”.
Claim 16 comprises inter alia the functions or steps of “A system comprising:
a processor;
a communications interface;
application variable fee manager;
a first scenario-specific machine learning model;
a second scenario-specific machine learning model;
a third scenario-specific machine learning model; and
a memory storing instructions that, when executed by the processor, cause the processor to:
receive an authorization request and transaction data for a pending transaction;
determine, from the transaction data, that the pending transaction is a cross-border or cross-currency transaction;
based on the determination, prior to forwarding the authorization request to an issuer, perform, by a variable fee manager, the following operations to determine a variable fee to be applied to the pending transaction:
obtain, for the pending transaction, raw data associated with a plurality of transactions;
prepare the raw data for analysis by a selected scenario-specific machine learning model, selected from among the first, second, and third scenario-specific machine learning models, the preparing comprising:
pre-processing the raw data to produce cleaned data by standardizing formats and removing outliers from the raw data; and extracting transaction dimensions from the cleaned data, the transaction dimensions comprising issuer country, dynamic currency conversion (DCC), merchant country, merchant category code (MCC), card portfolio, card-present code, approved amount, and fraud rate;
rank each of the plurality of transactions based on a ratio of maximum approved transaction amounts to fraud likelihood;
based on the ranking, forming a first tier of transactions associated with a first scenario, a second tier of transactions associated with a second scenario, and a third tier of transactions associated with a third scenario, wherein the first scenario corresponds to the first scenario-specific machine learning model, the second scenario corresponds to the second scenario-specific machine learning model, and a third scenario corresponds to the third scenario-specific machine learning model; and assign a scenario to the pending transaction, the assigned scenario corresponding to one of the following:
the first tier of transactions associated with the first Application Docket No scenario, the second tier of transactions associated with the second scenario, or the third tier of transactions associated with the third scenario;
determining the selected scenario-specific machine learning model based on the assigned scenario, and applying the pending transaction to the selected-scenario-specific machine learning model, without executing the first, second, and third scenario-specific machine learning models other than the selected scenario-specific machine learning model, to generate a recommended variable fee for the pending transaction based on the prepared raw data; and
causing the recommended variable fee to be applied to the pending transaction”.
Claim 20 comprises inter alia the functions or steps of “A computer-readable medium storing instructions that, when executed by a processor, cause the processor to: maintain a variable fee manager and simultaneous availability of a first scenario- specific machine learning model, a second scenario-specific machine learning model, and a third scenario-specific machine learning model; receive an authorization request and transaction data for a pending transaction; determine, from the transaction data, that the pending transaction is a cross-border or cross-currency transaction; based on the determination, prior to forwarding the authorization request to an issuer, perform, by the variable fee manager, the following operations to determine a variable fee to be applied to the pending transaction: obtain, for the pending transaction, raw data associated with a plurality of transactions including the pending transaction; prepare the raw data for analysis by a selected scenario-specific machine learning model, selected from among the first, second, and third scenario-specific machine learning models, the preparing comprising: pre-processing the raw data to produce cleaned data by standardizing formats and removing outliers from the raw data; and extracting transaction dimensions from the cleaned data, the transaction dimensions comprising issuer country, dynamic currency conversion (DCC), merchant country, merchant category code (MCC), card portfolio, card-present code, approved amount, and fraud rate; rank each of the plurality of transactions based on a ratio of maximum approved transaction amounts to fraud likelihood; based on the ranking, form a first tier of transactions associated with a first scenario, a second tier of transactions associated with a second scenario, and a third tier of transactions associated with a third scenario, wherein the first scenario corresponds to the first scenario-specific machine learning model, the second scenario corresponds to the second scenario-specific machine learning model, and a third scenario corresponds to the third scenario-specific machine learning model; and assign a scenario to the pending transaction, the assigned scenario corresponding to one of the following first tier of transactions associated with the first scenario, the second tier of transactions associated with the second scenario or the third tier of transactions associated with the third scenario; determining the selected scenario-specific machine learning model based on the assigned scenario, and applying the pending transaction to the selected scenario-specific machine learning model, without executing the first, second, and third scenario-specific machine learning models other than the selected scenario-specific machine learning model, to generate a recommended variable fee for the pending transaction based on the prepared raw data; cause the recommended variable fee to be applied to the pending transaction; receive feedback from the issuer regarding the recommended variable fee; and train the selected scenario-specific model using the received feedback”.
Those claim limits in bold are identified as claim limitations which recite the abstract idea, while those that are un-bolded are identified as additional elements.
The cited limitations as drafted are systems and methods that, under their broadest reasonable interpretation, covers performance of a method of organizing human activity, but for the recitation of the generic computer components. Further, none of the limitations recite technological implementations details for any of the steps but, instead, only recite broad functional language being performed by the generic use of at least one processor. Computing and assign a variable fee for cross-border transactions is a fundamental economic practice long prevalent in commerce systems. If a claim limitation, under its broadest reasonable interpretation, covers a fundamental economic principle or practice but for the general linking to a technological environment, then it falls within the organizing human activity grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2, Next, it is determined whether the claim is directed to the abstract concept itself or whether it is instead directed to some technological implementation or application of, or improvement to, this concept, i.e., integrated into a practical application. See, e.g., Alice, 573 U.S. at 223, discussing Diamond v. Diehr, 450 U.S. 175 (1981). The mere introduction of a computer or generic computer technology into the claims need not alter the analysis. See Alice, 573 U.S. at 223—24. “[T]he relevant question is whether the claims here do more than simply instruct the practitioner to implement the abstract idea on a generic computer.” Alice, 573 U.S. at 225.
In the present case, the judicial exception is not integrated into a practical application. The claim limitations are not indicative of integration into a practical application by claiming an improvement to the functioning of the computer or to any other technology or technical field. Further, the claim limitations are not indicative of integration into a practical application by applying or using the judicial exception in some other meaningful way.
In particular, the claims contain the following additional elements: a computer; a system; a processor; a communications interface; a memory storing instructions; a network; a computer-readable medium storing instructions; a variable fee manager; a first scenario-specific machine learning model;
a second scenario-specific machine learning model; a third scenario-specific machine learning model. However, the specification description of the additional elements a computer ([Figure 2, element 202] [0031]); a system ([Figures 1 and 2] [0021-0022] [0030-0031]); a processor ([Figure 2, element 208] [0032]); a communications interface ([Figure 2, element 212] [0032]); a memory storing instructions ([Figure 2, element 204] [0032]); a network ([Figure 2, element 232] [0032]); a computer-readable medium storing instructions ([0097] [0139]); a variable fee manager (software [Figure 2, element 222] [0032] [0040] [0059]); a first scenario-specific machine learning model ([Figure 6A-B] [0018-0019] [0040]); a second scenario-specific machine learning model ([Figure 6A-B] [0018-0019] [0040]); a third scenario-specific machine learning model ([Figure 6A-B] [0018-0019] [0040]) are at a high level of generality using exemplary language or as part of a generic technological environment and are functions any general purpose computer performs such that it amount no more than mere instruction to apply the exception to a particular technological environment. Further, none of the limitations recite technological implementations details for any of the steps but, instead, only recite broad functional language being performed by the generic use of at least one processor. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaning limits on practicing the abstract idea. Thus, the claim is directed toward an abstract idea.
Step 2B, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more that the abstract idea(s). As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the abstract idea(s) amounts to no more than mere instructions to apply the exaction using a generic computer component. Mere instruction to apply an exertion using a generic computer component cannot provide an inventive concept. These generic computer components are claimed at a high level of generality to perform their basic functions which amount to no more than generally linking the use of the judicial exception to the particular technological environment of field of use (Specification as cited above for additional elements) and further see insignificant extra-solution activity MPEP § 2106.05 I. A. iii, 2106.05(b), 2106.05(b) III, 2106.05(g). Thus, the claims are not patent eligible.
As for dependent claims 2, 4-12, 15, and 17-23 these claims recite limitations that further define the same abstract idea using previously identified additional elements noted from the respective independent claims from which they depend. Therefore, the cited dependent claims are considered patent ineligible for the reasons given above.
Prior Art
Claims 1, 2, and 4-20 overcome the prior art of record such that none of the cited prior art reference’s disclosures can be applied to form the basis of a 35 USC § 102 rejection nor can they be combined to fairly suggest in combination, the basis of a 35 USC § 103 rejection when the limitations directed toward the abstract idea are read in the particular environment of the claims. Therefore, the claims may be allowable if amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action.
Response to Arguments
Applicant's arguments with regards to patent eligibility have been fully considered but they are not persuasive.
EXAMINER’S RESPONSE TO APPLICANT REMARKS CONCERNING Claim Rejections - 35 USC § 101: Applicant's arguments with regards to 35 USC § 101 have been fully considered but are not persuasive. Regarding applicant’s argument directed toward the amended claims involving scenario-specific machine learning models, paragraph [0019] states:
[0019] The technical solution of scenario-specific machine learning (ML) models is implemented in an unconventional manner at least by assigning a transaction to a scenario based on a tiered ranking that is formulated using a ratio of the transaction amount and likelihood of fraud, and executing the different ML model that is tied to one of the tiers in order to accomplish a specific purpose with the variable fee. For example, one ML model identifies a group of issuers and generates a similar variable fee to that generated by the other issuers, another model generates a minimal variable fee, and yet another ML model generates a variable fee to either recover operational costs or generate a profit. In some examples, assigning a particular transaction to a particular scenario, and therefore a particular model for calculating the variable fee, reduces the bandwidth and computing power required to generate the variable fee by identifying a single model to be used to generate the variable fee, and reduces the amount of time required to compute the variable fee. In other words, as opposed to implementing multiple models for a single transaction and identifying an optimal fee between the multiple models, implementations of the present disclosure reduce the computing power required to generate the recommended variable fee by first identifying a particular model to be used to generate the variable fee prior to executing the model.
Thus, the three scenario-specific machine learning models represent different business objectives and are selected based on which objective is desired. Thus in the amended claims “based on the ranking, forming a first tier of transactions associated with a first scenario, a second tier of transactions associated with a second scenario, and a third tier of transactions associated with a third scenario, wherein the first scenario corresponds to the first scenario-specific machine learning model, the second scenario corresponds to the second scenario-specific machine learning model, and a third scenario corresponds to the third scenario-specific machine learning model; and assigning a scenario to the pending transaction, the assigned scenario corresponding to one of the following: the first tier of transactions associated with the first scenario, the second tier of transactions associated with the second scenario, or the third tier of transactions associated with the third scenario; determining the selected scenario-specific machine learning model based on the assigned scenario, and applying the pending transaction to selected scenario-specific machine learning model, without executing the first, second, and third scenario-specific machine learning models other than the selected scenario-specific machine learning model, to generate a recommended variable fee for the pending transaction based on the prepared raw data;”
the selected scenario-specific machine learning model is an abstract idea. Thus, there is no technological improvement as in McRo. As such, the examiner maintains the rejection.
Conclusion
For prior art made of record and not relied upon is considered pertinent to applicant's disclosure see Notice of References Cited items A-G submitted 02/19/2025 used as prior art and in the conclusion section in the office action submitted 02/19/2025.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Gregory A Pollock whose telephone number is (571) 270-1465. The examiner can normally be reached M-F 8 AM - 4 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abhishek Vyas can be reached on 571 270-1836. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Gregory A Pollock/Primary Examiner, Art Unit 3691
04/02/2026
Prosecution Insights