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 amendment filed 01/29/2026.
Claims 1-3, 5-6, 8, 10-12, 14-15, 17, and 19-20 have been amended. Claims 1-6 and 8-21 are pending and have been examined on the merits (claims 1, 10, and 19 being independent).
The amendment filed 01/29/2026 to the claims has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/29/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant’s arguments and amendments filed 01/29/2026 have been fully considered.
Applicants assert that the pending claims fully comply with the requirement of 35 U.S.C. 101. Examiner respectfully disagrees. Applicant’s argument and amendments have been considered and are not persuasive. The rejections under 35 U.S.C. 101 have been maintained and clarified in view of the USPTO MPEP 2106.
Applicant’s arguments (see Applicant’s remarks, pages 10-15)
A. Applicant's Claims Are Not Directed to an Abstract Idea (see pages, 10-14)
(1) Applicant arguments that “The Office Action alleges, at page 8, that the claims are akin to the abstract idea subject matter grouping of: Certain Methods of Organizing Human Activity as fundamental economic principles or practices and commercial or legal interactions. Applicant respectfully disagrees. The pending claims do not recite methods of organizing human activity or economic practices. Instead, they recite specific, processor-implemented operations for training and retraining a machine learning model using identified training data sets and correction signals.” (see page 11), are not found persuasive.
In response (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 MPEP 2106.04. In the instant application, Examiner considers the cited limitations as drafted are systems and/or 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 (e.g., a payment processor, machine learning model). 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. Making a prediction of clearing message parameters (e.g., a clearing transaction amount and/or clearing latency) using the training data set such as historical financial 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 (e.g., a machine learning model), then it falls within the organizing human activity grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
(2) Applicant arguments that “Accordingly, in this case, "the specification sets forth an improvement in technology ... [and] the claim includes the components or steps of the invention that provide the improvement described in the specification," which is sufficient to establish a practical application. See MPEP § 2104.04(d)(l). For at least the reasons set forth above, the present claims are eligible under the second prong of Step 2A.” (see page 14), are not found persuasive.
In response (2): Under Step 2 A, Prong 2 of the 2019 Revised § 101 Guidance, 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. The Examiner considers the instant claims do not integrate the exception into a practical application because additional elements: 1) “a payment processor”, “a processor”, and “machine learning model” amount to simply applying the abstract idea to a computer component. (e.g. “apply it”) 2) “computing device” also amounts to simply applying the abstract idea to a computer. (e.g. “apply it” or the equivalent) do not apply, rely on, or use the judicial exception in a manner that that imposes a meaningful limitation on the judicial exception (i.e. generally linking the use of the judicial exception to a particular technological environment or field of use - see MPEP 2106.05(h) or apply it with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)). The instant recited claims including additional elements (e.g., processor, memory device, a payment processor, machine learning model, computing device) do not improve the functioning of the computer or improve another technology or technical field nor do they recite meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. Therefore, Applicant’s arguments are not persuasive.
B. Applicant's Claims Are Directed to "Significantly More" Than the Abstract Idea ( see pages 14-15)
(3) Applicant arguments that “One path to show that a claim recites "significantly more" is to identify "a specific limitation other than what is well-understood, routine, conventional activity in the field, or ... unconventional steps that confine the claim to a particular useful application." See MPEP § 2106.05(I)(A)(v).” (see page 14), are not found persuasive.
In response (3): Examiner considers the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration into a practical application, the additional elements (e.g., processor, memory device, a payment processor, machine learning model, computing device) amount to no more than mere instructions to apply the exactly using generic computer component. The claim elements when considered separately and in an ordered combination, do not add significantly more than implementing the abstract idea. The computer is merely a platform on which the abstract idea is implemented. Simply executing an abstract concept on a computer does not render a computer “specialized,” nor does it transform a patent-ineligible claim into a patent-eligible one. See Bancorp Servs., LLC v. Sun Life Assurance Co. of Can., 687 F.3d 1266, 1280 (Fed. Cir. 2012). There are no improvements to another technology or technical field, no improvements to the functioning of the computer itself, transformation or reduction of a particular article to a different state or thing or any other meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment as a result of performing the claimed method. Also, the addition of merely novel or non-routine components to the claimed idea does not necessarily turn an abstraction into something concrete (See Ultramercial, Inc. v. Hulu, LLC, _ F.3d_, 2014 WL 5904902, (Fed. Cir. Nov. 14, 2014). Hence, the claims do not recite significantly more than an abstract idea. Therefore, Applicant’s arguments are not persuasive.
With regard to the rejections of claims 1-6 and 8-21 under 35 U.S.C. 103, Applicant’s arguments and amendments have been considered but are moot as a new ground of rejection has been added and Examiner respectfully disagrees. Examiner notes that Applicant is arguing newly amended claim language. As noted in the citation above the prior art and it is addressed by the rejections under 35 USC 103.
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 and 8-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, (1) 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, (2a) 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 (2b), 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).
The claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more. In the instant case, the claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea.
Step (1): In the instant case, the claims are directed towards to a method for making a prediction of clearing message parameters (e.g., a clearing transaction amount and/or clearing latency) using the training data set such as historical financial transactions which contains the steps of identifying, training, receiving, outputting, receiving, comparing, deriving, and re-training. The claim recites a series of steps and, therefore, is a process. The claims do fall within at least one of the four categories of patent eligible subject matter because claim 1 is direct to a modeling platform, claim 10 is direct to a method, and claim 19 is direct to a non-transitory computer-readable medium, i.e. machines programmed to carrying out process steps, Step 1-yes.
Step (2A) Prong 1: A method for making a prediction of clearing message parameters (e.g., a clearing transaction amount and/or clearing latency) using the training data set such as historical financial transactions is akin to the abstract idea subject matter grouping of: Certain Methods of Organizing Human Activity as fundamental economic principles or practices and/or commercial or legal interactions. As such, the claims include an abstract idea.
The specific limitations of the invention are (a) identified to encompass the abstract idea include: { identify training data sets from retrieved subsets of data extracted from authorization request messages and clearing messages associated with a plurality of historical authorized transactions, each of the training data sets corresponding to one of the plurality of historical authorized transactions and comprising data elements associated with one or more authorization request messages and one or more clearing messages for the corresponding historical authorized transaction; train …. by applying the training data sets as inputs…; receive, ….. an authorization request message including authorization data in one or more data fields of the authorization request message; output a first output and a second output by applying the authorization data as one or more inputs to ….., the first output comprising a confidence prediction value corresponding to a likelihood that a transaction amount included in the authorization request message matches an actual clearing transaction amount included in an authorized clearing message received in response to processing the authorization request message, and the second output comprising a timing prediction associated with an actual clearing latency for clearing the transaction amount; in response to transmitting the first and second outputs, in near-real time, …., receive, …. the authorized clearing message including the actual clearing transaction amount and the actual clearing latency; compare the actual clearing transaction amount to the first output and the actual clearing latency to the second output; derive a correction signal based on results from the comparison; periodically re-train ….. using the correction signal. }
As stated above, this abstract idea falls into the (b) subject matter grouping of: Certain Methods of Organizing Human Activity as fundamental economic principles or practices and/or commercial or legal interactions as identifying the training data set as historical financial transactions, outputting a result, and making a prediction of clearing message parameters (e.g., a clearing transaction amount and/or clearing latency).
Step (2A) Prong 2: The instant claims do not integrate the exception into a practical application because additional elements: 1) “a payment processor”, “processor”, and “machine learning model” amount to simply applying the abstract idea to a computer component. (e.g. “apply it”) 2) “computing device” also amounts to simply applying the abstract idea to a generic computer. (e.g. “apply it” or the equivalent) do not apply, rely on, or use the judicial exception in a manner that that imposes a meaningful limitation on the judicial exception (i.e. generally linking the use of the judicial exception to a particular technological environment or field of use - see MPEP 2106.05(h) or apply it with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).
The instant recited claims including additional elements (e.g., processor, memory device, payment processor, machine learning model, computing device) do not improve the functioning of the computer or improve another technology or technical field nor do they recite meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. The limitations merely use a generic computing technology (Specification paragraph [0068]: a server computing device, an online platform or point of sale system of merchant, a server system of acquirer, a server system of payment processor, a server system of issuer, modelling platform, and/or operational predictive model module, and processor for executing instructions stored in a memory) as applying it with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)). Therefore, the claims are directed to an abstract idea
Step (2B): The claims do not include additional elements that are sufficient to 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 (Claims: e.g., processor, memory device, payment processor, machine learning model, computing device) amount to no more than mere instructions to apply the exactly using generic computer component. The claim elements when considered separately and in an ordered combination, do not add significantly more than implementing the abstract idea.
The computer is merely a platform on which the abstract idea is implemented. Simply executing an abstract concept on a computer does not render a computer “specialized,” nor does it transform a patent-ineligible claim into a patent-eligible one. See Bancorp Servs., LLC v. Sun Life Assurance Co. of Can., 687 F.3d 1266, 1280 (Fed. Cir. 2012). There are no improvements to another technology or technical field, no improvements to the functioning of the computer itself, transformation or reduction of a particular article to a different state or thing or any other meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment as a result of performing the claimed method. Also, the addition of merely novel or non-routine components to the claimed idea does not necessarily turn an abstraction into something concrete (See Ultramercial, Inc. v. Hulu, LLC, _ F.3d_, 2014 WL 5904902, (Fed. Cir. Nov. 14, 2014). Hence, the claims do not recite significantly more than an abstract idea. In conclusion, merely “linking/applying” the exception using generic computer components does not constitute ‘significantly more’ than the abstract idea. (MPEP 2106.05 (f)(h)). Therefore, the claims are not patent eligible under 35 USC 101.
Dependent claims 2-6, 8-9, 11-18, and 20-21 when analyzed as a whole and in an ordered combination are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea, as detailed below. The additional recited limitations in the dependent claims only refine the abstract idea.
For instance, in claims 2, 11 and 20, the step of “… transmit, …. , in real-time as part of an enhanced authorization request message, the first output and the second output, ...” (i.e., sending a message), in claims 3 and 12, the step of “… wherein the value comprises the transaction amount when the first output is greater than a first threshold value, indicating a high confidence the transaction amount…” (i.e., determining a confidence for the transaction amount), in claims 4 and 13, the step of “… wherein the timing prediction corresponds to a time period between authorization and receipt ...” (i.e., determining a time period), in claims 5 and 14, the step of “… retrieve the subsets of data ….. apply the model input data fields of each training data set as inputs… re-train … to adjust parameters … until an error between the at least one output and the at least one result data field falls below a threshold….” (i.e., updating parameters), in claims 6 and 15, the step of “… derive the model input data fields from data fields in the retrieved subsets of data extracted from the authorization request messages ...” (i.e., obtaining data), in claims 8 and 17, the step of “… wherein the at least one trained machine learning...” (i.e., using machine learning), in claims 9 and 18, the step of “… the parameters adjusted are respective weight values applied to one or more inputs ...” (i.e., parameters applied to inputs), in claim 16, the step of “… wherein the model input data fields include one or more of an authorization date, a clearing date, an authorization amount, a clearing amount, ...” (i.e., obtaining input data), and in claim 21, the step of “… generate a plurality of training data sets using historical authorized transaction data; ...” (i.e., generating data sets) are all processes that, under its broadest reasonable interpretation, covers performance of a fundamental economic practice but for the recitation of a generic computer component. Using predictive modeling to predict the outcome of future events based on historical transaction data is a most fundamental commercial process.
This is an abstract concept with nothing more and is also considered mere instructions to apply an exception akin to a commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd.; Gottschalk and Versata Dev. Group, Inc.; see MPEP 2106.05(f)(2).
In dependent claims 2-6, 8-9, 11-18, and 20-21, the step claimed are rejected under the same analysis and rationale as the independent claims 1, 10, and 19 above. Merely claiming the same process using predictive modeling to predict the outcome of future events based on historical transaction data does not change the abstract idea without an inventive concept or significantly more. Clearly, the additional recited limitations in the dependent claims only refine the abstract idea further. Further refinement of an abstract idea does not convert an abstract idea into something concrete.
Therefore, claims 1-6 and 8-21 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 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.
In the rejections below, where claims are currently amended, this is indicated by underlining.
Claims 1, 3-6, 8-10, 12-19, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Mori et al. (hereinafter Mori), US Publication Number 2022/0366412 A1 in view of Mach et al. (hereinafter Mach), US Publication Number 2021/0125179 A1 in view of Das et al. (hereinafter Das), US Publication Number 2021/1092641 A1 in further view of Mann et al. (hereinafter Mann), US Patent Number 11,790,411 B1.
Regarding claim 1:
Mori discloses the following:
A modelling platform comprising at least one processor in communication with at least one memory device and a payment processor, the at least one processor programmed to: (Mori: see paragraph [0011] “The system may consist of a processor and memory hosting an artificial intelligence (AI) engine, a database, an input processor, a clearing delay module, and a modification module. The database may be coupled to the processor and the memory and store a dataset corresponding to payment transactions between a plurality of customers to a plurality of merchants.”)
Mori does not explicitly disclose the following, however Mach further teaches:
identify training data sets from retrieved subsets of data extracted from authorization request messages and clearing messages associated with a plurality of historical authorized transactions, each of the training data sets corresponding to one of the plurality of historical authorized transactions and comprising data elements associated with one or more authorization request messages and one or more clearing messages for the corresponding historical authorized transaction; (Mach: see paragraph [0053] “the machine-learned payment success prediction model can have been trained (e.g., using supervised training techniques) on a set of training data. The training data can include a plurality of training examples, where each training example includes a historical authorization request ( e.g., a historical authorization request message and/or its routing characteristics) that has been labeled, annotated, or otherwise associated with a ground-truth authorization outcome (e.g., an indication of whether the corresponding historical authorization request was authorized or declined). Through the training process, the machine-learned payment success prediction model 206 can learn to predict, for a given historical authorization request, a probability that such authorization request was successful (e.g., authorized) (e.g., the model 206 learns to predict a probability that matches the historical authorization outcome).”)
train at least one machine learning model by applying the training data sets as inputs into the at least one machine learning model; (Mach: see paragraph [0092] “the model trainer 560 can train the machine-learned models 520 and/or 540 based on a set of training data 562.”)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the machine learning architecture that is trained to analyze a likely outcome for a given set of inputs based on thousands or even millions of observations of previous customer/merchant transactions of Mori to include training the machine-learned models based on a set of training data including a historical authorization request, as taught by Mach, in order to predict success probabilities for payment authorization requests. (see Mach, [0013-0015])
Mori and Mach do not explicitly disclose the following, however Das further teaches:
receive, from the payment processor, an authorization request message including authorization data in one or more data fields of the authorization request message; (Das: see paragraphs [0017] “The one or more authorization records may be associated with an authorization request for a payment transaction of the one or more payment transactions.” and [0099] “transaction processing system 110 may receive an authorization record from issuer system 112 based on initiation of a payment transaction associated with the authorization record at merchant system 104 by user device 102.”, and notes: As cited above, an authorization record is associated with an authorization request message for a payment transaction.)
output a first output (reads on “generate an updated clearing record based on transaction processing system 110 appending an original transaction amount of the authorization record to a clearing record”) and a second output (reads on “generate an estimated time delay”) by applying the authorization data as one or more inputs to the at least one trained machine learning model, the first output comprising a confidence prediction value corresponding to a likelihood that a transaction amount included in the authorization request message matches an actual clearing transaction amount included in an authorized clearing message received in response to processing the authorization request message, and the second output comprising a timing prediction associated with an actual clearing latency for clearing the transaction amount; (Das: see paragraphs [0110] “transaction processing system 110 may generate a prediction (e.g., an output representative of a likelihood of a clearing record matching an authorization record) based on transaction processing system 110 providing the clearing record and the authorization record as input to the machine learning model. The prediction may be associated with a confidence score (e.g., a score indicating a likelihood that that a clearing record matches and/or partially matches an authorization record). transaction processing system 110 may generate an updated clearing record based on transaction processing system 110 appending a confidence score to the clearing record. In some non-limiting embodiments or aspects, transaction processing system 110 may generate an updated clearing record based on transaction processing system 110 appending an original transaction amount of the authorization record to a clearing record. For example, transaction processing system 110 may append an original transaction amount of an authorization record to a clearing record based on transaction processing system 110 determining that the authorization record matches and/or partially matches the clearing record.” and [0136] “The machine learning model may, given an input of a merchant identifier, acquirer identifier, and/or other transaction data of a clearing record, generate an estimated time delay (e.g., a delay in time from clearing records being received relative to authorization records being received) that is associated with the merchant that originated the clearing record, and generate confidence scores for the unmatched clearing records.”, and see also [0140])
in response to transmitting the first and second outputs, in near-real time, to the payment processor, receive (reads on [0110] “an updated clearing record based on transaction processing system 110 appending an original transaction amount of the authorization record to a clearing record” and [0136] “an estimated time delay (e.g., a delay in time from clearing records being received relative to authorization records being received)”), from the payment processor, the authorized clearing message including the actual clearing transaction amount (reads on “an updated clearing record”) and the actual clearing latency (reads on “an estimated time delay”); (Das: see paragraphs [0110] and [0136])
compare the actual clearing transaction amount (reads on [0126] “it may be determined whether only the transaction amount of a clearing record does not match an authorization record”) to the first output and the actual clearing latency (reads on [0140] “the estimated clearing delay and confidence score may be output from the second process 419. For example, the transaction processing system 110 may output the estimated clearing delay and confidence score for each clearing record having a confidence score that satisfied a predetermined threshold in step 609.”) to the second output; (Das: see paragraphs [0126] and [0140])
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the machine learning architecture that is trained to analyze a likely outcome for a given set of inputs based on thousands or even millions of observations of previous customer/merchant transactions of Mori to include a confidence score (e.g., a score indicating a likelihood that that a clearing record matches and/or partially matches an authorization record, as taught by Das, in order to provide more success probability for each authorization request. (see Das, [0110])
Mori, Mach, and Das do not explicitly disclose the following, however Mann further teaches:
derive (reads on “output an instruction to re-train machine learning model 210.”) a correction signal based on results from the comparison; and (Mann: see column 12, lines 54-63: “ if performance monitoring unit 238 determines that the accuracy of machine learning model 210 is below a threshold accuracy value (e.g., 90%), performance monitoring unit 238 may output an instruction to re-train machine learning model 210.”)
periodically re-train (reads on “periodically (e.g., monthly, bimonthly, yearly, or the like) re-train machine learning model 210”) the at least one trained machine learning model using the correction signal. (Mann: see column 12, lines 63-65: “ Training unit 230 may periodically (e.g., monthly, bimonthly, yearly, or the like) re-train machine learning model 210 based on an updated set of training data. The updated set of training data may include part or all of the plurality of messages of training data 209.” and column 18, lines 15-23: “training unit 230 may re-train machine learning model 210 based on a new set of training data including the training data 209 and the set of messages selected for quality control and determined to be correctly or not correctly classified using machine learning model 210.”, and see also column 12, lines 54-68)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the machine learning architecture that is trained to analyze a likely outcome for a given set of inputs based on thousands or even millions of observations of previous customer/merchant transactions of Mori to include outputting an instruction to re-train machine learning model, as taught by Mann, in order to improve performance. (see Mann, column 18, lines 15-28)
Regarding claim 3:
Mori and Mach do not explicitly disclose the following, however Das further teaches:
The modelling platform of claim 2, wherein the value comprises the transaction amount when the first output is greater than a first threshold value, indicating a high confidence the transaction amount will match the actual clearing transaction amount. (Das: See paragraph [0110] “The prediction may be associated with a confidence score (e.g., a score indicating a likelihood that that a clearing record matches and/or partially matches an authorization record). Transaction processing system 110 may generate an updated clearing record based on transaction processing system 110 authorization record to a clearing record. For example, transaction processing system 110 may append an original transaction amount of an authorization record to a clearing record based on transaction processing system 110 determining that the authorization record matches and/or partially matches the clearing record.”, and see also [0074] and [0126])
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the machine learning architecture that is trained to analyze a likely outcome for a given set of inputs based on thousands or even millions of observations of previous customer/merchant transactions of Mori to include a confidence score (e.g., a score indicating a likelihood that that a clearing record matches and/or partially matches an authorization record, as taught by Das, in order to provide more success probability for each authorization request. (see Das, [0110])
Regarding claim 4:
Mori discloses the following:
The modelling platform of claim 1, wherein the timing prediction corresponds to a time period between authorization and receipt by the payment processor of a corresponding clearing message. (Mori: See paragraph [0036] “a clearing delay estimate layer 402 may determine a predicted clearing delay 404 from observed inputs 404A, 404B.”), and see also Claim 1)
Regarding claim 5:
Mori discloses the following:
The modelling platform of claim 1,
re-train (reads on “the data 122A may be used to train a first or second machine learning (ML) module”) the at least one trained machine learning model by applying a machine learning algorithm to adjust parameters of the at least one trained machine learning model until an error between the at least one output and the at least one result data field falls below a threshold; and (Mori: See paragraph [0010] “The method may also receive first data corresponding to a purchase transaction at a merchant and determine a clearing delay estimate for the purchase transaction based on an analysis by the artificial intelligence engine of the first data. The method may then modify one or more of an authorization process, a clearing process, and a settlement process for the purchase transaction in response to the clearing delay estimate being above a threshold.” And [0039] “the data 122A may be used to train a first or second machine learning (ML) module 112A, 112B to determine a clearing delay estimate 119.”)
Mori does not explicitly disclose the following, however Mach further teaches:
wherein each of the training data sets includes model input data fields and at least one result data field, the at least one result data field representing a clearing message parameter for the plurality of historical authorized transactions, and wherein the at least one processor is further programmed to: (Mach: see paragraph [0053] “the machine-learned payment success prediction model can have been trained (e.g., using supervised training techniques) on a set of training data. The training data can include a plurality of training examples, where each training example includes a historical authorization request ( e.g., a historical authorization request message and/or its routing characteristics) that has been labeled, annotated, or otherwise associated with a ground-truth authorization outcome (e.g., an indication of whether the corresponding historical authorization request was authorized or declined). Through the training process, the machine-learned payment success prediction model 206 can learn to predict, for a given historical authorization request, a probability that such authorization request was successful (e.g., authorized) (e.g., the model 206 learns to predict a probability that matches the historical authorization outcome).”)
retrieve the subsets of data from a transaction history database, wherein the transaction history database stores data extracted from the authorization request messages and the clearing messages for the plurality of historical authorized transactions processed by the payment processor; (Mach: See paragraph [0071] “the smart routing system 402 can access a database 406 that contains authorization message, routing information, outcomes of recent similar transactions using each payment processor, and/or other data. Specifically, the database 406 can obtain information about historical variables, transaction constants, policy rules, or other information. The historical variables can include some or all of: country code/domicile, acquirer, merchant ID, MCC, transaction type, expiration date, etc.”)
apply the model input data fields of each training data set as the inputs to the at least one trained machine learning model, wherein each of the at least one trained machine learning model is programmed to produce, for each training data set, at least one output corresponding to a value of the at least one result data field of the training data set; (Mach: See paragraph [0014] “The method includes obtaining, by one or more computing devices, a set of historical payment data that comprises a plurality of training pairs, each training pair comprising an example authorization request having a particular combination of values for one or more variable request parameters and an example authorization outcome associated with the example authorization request. The method includes processing each example authorization request with the machine-learned payment success prediction model to receive a respective predicted authorization outcome for each example authorization request.”)
in response to the error falling below the threshold, upload one or more of the at least one trained machine learning model to an operational predictive model module. (Mach: See paragraph [0053] “Through the training process, the machine-learned payment success prediction model 206 can learn to predict, for a given historical authorization request, a probability that such authorization request was successful (e.g., authorized) (e.g., the model 206 learns to predict a probability that matches the historical authorization outcome). Thus, payment data such as authorization requests and their associated authorization outcomes can be logged over time and can be used to train the machine-learned payment success prediction model 206 to accurately predict a success probability for a particular authorization request.”)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the machine learning architecture that is trained to analyze a likely outcome for a given set of inputs based on thousands or even millions of observations of previous customer/merchant transactions of Mori to include training the machine-learned models based on a set of training data including a historical authorization request, as taught by Mach, in order to predict success probabilities for payment authorization requests. (see Mach, [0013-0015])
Regarding claim 6:
Mori does not explicitly disclose the following, however Mach further teaches:
The modelling platform of claim 5, wherein the at least one processor is further programmed to derive the model input data fields from data fields in the retrieved subsets of data extracted from the authorization request messages processed by the payment processor. (Mach: See paragraph [0053] “the machine-learned payment success prediction model can have been trained (e.g., using supervised training techniques) on a set of training data. The training data can include a plurality of training examples, where each training example includes a historical authorization request (e.g., a historical authorization request message”)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the machine learning architecture that is trained to analyze a likely outcome for a given set of inputs based on thousands or even millions of observations of previous customer/merchant transactions of Mori to include training the machine-learned models based on a set of training data including a historical authorization request, as taught by Mach, in order to predict success probabilities for payment authorization requests. (see Mach, [0013-0015])
Regarding claim 8:
Mori does not explicitly disclose the following, however Mach further teaches:
The modelling platform of claim 5, wherein the at least one trained machine learning model includes a neural network. (Mach: See paragraph [0055] “the machine-learned model can be an artificial neural network, a random forest model, a logistic regression model”)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the machine learning architecture that is trained to analyze a likely outcome for a given set of inputs based on thousands or even millions of observations of previous customer/merchant transactions of Mori to include training the machine-learned models based on a set of training data including a historical authorization request, as taught by Mach, in order to predict success probabilities for payment authorization requests. (see Mach, [0013-0015])
Regarding claim 9:
Mori discloses the following:
The modelling platform of claim 8, wherein the neural network comprises one or more layers of nodes, and the parameters adjusted are respective weight values applied to one or more inputs to each of the nodes. (Mori: See paragraphs [0032] “ML architecture 300 may include an input layer 302, a hidden layer 304, and an output layer 306. The input layer 302 may include inputs 308A, 308B, etc., coupled to the data integration module 112C and represent those inputs that are observed from actual customer and merchant data in transactions. The hidden layer 304 may include weighted nodes 310 that have been trained for the transactions being observed. Each node 310 of the hidden layer 304 may receive the sum of all inputs 308A, 308B, etc., multiplied by a corresponding weight. The output layer 306 may present various outcomes 312 based on the input values 308A, 308B, etc., and the weighting of the hidden layer 304.”)
Regarding claim 16:
Mori does not explicitly disclose the following, however Mach further teaches:
The method of claim 15, wherein the model input data fields include one or more of an authorization date, a clearing date, an authorization amount, a clearing amount, a merchant identifier, a merchant category code, an account number entry code, a pre- authorization code, an acquirer code, or a country code. (Mach: See paragraph [0048] “the variable request parameters can include variable message parameters associated with the authorization request message and/or variable routing parameters associated with the routing of the request message. As examples, the variable message parameters can include: a merchant domicile; a merchant ID; a merchandise category code ("MCC") (e.g., digital goods, travel, hardware, music, subscription, utility, etc.); a transaction type (e.g., recurring vs. e-commerce vs one-off); an encryption type or format; customer reputation/value; currency; an expiration date; and/or other portions of the authentication request message.”, and see also [0071])
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the machine learning architecture that is trained to analyze a likely outcome for a given set of inputs based on thousands or even millions of observations of previous customer/merchant transactions of Mori to include training the machine-learned models based on a set of training data including a historical authorization request, as taught by Mach, in order to predict success probabilities for payment authorization requests. (see Mach, [0013-0015])
Regarding claim 21:
Mori and Mach do not explicitly disclose the following, however Das further teaches:
The modelling platform of claim 1, wherein the at least one processor is further programmed to:
generate a plurality of training data sets using historical authorized transaction data; (Das: See paragraph [0138] “continually regenerate the estimates (e.g., re-train and re-execute the model) as additional data becomes available and is added to the historical transaction data 607 that may be used to train the machine learning model.”)
generate at least one machine learning model using the plurality of training data sets; and (Das: See paragraph [0138] “the machine learning model may, after being trained on the historical transaction data 607, generate a prediction”)
train the at least one machine learning model by applying additional training data sets to the at least one machine learning model. (Das: See paragraph [0138] “The machine learning model may continually regenerate the estimates (e.g., re-train and re-execute the model) as additional data becomes available and is added to the historical transaction data 607 that may be used to train the machine learning model”)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the machine learning architecture that is trained to analyze a likely outcome for a given set of inputs based on thousands or even millions of observations of previous customer/merchant transactions of Mori to include a confidence score (e.g., a score indicating a likelihood that that a clearing record matches and/or partially matches an authorization record, as taught by Das, in order to provide more success probability for each authorization request. (see Das, [0110])
Regarding claims 10 and 19: it is similar scope to claim 1, and thus it is rejected under similar rationale.
Regarding claim 12: it is similar scope to claim 3, and thus it is rejected under similar rationale.
Regarding claim 13: it is similar scope to claim 4, and thus it is rejected under similar rationale.
Regarding claim 14: it is similar scope to claim 5, and thus it is rejected under similar rationale.
Regarding claim 15: it is similar scope to claim 6, and thus it is rejected under similar rationale.
Regarding claim 17: it is similar scope to claim 8, and thus it is rejected under similar rationale.
Regarding claim 18: it is similar scope to claim 9, and thus it is rejected under similar rationale.
Claims 2, 11, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Mori in view of Mach in view of Das in view of Mann in further view of Yoshida et al. (hereinafter Yoshida), US Publication Number 2002/0004760 A1.
Regarding claim 2:
Mori and Mach do not explicitly disclose the following, however Das further teaches:
The modelling platform of claim 1, wherein the at least one processor is further programmed to:
transmit, to an issuer computing device, in real-time as part of an enhanced authorization request message, the first output and the second output, (Das: See paragraph [0121] “transaction processing system 110 may transmit the updated clearing batch file to issuer system 112 based on transaction processing system 110 determining that the clearing batch file and/or the one or more authorization records that correspond to clearing records included in the clearing batch file are associated with issuer system 112.”, and see also [0140], notes: the updated clearing batch file includes an updated clearing record by modifying and/or appending a key filed of the clearing record to include the estimated clearing delay and the confidence score read on the limitation of “the first output and the second output”)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the machine learning architecture that is trained to analyze a likely outcome for a given set of inputs based on thousands or even millions of observations of previous customer/merchant transactions of Mori to include a confidence score (e.g., a score indicating a likelihood that that a clearing record matches and/or partially matches an authorization record, as taught by Das, in order to provide more success probability for each authorization request. (see Das, [0110])
Mori, Mach, Das, and Mann do not explicitly disclose the following, however Yoshida further teaches:
wherein the enhanced authorization request message instructs the issuer computing device to cause a value associated with an actual transaction amount to be displayed on a user computing device associated with an account holder, and wherein the value is one of the transaction amount and the actual clearing transaction amount. (Yoshida: See paragraph [0085] “The account handling institute obtains a new account balance by referring to the account database and transfers the data to the claim management server. The claim management server generates a list of unsettled payment from both the updated unsettled payment data and account balance data, and presents the list to the user. In this way, the shopping item settled by the user is deleted from the list of unsettled payment, and the remaining unsettled shopping items and the account balance are displayed.”)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the machine learning architecture that is trained to analyze a likely outcome for a given set of inputs based on thousands or even millions of observations of previous customer/merchant transactions of Mori to include the updated unsettled payment data and account balance data, and presents the list to the user, as taught by Das, in order to provide account holders with more accurate account balance based on pending transactions. (see Yoshida, [0105])
Regarding claims 11 and 20: it is similar scope to claim 2, and thus it is rejected under similar rationale.
Conclusion
The prior art made of record but not relied upon herein but pertinent to Applicant’s disclosure is listed in the enclosed PTO-892.
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 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 mailing date of this final action.
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/YONGSIK PARK/Examiner, Art Unit 3694
March 26, 2026
/BENNETT M SIGMOND/Supervisory Patent Examiner, Art Unit 3694
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