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 .
The following is a Non-Final Office Action in response to communications received June 16, 2026. Claim(s) 9 has been canceled. Claims 1, 5 and 16 have been amended. No new claims have been added. Therefore, claims 1-8 and 10-20 are pending and addressed below.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17 (e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant’s submission has been entered.
Priority
Application 18145627 filed 12/22/2022 Claims Priority from Provisional Application 63294406, filed 12/29/2021.
Applicant Name/Assignee: Mastercard International Incorporated
Inventor(s): Baguley, Nicholas; Ban Young Mi Catarina; Campbell, William; Lo Faro, Wally; Parkinson, William; Gagon, Serenie, Hosny, Ahmed; Liberson, Alexander; Woolston, Gayley; Thomas, Nicholas; Ragozzine, Brett; Roper, Daniel; Harnish, Justin; Moosavi, Abbas; Saini, Swati; Bell, Cameron; Arunachalam, Natesh Babu; Mehrhoff, Shawn
Response to Amendment/Amendment
Claim Rejections - 35 USC § 112(a)
Applicant's arguments filed June 16, 2026 have been fully considered but they are not persuasive.
In the remarks applicant argues that according to Ariad Pharms Ince, the exact claim terminology is not needed as long as long as the written description suggest the limitation. Applicant argues the specification para 0049 states the “machine learning …may provided with example inputs and their associated outputs and may seek to discover a general rule that maps inputs…accurately predict the correct output”. Applicant argues the “providing a model with example inputs and their associated outputs so that the model discovers a general input-to-output rule for predicting outputs…” means to “train a machine learning model”. Applicant’s argument is not persuasive. The definition of the term “provide” is to make available for use/supply (something wanted/needed); to make something available (afford) : to have as a condition (stipulate)
To “train” a machine learning model is a specific technical process. According to its common meaning in the art, training a machine learning model is the process of teaching a machine learning how to learn from data and make predictions or decisions, teaching an algorithm how to solve a problem using data. The examiner disagrees that the “providing” as set forth in the specification goes beyond merely making available a learning model for use in analyzing data inputted used to generate patterns or correlations to estimate a likelihood as set forth in the specification. The specification disclosure possession of “method or program may be provided with example inputs and their associated outputs, and may seek to discover a general rule that maps inputs to outputs, so that when subsequent novel inputs are provided the processing element may, based upon the discovered rule, accurately predict the correct output” is not analogous to a specific technical process of “training” a machine learning model. This is because the “provide” of para 0049 does not suggest or imply to one of ordinary skill in the art that the learning model is trained as the “provide” language in the specification does not suggest or imply teaching a machine learning how to learn from data and make predictions or decisions, teaching an algorithm how to solve a problem using data. The examiner maintains that the specification discloses the use of machine learning model in analyzing financial related data, but does not have possession of a process either explicitly or by implication a machine learning training process. The rejection is maintained.
[0047] For instance, one of ordinary skill will appreciate that pattern recognition may be
employed to estimate or project the impact that each of variables 404, 406, 408 will have on the
available balance 402 between the date on which the payment processor calculates a scaled score
400 and the date on which the settlement is to occur. For example, in one or more embodiments,
machine learning models and/or algorithms may be used to generate patterns or correlations for
historical income or deposits and corresponding day(s) (e.g., on a given day each month or quarter,
a certain deposit is typically seen, at least within the previous eighteen (18) month period). For
another example, such models may estimate the likelihood and amount of regular payments that
will be made during the intervening period prior to settlement. Finally, other categories of spending
(e.g., discretionary spending or the like) may be estimated on a monthly basis, and possibly in
view of seasonal changes, to project other expenditures likely to occur during the intervening
period.
[0048] Various methods and models described herein may utilize machine learning programs
or techniques to perform the analyses outlined below. For instance, machine learning program(s)
may recognize or determine patterns or correlations between customer spending or deposit
behavior on the one hand, and date, time of the month and/or season on the other hand. The
machine learning techniques or programs may include curve fitting, regression model builders,
convolutional or deep learning neural networks, combined deep learning, pattern recognition, or
the like. Based upon this data analysis, the computer-implemented methods and/or machine
learning program(s) may estimate income and expenditures likely to occur in the customer's
account in the intervening period between the time of calculation or estimation and the date of
settlement.
[0049] In supervised machine learning, a computer-implemented method or program may be
provided with example inputs and their associated outputs, and may seek to discover a general rule
that maps inputs to outputs, so that when subsequent novel inputs are provided the processing
element may, based upon the discovered rule, accurately predict the correct output. In
unsupervised machine learning, the computer-implemented method or program may be required
to find its own structure in unlabeled example inputs.
[0050] The computer-implemented methods or programs herein may utilize classification
algorithms such as Bayesian classifiers and decision trees, sets of pre-determined rules, and/or
other algorithms to generate estimated income and expenses for each customer account.
[0052] In one or more embodiments of the present invention, the payment router may access historical transaction records for each merchant. The payment router may submit the historical transaction data - e.g., reflecting transaction amounts, customers, chosen rails and/or dates of settlement (e.g., as compared to corresponding transaction initiation dates) - to a machine learning program or algorithm. The machine learning program or algorithm may automatically determine - for each merchant and based on pattern recognition - a merchant's preference(s) relating to risk tolerance and/or timing sensitivity. The payment router may use this determined risk tolerance and/or timing sensitivity information to select an optimized payment rail for each future transaction (e.g., according to the method 500 discussed in more detail below) without departing from the spirit of the present invention. One of ordinary skill will appreciate, however, that a merchant may manually input and/or select risk tolerance, timing sensitivity or the like for use by the payment router within the scope of the present invention.
Claim Rejections - 35 USC § 101
Applicant's arguments filed June 16, 2026 have been fully considered but they are not persuasive.
In the remarks applicant argues that the claimed limitations of claim 1 cannot reasonably be directed toward the abstract concept of mental process. Applicant is arguing an abstract category that has not been applied in the previous rejection and therefore is moot.
In the remarks applicant argues that under step 2A prong 2, the claimed limitations are patent eligible. Applicant argues the “training per-rail machine learning models on historical data, generating per-rail scaled scores predicting settlement or insufficient funds likelihood, determining merchant risk tolerance preferences via preference ML model, selecting payment rail based on scores and the merchant preferences and initiating transaction over selected rail improves the technical process of routing payment transactions. Applicant argues existing systems route payments according to predetermined settings of the merchant leading to suboptimal payment processing/failed transaction and higher settlement cost (¶ 0003). Applicant argues pointing to McRo, Finjan Inc v Blue Coat System decisions that the claim limitations address the issues. Applicant’s argument is not persuasive. The claim limitations merely use technology as a tool to analyze data in order to determine risk related to the transaction and available payment rails used to perform the transaction. Unlike McRO which provides a technical solution to a deficit within the technology itself or Finjan Inc v Blue Coat System which provided a behavior based virus scanning process rather than code-matching virus scanning process to provide a solution to a problem in the technology itself., the current applications is providing a solution to a problem in the abstract idea (e.g. predetermined settings of the merchant leading to suboptimal payment processing/failed transaction and higher settlement cost). The rejection is maintained.
In the remarks applicant argues that the previous Office action response with respect to example 47, claim 3 dismisses the guidance on the training of neural networks and use of the networks output to take a specific automated action and eligibility. Applicant argues the current limitations find analogous patent eligibility as that of example 47. This is because the present claims train machine learning modules and use the outputs to take concrete actions of selecting payment rails and initiating transaction. The examiner respectfully disagrees with the premise of applicant’s argument. Example 47 claim 3 which recites training an artificial network to identify anomalies, detect anomalies and provide a solution to the technical problem of network intrusion by malware by dropping malicious networks and blocking future traffic from corresponding source address. Patent eligibility is not found in the training of the artificial network but instead in applying the result of the analysis perform a solution to the technical problem of network intrusion by malware. The claimed model generates scores for payment rails in connection with a transaction and selects a payment rail for the transaction and initiates the transaction. The current application similarly uses machine learning to analyze data, however the result of the analysis of the current application is not to provide a solution to a problem rooted in technology itself but instead to apply the results to reduce transaction risk which is directed toward an abstract idea. The technology claimed is merely acting as a tool to automate data analysis based on transaction history and account balance changes in order to generate a score representing risk of payment for a transaction.
In the remarks applicant argues that under step 2B, the claimed subject matter is patent eligible. Applicant points to BASCOM arguing that similar to BASCOM, the current limitations recite non-conventional and non-generic arrangement of elements. Specifically, the ordered combination of the “per-rail trained settlement-likelihood models, preference machine learning model and rail selection conditioned on per-rail scores and merchant preferences learned which is not well understood arrangement. The examiner respectfully disagrees with the premise of applicant’s argument. The claim limitations of claim 1, except for the additional element “machine learning model” applied to determine preferences of the merchant which is mere data analysis for use in analyzing transaction risk and the additional element “payment rails” applied for initiating payment transaction, the claim limitations do not recite any technical process. The operations performed by the machine learning model and payment rails are not performed in an “ordered combination” but instead as separate functions in the steps recited to perform risk analysis of a transaction and perform the transaction using selected payment rails based on the risk determined. Claim 16 is similar except for the recitation of the additional elements “computer readable storage media having instructions stored and executed by at least one processor to execute instructions corresponding to method claim 1 and similar to claim 1, the order combination of the executed instruction is not to perform a particular technological technique but instead to perform a risk mitigating transaction process. The rejection is maintained.
Claim Rejections - 35 USC § 103
Applicant's arguments filed June 16, 2026 have been fully considered but they are not persuasive.
In the remarks applicant argues the prior art references fail to teach “for each of a plurality of potential payment rails, train a machine learning model on historical transaction data to correlate account data variables with account balance changes for an account", the examiner respectfully disagrees, the prior art reference Jia teaches abstract; para 0039 wherein the prior art teaches applying prediction process including calculated routing and risk score calculated by risk model which include route control action determining which of a number of payment processors to select for accepting purchase; para 0048-0050 wherein the prior art teaches building machine learning models to perform risk modeling to score risk including historical transaction information including bank rejected purchase transaction and reason codes, para 0053 wherein the prior art teaches account information including lack of funds, incomplete/incorrect account information, account history and times of day, week which may impact bank decision
In the remarks applicant argues the prior art references fail to teach “ (ii) generating each scaled score "by inputting account data including an existing balance of funds eligible to be debited into the corresponding one of the machine learning models to predict account balance changes and corresponding likelihood of insufficient funds for the account on the date", Applicant’s argument is moot as a new reference has been applied below in response to the submitted amendment, see rejection below.
In the remarks applicant argues the prior art references fail to teach “ (iii) determining merchant "timing sensitivity based on historical transaction records of the merchant.", the examiner respectfully disagrees. The prior art Jia explicitly teaches para 0028 wherein the prior art teaches using routing score to increase settle rate, para 0033-0034 wherein the prior art teaches matching target risk predefined action based on likelihood determined by merchant controlled risk models, para 0039 wherein the prior art teaches merchant control actions guided by merchant prediction service that includes presenting a higher than initially expected risk , para 0042 wherein the prior art teaches generating retry score based on decision of risk action. When considered in combination with Howe, which teaches Col 1 lines 60-Col 2 lines 1-10, lines 21-Col 3 lines 1-5 wherein the prior art teaches merchant transaction sub-group determined and identifying for each date of predetermined dates preferred debit network and for each date a routing table , Col 3 lines 15-40 wherein the prior art teaches identifying merchant transaction sub-group and identifying for each date of a range of dates preferred debit network and teaches determining a change in debit routing for the merchant based on routing tables for predetermined range of dates, Col 6 lines 25-38 wherein the prior art teaches identifying a routing table for the merchant based on detecting changes in merchant routing table where the routing table is identified for each day of transaction entities in close proximity to the day the changes in the routing table occurs, Col 7 lines 20-55 wherein the prior art teaches identifying merchant and identify routing table for specific date due to complex pricing structures. The rejection is maintained.
In the remarks applicant argues the prior art references combination are a product of impermissible hindsight. Applicant does not explain how the reasons to combine in the rejection below implemented hindsight. According to MPEP 2145, the burden shifts to the applicant to come forward with arguments and/or evidence to rebut the prima facie case. See, e.g., In re Dillon, 919 F.2d 688, 692, 16 USPQ2d 1897, 1901 (Fed. Cir. 1990) (en banc). Applicant may also present rebuttal evidence and arguments prior to an Office action in anticipation of possible prior art rejections. Applicant has not provided evidence or arguments as to the hindsight applied in the reasons to combine.
In the remarks applicant argues the prior art references fail to teach “(i) a respective machine learning model is trained per payment rail to predict account balance changes and insufficient-funds likelihood”, Applicant’s argument is moot as a new reference has been applied below in response to the submitted amendment, see rejection below.
In the remarks applicant argues the prior art references fail to teach “ (iii) a payment rail is selected based on both the per- rail scaled scores and those learned merchant preferences before initiating the transaction. The examiner respectfully disagrees. The prior art Jia teaches in at least para 0028 wherein the prior art teaches using routing score to increase settle rate, para 0033-0034 wherein the prior art teaches matching target risk predefined action based on likelihood determined by merchant controlled risk models, para 0039 wherein the prior art teaches merchant control actions guided by merchant prediction service that includes presenting a higher than initially expected risk , para 0042 wherein the prior art teaches generating retry score based on decision of risk action,
In the remarks applicant argues that based on arguments above the limitations of claims are allowable over the art, the examiner respectfully disagrees. See response above.
Claim Interpretation
With respect to the limitation “preference machine learning model”, the specification does not distinguish any particular machine learning machine as a “preference machine learning machine”. Rather the specification discloses “The machine learning program or algorithm may automatically determine- for each merchant and based on pattern recognition - a merchant's preference(s) relating to risk tolerance and/or timing sensitivity.”(¶ 0052). And discloses application or “machine learning models and/or algorithms” used to generate patterns and correlate data (¶ 0048-0040). Accordingly the examiner is interpreting the “preference machine learning machine” as a generic learning model of the one of the machine learning models used to predict account balance changes, generate patterns and correlate data.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim(s) 1-8 and 10-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
In reference to Claims 1-4: Claims 5-8 and 10-15: Claims 16-20:
Independent claims 1, 5 and 16 recites the limitations for each of a plurality of potential payment rails, “train a machine learning model on historical transaction data to correlate account data variables with account balance changes for an account”, this is because the original written description is silent with respect to a training of the machine learning model. The specification has support for:
[0047] For instance, one of ordinary skill will appreciate that pattern recognition may be employed to estimate or project the impact that each of variables 404, 406, 408 will have on the available balance 402 between the date on which the payment processor calculates a scaled score 400 and the date on which the settlement is to occur. For example, in one or more embodiments, machine learning models and/or algorithms may be used to generate patterns or correlations for historical income or deposits and corresponding day(s) (e.g., on a given day each month or quarter, a certain deposit is typically seen, at least within the previous eighteen (18) month period). For another example, such models may estimate the likelihood and amount of regular payments that will be made during the intervening period prior to settlement. Finally, other categories of spending (e.g., discretionary spending or the like) may be estimated on a monthly basis, and possibly in view of seasonal changes, to project other expenditures likely to occur during the intervening period.
[0048] Various methods and models described herein may utilize machine learning programs or techniques to perform the analyses outlined below. For instance, machine learning program(s) may recognize or determine patterns or correlations between customer spending or deposit behavior on the one hand, and date, time of the month and/or season on the other hand. The machine learning techniques or programs may include curve fitting, regression model builders, convolutional or deep learning neural networks, combined deep learning, pattern recognition, or the like. Based upon this data analysis, the computer-implemented methods and/or machine learning program(s) may estimate income and expenditures likely to occur in the customer's account in the intervening period between the time of calculation or estimation and the date of
settlement.
[0049] In supervised machine learning, a computer-implemented method or program may be provided with example inputs and their associated outputs, and may seek to discover a general rule that maps inputs to outputs, so that when subsequent novel inputs are provided the processing element may, based upon the discovered rule, accurately predict the correct output. In unsupervised machine learning, the computer-implemented method or program may be required.
[0050] The computer-implemented methods or programs herein may utilize classification algorithms such as Bayesian classifiers and decision trees, sets of pre-determined rules, and/or other algorithms to generate estimated income and expenses for each customer account.
[0052] In one or more embodiments of the present invention, the payment router may access historical transaction records for each merchant. The payment router may submit the historical transaction data - e.g., reflecting transaction amounts, customers, chosen rails and/or dates of settlement (e.g., as compared to corresponding transaction initiation dates) - to a machine learning program or algorithm. The machine learning program or algorithm may automatically determine - for each merchant and based on pattern recognition - a merchant's preference(s) relating to risk tolerance and/or timing sensitivity. The payment router may use this determined risk tolerance and/or timing sensitivity information to select an optimized payment rail for each future transaction (e.g., according to the method 500 discussed in more detail below) without departing from the spirit of the present invention. One of ordinary skill will appreciate, however, that a merchant may manually input and/or select risk tolerance, timing sensitivity or the like for use by the payment router within the scope of the present invention.
Independent claims 5 recites the similar limitations ““for each of a plurality of potential payment rails, train a machine learning model on historical transaction data to correlate account data variables with account balance changes for an account” and independent claim 16 limitation ““for each of a plurality of potential payment rails, train a machine learning model on historical transaction data to correlate account data variables with account balance changes for an account”” and therefore also include new matter with respect to the “training” limitations. Dependent claims 2-4 of claim 1; dependent claims 6-8 and 10-15 of claim 5 and dependent claims 17-20 of claim 16 are also rejected under 112(a).
Based on the dependencies of claims 2-4; 6-8 and 10-15; and claims 17-20 on independent claims 1, 5 and 16 respectively and contain the same deficiencies as discussed above with respect to the independent claims. Therefore, claims 1-4, 5-8 and 10-15; and claims 16-20 are rejected under 112(a).
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 1-8 and 10-20 are rejected under 35 U.S.C. § 101 because the instant application is directed to non-patentable subject matter. Specifically, the claims are directed toward at least one judicial exception without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of USPTO, applies to all statutory categories, and is explained in detail below.
In reference to Claims 1-4:
STEP 1. Per Step 1 of the two-step analysis, the claims are determined to include a system, as in independent Claim 1 and the dependent claims. Such systems fall under the statutory category of "machine." Therefore, the claims are directed to a statutory eligibility category.
STEP 2A Prong 1. The claimed invention is directed to an abstract idea without significantly more. System claim 1 recites a functional process 1) receive payment message 2) train machine learning model 3) generate scaled score and inputting data into model 4) determine merchant risk tolerance/sensitivity timing preference 5) select payment rail/route 6) initiate payment transaction.
The claimed limitations which under its broadest reasonable interpretation, covers performance of a transaction which is in the abstract category of a commercial interaction. The claimed subject matter is directed toward a analyzing historical transaction data to generate a settlement risk score and merchant risk preferences for use in a sales activity of selecting payment rail/route and initiating transaction. The specification titled “Computer-Implemented Systems and Methods for Payment Routing” where in the background the specification discloses “a merchant may use a terminal or other payment device to initiate a payment transaction or method and will provide information relating thereto via a payment transaction network. Existing payment systems route payments according to predetermined settings of the merchant, potentially leading to suboptimal payment processing, or failed transactions.
Additionally, the predetermined settings of the merchant lead to higher average costs associated with settling payment transaction. Improved routing methods are needed.”(spec ¶ 0003). The specification in ¶ 0006-0008 discloses that the focus of the claimed invention is to address the concern of settlement risk with a payment routing accounting to likelihood of settlement. The process includes analyzing receiving transaction data and generating a risk settlement score on a date for each of plurality of payment rails and based on the analysis result and score, select a payment rail and perform a transaction using generic high level functions and computer technology to perform the abstract idea.
These concepts are enumerated in Section I of the 2019 revised patent subject matter eligibility guidance published in the federal register (84 FR 50) on January 7, 2019) is directed toward abstract category of methods of organizing human activity.
STEP 2A Prong 2: The identified judicial exception is not integrated into a practical application because the additional elements “transceivers/processors”, “preference machine learning model” and “payment rail” fail to integrate the judicial exception into a practical application. The additional element transceiver/processor is merely being applied as a tool to automate the transaction process including “receiving data” without technical details of implementation. According to which according to MPEP 2106.05(d) II (see also MPEP 2106.05(g)) are directed toward extra solution activity. The courts have recognized the following computer functions are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014)
The transceiver/processors is also applied at a high level to perform the operations “train a learning model on …data to correlate account data variables with account balance changes”, “generate a …score representing…likelihood of settlement” and “select a payment rail”, which are processes which are not directed toward indications of patent eligibility but instead risk analysis for a transaction.
The additional element beyond the abstract idea “machine learning model” is merely applied to “determine preferences of the merchant for …risk tolerance and timing sensitivity based on …transaction records” which is not directed toward improvement to technology or providing a solution to a problem rooted in technology, but instead analysis of transaction risk and human behavior. The additional element beyond the abstract idea “payment rail” is merely applied as a tool to initiate transaction.
According to MPEP 2106.04 (d) I, merely “applying” technology with the judicial exception or merely including instructions to implement the abstract idea on a computer component (processor/transceiver or machine learning model) is merely using a computer as a tool to perform an abstract idea. The claim limitations simply generally at a high level of linking the use of the judicial exception to a particular technological environment or field of use (see also MPEP 2106.05(h)).
The wherein clause limits the predicting of account balances by limiting the data acted upon within time periods of score generation and dates but does not further limit any technology.
The functions of the transceiver/processor and learning model are is recited at a high-level of generality such that it amounts to no more than applying the exception using generic computer components. Taking the claim elements separately, the operation performed by the system at each step of the process is purely in terms of results desired and devoid of implementation of technical details. Technology is not integral to the process as the claimed subject matter is so high level that any generic programming could be applied and the functions could be performed by any known means. Furthermore, the claimed functions do not provide an operation that could be considered as sufficient to provide a technological implementation or application of/or improvement to this concept (i.e. integrated into a practical application).
When the claims are taken as a whole, as an ordered combination, the combination of limitations 1-3 are directed toward in response to receiving transaction message analyze and applying model technology to model historical data to correlate account data variable with account balances changes and scoring transaction route cost is directed toward a business practice. The combination of limitations 1-3 and 4 (determine merchant risk tolerance/sensitivity timing) and 5-6 are directed toward analyzing merchant preference risk which is applied with the generated score for use in selecting a payment route based on limitations 1-5 and initiating a transaction-directed toward a transaction process. The combinations of parts is not directed toward any technical process or technological technique or technological solution to a problem rooted in technology.
In addition, when the claims are taken as a whole, as an ordered combination, the combination of steps not integrate the judicial exception into a practical application as the claim process fails to impose meaningful limits upon the abstract idea. This is because the claimed subject matter fails to provide additional elements or combination or elements to apply or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The functions recited in the claims recite the concept of in response to receiving a transaction payment message generate a scaled score for cost of payment route/rail which is applied to select a payment route/rail and initiating a transaction which is a process directed toward a business practice.
The integration of elements do not improve upon technology or improve upon computer functionality or capability in how computers carry out one of their basic functions. The integration of elements do not provide a process that allows computers to perform functions that previously could not be performed. The integration of elements do not provide a process which applies a relationship to apply a new way of using an application. The instant application, therefore, still appears only to implement the abstract idea to the particular technological environments apply what generic computer functionality in the related arts. The steps are still a combination made to reduce cost by selecting least cost payment route/rail and then initiating a payment and does not provide any of the determined indications of patent eligibility set forth in the MPEP 2106.
The additional steps only add to those abstract ideas using generic functions, and the claims do not show improved ways of, for example, an particular technical function for performing the abstract idea that imposes meaningful limits upon the abstract idea. Moreover, Examiner was not able to identify any specific technological processes that goes beyond merely confining the abstract idea in a particular technological environment, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to 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 as discussed above with respect to concepts of the abstract idea into a practical application. The additional elements recited in the claim beyond the abstract idea include a system comprising one or more processors to perform the functions of receiving message, generate scale score, select payment rail/route and initiate a transaction and a machine learning model used to determine merchant preferences and payment rail applied to route payments----are some of the most basic functions of a computer. Taking the claim elements separately, the function performed by the computer at each step of the process is purely conventional. When the claims are taken as a whole, as an ordered combination, the combination of steps does not add “significantly more” by virtue of considering the steps as a whole, as an ordered combination. All of these computer functions are generic, routine, conventional computer activities that are performed only for their conventional uses. See Elec. Power Grp. v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016). Also see In re Katz Interactive Call Processing Patent Litigation, 639 F.3d 1303, 1316 (Fed. Cir. 2011) Absent a possible narrower construction of the terms “receiving”, “generate scaled score”, “select payment rail/route”, “initiate payment transaction” using the selected payment rail ... are functions can be achieved by any general purpose computer without special programming"). None of these activities are used in some unconventional manner nor do any produce some unexpected result. Applicants do not contend they invented any of these activities. In short, each step does no more than require a generic computer to perform generic computer functions.
As to the data operated upon, "even if a process of collecting and analyzing information is 'limited to particular content' or a particular 'source,' that limitation does not make the collection and analysis other than abstract." SAP America, Inc. v. Invest Pic LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018). Considered as an ordered combination, the computer components of Applicant’s claimed functions add nothing that is not already present when the steps are considered separately. The sequence of data reception-analysis modification-transmission is equally generic and conventional. See Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014) (sequence of receiving, selecting, offering for exchange, display, allowing access, and receiving payment recited as an abstraction), Inventor Holdings, LLC v. Bed Bath & Beyond, Inc., 876 F.3d 1372, 1378 (Fed. Cir. 2017) (sequence of data retrieval, analysis, modification, generation, display, and transmission), Two-Way Media Ltd. v. Comcast Cable Communications, LLC, 874 F.3d 1329, 1339 (Fed. Cir. 2017) (sequence of processing, routing, controlling, and monitoring). The ordering of the steps is therefore ordinary and conventional. The analysis concludes that the claims do not provide an inventive concept because the additional elements recited in the claims do not provide significantly more than the recited judicial exception.
According to 2106.05 well-understood and routine processes to perform the abstract idea is not sufficient to transform the claim into patent eligibility. As evidence the examiner provides:
[0020] Turning briefly to Figure 2, generally the computing device 102 may comprise tablet computers, laptop computers, desktop computers, workstation computers, smart phones, smart watches, and the like. Also, or in addition, the computing device 102 may include a plurality of copiers, printers, routers, switches, servers, and any other device that can connect to an internal or external network, and/or communication network. For example, the computing device 102 may also include a plurality of proxy servers, web servers, communications servers, routers, load balancers, and/or firewall servers, as are commonly known. Each computing device 102 may respectively include a processing element 200 and a memory element 204. Each computing device 102 may also respectively include circuitry capable of wired and/or wireless communication with the card issuer I 04, merchant I 06, account data storage device 108, databases 110, and/or financial institution 112, including, for example, transceiver element 202. Further, the computing device 102 may include software configured with instructions for performing and/or enabling performance of at least some of the steps set forth herein. In an embodiment, the software comprises programs stored on computer-readable media of memory elements 204.
[0027] The processing element 200 may include electronic hardware components such as processors. The processing element 200 may include digital processing unit(s). The processing element 200 may include microprocessors (single-core and multi-core), microcontrollers, digital signal processors (DSPs), field-programmable gate arrays (FPGAs), analog and/or digital application-specific integrated circuits (ASICs), or the like, or combinations thereof. The processing element 200 may generally execute, process, or run instructions, code, code segments, software, firmware, programs, applications, apps, processes, services, daemons, or the like. For instance, the processing element 200 may execute software applications/programs stored on the memory element 204 in connection with performing all or some of the steps described herein. The processing element 200 may also include hardware components such as finite-state machines, sequential and combinational logic, and other electronic circuits that can perform the functions necessary for the operation of the current invention. The processing element 200 may be in communication with the other electronic components through serial or parallel links that include universal busses, address busses, data busses, control lines, and the like.
[0028] Through hardware, software, firmware, or various combinations thereof, the processing element 200 may - alone or in combination with other processing elements - be configured to perform the operations of embodiments of the present invention. Specific embodiments of the technology will now be described in connection with the attached drawing figures. The embodiments are intended to describe aspects of the invention in sufficient detail to enable those skilled in the art to practice the invention. Other embodiments can be utilized, and changes can be made without departing from the scope of the present invention. The system may include additional, less, or alternate functionality and/or device(s), including those discussed elsewhere herein. The following detailed description is, therefore, not to be taken in a limiting sense. The scope of the present invention is defined only by the appended claims, along with the full scope of equivalents to which such claims are entitled.
With respect to the “training” limitations the specification lacks technical disclosure as the specification is silent with respect to a “training” process of learning models.
The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is generic components and functions in the related arts. The claim is not patent eligible.
The remaining dependent claims—which impose additional limitations—also fail to claim patent-eligible subject matter because the limitations cannot be considered statutory. In reference to claims 2-4 these dependent claim have also been reviewed with the same analysis as independent claim 1. Dependent claim 2 is directed toward processing data from a different transaction entity sources in order to generate a score- mathematical process and business process for a sales activity. Dependent claim 3 is directed toward data is encrypted- common application of technology in a transaction. Dependent claim 4 is directed toward applying a mobile device in a transaction- applying known technology to perform a transaction.
The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 1. Where all claims are directed to the same abstract idea, “addressing each claim of the asserted patents [is] unnecessary.” Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat 7 Ass ’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims 2-4 are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter.
In reference to Claims 5-8 and 10-15:
STEP 1. Per Step 1 of the two-step analysis, the claims are determined to include a method, as in independent Claim 5 and the dependent claims. Such methods fall under the statutory category of "process." Therefore, the claims are directed to a statutory eligibility category.
STEP 2A Prong 1. The steps of Method claim 5 corresponds to the functions of system claim 1. Therefore, claim 5 has been analyzed and rejected as being directed toward an abstract idea of the categories of concepts directed toward methods of organizing human activity previously discussed with respect to claim 1.
STEP 2A Prong 2: Method claim 5 corresponds to the functions of system claim 1. Therefore, claim 5 has been analyzed and rejected as failing to provide limitations that are indicative of integration into a practical application, as previously discussed with respect to claim 1.
STEP 2B; The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to concepts of the abstract idea into a practical application. The additional elements beyond the abstract idea include a one or more transceivers and/or processors to perform the operations of claim 1–is purely functional and generic. Nearly every computer element application for implementing a method will include a “processor” capable of performing the basic computer functions -of “receiving message, generate scale score, select payment rail/route and initiate a transaction” - As a result, none of the hardware recited by the method claim offers a meaningful limitation beyond generally linking the use of the method to a particular technological environment, that is, implementation via one or more transceivers and/or processors.
Method claim 5 steps corresponds to system functions claim 1. Therefore, claim 5 has been analyzed and rejected as failing to provide additional elements that amount to an inventive concept –i.e. significantly more than the recited judicial exception. Furthermore, as previously discussed with respect to claim 1, the limitations when considered individually, as a combination of parts or as a whole fail to provide any indication that the elements recited are unconventional or otherwise more than what is well understood, conventional, routine activity in the field.
According to 2106.05 well-understood and routine processes to perform the abstract idea is not sufficient to transform the claim into patent eligibility. As evidence the examiner provides:
[0027] The processing element 200 may include electronic hardware components such as processors. The processing element 200 may include digital processing unit(s). The processing element 200 may include microprocessors (single-core and multi-core), microcontrollers, digital signal processors (DSPs), field-programmable gate arrays (FPGAs), analog and/or digital application-specific integrated circuits (ASICs), or the like, or combinations thereof. The processing element 200 may generally execute, process, or run instructions, code, code segments, software, firmware, programs, applications, apps, processes, services, daemons, or the like. For instance, the processing element 200 may execute software applications/programs stored on the memory element 204 in connection with performing all or some of the steps described herein. The processing element 200 may also include hardware components such as finite-state machines, sequential and combinational logic, and other electronic circuits that can perform the functions necessary for the operation of the current invention. The processing element 200 may be in communication with the other electronic components through serial or parallel links that include universal busses, address busses, data busses, control lines, and the like.
[0028] Through hardware, software, firmware, or various combinations thereof, the processing element 200 may - alone or in combination with other processing elements - be configured to perform the operations of embodiments of the present invention. Specific embodiments of the technology will now be described in connection with the attached drawing figures. The embodiments are intended to describe aspects of the invention in sufficient detail to enable those skilled in the art to practice the invention. Other embodiments can be utilized, and changes can be made without departing from the scope of the present invention. The system may include additional, less, or alternate functionality and/or device(s), including those discussed elsewhere herein. The following detailed description is, therefore, not to be taken in a limiting sense. The scope of the present invention is defined only by the appended claims, along with the full scope of equivalents to which such claims are entitled.
With respect to “train” model, the specification is silent with respect to a training process and lacks technical disclosure.
The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is generic components and functions in the related arts. The claim is not patent eligible.
The remaining dependent claims—which impose additional limitations—also fail to claim patent-eligible subject matter because the limitations cannot be considered statutory. In reference to claims 6-8 and 10-18 these dependent claim have also been reviewed with the same analysis as independent claim 5. The elements and steps of method claim 6 is directed toward transaction data – sales activity and non-functional descriptive data. The elements and steps of method claim(s) 7 is directed toward the settlement date of the transaction- sales activity and non-functional descriptive data. Dependent claim(s) 8 is directed toward transaction data score based on risk assessment- risk mitigation. Dependent claim(s) 10 is directed toward historical data applied in analysis. Dependent claim(s) 11 is directed toward weighted score generated and values corresponding to higher likelihood- risk mitigation and sales analysis activity. Dependent claim 12 is directed toward generating low score related to error or insufficient funds- risk mitigation. Dependent claim 13 is directed toward determining settlement data for initiation of transaction- sales activity. Dependent claim 14 is directed toward settlement date for payment rails date related to cost of transaction-sales activity. Dependent claim 15 is directed toward payment rail used to initiate transaction to process transaction on settlement data- sales activity.
The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 10. Where all claims are directed to the same abstract idea, “addressing each claim of the asserted patents [is] unnecessary.” Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat 7 Ass ’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims 6-8 and 10-15 are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter.
In reference to Claims 16-20:
STEP 1. Per Step 1 of the two-step analysis, the claims are determined to include a non-transitory computer-readable storage media, as in independent Claim 16 and the dependent claim 17-20. Such mediums fall under the statutory category of “manufacture." Therefore, the claims are directed to a statutory eligibility category.
STEP 2A Prong 1. The instructions of medium claim 16 corresponds to the functions of system claim 1. Therefore, claim 16 has been analyzed and rejected as being directed toward an abstract idea of the categories of concepts directed toward methods of organizing human activity previously discussed with respect to claim 1.
STEP 2A Prong 2: The instructions of medium claim 16 corresponds to the functions of system claim 1. Therefore, claim 16 has been analyzed and rejected as failing to provide limitations that are indicative of integration into a practical application, as previously discussed with respect to claim 1.
STEP 2B; The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to concepts of the abstract idea into a practical application. The additional elements beyond the abstract idea include a non-transitory computer-readable storage media having compute executable instructions when executed by at least one or more processor the executable instructions causing the processor to perform the operations of claim 1–is purely functional and generic. Nearly every non-transitory computer readable media will include instructions executed by one or more processors for implementing the instructions corresponding to the functions of system claim 1 -of “receiving message, inputting data, retrieving data and retraining models” - As a result, none of the computer software and hardware recited by the media claim offers a meaningful limitation beyond generally linking the use of the method to a particular technological environment, that is, implementation via one or more transceivers and/or processors.
The instructions of medium claim 16 corresponds to the functions of system claim 1. Therefore, claim 16 has been analyzed and rejected as failing to provide additional elements that amount to an inventive concept –i.e. significantly more than the recited judicial exception. Furthermore, as previously discussed with respect to claim 1, the limitations when considered individually, as a combination of parts or as a whole fail to provide any indication that the elements recited are unconventional or otherwise more than what is well understood, conventional, routine activity in the field.
According to 2106.05 well-understood and routine processes to perform the abstract idea is not sufficient to transform the claim into patent eligibility. As evidence the examiner provides:
[0020] Turning briefly to Figure 2, generally the computing device 102 may comprise tablet computers, laptop computers, desktop computers, workstation computers, smart phones, smart watches, and the like. Also, or in addition, the computing device 102 may include a plurality of copiers, printers, routers, switches, servers, and any other device that can connect to an internal or external network, and/or communication network. For example, the computing device 102 may also include a plurality of proxy servers, web servers, communications servers, routers, load balancers, and/or firewall servers, as are commonly known. Each computing device 102 may respectively include a processing element 200 and a memory element 204. Each computing device 102 may also respectively include circuitry capable of wired and/or wireless communication with the card issuer I 04, merchant I 06, account data storage device 108, databases 110, and/or financial institution 112, including, for example, transceiver element 202. Further, the computing device 102 may include software configured with instructions for performing and/or enabling performance of at least some of the steps set forth herein. In an embodiment, the software comprises programs stored on computer-readable media of memory elements 204.
[0026] The memory element 204 may include electronic hardware data storage components such as read-only memory (ROM), programmable ROM, erasable programmable ROM, random access memory (RAM) such as static RAM (SRAM) or dynamic RAM (DRAM), cache memory, hard disks, floppy disks, optical disks, flash memory, thumb drives, universal serial bus (USB) drives, or the like, or combinations thereof. In some embodiments, the memory element 204 may be embedded in, or packaged in the same package as, the processing element 200. The memory element 204 may include, or may constitute, a "computer-readable medium." The memory element 204 may store the instructions, code, code segments, software, firmware, programs, applications, apps, services, daemons, or the like that are executed by the processing element 200. In an embodiment, the memory element 204 respectively stores software applications. The memory element 204 may also store settings, data, documents, sound files, photographs, movies, images, databases, and the like.
[0027] The processing element 200 may include electronic hardware components such as processors. The processing element 200 may include digital processing unit(s). The processing element 200 may include microprocessors (single-core and multi-core), microcontrollers, digital signal processors (DSPs), field-programmable gate arrays (FPGAs), analog and/or digital application-specific integrated circuits (ASICs), or the like, or combinations thereof. The processing element 200 may generally execute, process, or run instructions, code, code segments, software, firmware, programs, applications, apps, processes, services, daemons, or the like. For instance, the processing element 200 may execute software applications/programs stored on the memory element 204 in connection with performing all or some of the steps described herein. The processing element 200 may also include hardware components such as finite-state machines, sequential and combinational logic, and other electronic circuits that can perform the functions necessary for the operation of the current invention. The processing element 200 may be in communication with the other electronic components through serial or parallel links that include universal busses, address busses, data busses, control lines, and the like.
[0028] Through hardware, software, firmware, or various combinations thereof, the processing element 200 may - alone or in combination with other processing elements - be configured to perform the operations of embodiments of the present invention. Specific embodiments of the technology will now be described in connection with the attached drawing figures. The embodiments are intended to describe aspects of the invention in sufficient detail to enable those skilled in the art to practice the invention. Other embodiments can be utilized, and changes can be made without departing from the scope of the present invention. The system may include additional, less, or alternate functionality and/or device(s), including those discussed elsewhere herein. The following detailed description is, therefore, not to be taken in a limiting sense. The scope of the present invention is defined only by the appended claims, along with the full scope of equivalents to which such claims are entitled.
With respect to the “train model” limitation, the specification is silent with respect to a training process and lacks technical disclosure.
The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is generic components and functions in the related arts. The claim is not patent eligible.
The remaining dependent claims—which impose additional limitations—also fail to claim patent-eligible subject matter because the limitations cannot be considered statutory. In reference to claims 17-20 these dependent claim have also been reviewed with the same analysis as independent claim 16. Dependent claim 17 is directed toward transaction data content -non-functional descriptive subject matter. Dependent claim 18 is directed toward transaction data content – non-functional descriptive subject matter. Dependent claim 19 is directed toward each score is part of risk assessment score- mitigation of risk. Dependent claim 20 is directed toward using analyzed account data for generating a score- mathematical relationship.
The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 16. Where all claims are directed to the same abstract idea, “addressing each claim of the asserted patents [is] unnecessary.” Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat 7 Ass ’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims 17-20 are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible 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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-2, 5 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pub No. 2020/0134628 A1 by Jia et al (Jia) in view of US Pub. No. 2022/0198432 A1 by Jones et al (Jones) and further in view of US Patent No. 8,655,775 B1 by Howe (Howe)
In reference to Claim 1:
Jia teaches:
(Currently Amended) A system for payment routing according to a likelihood of settlement, the system comprising one or more processors and/or transceivers individually or collectively programmed ((Jia) in at least Fig. 1, FIG. 3, FIG. 11; para 0007) to:
receive a payment transaction message relating to a putative payment transaction, the payment transaction message containing putative payment transaction data including a customer identification (ID) for an account corresponding to the putative payment transaction, and a transaction amount corresponding to the putative payment transaction ((Jia) in at least FIG. 3, FIG. 9; para 0007-0010 wherein the prior art teaches receiving a transaction, para 0030 wherein the prior art teaches receiving transaction information which includes account information, order information, merchandise information; para 0053 wherein the prior art teaches information of transaction includes purchase amount) ;
for each of a plurality of potential payment rails, train a machine learning model on historical transaction data to correlate account data variables with account balance …((Jia) in at least abstract; para 0039 wherein the prior art teaches applying prediction process including calculated routing and risk score calculated by risk model which include route control action determining which of a number of payment processors to select for accepting purchase; para 0048-0050 wherein the prior art teaches building machine learning models to perform risk modeling to score risk including historical transaction information including bank rejected purchase transaction and reason codes, para 0053 wherein the prior art teaches account information including lack of funds, incomplete/incorrect account information, account history and times of day, week which may impact bank decision)
responsive to receipt of said putative transaction data, generate a scaled score representing the likelihood of settlement of the putative payment transaction on at least one date for each of the plurality of potential payment rails, each of the scaled scores being generated in part by inputting account data …((Jia) in at least para 0028, para 0039-0041, para 0047, para 0050, para 0053, para 0080 wherein the prior art teaches feature wise contributions lead based on customer features to certain scores where a score is calculated using feature contribution, para 0063);
determine, via a preference machine learning model, preference of a merchant for one or both of risk tolerance and timing …based on historical transaction records of the merchant((Jia) in at least Abstract; FIG. 9; para 0028 wherein the prior art teaches merchant making decisions on transaction which includes risk decisions, routing decisions, based on outputted risk score, where the decisions occur in response to transaction being approved by risk model; para 0031-0033 wherein the prior art teaches merchant device prediction action models evaluation purchase transaction and producing prediction information on how to proceed with purchase including subset of risk and routing models including determining likelihood of success on purchase transaction settlement, para 0036 , para 0039-0041 wherein the prior art teaches merchant control actions within the payment system in processing transaction decided using risk model score calculated, including routing score, para 0048-0050, para 0053-0054 wherein the prior art teaches risk model scoring transaction risk on probability of lack of funds and other factors such as time of day, day of week which may impact bank decision to provide funds, para 0060 wherein the prior art teaches merchant model configured to determine a cut-off score in determining of risk score; para 0063-0064, para 0068, para 0091 wherein the prior art teaches applying two or more models for evaluation and prediction where the models are a joint learned model including risk model and routing models);
based at least in part on the at least one scaled score for each of the plurality of potential payment rails and on the preferences of the merchant for one or both of risk tolerance and timing sensitivity, select a payment rail from the plurality of payment rails ((Jia) in at least para 0028 wherein the prior art teaches using routing score to increase settle rate, para 0033-0034 wherein the prior art teaches matching target risk predefined action based on likelihood determined by merchant controlled risk models, para 0039 wherein the prior art teaches merchant control actions guided by merchant prediction service that includes presenting a higher than initially expected risk , para 0042 wherein the prior art teaches generating retry score based on decision of risk action); and
initiate, via the selected payment rail, a payment transaction corresponding to the putative payment transaction.((Jia) in at least para 0039, para 0050, para 0053-0054, para 0064)
Jia does not explicitly teach:
for each of a plurality of potential payment rails, train a machine learning model on historical transaction data to correlate account data variables with account balance changes for an account;
…by inputting account data including an existing balance of funds eligible to be debited into the corresponding one of the machine learning models to predict account balance changes and corresponding likelihood of insufficient funds for the account corresponding to the respective one of the plurality of potential payment rails on the date, …, wherein predicting the account balance changes comprises projecting, via the corresponding one of the machine learning models, income and expenditures likely to occur in the account during an intervening period between a time at which the scaled score is generated and the date;
timing sensitivity based on historical transaction records of the merchant
Jones teaches:
receive a payment transaction message relating to a putative payment transaction, the payment transaction message containing putative payment transaction data including a customer identification (ID) for an account corresponding to the putative payment transaction and a transaction amount corresponding to the putative payment transaction((Ankur) in at least para 0018, para 0028, para 0035, para 0047-0049)
for each of a plurality of potential payment rails, train a machine learning model on historical transaction data to correlate account data variables with account balance changes for an account ((Jones) in at least Abstract; para 0008-0010, para 0017, para 0021, para 0037, para 0044-0046, para 0064-0067, para 0085, para 0102, para 0119, para 0121),
responsive to receipt of said putative transaction data, generate a scaled score representing the likelihood of settlement of the putative payment transaction on at least one date for each of the plurality of potential payment rails, each of the scaled scores being generated in part by inputting account data including an existing balance of funds eligible to be debited into the corresponding one of the machine learning models to predict account balance changes and corresponding likelihood of insufficient funds for the account corresponding to the respective one of the plurality of potential payment rails on the date, wherein predicting the account balance changes comprises projecting, via the corresponding one of the machine learning models, income and expenditures likely to occur in the account during an intervening period between a time at which the scaled score is generated and the date ((Jones) in at least para 0017, para 0019, para 0029, para 0036, para 0048, para 0075, para 0078-0084, para 0086, para 0090, para 0093, para 0096-0098, para 0101, para 0108, para 0121, para 0214)
Both Jia and Jones are directed toward analyzing transaction account data associated in order to determine transaction risk which includes account funds available. Jones teaches the motivation of analyzing and scoring transaction account data for routing purchase request in order to determine the probability of account funds for use in transactions by analyzing historical balance data, income and spending data using a particular selected time period in order to determine whether in response to a request for payment to use a routing network a balance is available. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the transaction account data analyzed in determining selection of payment routing networks of Jia to include analyzing historical account balance data as it relates to a particular selected time period as taught by Jones since Jones teaches the motivation of analyzing and scoring transaction account data for routing purchase request in order to determine the probability of account funds for use in transactions by analyzing historical balance data, income and spending data using a particular selected time period in order to determine whether in response to a request for payment to use a routing network a balance is available
Howe teaches:
determine, via a preference machine learning model, preference of a merchant for one or both of risk tolerance and timing sensitivity based on historical transaction records of the merchant ((Howe) in at least abstract; FIG. 2. Fig. 4-6, Fig. 9; Col 1 lines 60-Col 2 lines 1-10, lines 21-Col 3 lines 1-5 wherein the prior art teaches merchant transaction sub-group determined and identifying for each date of predetermined dates preferred debit network and for each date a routing table , Col 3 lines 15-40 wherein the prior art teaches identifying merchant transaction sub-group and identifying for each date of a range of dates preferred debit network and teaches determining a change in debit routing for the merchant based on routing tables for predetermined range of dates, Col 6 lines 25-38 wherein the prior art teaches identifying a routing table for the merchant based on detecting changes in merchant routing table where the routing table is identified for each day of transaction entities in close proximity to the day the changes in the routing table occurs, Col 7 lines 20-55 wherein the prior art teaches identifying merchant and identify routing table for specific date due to complex pricing structures, col 8 lines 40-53, Col 10 lines 58-Col 11 lines 1-18)
Both Jia and Howe are directed toward analyzing transaction data which includes date considerations for transaction selection and routing of the transaction selection. Howe teaches the motivation of merchants selecting networks based on detecting changes in routing tables on predetermined dates and for expense charges for processing transaction and teaches a preferred network based on transaction data where the transaction date is specific and identifying for each date of a range of dates the preferred network for each network subgroup with respect to charges and network preferences of the merchant and detection changes in routing tables. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the routing model analysis for determination of transaction routes of Jia to include determining merchant preferences for transaction dates as taught by Howe since . Howe teaches the motivation of merchants selecting networks based on detecting changes in routing tables on predetermined dates and for expense charges for processing transaction and teaches a preferred network based on transaction data where the transaction date is specific and identifying for each date of a range of dates the preferred network for each network subgroup with respect to charges and network preferences of the merchant and detection of changes in routing tables.
In reference to Claim 2:
The combination of Jia, Jones and Howe discloses the limitations of independent claim 1. Jia further discloses the limitations of dependent claim 2
(Previously Presented) The system of claim 1, wherein generation of the scaled score for the at least one date for each of the plurality of potential payment rails(see rejection of claim 1 above) includes
processing data from at least one of a merchant, a card issuer, a financial institution, an account data storage device and a database. ((Jia) in at least para 0030 wherein the prior art teaches receiving transaction information which includes account information, order information, merchandise information; para 0053 wherein the prior art teaches information of transaction includes purchase amount, para 0106)
In reference to Claim 5:
The combination of Jia, Jones and Howe and discloses the limitations of independent claim 5.
The Steps of method claim 5 correspond to the functional process of system claim 1. The additional limitations recited in claim 5 that go beyond the limitations of claim 1 include the one or more transceivers .((Twombly) in at least para 0062, para 0065)
:Therefore, claim 5 has been analyzed and rejected as previously discussed with respect to claim 1.
According to KSR, simple substitution of one known element for another to obtain predictable results is an obvious combination. The prior art Jia contained communication computing device which differed from the claimed device with another communication computing device. The prior art Twombly provides evidence that the substituted computing devices and their functions were known in the art and performed similar functions. Therefore, common sense rationale makes obvious to one of ordinary skill in the art could have substituted one known communication computing device for another and the results of the substation would have been predicable.
Both Jia and Twombly teach applying computers to perform transaction with retailers. Twombly teaches the motivation that seller or user devices for use in a transaction process as such devices are able to communicate to perform transaction activity. It would have been obvious to one having ordinary skill at the time of effective filing the invention to modify the computing devices applied in the transaction of Jia to include mobile devices as taught by Twombly since Twombly teaches the motivation that seller or user devices for use in a transaction process as such devices are able to communicate to perform transaction activity.
In reference to Claim 16:
The combination of Jia, Jones and Howe discloses the limitations of independent claim 16.
The instructions of non-transitory computer readable storage media claim 16 correspond to the functions of system claim 1 and steps of method claim 5. The additional limitations recited in claim 16 that go beyond the limitations of claim 1 include a non-transitory computer-readable storage media having executable instructions executed by a processor ((Jia) in at least para 0006, para 0109) to perform the operations corresponding to the functions of claim 1.
Therefore, claim 16 has been analyzed and rejected as previously discussed with respect to claim 1.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pub No. 2020/0134628 A1 by Jia et al (Jia) in view of US Pub. No. 2022/0198432 A1 by Jones et al (Jones) in view of US Patent No. 8,655,775 B1 by Howe (Howe) as applied to claim 1 above, and further in view of WO 2021146727 A1 by Caldwell (Caldwell)
In reference to Claim 3:
The combination of Jia, Jones and Howe discloses the limitations of dependent claim 2. Jia further discloses the limitations of dependent claim 3.
(Original) The system of claim 2 (see rejection of claim 2 above),
Jia does not explicitly teach:
wherein the data is encrypted.
Caldwell teaches:
wherein the data is encrypted. ((Caldwell) in at least page 34 lines 11-22)
Both Jia and Caldwell are directed toward a transaction process where payment request of clients are implemented. Caldwell teaches the motivation of encrypting data in order to secure the users data in transactions. It would have been obvious to one having ordinary skill at the time of effective filing the invention to modify the process for data reception, storage and transmission of Jia to include encryption of data of Caldwell since Caldwell teaches the motivation of encrypting data in order to secure the users data in transactions.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pub No. 2020/0134628 A1 by Jia et al (Jia) in view of US Pub. No. 2022/0198432 A1 by Jones et al (Jones) in view of US Patent No. 8,655,775 B1 by Howe (Howe) as applied to claim 1 above, and further in view of US Pub No. 2018/0287927 A1 by Twombly et al. (Twombly)
In reference to Claim 4:
The combination of Jia, Jones and Howe discloses the limitations of independent claim 1. Jia further discloses the limitations of dependent claim 4
(Original) The system of claim 1 (see rejection of claim 1 above),
Jia does not explicitly teach:
wherein the putative transaction is initiated by a mobile device
Twombly teaches:
wherein the putative transaction is initiated by a mobile device.((Twombly) in at least para 0062, para 0065)
According to KSR, simple substitution of one known element for another to obtain predictable results is an obvious combination. The prior art Jia contained computing device which differed from the claimed device with another computing device. The prior art Twombly provides evidence that the substituted computing devices and their functions were known in the art and performed similar functions. Therefore, common sense rationale makes obvious to one of ordinary skill in the art could have substituted one known computing device for another and the results of the substation would have been predicable.
Both Jia and Twombly teach applying computers to perform transaction with retailers. Twombly teaches the motivation that seller or user devices for use in a transaction process as such devices are able to communication perform transaction activity. It would have been obvious to one having ordinary skill at the time of effective filing the invention to modify the computing devices applied in the transaction of Jia to include mobile devices as taught by Twombly since Twombly teaches the motivation that seller or user devices for use in a transaction process as such devices are able to communication perform transaction activity.
According to KSR, simple substitution of one known element for another to obtain predictable results is an obvious combination. The prior art Jia contained computing communication device which differed from the claimed device with another communication computing device. The prior art Twombly provides evidence that the substituted communication computing devices and their functions were known in the art and performed similar functions. Therefore, common sense rationale makes obvious to one of ordinary skill in the art could have substituted one known communication computing device for another and the results of the substation would have been predicable.
Both Jia and Twombly teach applying computers to perform transaction with retailers. Twombly teaches the motivation that seller or user devices for use in a transaction process as such communication devices are able to communicate perform transaction activity as claimed. It would have been obvious to one having ordinary skill at the time of effective filing the invention to modify the computing devices applied in the transaction of Jia to include mobile devices as taught by Twombly since Twombly teaches the motivation that seller or user devices for use in a transaction process as such communication devices are able to communication perform transaction activity.
Claim(s) 6-7, 13-15 of claim 5 above, Claim 17-18 of claim 16 above is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pub No. 2020/0134628 A1 by Jia et al (Jia) in view of US Pub. No. 2022/0198432 A1 by Jones et al (Jones) in view of US Patent No. 8,655,775 B1 by Howe (Howe) and further in view of US Pub No. 2014/0143024 A1 by Whitler (Whitler)
In reference to Claim 6:
The combination of Jia, Jones and Howe discloses the limitations of independent claim 5. Jia further discloses the limitations of dependent claim 6.
(Original) The computer-implemented method of claim 5 (see rejection of claim 5 above), wherein the putative payment transaction data further comprises
Jia does not explicitly teach:
a latest settlement date for the putative transaction and the at least one date for each of the plurality of potential payment rails is on or before the latest settlement date.
Whitler teaches:
a latest settlement date for the putative transaction and the at least one date for each of the plurality of potential payment rails is on or before the latest settlement date. ((Whitler) in at least para 0245-0248)
Both Jia and Whitler are directed toward determining routing cost for payment. Whitler teaches the motivation of payment transaction data including latest and previous transaction dates in order to determine average transaction cost based on the historical dates and settlement routing cost. It would have been obvious to one having ordinary skill before the time of effective filing the invention to modify the process for determining payment route cost of Jia to include applying transaction payment date of settlement as taught by Whitler since Whitler teaches the motivation of payment transaction data including latest and previous transaction dates in order to determine average transaction cost based on the historical dates and settlement routing cost.
In reference to Claim 7:
The combination of Jia, Jones, Howe and Whitler discloses the limitations of dependent claim 6. Jia further discloses the limitations of dependent claim 7.
(Original) The computer-implemented method of claim 6 (see rejection of claim 6 above),
Jia does not explicitly teach:
wherein the latest settlement date is a date upon which the putative transaction was initiated.
Whitler teaches:
wherein the latest settlement date is a date upon which the putative transaction was initiated. ((Whitler) in at least para 0245-0248)
Both Jia and Whitler are directed toward determining routing cost for payment. Whitler teaches the motivation of payment transaction data including latest and previous transaction dates in order to determine average transaction cost based on the historical dates and settlement routing cost. It would have been obvious to one having ordinary skill before the time of effective filing the invention to modify the process for determining payment route cost of Jia to include applying transaction payment date of settlement as taught by Whitler since Whitler teaches the motivation of payment transaction data including latest and previous transaction dates in order to determine average transaction cost based on the historical dates and settlement routing cost.
In reference to Claim 13:
The combination of Jia, Jones, Howe and Whitler discloses the limitations of dependent claim 6. Miller further discloses the limitations of dependent claim 13.
(Original) The computer-implemented method of claim 6 (see rejection of claim 6 above), wherein the method
Jia does not explicitly teach:
determines a settlement date by assessing a plurality of dates including a date upon which the putative transaction was initiated up to a latest settlement date.
Whitler teaches:
determines a settlement date by assessing a plurality of dates including a date upon which the putative transaction was initiated up to a latest settlement date. ((Whitler) in at least para 0061, para 0239-0240, para 0245-0248)
Both Jia and Whitler are directed toward determining routing cost for payment. Whitler teaches the motivation of payment transaction data including latest and previous transaction dates in order to determine average transaction cost based on the historical dates and settlement routing cost. It would have been obvious to one having ordinary skill before the time of effective filing the invention to modify the process for determining payment route cost of Jia Miller to include applying transaction payment date of settlement as taught by Whitler since Whitler teaches the motivation of payment transaction data including latest and previous transaction dates in order to determine average transaction cost based on the historical dates and settlement routing cost.
In reference to Claim 14:
The combination of Jia, Jones, Howe and Whitler discloses the limitations of dependent claim 13. Miller further discloses the limitations of dependent claim 14.
(Original) The computer-implemented method of claim 13 (see rejection of claim 13 above),
Jia does not explicitly teach:
wherein the settlement date for each of the plurality of potential payment rails is a date on which a cost to execute the putative payment transaction for the corresponding one of the plurality of potential payment rails is the lowest
Whitler teaches:
wherein the settlement date for each of the plurality of potential payment rails is a date on which a cost to execute the putative payment transaction for the corresponding one of the plurality of potential payment rails is the lowest ((Whitler) in at least para 0061, para 0239-0240, para 0245-0248)
Both Jia and Whitler are directed toward determining routing cost for payment. Whitler teaches the motivation of payment transaction data including latest and previous transaction dates in order to determine average transaction cost based on the historical dates and settlement routing cost. It would have been obvious to one having ordinary skill before the time of effective filing the invention to modify the process for determining payment route cost of Jia to include applying transaction payment date of settlement as taught by Whitler since Whitler teaches the motivation of payment transaction data including latest and previous transaction dates in order to determine average transaction cost based on the historical dates and settlement routing cost.
In reference to Claim 15:
The combination of Jia, Jones, Howe and Whitler discloses the limitations of dependent claim 13. Miller further discloses the limitations of dependent claim 15.
(Original) The computer-implemented method of claim 13 (see rejection of claim 13 above),
Jia does not explicitly teach:
wherein the payment rail used to initiate the putative payment transaction is available to process the putative payment transaction on the corresponding settlement date.
Jia teaches:
wherein the payment rail used to initiate the putative payment transaction is available to process the putative payment transaction on the corresponding settlement date. ((Whitler) in at least para 0061, para 0239-0240, para 0245-0248)
Both Jia and Whitler are directed toward determining routing cost for payment. Whitler teaches the motivation of payment transaction data including latest and previous transaction dates in order to determine average transaction cost based on the historical dates and settlement routing cost. It would have been obvious to one having ordinary skill before the time of effective filing the invention to modify the process for determining payment route cost of Jia to include applying transaction payment date of settlement as taught by Whitler since Whitler teaches the motivation of payment transaction data including latest and previous transaction dates in order to determine average transaction cost based on the historical dates and settlement routing cost.
In reference to Claim 17:
The combination of Jia, Jones, Howe and Whitler discloses the limitations of dependent claim 16. Miller further discloses the limitations of dependent claim 17.
The instruction of media claim 17 corresponds to functions of method claim 6. Therefore, claim 17 has been analyzed and rejected as previously discussed with respect to claim 6
In reference to Claim 18:
The combination of Jia, Jones, Howe and Whitler discloses the limitations of dependent claim 17. Miller further discloses the limitations of dependent claim 18.
The instruction of media claim 18 corresponds to functions of method claim 7. Therefore, claim 18 has been analyzed and rejected as previously discussed with respect to claim 7
Claim(s) 8 of claim 5 above; Claim(s) 19 of claim 16 above is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pub No. 20200134628 /A1 by Jia et al (Jia) in view of US Pub. No. 2022/0198432 A1 by Jones et al (Jones) in view of US Patent No. 8,655,775 B1 by Howe (Howe) and further in view of US Patent No. 11,023,878 B1 by Hernandez et al. (Hernandez)
In reference to Claim 8:
The combination of Jia, Jones and Howe discloses the limitations of independent claim 5. Jia further discloses the limitations of dependent claim 8.
(Original) The computer-implemented method of claim 5 (see rejection of claim 5 above),
Jia does not explicitly teach:
wherein the putative payment transaction data includes, and each scaled score is based in part on, an industry specific risk assessment score.
Hernandez teaches:
wherein the putative payment transaction data includes, and each scaled score is based in part on, an industry specific risk assessment score. ((Hernandez) in at least Col 23 lines 22-41, Col 25 lines 30-38)
Both Jia and Hernandez are directed toward determining least cost routing. Hernandez teaches the motivation in the specific transaction industry of card not present that the likelihood of fraud the transaction fee is higher and that for specific fund transfer request that it is needed to run a risk machine in order to yield a safe score in the implementation of processing payments. It would have been obvious to one having ordinary skill before the time of effective filing the invention to modify the scoring particulars of Jia to include the scoring elements as taught by Hernandez since Hernandez teaches the motivation in the specific transaction industry of card not present that the likelihood of fraud the transaction fee is higher and that for specific fund transfer request that it is needed to run a risk machine in order to yield a safe score in the implementation of processing payments.
In reference to Claim 19:
The combination of Jia, Jones, Howe and Whitler discloses the limitations of dependent claim 16. Miller further discloses the limitations of dependent claim 19.
The instruction of media claim 19 corresponds to functions of method claim 8. Therefore, claim 19 has been analyzed and rejected as previously discussed with respect to claim 8
Claim(s) 10 of claim 5 above; Claim(s) 20 of claim 16 above is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pub No. 2020/0134628A1 by Jia et al (Jia) in view of US Pub. No. 2022/0198432 A1 by Jones et al (Jones) in view of US Patent No. 8,655,775 B1 by Howe (Howe) as applied to claim 5 above, and further in view of CA 2397936 A1 by Lacerte et al. (Lacerte) as annotated by the examiner
In reference to Claim 10:
The combination of Jia, Howe and Jones discloses the limitations of independent claim 5. Jia further discloses the limitations of dependent claim 10
(Previously Presented) The computer-implemented method of claim 9 (see rejection of claim 5 above), wherein the account data comprise
Jia does not explicitly teach:
… historical data regarding — instances in which funds eligible to be debited from the corresponding account were insufficient for previously attempted transactions, instances in which funds were debited from the corresponding account, and instances in which funds were credited to the corresponding account
Lacerte teaches:
an existing balance of funds eligible to be debited from the corresponding account and historical data regarding — instances in which funds eligible to be debited from the account were insufficient for previously attempted transactions, instances in which funds were debited from the corresponding account, and instances in which funds were credited to the corresponding account ((Lacerte) in at least Abstract; para 0007, para 0015, para 0018, para 0033, para 0035, para 0038-0039, para 0047, para 0130, para 0144-0146, para 0187)
Both Jia and Lacerte are directed toward routing payment transactions for payment where the data analyzed includes account data. Lacerte teaches the motivation of providing in the transaction process eligible fund balances and historical data of transactions in order to determine whether the amount of funds to transfer is sufficient and in order to reduce risk of automated mechanisms having access to the senders account balance (see para 0035). It would have been obvious to one having ordinary skill before the time of effective filing the invention to modify the payment transaction routing process which includes account balance of Jia to include determining eligible funds and historical transaction data as taught by Lacerte since Lacerte teaches the motivation of providing in the transaction process eligible fund balances and historical data of transactions in order to determine whether the amount of funds to transfer is sufficient and in order to reduce risk of automated mechanisms having access to the senders account balance.
In reference to Claim 20:
The combination of Jia, Jones and Howe discloses the limitations of dependent claim 16. Jia further discloses the limitations of dependent claim 20.
(Previously Presented) the non-transitory computer-readable storage media of claim 16 (see rejection of claim 16 above), wherein each scalded score:
Jia does not explicitly teach:
Is generated in part by analyzing… historical data regarding — instances in which funds eligible to be debited from the corresponding account were insufficient for previously attempted transactions, instances in which funds were debited from the corresponding account, and instances in which funds were credited to the corresponding account
Lacerte teaches:
Is generated in part by analyzing… historical data regarding — instances in which funds eligible to be debited from the corresponding account were insufficient for previously attempted transactions, instances in which funds were debited from the corresponding account, and instances in which funds were credited to the corresponding account ((Lacerte) in at least Abstract; para 0007, para 0015, para 0018, para 0033, para 0035, para 0038-0039, para 0047, para 0130, para 0144-0146, para 0187)
Both Jia and Lacerte are directed toward routing payment transactions for payment. Lacerte teaches the motivation of providing in the transaction process eligible fund balances and historical data of transactions in order to determine whether the amount of funds to transfer is sufficient and in order to reduce risk of automated mechanisms having access to the senders account balance (see para 0035). It would have been obvious to one having ordinary skill before the time of effective filing the invention to modify the data for scaled scoring of Jia to include determining eligible funds and historical transaction data as taught by Lacerte since Lacerte teaches the motivation of providing in the transaction process eligible fund balances and historical data of transactions in order to determine whether the amount of funds to transfer is sufficient and in order to reduce risk of automated mechanisms having access to the senders account balance.
Claim(s) 11-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pub No. 2020/0134628A1 by Jia et al (Jia) in view of US Pub. No. 2022/0198432 A1 by Jones et al (Jones) in view of US Patent No. 8,655,775 B1 by Howe (Howe) in view of US Pub No. 2014/0143024 A1 by Whitler (Whitler) as applied to claim 6 above, and further in view of WO 2021146727 A1 by Caldwell (Caldwell)
In reference to Claim 11:
The combination of Jia, Jones, Howe and Whitler discloses the limitations of dependent claim 6. Jia further discloses the limitations of dependent claim 11
(Original) The computer-implemented method of claim 6 (see rejection of claim 6 above),
Jia does not explicitly teach:
wherein each scaled score is generated by a weighted summation of factors and a greater value corresponds to a higher likelihood of settlement.
Caldwell teaches:
wherein each scaled score is generated by a weighted summation of factors and a greater value corresponds to a higher likelihood of settlement. ((Caldwell) in at least page 24 lines 29-page 25 lines 1-10)
Both Jia and Caldwell teach scoring payment properties when analyzing payment transaction data for settlements. Caldwell teaches the motivation of scoring the risk of funds for transactions when processing a payment which also includes determining whether the hardware/server for routing transactions is available or to determine an alternate route, whereas Jia teaches scoring settlement transaction, customer merchant and routing attributes in order to optimize the transaction for participants. According to KSR, known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art. The prior art scope and content whether in the same field of endeavor as that of the applicant’s invention or a different field of endeavor included a similar/analogous process (scoring payment parameters). Both Jia and Caldwell provides evidence that the differences between the claimed invention and the prior art were encompassed in known variations or in a principle known in the prior art. Accordingly based on the design incentives of the respective prior art references, one of ordinary skill in the art could have implemented the claimed variations would have been predictable to one of ordinary skill in the art.
In reference to Claim 12:
The combination of Jia, Jones, Howe, Whitler and Caldwell discloses the limitations of dependent claim 11. Jia further discloses the limitations of dependent claim 12
(Original) The computer-implemented method of claim 11 (see rejection of claim 11 above),
Jia does not explicitly teach:
wherein the weighted summation generates a low score if an error or insufficient funds event is likely to occur.
Caldwell teaches:
wherein the weighted summation generates a low score if an error or insufficient funds event is likely to occur. ((Caldwell) in at least page 15 lines 32-page 16 lines 1-5 wherein the prior art teaches determining availability of funds for transfer, page 22 lines 17-31, page 24 lines 29-page 25 lines 1-10)
Both Jia and Caldwell teach scoring payment properties when analyzing payment transaction data for settlements. Caldwell teaches the motivation of scoring the risk of funds for transactions when processing a payment which also includes determining whether the hardware/server for routing transactions is available or to determine an alternate route and determining whether sources for funds are allowed and determining available balance for fund transfer and to prevent float periods and eliminate fees, whereas Miller teaches scoring settlement details where settlement details include bank rules and settlement requirements in order to quantize settlement terms for source banks and other parameters in order to determine which payment routes/rails are best, available or meet criteria. According to KSR, known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art. The prior art scope and content whether in the same field of endeavor as that of the applicant’s invention or a different field of endeavor included a similar/analogous process (scoring payment parameters). Both Jia and Caldwell provides evidence that the differences between the claimed invention and the prior art were encompassed in known variations or in a principle known in the prior art. Accordingly based on the design incentives of the respective prior art references, one of ordinary skill in the art could have implemented the claimed variations would have been predictable to one of ordinary skill in the art.
According to KSR, simple substitution of one known element for another to obtain predictable results is an obvious combination. The prior art Jia contained score result when analyzing transaction data which differed from the claimed analysis score of the claimed device by the substitution of one type of score with another score type. The prior art Caldwell provides evidence that the substituted score and their functions were known in the art. Therefore, common sense rationale makes obvious to one of ordinary skill in the art could have substituted one known element for another and the results of the substation would have been predicable.
Both Jia and Caldwell teach scoring payment properties when analyzing payment transaction data for settlements. Caldwell teaches the motivation of determining a scaled score in order to provide relevance to merchants of a risk level so that the merchant can decide whether to take the risk for settling a payment request. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the generic score of Jia to be a scaled score as taught by Caldwell since Caldwell teaches the motivation of determining a scaled score in order to provide relevance to merchants of a risk level so that the merchant can decide whether to take the risk for settling a payment request.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US Pub No. 2023/0377034 A1 by Ball et al
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARY M GREGG whose telephone number is (571)270-5050. The examiner can normally be reached M-F 9am-5pm.
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/MARY M GREGG/ Examiner, Art Unit 3695
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