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 Final Office Action in response to communications received June 16, 2026. No Claim(s) have been canceled. Claim(s) 1 and 11 have been amended. No new claims have been added. Therefore, claims 1-20 are pending and addressed below.
Priority
Application No. 18472039 filed 09/21/2023 is a Continuation in Part of 18145627, filed 12/22/2022 and having 1 RCE-type filing therein 18145627 Claims Priority from Provisional Application 63294406 , filed 12/29/2021.
Applicant Name/Assignee: Mastercard International Incorporated
Inventor(s): Baguley, Nick; Gagon, Serenie; Arunachalam, Natesh; Harnish, Justin, Bell, Cameron; Roper, Daniel
Response to Arguments
Claim Interpretation
Applicant's amendments filed June 16, 2026 have been fully considered and are sufficient to provide specificity with respect to the term “retrain”. The claim interpretation has been withdrawn.
Claim Rejections - 35 USC § 101
Applicant's arguments filed December 02, 2025 have been fully considered but they are not persuasive.
In the remarks applicant argues that the amended limitations are patent eligible. Pointing to the USPTO 2019 patent eligibility guidance incorporating the Alice/Mayo framework and MPEP 2106.04(a)(2). Applicant argues the claim limitations recitation of the “retraining” of the algorithm process are not a commercial interaction. Accordingly the claimed limitations under step 2A prong 1 are patent eligible. The examiner respectfully disagrees. The claim limitations recited “receiving payment …message”, “input historical data…” for the intended use to generate a score representing risk, “generate …payment routing recommendation identifying a recommended date and …recommended payment rail “, “output...the scores and payment routing recommendation, designate feedback data together with scaled score…”, “retrain …algorithm using feedback data…and …scores”. The claim limitations as a whole are not directed toward retraining model technology but rather directed toward analysis of transaction data in order to provide payment routing recommendation identifying a recommended date and …recommended payment rail” a commercial activity. The rejection is maintained.
In the remarks applicant points to the USPTO 2024 July 101 guidance arguing that the claimed limitations recite specific improvements in which machine learning models operate or trained. The independent claims recite a particular technique for improving the score algorithm using feedback data with previously generated scores as labeled data, grouping factors via regression or clustering analysis adjusting the algorithms weights which analogous to example 39 is patent eligible. The amended limitations improve predictive accuracy of the model therefore improving technology which is patent eligible. Applicant’s argument is not persuasive. With respect to example 39, patent eligibility was found in an expanded training process which allowed computer operations to solve a problem created by technology itself in that the use of stochastic learning with backpropagation using loss functions to adjust weights increased false positive when classifying images of facial images and animation. The improvement and solution providing patent eligibility provided a combination of features by using expanded training set of fail images and applying mathematical transformation functions on the set of facial images which provided robust face detection in distorted images while limiting false positives. This is not the case of the current application. The retraining as claimed is not to provide a solution rooted in technology itself with an improve process that address the issues of the technology, rather the adjusting of weights using feedback data is to retrain the score for a more accurate score generated to represent risk (see spec ¶ 0146-0147). The argues “regression or clustering analysis” applied in the retraining is merely applying known generic machine learning techniques for analysis and processing of the algorithm. The rejection is maintained.
In the remarks applicant argues that under step 2A prong 2, the recitation of closed loop routing architecture, scaled score algorithm generating scaled scores representing likelihood of settlement (risk) on settlement data and payment rail, routing recommendations identifying recommended date and payment rail generated from scores and output to merchant, the merchants outputting outcome the data of the processing and an indicator of whether the processing was completed returned as feedback where the feedback and prior scores are applied to retrain the algorithm provide specific improvement to payment routing. Applicant argues the claimed process improves payment routing technology reducing failed payments reducing costs resulting from routing payments according to settings by recommendation of dates and rails derived from model outputs. Applicant’s argument is not persuasive. The retraining to adjust weights applied in the calculation of settlement scores based on prior scores and transaction result feedback for use in generating scores based on adjusted weights does not change or improve payment routing technology. Rather the recommended dates and routing rails for payment are to mitigation risk of settlement which is an improvement to the judicial exception and not payment routing technology. The rejection is maintained.
In the remarks applicant argues the previous Office action reliance on Electric Power group, Intellectual Ventures and SAP America decisions, is misplaced, Applicant argues the cited cases recite generic analysis and output without technical details. This is not the case of the current amended limitations which provide specific structure and operations (scaled score algorithm comprising account balance component, general transaction behavior component, payment routing, date and rail recommendation and a retraining operation that relied upon factors via regression and clustering analysis and adjust weights using labeled feedback. Applicant argues the amended limitations recite how decisions found lacking confining the claimed subject matter to a particular technologically grounded implementation rather that results by any known means. The examiner respectfully disagrees. Electric Power Group receives data a plurality of data elements which includes measurements from data streams, stability metrics, grid data, non-grid data associated with wide/local area portions of the interconnected power grid and then teaches an analysis of the data including combination of the measurements, computations of measurement and the other data received and outputs the results. The claimed limitations recite receiving a plurality of data elements which includes payment transaction data, transaction amount, historical data for analysis that include computation of different transaction and account data to calculate a score and recommendation and outputs the results. This uses of technology to collect data, analyze data and output the result is analogous to Electric Power group. The additional limitations presented in the amendments “designate feedback data” and “retrain …algorithm using feedback data…” similar to Intellectual Venture discloses result oriented solution without sufficient details as to technical implementation. The retraining as claimed recites using known generic learning analysis technique “regression” or “clustering” analysis and the data acted upon used to adjust weights for calculating scores. See also Recentive Analytics. The rejection is maintained.
In the remarks applicant argues that under 2B, the claimed subject matter amended claims recite significantly more than any alleged abstract idea. Applicant argues the ordered combination of the limitation “generating per-date and per-rail settlement likelihood scores using a multi-component algorithm, deriving and outputting a date and rail routing recommendation, and receiving merchant feedback and prior scores for use in retraining the model improving the model and payment routing system, pointing to BASCOM, Amdocs, Berkheimer and MPEP 2106.05(d). Applicant’s argument is not persuasive. The specification discloses “deep neural network …used as a “component” …”generate …values of the …score algorithm” (para 0062), disclosing the algorithm includes “account balance prediction component to determine account balance and analyze historical data of account holder, transaction behavior component to analyze historical data of a plurality of account holders to determine factors impacting projected account balance in the account data (para 0063). The specification does not describe any details as to the technical process the algorithm performs, but instead recites generic computer elements “components” that are applied at a high level to perform analysis for an expected outcome. Applicant’s argument is not persuasive. The rejection is maintained.
In the remarks applicant argues that based on the arguments above, the dependent claims are patent eligible. The examiner respectfully disagrees. See response above.
Claim Rejections - 35 USC § 103
Applicant's arguments filed December 02, 2025 have been fully considered but they are not persuasive.
In the remarks applicant argues that the prior art combination reflects impermissible hindsight without identifying reasons to combine. The examiner respectfully disagrees. In the previous Office Action in paragraph 37, the reasons to combine, applying KSR providing the design incentives of Pandian and/or market forces that would have prompted adaptation to calculate and output risk scores. Applicant argues that the reference Pandian fails to teach calculating probability of balance available on a certain date of Chen, instead Pandian teaches probability of fraud. The examiner respectfully disagrees with the premise of applicant’s argument. Pandian was applied to provide teaching of outputting the results of the generated scores representing probability that a payment would not be completed, that data received for applying in the analysis would be feedback data from the merchant including date of attempted payment and processing completed (Col 21, Col 25), and designating feedback data together with the score for retraining (Col 14, Col 25). The prior art Pandian teaches receiving data from a merchant which includes date of transactions to be analyzed for attempted payments which is used with prior scores for retraining In para 0039, the previous Office Action para 0039 where the action provided the motivation to send the calculated score to the merchant in order to provide to the merchant information indicating actions to implement to take in response to the risk scored. The rejection is maintained. With respect to applicant’s argument related to the amendments see response below.
In the remarks applicant argues that the application of KSR for substitution is improper. Applicant has not explained how the substitution of analyzing risk of probability for transaction fraud is not obvious substitution of risk sufficient funds for completion of transaction. Both are analysis of risk related to transaction payments. Applicant has not explained how one of ordinary skill in the art would not have found it common sense to analyze different risk related to transaction completion.
In the remarks applicant argues the prior art references fail to teach “using feedback data and the plurality of scores as labeled data in learning including grouping via regression or clustering analysis, the factors relied upon by the algorithm with generating scores adjusting weights. Applicant’s argument is not persuasive. The prior art Chen teaches inputting historical transaction data for the accountholder, transaction amount to a scaled score algorithm to generate a plurality of scaled scores, each of the plurality of scaled scores representing the likelihood of settlement of the putative payment transaction on a corresponding date and payment rail, the scaled score algorithm comprising an account balance prediction component configured to for each of the plurality of scaled scores, determine an existing account balance in an account of the accountholder and to analyze prior withdrawals and deposits of the historical transaction data for the account to project an account balance in the account on the corresponding date, and a general transactional behavior component configured to analyze transactions of a plurality of accountholders to determine one or more factors impacting the projected account balance in the account on the corresponding date. The prior art Chen teaches para 0014 wherein the prior art teaches models analyze risk assessment associated with probability of forecast global balance may have available with one or more financial institutions at a particular time and date, para 0015-0016 wherein the prior art teaches monitoring bank /financial accounts to determine available funds, cash funds burn rate including credit, debit, through wire transfers, funding sources, para 0021-0022 wherein the prior art teaches forecasting balance of entity at specific time/date and determine/forecast changes on balances over time according to user transaction/spend behavior, para 0034, para 0047-0049, para 0072. The prior art Chen teaching of the limitation “retrain the scaled score algorithm using the feedback data and the plurality of scaled scores as labeled data in a supervised learning operation, the retraining including grouping, via a regression or clustering analysis “ teaches model trained using XG boost model training, para 0047, para 0053-0054, para 0066, para 0073), one or more factors relied on by the scaled score algorithm in generating the plurality of scaled scores, and adjusting one or more weights applied by the scaled score algorithm to the one or more factors based on the feedback data ((Chen) in at least para 0022- para 0024, para 0053, para 0055, para 0058-0059, para 0066, para 0069-0070, para 0073, para 0075-0077. The XGBoost model for analysis. XGBoost modeling techniques apply regression analysis and classification of data element (e.g. labeled data). The rejection is maintained.
In the remarks applicant argues the prior art references Chen and Pandian fail to teach “per-rail settlement likelihood scores or payment routing recommendations. Applicant's arguments are moot in light of the new ground of rejection that was necessitated by Applicant's amendments. Based on an updated search of the art, a new reference was used in the rejection below
In the remarks applicant argues that the prior art references Liu, Jia, Hinghole and Hubard do not cure the deficits discussed above. Accordingly the limitations are allowable over the prior art. The examiner disagrees with the premise of applicant’s argument. See response above.
Examiner Note
With respect to machine learning techniques XG boost model training is designed for prediction tasks such as regression and classification labelling. As evidence see “Understanding XGBoost Algorithm, What is XGBoost Algorithm? by Great Learning Editorial Team
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 1-20 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 Claim(s) 1-10:
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 operations (1) receive message (2) input historical data and transaction amount into algorithm (3) generate payment routing recommendation (4) output generated scaled score (5) receive feedback data of indication payment completed (6) designate data (7) retrain algorithm using feedback data and prior scores via regression or clustering analysis (8) generate scores and (9) adjusting weights applied to factors based feedback data. The claimed limitations which under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic algorithm and computer. The claimed physical structures (processors and/or transceivers) are generic computer components and tools to perform the mental processes. The computer components are recited at a high level of generality and merely automates functions that could reasonably be performed using mental concepts, therefore acting as a generic computer to perform the abstract idea.
The limitation “retrain” the algorithm using feedback data for using and determining a score output which is a mathematical process. According to the specification, describes “retraining” to be designate feedback data for use in retraining (¶ 0144) and “may more heavily weight or otherwise favor the corresponding time period(s)” as “the feedback data may describe which day chosen by the merchant for the payment and whether the payment was successful…regression or clustering analysis and techniques may be used to group factors/variables relied upon by the algorithm in generating scores…the training may more heavily weight/factor time periods in future score generation”. (¶ 0146) The court also has “treated analyzing information by steps people go through in their minds, or by mathematical algorithms, without more, as essentially mental processes within the abstract-idea category. (see Benson). Therefore, the limitations, mimic human thought processes of observation, evaluation, analysis, calculation and communication of result which, where the data interpretation is perceptible only in the human mind “See In re TLl Commc'ns LLC Patent Litig., 823 F.3d 607, 611 (Fed. Cir. 2016); FairWarning IP, LLC v. latric Sys., Inc., 839 F.3d 1089, 1093-94 (Fed. Cir. 2016)
The specification titled “Computer Implemented Systems and Methods for payment Routing”, disclose payment systems routing payments according to predetermined settings potentially lead to failed transactions and higher than average costs associated with payment transactions (para 0025). The specification discloses that transaction data may include industry specific risk and that the risk assessment may be transmitted in the format of a risk assessment score. (para 0045). The specification discloses risk assessment of transaction data in the format of a risk assessment score where the risk assessment may affect calculation of a scaled score or likelihood of settlement (Spec ¶ 0045). It is clear from the Specification (including the claim language) that claim 1 focuses on an abstract idea, and not on patent eligible subject matter (e.g. improvement to technology and/or a technical field). This is because the focus of the specification and claim language is on assessing risk where a score is generate representing the risk, the limitations are directed toward analyzing a sales activity in order to calculate a score representing the risk of likelihood of a settlement. This is because the claim limitations “receive message”, “input data”, “output likelihood scores”, “receive feedback data indicating payment complete” and “retrain algorithm” used for scoring when considered as a whole in light of the specification is directed toward a sales activity. This concept is a sub-category of the abstract category of methods of organizing human activity.
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 claims fail to provide indications of patent eligible subject matter that integrate the alleged 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/transreceivers programmed to perform the claimed process and scaled algorithm comprising prediction and behavior components used by the algorithm to analyze data.
The claimed additional elements beyond the abstract idea include one or more processors/transceivers to perform the functions “receive…message”, “input …data”, “output …score” and “receive feedback data” which according to the courts have recognized the following computer functions are claimed in a merely generic manner (e.g., at a high level of generality) where technology is merely applied to perform the abstract idea 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 claim limitations (receive.. message, input…data, receive …data) are recited at a high level of generality without details of technical implementation and thus are insignificant extra solution activity.
The claimed additional elements beyond the abstract idea include one or more processors/transceivers to perform the functions “generate …recommendations”, “designate …data”, “retrain …algorithm”, “generate…scores” and “adjusting weights whole operations are recited at a high level of generality to analyze data to generate recommendations and designate data, generate scores and adjust weights. The claimed algorithm applies general regression or clustering machine learning techniques in the analysis process. The specification makes clear that the feedback data provides the success or lack of success in the completion of payments when dates for those payments are considered in the calculation. The algorithm weights the information accordingly as part of the retraining process. The current state of the law does not find that gathering of values for use in a mathematical process to be under step 2A prong 2, an indication of patent eligible subject matter.
Taking the claim elements separately, the operation performed by the system processor at each step of the process is purely in terms of results desired and devoid of implementation of details. This is true with respect to the limitations “receiving data”, “inputting data”, “generating a score” , “outputting the result”, “receive feedback data”, “designate data for retraining” and “retrain…using data”, as the claim recited limitations fail to recite any details on technical implementation. 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). The combinations of parts is not directed toward any technical process or technological technique or technological solution to a problem rooted in technology.
When considered as a combination of parts, the combination of limitations (1)-(4) is directed toward receiving data used to generate a score related to transaction and output the result. The combination of limitations (5)-(7) is directed toward receiving attempted payment data, designating data for use in retraining/further analysis, retraining the algorithm using data designated – which is directed toward receiving and analyzing data for use a transaction process. The combination of limitations (8)-(9) are directed toward using feedback data and scores generated in limitations (1)-(7) used to adjust weights applied to factors based on feedback data which is merely adjusting the weights/reliance of data reflected in generating scores.
When considered as a whole the claimed limitations are directed toward generating a risk score using an algorithm that can be updated/retrained based on receiving additional information and not a process where the technology imposes meaningful limits upon the generating of a risk score or solving a problem rooted in technology or improving upon the underlying technology itself. Accordingly, the combination the steps simply call for using a generic system processor to function as one of ordinary skill in the art would expect system processors to function, that is perform a transaction process of receiving transaction data that is inputted into an algorithm where the algorithm outputs a determined score representing probability of settlement based on the received inputted data, and the algorithm receiving additional data that is applied to “retrain” the algorithm. The algorithm is generally used to apply the abstract idea without limiting how the algorithm functions or is “retrain[ed]”. The algorithm is described at such a high level that the limitations amounts to using a process with the algorithm to apply the abstract idea. When considering the limitations as claimed, the limitations recite the outcomes of the “receive”, “input”, “designate,” “output” and “retrain” without any details as to how the outcomes are accomplished. The processor applied is nominally mentioned vaguely tied to the operations in the body of the claim.
The functions are is recited at a high-level of generality such that it amounts to no more than applying the exception using generic system processor. The claim limitations and specification lacks technical disclosure on what the technical problem was and how the claimed limitations provide a technical solution to a technical problem or improvement to technology or a process where the technology imposes meaningful limits upon the identified abstract idea, rather than a solution to a problem found in the abstract idea.
The recited operations of the system processor simply applies the process to receive/input data, generate a score and output the results. Claim 1, consist solely on result-oriented functional language omitting any specific requirements on how these steps of generating a score are performed. The claim limitations generalize receiving and inputting data into an algorithm that is applied to analyze the received data and rules in order to generate a score for measuring risk and outputting the value generated using nothing more that high level generic processor and processor operations. According to Electric Power Group, the steps of collecting data, analyzing data and outputting the results when claimed as a high level of generality are abstract concepts. The system processor is described in general terms, functions (receiving, inputting, outputting, designating and retrain[ing]) for generating a risk score.
The integration of elements do not improve upon technology or improve upon computer functionality or capability in how computers processors carry out 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 identify accounts to use for a payment and generate a likelihood score of settlements and does not provide any of the determined indications of patent eligibility set forth in the 2019 USPTO 101 guidance. 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.
The analysis find no indication in the claim language that the structure and/or the manner in which a computer system or its components basic operations are changed in any way. The Specification describes the challenges transaction risk.
Similar to the claims at issue in Intellectual Ventures I LLC v. Capital One Financial Corp., 850 F.3d 1332 (Fed. Cir. 2017), “the claim language . . . provides only a result-oriented solution with insufficient detail for how a computer accomplishes it. Our law demands more.” Intellectual Ventures, 850 F.3d at 1342 (citing Elec. Power Grp. LLC v. Alstom, S.A., 830 F.3d 1350, 1356 (Fed. Cir. 2016)). Accordingly, the finding of claim 1, when considered as a whole, does not reflect an improvement in computer functionality, an improvement in technology or a technical field, or that the claim otherwise integrates the recited abstract idea into a “practical application,”
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 and/or transceivers programmed to perform the claimed process and scaled algorithm comprising prediction and behavior components used by the algorithm to analyze data to perform the operations of “receive message”, “input transaction data at an algorithm”, “generate payment routing recommendation “, “output generate a scaled score”, “receive feedback data of indication payment completed” designate data”, “retrain algorithm using feedback data and prior scores via regression or clustering analysis”, “generate scores” and “adjusting weights applied to factors based feedback data”. The limitations “receive …feedback data and payment complete indicator”, “designate feedback data with scores for retraining” and “retraining using data/scores” do not as a combination provided the needed “significantly more” than the identified abstract idea. Claim 1 does not describe the system processors and/or transceivers in any further technical detail that would distinguish them from their generic counterparts. Each is functionally described as either "receive”, “input”, "output" , “designate” and “retrain” are at such a high level that such functions can be associated with generic system processors capable of performing these operations using any generic programming. The Specification attributes no special technical meaning to any of these operations, individually or in the combination, as claimed. Accordingly, these are common processing functions that one of ordinary skill in the art at the time of the invention would have known generic processors were capable of performing and would have associated with such generic computer elements and functionality. Cf OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015). Taking the claim elements separately, the function performed by the computer at each step of the process is purely conventional. The application of one or more processor of a system to perform the “receive”, “input”, “output” score and “receive” feedback, “designate data” and “retrain” operations of the claim ----are some of the most basic functions of a system processors. The generic system processors are employed in a customary manner such that they were insufficient to transform the abstract idea into a patent-eligible invention. Therefore, it concluded that the claims still “simply recite conventional actions in a generic way” (e.g., receiving a transaction message, inputting data at an algorithm and generating score of likelihood) and “do not purport to improve any underlying technology” and is not enough to qualify as “significantly more” include “apply it” (or an equivalent) with an abstract idea, mere instructions to implement the abstract idea by system processors or requiring no more than a generic compute to perform generic computer functions that are well understood activities known to the industry. As a result, none of the hardware recited by the system claims offers a meaningful limitation beyond generally linking the use of the abstract idea to a particular technological environment, that is, implementation via system processors.... The claim limitations do not recite that any of the “devices” perform more than a high level generic function .... None of the limitations recite technological implementation details for any of these steps, but instead recite only results desired to be achieved by any and all possible means.... Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. 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 “generating”, “transmitting”, “intercepting”, identifying”, “determining”, “replacing” and “routing' ... are functions can be achieved by any general purpose computer without special programming"). None of the claimed operations/activities are used in some unconventional manner nor do any produce some unexpected result. In short, each operation 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:
[0026] Figure 1 depicts an exemplary environment 100 for payment routing according to
embodiments of the present invention. The environment 100 may include a computing device
102, a card issuer 104, a merchant 106, an account data storage device 108, a database 110, one or
more financial institutions 112A, 112B and the like, and communication links 114. The computing
device 102 may be located within network boundaries of a large organization, such as a payment
network or interchange. The computing device 102 may also be external to the organization.
[0038] 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.
[0146] Referring to step 910, the feedback data may be used to retrain the scaled score
algorithm. For example, the feedback data may describe which day(s) were chosen by the merchant
for the attempted payment processing and/or whether the attempted payment processing was
successful. Moreover, regression or clustering analyses and techniques may be used to group
factors or variables relied on by the scaled score algorithm in generating the scaled scores and
which were apparently important to the merchant in selecting the date, account and/or payment
rail used for the attempted payment processing. Put differently, if a relatively large dataset
reflecting a multitude of attempted payment processing transactions reveals that one or more
merchants consistently select a date that is after the fifth (5 th) of each month for attempted payment
processing, even where the scaled scores are more favorable in preceding days, the retraining may
more heavily weight or otherwise favor the corresponding time period(s) in future scaled score
generation.
[0156] The transmission may be automated, and may occur intermittently, periodically,
continuously and/or based on a trigger (e.g., approach or passage of the aforementioned last occurring day in the group of potential dates for settlement). Moreover, each of the payment
transaction message, the scaled scores, and the feedback data (and, optionally, the transaction
request itself) may be labeled with a unique identifier (e.g., a unique alphanumeric code or the
like) that may be used to link such data together with the same payment transaction message and automatically designate same for retraining processes.
[0157] Referring to step 1008, the actual account balance data may be used to retrain the scaled
score algorithm. In one or more embodiments, the account balance prediction component of the
scaled score algorithm - discussed in more detail above in connection with description of the
scaled score 400 calculation - may be principally retrained with the actual account balance data.
For example, overall actual account balance on a day for which a scaled score was calculated may be significantly different than that predicted by the account balance prediction component,
revealing one or more flaws in the methodology implemented by the scaled score algorithm for
calculating the predicted account balance. More particularly, one or more debits or credits may
have been predicted or projected by the account balance prediction component, but may have not been realized in the account (as revealed by the actual account balance data), permitting
corresponding adjustment of the scaled score algorithm. However, it is foreseen that other
components of the scaled score algorithm - such as the general transactional behavior component - may be retrained based at least in part on the actual account balance data within the scope of the present invention.
[0158] Regression or clustering analyses techniques may be used to group factors or variables
relied on by the scaled score algorithm and/or its account balance prediction component in
generating the scaled scores. For example, regression may be used to evaluate the debit and credit data of the actual account balance data to determine periodicity for retraining.
[0159] For another example, wherever the actual account balance data reveal that success rates
for attempted payment transactions sharing a given characteristic, trait or factor reflected in the
actual account balance data - such as those for which favorable scaled scores were given on
particular days in large part based on assumptions regarding debits without well-established
periodicity- are unexpectedly low, the retraining may weight or otherwise adjust reliance on those
debit-related assumptions to more accurately reflect their impact on accurate scaled score generation. More generally, the weight accorded any particular output or component of the scaled
score algorithm may be changed through retraining, for example to reflect situations where
periodicity of a debit or credit or grouping thereof is shown to be more or less reliable than
originally anticipated based on analysis of the actual account balance data and/or where a dollar
amount of a debit or credit influences the reliability of prediction of future instances of
reoccurrence.
[0164] Certain embodiments are described herein as including logic or a number of routines,
subroutines, applications, or instructions. These may constitute either software (e.g., code
embodied on a machine-readable medium or in a transmission signal) or hardware. In hardware,
the routines, etc., are tangible units capable of performing certain operations and may be
configured or arranged in a certain manner. In example embodiments, one or more computer
systems (e.g., a standalone, client or server computer system) or one or more hardware modules
of a computer system (e.g., a processor or a group of processors) may be configured by software
(e.g., an application or application portion) as computer hardware that operates to perform certain
operations as described herein.
[0165] In various embodiments, computer hardware, such as a processing element, may be
implemented as special purpose or as general purpose. For example, the processing element may comprise dedicated circuitry or logic that is permanently configured, such as an application specific integrated circuit (ASIC), or indefinitely configured, such as an FPGA, to perform certain
operations. The processing element may also comprise programmable logic or circuitry (e.g., as
encompassed within a general-purpose processor or other programmable processor) that is
temporarily configured by software to perform certain operations. It will be appreciated that the
decision to implement the processing element as special purpose, in dedicated and permanently
configured circuitry, or as general purpose (e.g., configured by software) may be driven by cost
and time considerations.
[0166] Accordingly, the term "processing element" or equivalents should be understood to
encompass a tangible entity, be that an entity that is physically constructed, permanently
configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain
manner or to perform certain operations described herein. Considering embodiments in which the
processing element is temporarily configured (e.g., programmed), each of the processing elements need not be configured or instantiated at any one instance in time. For example, where the processing element comprises a general-purpose processor configured using software, the general purpose processor may be configured as respective different processing elements at different times. Software may accordingly configure the processing element to constitute a particular hardware configuration at one instance of time and to constitute a different hardware configuration at a different instance of time.
[0167] Computer hardware components, such as transceiver elements, memory elements,
processing elements, and the like, may provide information to, and receive information from, other computer hardware components. Accordingly, the described computer hardware components may be regarded as being communicatively coupled. Where multiple of such computer hardware components exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) that connect the computer hardware components. In embodiments in which multiple computer hardware components are configured or instantiated at different times, communications between such computer hardware components may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple computer hardware components have access. For example, one computer hardware component may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further computer hardware component may then, at a later time, access the memory device to retrieve and process the stored output. Computer hardware components may also initiate communications with input or output devices, and may operate on a resource (e.g., a collection of information).
With respect to the training of the algorithm – see Recentive Analytics v Fox
Claim 1 does not describe the system and its processors and/or transceiver in any further technical detail that would distinguish them from their generic counterparts. Each is functionally described as either “receive”, “input”, “output”, “receive” certain information and “retrain” algorithm functions associated with generic processor and/or transceiver of the system is further described in functional terms; that is, it is configured to perform information-receiving, outputting steps to obtain a risk score from received data and retrain the algorithms without details except for the data applied. Putting it together, these functions simply call for using a generic system processor and/or transceivers to function as one of ordinary skill in the art would expect such a system processor and/or transceivers to function, that is, to perform, inter alia, receive, output and retrain functions. Claim 1 "consists solely of result-orientated, functional language and omits any specific requirements as to how these functions of the system processor and/or receivers are performed." Mobile Acuity Ltd. v. Blippar Ltd., 110 F.4th 1280, 1292-93 (Fed. Cir. 2024). With respect to the receive, input, output and retrain functions, the Specification attributes no special technical meaning to any of these operations, individually or in the combination, as claimed. Accordingly, in light of the specification and limitations,
these are common processing functions that one of ordinary skill in the art at
the time of the invention would have known generic system and corresponding processors/transceivers were capable of performing and would have associated with such generic devices. Cf OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015). 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 2-10 these dependent claim have also been reviewed with the same analysis as independent claim 1. 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. Dependent claim 2 is directed toward maintaining API to receive data and designate data for retraining – insignificant extra solution activity of gathering data and a business practice for use of the data in the analysis. Dependent 3 is directed toward the mathematical concepts applied for the algorithm scoring process- mathematical concepts. Dependent claim 4 is directed retraining using feedback data in a confusion matrix-data analysis and mathematical concepts. Dependent claim(s) 5-7 is directed toward plurality of dates and rail/routes for different dates- a transaction process. Dependent claim 8 is directed toward data content- non-functional descriptive subject matter. Dependent claim 9 is directed toward determine account balance, withdrawals/deposits; analyze transactions of a plurality of accountholders to determined factors impacting account balance- analyzing financial data a business practice. Dependent claim 10 is directed toward overdraft policies- business practice.
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 2-10 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 Claim(s) 11-20:
STEP 1. Per Step 1 of the two-step analysis, the claims are determined to include a method, as in independent Claim 11 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 11 corresponds to operations of system claim 1. Therefore, claim 11 has been analyzed and rejected as being directed toward an abstract idea of the categories of concepts directed toward mental processes and methods of organizing human activity previously discussed with respect to claim 1.
STEP 2A Prong 2: Method claim 11 corresponds to operations of system claim 1. Therefore, claim 11 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 “via, one or more transceivers and/or processors”–is purely functional and generic. Nearly every computer implemented method will include a “processor” is capable of “receiving” data, “inputting” data, “outputting” data, “receiving” data and “retraining” algorithm - As a result, the claimed “via processor” recited by the method claims fails to offer a meaningful limitation beyond generally linking the use of the processor to perform the method, that is, implementation via “one or more processors”.
The steps of Method claim 11 corresponds to operations of system claim 1. Therefore, claim 11 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:
With respect to the training of the algorithm – see Recentive Analytics v Fox
[0026] Figure 1 depicts an exemplary environment 100 for payment routing according to
embodiments of the present invention. The environment 100 may include a computing device
102, a card issuer 104, a merchant 106, an account data storage device 108, a database 110, one or
more financial institutions 112A, 112B and the like, and communication links 114. The computing
device 102 may be located within network boundaries of a large organization, such as a payment
network or interchange. The computing device 102 may also be external to the organization.
[0038] 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.
[0146] Referring to step 910, the feedback data may be used to retrain the scaled score
algorithm. For example, the feedback data may describe which day(s) were chosen by the merchant
for the attempted payment processing and/or whether the attempted payment processing was
successful. Moreover, regression or clustering analyses and techniques may be used to group
factors or variables relied on by the scaled score algorithm in generating the scaled scores and
which were apparently important to the merchant in selecting the date, account and/or payment
rail used for the attempted payment processing. Put differently, if a relatively large dataset
reflecting a multitude of attempted payment processing transactions reveals that one or more
merchants consistently select a date that is after the fifth (5 th) of each month for attempted payment
processing, even where the scaled scores are more favorable in preceding days, the retraining may
more heavily weight or otherwise favor the corresponding time period(s) in future scaled score
generation.
[0157] Referring to step 1008, the actual account balance data may be used to retrain the scaled
score algorithm. In one or more embodiments, the account balance prediction component of the
scaled score algorithm - discussed in more detail above in connection with description of the
scaled score 400 calculation - may be principally retrained with the actual account balance data.
For example, overall actual account balance on a day for which a scaled score was calculated may be significantly different than that predicted by the account balance prediction component,
revealing one or more flaws in the methodology implemented by the scaled score algorithm for
calculating the predicted account balance. More particularly, one or more debits or credits may
have been predicted or projected by the account balance prediction component, but may have not been realized in the account (as revealed by the actual account balance data), permitting
corresponding adjustment of the scaled score algorithm. However, it is foreseen that other
components of the scaled score algorithm - such as the general transactional behavior component - may be retrained based at least in part on the actual account balance data within the scope of the present invention.
[0158] Regression or clustering analyses techniques may be used to group factors or variables
relied on by the scaled score algorithm and/or its account balance prediction component in
generating the scaled scores. For example, regression may be used to evaluate the debit and credit data of the actual account balance data to determine periodicity for retraining.
[0159] For another example, wherever the actual account balance data reveal that success rates
for attempted payment transactions sharing a given characteristic, trait or factor reflected in the
actual account balance data - such as those for which favorable scaled scores were given on
particular days in large part based on assumptions regarding debits without well-established
periodicity- are unexpectedly low, the retraining may weight or otherwise adjust reliance on those
debit-related assumptions to more accurately reflect their impact on accurate scaled score generation. More generally, the weight accorded any particular output or component of the scaled
score algorithm may be changed through retraining, for example to reflect situations where
periodicity of a debit or credit or grouping thereof is shown to be more or less reliable than
originally anticipated based on analysis of the actual account balance data and/or where a dollar
amount of a debit or credit influences the reliability of prediction of future instances of
reoccurrence.
[0164] Certain embodiments are described herein as including logic or a number of routines,
subroutines, applications, or instructions. These may constitute either software (e.g., code
embodied on a machine-readable medium or in a transmission signal) or hardware. In hardware,
the routines, etc., are tangible units capable of performing certain operations and may be
configured or arranged in a certain manner. In example embodiments, one or more computer
systems (e.g., a standalone, client or server computer system) or one or more hardware modules
of a computer system (e.g., a processor or a group of processors) may be configured by software
(e.g., an application or application portion) as computer hardware that operates to perform certain
operations as described herein.
[0165] In various embodiments, computer hardware, such as a processing element, may be
implemented as special purpose or as general purpose. For example, the processing element may comprise dedicated circuitry or logic that is permanently configured, such as an application specific integrated circuit (ASIC), or indefinitely configured, such as an FPGA, to perform certain
operations. The processing element may also comprise programmable logic or circuitry (e.g., as
encompassed within a general-purpose processor or other programmable processor) that is
temporarily configured by software to perform certain operations. It will be appreciated that the
decision to implement the processing element as special purpose, in dedicated and permanently
configured circuitry, or as general purpose (e.g., configured by software) may be driven by cost
and time considerations.
[0166] Accordingly, the term "processing element" or equivalents should be understood to
encompass a tangible entity, be that an entity that is physically constructed, permanently
configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain
manner or to perform certain operations described herein. Considering embodiments in which the
processing element is temporarily configured (e.g., programmed), each of the processing elements need not be configured or instantiated at any one instance in time. For example, where the processing element comprises a general-purpose processor configured using software, the general purpose processor may be configured as respective different processing elements at different times. Software may accordingly configure the processing element to constitute a particular hardware configuration at one instance of time and to constitute a different hardware configuration at a different instance of time.
[0167] Computer hardware components, such as transceiver elements, memory elements,
processing elements, and the like, may provide information to, and receive information from, other computer hardware components. Accordingly, the described computer hardware components may be regarded as being communicatively coupled. Where multiple of such computer hardware components exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) that connect the computer hardware components. In embodiments in which multiple computer hardware components are configured or instantiated at different times, communications between such computer hardware components may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple computer hardware components have access. For example, one computer hardware component may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further computer hardware component may then, at a later time, access the memory device to retrieve and process the stored output. Computer hardware components may also initiate communications with input or output devices, and may operate on a resource (e.g., a collection of information).
Claim 11 does not describe the processor(s) in any further technical detail that would distinguish them from their generic counterparts. Each is functionally described as either “receive”, “input”, “output”, “receive” certain information and “retrain” algorithm functions associated with generic processor is further described in functional terms; that is, it is configured to perform information-receiving, outputting steps to obtain a risk score from received data and retrain the algorithms without details except for the data applied. Putting it together, these functions simply call for using a generic processor to function as one of ordinary skill in the art would expect such a processor to function, that is, to perform, inter alia, receive, output and retrain functions. Claim 11 "consists solely of result-orientated, functional language and omits any specific requirements as to how these functions of the processor are performed." Mobile Acuity Ltd. v. Blippar Ltd., 110 F.4th 1280, 1292-93 (Fed. Cir. 2024). With respect to the receive, input, output and retrain functions, the Specification attributes no special technical meaning to any of these operations, individually or in the combination, as claimed. Accordingly, in light of the specification and limitations, these are common processing functions that one of ordinary skill in the art at the time of the invention would have known generic processors were capable of performing and would have associated with such generic devices. Cf OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015). 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 12-20 these dependent claim have also been reviewed with the same analysis as independent claim 11. The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 11. Dependent claim 12 is directed toward maintaining API to receive data and designate data for retraining – insignificant extra solution activity of gathering data and a business practice for use of the data in the analysis. Dependent 13 is directed toward the mathematical concepts applied for the algorithm scoring process- mathematical concepts. Dependent claim 14 is directed retraining using feedback data in a confusion matrix-data analysis and mathematical concepts. Dependent claim(s) 15-17 is directed toward plurality of dates and rail/routes for different dates- a transaction process. Dependent claim 18 is directed toward data content- non-functional descriptive subject matter. Dependent claim 19 is directed toward determine account balance, withdrawals/deposits; analyze transactions of a plurality of accountholders to determined factors impacting account balance- analyzing financial data a business practice. Dependent claim 20 is directed toward overdraft policies- business practice.
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 12-20 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) 1-3, 5 and 7; Claim(s) 11-13, 15 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pub No. 2023/0005055 A1 by Chen et al (Chen) in view of US Patent No. 11,625,723 B2 by Pandian et al (Pandian) and further in view of US Pub No. 2012/0271765 A1 by Cervenka et al. (Cervenka)
(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 ((Chen) in at least Abstract; para 0080) to:
receive a payment transaction message relating to a putative payment transaction, the payment transaction message containing putative payment transaction data identifying an accountholder and a transaction amount corresponding to the putative payment transaction ((Chen) in at least para 0014, para 0016, para 0018, para 0021-0022, para 0025-0027, para 0041);
input historical transaction data for the accountholder and at least a portion of the transaction amount to a scaled score algorithm to generate a plurality of scaled scores, each of the plurality of scaled scores representing the likelihood of settlement of the putative payment transaction on a corresponding date and payment rail, the scaled score algorithm comprising an account balance prediction component configured to for each of the plurality of scaled scores, determine an existing account balance in an account of the accountholder and to analyze prior withdrawals and deposits of the historical transaction data for the account to project an account balance in the account on the corresponding date, and a general transactional behavior component configured to analyze transactions of a plurality of accountholders to determine one or more factors impacting the projected account balance in the account on the corresponding date ((Chen) in at least para 0014 wherein the prior art teaches models analyze risk assessment associated with probability of forecast global balance may have available with one or more financial institutions at a particular time and date, para 0015-0016 wherein the prior art teaches monitoring bank /financial accounts to determine available funds, cash funds burn rate including credit, debit, through wire transfers, funding sources, para 0021-0022 wherein the prior art teaches forecasting balance of entity at specific time/date and determine/forecast changes on balances over time according to user transaction/spend behavior, para 0034, para 0047-0049, para 0072)….
retrain the scaled score algorithm using the feedback data and the plurality of scaled scores as labeled data in a supervised learning operation, the retraining including grouping, via a regression or clustering analysis ((Chen) in at least para 0022 wherein the prior art teaches model trained using XG boost model training, para 0047, para 0053-0054, para 0066, para 0073), one or more factors relied on by the scaled score algorithm in generating the plurality of scaled scores, and adjusting one or more weights applied by the scaled score algorithm to the one or more factors based on the feedback data ((Chen) in at least para 0022- para 0024, para 0053, para 0055, para 0058-0059, para 0066, para 0069-0070, para 0073, para 0075-0077)
Chen does not explicitly teach:
generate, using the plurality of scaled scores, a payment routing recommendation identifying a recommended date and a recommended payment rail for processing the putative payment transaction; …
output the plurality of scaled scores and the payment routing recommendation to a merchant in response to the payment transaction message;
receive, from the merchant and in response to the output, feedback data for the putative payment transaction, the feedback data including a date of attempted payment processing for the putative payment transaction and an indicator of whether the attempted payment processing was completed;
designate the feedback data together with the plurality of scaled scores for retraining processes; and
Pandian teaches:
receive a payment transaction message relating to a putative payment transaction, the payment transaction message containing putative payment transaction data identifying an accountholder and a transaction amount corresponding to the putative payment transaction ((Pandian) in at least FIG. 5; Col 6 lines 50-Col 7 lines 1-15, Col 7 lines 55-Col 8 lines 1-2, Col 24 lines Col 24 lines 1-25, lines 45-63),
output the plurality of scaled scores to a merchant in response to the payment transaction message ((Pandian) in at least Col 12 lines 45-Col 13 lines 1-9, Col 20 lines 4-10);
receive, from the merchant and in response to the output, feedback data for the putative payment transaction, the feedback data including a date of attempted payment processing for the putative payment transaction and an indicator of whether the attempted payment processing was completed ((Pandian) in at least Fig. 6; Col 14 lines 9-47, Col 15 lines 29-Col 16 lines 21-34, Col 21 lines 14-41, Col 24 lines 1-25, Col 25 lines 1-33);
designate the feedback data together with the plurality of scaled scores for retraining processes ((Pandian) in at least FIG. 6; Col 14 lines 9-47 wherein the prior art teaches merchant can provide event information over a lifecycle of interactions identified as prohibited, suspicious or illegal and generate an adjusted risk score; Col 21 lines 14-41, Col 25 lines 45-61 wherein the prior art teaches flagging transactions and feedback provided by merchant); and
retrain the scaled score algorithm using the feedback data and the plurality of scaled ((Pandian) in at least FIG. 6; Col 14 lines 26-47, Col 25 lines 45-61 wherein the prior art teaches flagging transactions and feedback provided by merchant and retraining by adjusting weights);
Although, the prior art Pandian calculates a transaction risk score which indicates probability of fraud rather than the transaction score representing probability of a balance available on a certain date as taught by Chen, according to KSR, simple substitution of one known element for another to obtain predictable results is common sense rationale. The prior art Pandian calculating a probability of risk for transaction completion which differed from the claimed calculated of probability of risk for transaction for completion by the substitution of calculations of probability of fraud instead of probability of sufficient funds to complete a transaction. The prior art Chen provides evidence that the substituted components and their functions were known in the art. Common sense rationale makes evident to one of ordinary skill in the art that the calculated score for probability of account balance available at time of transaction of Chen could have substituted for the calculated score of probability of fraud which negatively impacts payments at the time of the transaction, and the results of the substitution would have been predictable
According to KSR, known work in one field of endeavor may prompt variations of it for use in either the same field of a different one based on design incentives or other market forces if the variations are predictable. The scope and content calculated score for probability of completion of a transaction that is transmitted, whether in the same field of endeavor as that of the applicant’s invention included a similar or analogous communication of the calculated result to a participant in the transaction that was not the payor, but rather the participant that acquired risk in the transaction for completion of a transaction. The prior art Pandian provides design incentives or market forces that would have prompted adaptation of who would receive the calculated risk score based on transaction data for indications of probability of completion of a transaction. Accordingly the differences between the claimed invention and the prior art where encompassed in known variations or in a principle known in the prior art. Therefore, based on the teaching of Pandian, one of ordinary skill in the art, in view of the identified design incentives or other market forces, could have implemented the claimed variation of the prior art, and the claimed variation would have been predictable to one of ordinary skill in the art.
Both Chen and Pandian are directed toward collecting transaction data and generating scores representing transaction risk that is provided to participants in the transaction. Pandian teaches the motivation of sending the calculated score to the merchant in order to provide the merchant information that can be applied indicating remedial actions that could be taken as part of the assessment score provided in the message. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the participant receiving the score calculated of Chen to include the merchant as taught by Pandian since Pandian teaches the motivation of sending the calculated score to the merchant in order to provide the merchant information that can be applied indicating remedial actions that could be taken as part of the assessment score provided in the message.
Both Chen and Pandian are directed toward collecting transaction data and generating scores representing transaction risk using Machine learning algorithms that can be retrained using feedback data and previously generated scores. Pandian teaches the motivation of using as training data for retraining the machine learning model flagged feedback data from the merchant in order to adjust the weights and values of the data used for calculating the scores. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the feedback data provided for retraining the model of Chen to include flagged feedback data as taught by Pandian since Pandian teaches the motivation of using as training data for retraining the machine learning model flagged feedback data from the merchant in order to adjust the weights and values of the data used for calculating the scores
Cervenka teaches:
generate, using the plurality of scaled scores [optimal path] , a payment routing recommendation identifying a recommended date and a recommended payment rail for processing the putative payment transaction ((Cervenka) in at least para 0023, para 0026, para 0040, para 0042-0044, para 0050-0054, para 0073, para 0080);; …
output the … the payment routing recommendation to a merchant in response to the payment transaction message ((Cervenka) in at least Fig. 3A-B; para 0036, para 0041, para 0070, para 0073)
Both Chen and Cervenka are directed toward receiving and analyzing transaction payment information. Cervenka teaches the motivation of analyzing payment processing routing networks that are available and identifying routing networks with a high number of declines or timeouts leading to cost for gateway processing service and that it is desirable to have a transaction routing system capable of bypassing networks exhibiting signs of degraded service. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the data analyzed for evaluating probability of payment settlement of Chen to include analyzing payment processing routing networks for determining which networks have a high number of declines or timeouts as taught by Cervenka since Cervenka teaches the motivation of analyzing payment processing routing networks that are available and identifying routing networks with a high number of declines or timeouts leading to cost for gateway processing service and that it is desirable to have a transaction routing system capable of bypassing networks exhibiting signs of degraded service.
In reference to Claim 2:
The combination of Chen, Cervenka and Pandian discloses the limitations of independent claim 1. Chen further discloses the limitations of dependent claim 2
(Original) The system of claim 1 (see rejection of claim 1 above),
Chen does not explicitly teach:
the one or more processors and/or transceivers being further individually or collectively programmed to maintain an application programming interface (API) configured to automatically receive the feedback data from the merchant and designate the feedback data for the retraining.
Pandian teaches:
the one or more processors and/or transceivers being further individually or collectively programmed to maintain an application programming interface (API) configured to automatically receive the feedback data from the merchant and designate the feedback data for the retraining. ((Pandian) in at least FIG. 6; Abstract; Col 2 lines 66-Col 3 lines 1-25, Col 10 lines 18-35, Col 14 lines 4-47 wherein the prior art teaches merchant can provide event information over a lifecycle of interactions identified as prohibited, suspicious or illegal and generate an adjusted risk score; Col 21 lines 14-41, Col 25 lines 45-61 wherein the prior art teaches flagging transactions and feedback provided by merchant)
According to KSR, simple substitution of one known element for another to obtain predictable results is common sense rationale. The prior art Chens generic interface applied to receiving/transmit data between devices differed from the claimed API interface for use in receiving/transmitting data between devices. The prior art Pandian provides evidence that the substituted components and their functions were known in the art. Common sense rationale makes evident to one of ordinary skill in the art that the API of Pandian could have been substituted for the generic API, and the results of the substitution would have been predictable
Both Chen and Pandian teach applying interface technology in order to communicate data between entities. Pandian teaches the motivation of applying API interface so that the merchant server can provide information to the machine model for use in calculating probability risk score. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the generic interface of Chen to include the API interface of Pandian since Pandian teaches the motivation of applying API interface so that the merchant server can provide information to the machine model for use in calculating probability risk score
Both Chen and Pandian are directed toward collecting transaction data and generating scores representing transaction risk using Machine learning algorithms that can be retrained using feedback data and previously generated scores. Pandian teaches the motivation of using as training data for retraining the machine learning model flagged feedback data from the merchant in order to adjust the weights and values of the data used for calculating the scores. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the feedback data provided for retraining the model of Chen to include flagged feedback data as taught by Pandian since Pandian teaches the motivation of using as training data for retraining the machine learning model flagged feedback data from the merchant in order to adjust the weights and values of the data used for calculating the scores
In reference to Claims 3:
The combination of Chen, Cervenka and Pandian discloses the limitations of independent claim 1. Chen further discloses the limitations of dependent claim 3
(Original) The system of claim 1 (see rejection of claim 1 above),
wherein the scaled score algorithm includes a decision tree with boosted gradient trees. ((Chen) in at least para 0022, para 0047, para 0053-0055)
In reference to Claim 5:
The combination of Chen, Cenvenka and Pandian discloses the limitations of independent claim 1. Chen further discloses the limitations of dependent claim 5
(Original) The system of claim 1 (see rejection of claim 1 above),
wherein the corresponding plurality of dates …includes multiple different dates. ((Chen) in at least para 0021, para 0041, para 0048, para 0057, para 0066, para 0068, para 0073-0074)
Chen does not explicitly teach:
plurality of … rails
Cervenka teaches:
plurality of … rails ((Cervenka) in at least FIG. 3A-B; para 0021, para 0032, para 0034, para 0045, para 0048, para 0068, para 0084)
Both Chen and Cervenka are directed toward receiving and analyzing transaction payment information. Cervenka teaches the motivation of analyzing payment processing routing networks that are available and identifying routing networks with a high number of declines or timeouts leading to cost for gateway processing service and that it is desirable to have a transaction routing system capable of bypassing networks exhibiting signs of degraded service. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the data analyzed for evaluating probability of payment settlement of Chen to include analyzing payment processing routing networks for determining which networks have a high number of declines or timeouts as taught by Cervenka since Cervenka teaches the motivation of analyzing payment processing routing networks that are available and identifying routing networks with a high number of declines or timeouts leading to cost for gateway processing service and that it is desirable to have a transaction routing system capable of bypassing networks exhibiting signs of degraded service.
In reference to Claim 7:
The combination of Chen, Cenvenka and Pandian discloses the limitations of independent claim 1. Chen further discloses the limitations of dependent claim 7
(Original) The system of claim 1 (see rejection of claim 1 above),
Chen does not explicitly teach:
wherein the corresponding plurality of dates and rails includes multiple different rails.
Cervenka teaches:
wherein the corresponding plurality of dates and rails includes multiple different rails. ((Cervenka) in at least FIG. 3A-B; para 0021-0023, para 0032, para 0034, para 0042-0043, para 0045, para 0048, para 0068, para 0080, para 0084)
Both Chen and Cervenka are directed toward receiving and analyzing transaction payment information. Cervenka teaches the motivation of analyzing payment processing routing networks for processing degradation because the volume of transactions to date in a period of time of each payment processing route increase probability of timeouts or that determined periods have increase probability of settlement completions degradation. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the data analyzed for evaluating probability of payment settlement of Chen to include analyzing payment processing routing networks for volume of transactions on specified time periods in a month as taught by Cervenka since Cervenka teaches the motivation of analyzing payment processing routing networks for processing degradation because the volume of transactions to date in a period of time of each payment processing route increase probability of timeouts or that determined periods have increase probability of settlement completions degradation.
In reference to Claim 11:
The combination of Chen, Cervenka and Pandian discloses the limitations of independent claim 11.
The steps of method Machine claim 11 corresponds to operations of system claim 1. Therefore, claim 11 has been analyzed and rejected as previously discussed with respect to claim 1.
In reference to Claim 12:
The combination of Chen, Cervenka and Pandian discloses the limitations of independent claim 11. Chen further discloses the limitations of dependent claim 12
The steps of method claim 12 corresponds to functions of system claim 2. Therefore, claim 12 has been analyzed and rejected as previously discussed with respect to claim 2
In reference to Claim 13:
The combination of Chen, Cervenka and Pandian discloses the limitations of independent claim 11. Chen further discloses the limitations of dependent claim 13
The steps of method claim 13 corresponds to functions of system claim 3. Therefore, claim 13 has been analyzed and rejected as previously discussed with respect to claim 3
In reference to Claim 15:
The combination of Chen, Cervenka and Pandian discloses the limitations of independent claim 11. Chen further discloses the limitations of dependent claim 15
The steps of method claim 15 corresponds to functions of system claim 5. Therefore, claim 15 has been analyzed and rejected as previously discussed with respect to claim 5.
In reference to Claim 17:
The combination of Chen, Cervenka and Pandian discloses the limitations of independent claim 11. Chen further discloses the limitations of dependent claim 17
The steps of method claim 17 corresponds to functions of system claim 7. Therefore, claim 17 has been analyzed and rejected as previously discussed with respect to claim 7
Claim(s) 4 of claim 1 above, Claim(s) 14 of claim 11 above is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pub No. 2023/0005055 A1 by Chen et al (Chen) in view of US Patent No. 11,625,723 B2 by Pandian et al (Pandian), in view of US Pub No. 2012/0271765 A1 by Cervenka et al. (Cervenka) and further in view of US Pub No. 2020/0134387 A1 by Liu et al (Liu)
In reference to Claims 4:
The combination of Chen, Cervenka and Pandian discloses the limitations of dependent claim 3. Chen further discloses the limitations of dependent claim 4
(Original) The system of claim 3 (see rejection of claim 3 above),
Chen does not explicitly teach:
wherein the retraining includes incorporating the feedback data into a confusion matrix.
Liu teaches:
wherein the retraining includes incorporating the feedback data into a confusion matrix. ((Liu) in at least para 0019, para 0057, para 0060, para 0062, para 0064-0067, para 0071, para 0073, para 0077, para 0081; Table 1-2)
Both Chen and Liu are directed toward applying predictive engines/models which incorporate boosted gradient trees for analyzing financial data for assessing risk in order to generate an evaluation metric. Liu teaches the motivation of using confusion matrix techniques in the analysis in order to compute an evaluation metric based on the comparison of data values from different categories in order to provide indications of the accuracy of the modeling/predictive engine which can be used to alter one or more of the machine environments based on the evaluation accuracy. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the predictive engine analysis of risk data techniques of Chen to include applying confusion matrix techniques in the analysis of Liu since Liu teaches the motivation of using confusion matrix techniques in the analysis in order to compute an evaluation metric based on the comparison of data values from different categories in order to provide indications of the accuracy of the modeling/predictive engine which can be used to alter one or more of the machine environments based on the evaluation accuracy.
In reference to Claim 14:
The combination of Chen, Cervenka and Pandian discloses the limitations of dependent claim 13. Chen further discloses the limitations of dependent claim 14
The steps of method claim 14 corresponds to functions of system claim 4. Therefore, claim 14 has been analyzed and rejected as previously discussed with respect to claim 4
Claim(s) 8 of claim 1 above, Claim(s) 18 of claim 11 above, is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pub No. 2023/0005055 A1 by Chen et al (Chen) in view of US Patent No. 11,625,723 B2 by Pandian et al (Pandian), in view of US Pub No. 2012/0271765 A1 by Cervenka et al. (Cenvenka) and further in view US Pub No. 2020/0134628 A1 by Jia et al. (Jia)
In reference to Claim 8:
The combination of Chen, Cervenka, Pandian and Jia discloses the limitations of dependent claim 7. Chen further discloses the limitations of dependent claim 8
(Original) The system of claim 7 (see rejection of claim 7 above), wherein the feedback data includes:
Chen does not explicitly teach:
a date of initiation of an attempted transaction corresponding to the putative payment transaction;
an indicator of whether the attempted transaction was successfully completed;
a date of successful completion of the attempted transaction a rail of the attempted transaction, and a rail of the attempted transaction, the rail being one of the multiple different rails.
Jia teaches:
a date of initiation of an attempted transaction corresponding to the putative payment transaction ((Jia) in at least para 0030, para 0034, para 0037, para 0039, para 0041, para 0045, para 0047-0050, para 0053),
initiate, responsive to the transaction request, the payment transaction ((Jia) in at least para 0037-0039, para 0045, para 0053-0054);
an indicator of whether the attempted transaction was successfully completed ((Jia) in at least para 0037-0039, para 0045, para 0053-0054). ;
a date of successful completion of the attempted transaction ((Jia) in at least para 0058); and
a rail of the attempted transaction, the rail being one of the multiple different rails ((Jia) in at least para 0028, para 0039, para 0041-0042, para 0050, para 0064-0065).
According to KSR, common sense rationale, known work in one field of endeavor may prompt variations of its for use based on design incentives or market forces if the variations are predictable to one of ordinary skill in the art. The prior art provides evidence that the scope and content in the same field of endeavor as that of the applicant’s invention included a similar/analogous generation of transaction risk score analysis used for selection of different options in a transaction process. The prior art Jia teaches that there is a need in the market for merchants to adapt the teaching of Chen to include merchant settlement risk probability as taught by Jia. The prior art references provide evidence that the differences between the claimed invention and the prior art where encompassed in known variations or in a principle known in the art. Accordingly, one of ordinary skill in the art, in view of the identified design incentives or other market forces (e.g. identify which route/rail has a higher settlement rate which is included in the score for use in selection of a route/rail decision in the transaction process) and the claimed variations would have been predictable to one of ordinary skill in the art.
Both Chen and Jia are directed toward participants in a transaction requesting a transaction where the transaction data is used in order to determine a settlement probability risk score where the probability of user account having sufficient funds is a consideration. Jia teaches the motivation that merchants also have the need to score settlement risk score in order to identify the probability of a transaction request failing and teaches the motivation of the analysis determining whether it is likely the account balance is sufficient based on analysis of historical transaction in the determination of proceeding with the transaction. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the participant applying a transaction settlement risk score based on received and inputted transaction data for analysis in determining a risk settlement score of Chen to include the counterparty (merchant) as taught by Jia since Jia teaches the motivation that merchants also have the need to score settlement risk score in order to identify the probability of a transaction request failing and teaches the motivation of the analysis determining whether it is likely the account balance is sufficient based on analysis of historical transaction in the determination of proceeding with the transaction.
In reference to Claim 18:
The combination of Chen, Cervenka, Pandian and Jia discloses the limitations of dependent claim 17. Chen further discloses the limitations of dependent claim 18
The steps of method claim 18 correspond to the operations of system claim 8.
Therefore, claim 18 has been analyzed and rejected as previously discussed with respect to claim 8
Claim(s) 6 of claim 5 above and 9 of claim 1 above, Claim(s) 16 of claim 15 above, and 19 of claim 11 above, is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pub No. 2023/0005055 A1 by Chen et al (Chen) in view of US Patent No. 11,625,723 B2 by Pandian et al (Pandian), in view of US Pub No. 2012/0271765 A1 by Cervenka et al. (Cervenka), and further in view of US Patent No. 8,560,447 B1 by Hinghole et al. (Hinghole)
In reference to Claim 6:
The combination of Chen, Cenrvenka and Pandian discloses the limitations of dependent claim 5. Chen further discloses the limitations of dependent claim 6
(Original) The system of claim 5 (see rejection of claim 5 above),
Chen does not explicitly teach:
wherein the feedback data is received on or after a last-occurring date of the multiple different dates
Hinghole teaches:
wherein the feedback data is received on or after a last-occurring date of the multiple different dates. ((Hinghole) in at least Col 10 lines 60-67 wherein the prior art teaches determined date selected by the payor; Col 16 lines 62-col 17 lines 1-10 wherein the prior at teaches payor confirming account selected or rejects account selected or selects another account)
Both Chen and Hinghole teach applying dates for payments due when forecasting probability of available balances for a transaction and teach applying feedback data for use in calculating probability scores. Hinghole teaches the motivation of using feedback data which is receive prior to payment due in order to confirm which account are selected/rejected for use in the calculation of payment probability. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the billing data according to billing periods and feedback data used for calculating probability scores of Chen to include feedback data provided prior to a billing date as taught by Hinghole since Hinghole teaches the motivation of using feedback data which is receive prior to payment due in order to confirm which account are selected/rejected for use in the calculation of payment probability.
In reference to Claim 9:
The combination of Chen, Pandian and Cervenka discloses the limitations of independent claim 1. Chen further discloses the limitations of dependent claim 9
(Original) The system of claim 1 (see rejection of claim 1 above), wherein the scaled score algorithm includes:
a general transactional behavior component configured to analyze transactions of a plurality of accountholders to determine one or more factors impacting the projected account balance in the account on the corresponding date. ((Chen) in at least para 0021, para 0072)
Chen does not explicitly teach:
an account balance prediction component configured to, for each of the plurality of scaled scores, determine an existing account balance in an account of the accountholder and to analyze prior withdrawals and deposits of the historical transaction data for the account to project an account balance in the account on the corresponding date,
Hinghole teaches:
an account balance prediction component configured to, for each of the plurality of scaled scores, determine an existing account balance in an account of the accountholder and to analyze prior withdrawals and deposits of the historical transaction data for the account to project an account balance in the account on the corresponding date ((Hinghole) in at least Col 4 lines 33-56, Col 9 lines 62-Col 10 lines 1-5, lines 52-Col 11 lines 1-14, Col 14 lines 42-50),
a general transactional behavior component configured to analyze transactions of a plurality of accountholders to determine one or more factors impacting the projected account balance in the account on the corresponding date. ((Hinghole) in at least Col 10 lines 52-Col 11 lines 1-28, Col 12 lines 19-50)
Both Chen and Hinghole teach applying different account information and dates for payments due when forecasting probability of available balances for a transaction and teach applying feedback data for use in calculating probability scores. Hinghole teaches the motivation of applying for each score an account balance data which include existing account balance, withdrawals/deposits and general transaction for use in calculating probability score of available balance for each account so that accounts can be selected for payment for specific dates according to cashflow indications. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the data used for calculating probability scores of Chen to include account cashflow data of Hinghole since Hinghole teaches the motivation of applying for each score an account balance data which include existing account balance, withdrawals/deposits and general transaction for use in calculating probability score of available balance for each account so that accounts can be selected for payment for specific dates according to cashflow indications
In reference to Claim 16:
The combination of Chen, Cervenka and Pandian discloses the limitations of dependent claim 15. Chen further discloses the limitations of dependent claim 16
The steps of method claim 16 correspond to the operations of system claim 6.
Therefore, claim 16 has been analyzed and rejected as previously discussed with respect to claim 6.
In reference to Claim 19:
The combination of Chen, Cervenka, Pandian and Jia discloses the limitations of dependent claim 11. Chen further discloses the limitations of dependent claim 19
The steps of method claim 19 correspond to the operations of system claim 9.
Therefore, claim 19 has been analyzed and rejected as previously discussed with respect to claim 9.
Claim(s) 10 of claim 9 above, Claim(s) 20 of claim 19 above, is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pub No. 2023/0005055 A1 by Chen et al (Chen) in view of US Patent No. 11,625,723 B2 by Pandian et al (Pandian), in view of US Pub No. 2012/0271765 A1 by Cervenka et al (Cervenka) in view of US Patent No. 8,560,447 B1 by Hinghole et al (Hinghole), and further in view of US Pub No. 2022/0122171 A1 by Hubard et al. (Hubard)
In reference to Claim 10:
The combination of Chen, Pandian, Cervenka and Hinghole discloses the limitations of dependent claim 9. Chen further discloses the limitations of dependent claim 10
(Original) The system of claim 9 (see rejection of claim 9 above),
Chen does not explicitly teach:
the one or more factors including a non-sufficient funds overdraft protection policy of a financial institution corresponding to the account.
Hubard teaches:
the one or more factors including a non-sufficient funds overdraft protection policy of a financial institution corresponding to the account. ((Hubard) in at least FIG. 11, FIG. 111A; para 0048, para 0054, para 0120, para 0122, para 0133, para 0167, para 0186)
Both Chen and Hubard are directed toward analyzing and scoring transaction risk data. Hubard teaches the motivation of analyzing transaction and behavior data that show adverse credit risks such as number of overdrafts of bank accounts in order to determine positive/negative risk behavior. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the transaction data analyzed of Chen to include overdraft information as taught by Hubard since Hubard teaches the motivation of analyzing transaction and behavior data that show adverse credit risks such as number of overdrafts of bank accounts in order to determine positive/negative risk behavior.
In reference to Claim 20:
The combination of Chen, Pandian, Cervenka and Hinghole discloses the limitations of dependent claim 19. Chen further discloses the limitations of dependent claim 20
The steps of method claim 20 correspond to the operations of system claim 10.
Therefore, claim 20 has been analyzed and rejected as previously discussed with respect to claim 10.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to 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
/CHRISTINE M Tran/Supervisory Patent Examiner, Art Unit 3695
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