DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The following is a Non-Final Office Action in response to communications received August 03, 2026. No Claim(s) have been canceled. Claims 1, 10 and 19 have been amended. No new claims have been added. Therefore, claims 1-20 are pending and addressed below.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17 (e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant’s submission has been entered.
Priority
Application 18472043 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, Nicholas; Gagon, Serenie; Roper, Daniel; Harnish, Justin; Bell, Cameron; Arunachalam, Natesh Babu
Response to Amendment/Arguments
Claim Rejections - 35 USC § 101
Applicant's arguments filed 08/03/2026 have been fully considered but they are not persuasive.
In the remarks applicant discusses the two step analysis guidelines for determination of patent eligibility pointing to MPEP 2106.04 II, MPEP 2106.04(a)(2) III C, Enfish and Alice/Mayo decisions. Applicant argues the claim limitations cannot reasonably be performed using mental processes. Applicant discusses the “generating a plurality of scaled scores by inputting …data and transaction amount to a scaled score algorithm – which per claim 3 comprises a decision tree with booted gradient trees- retrieving account balances from a financial institution across a plurality of settlement dates, retraining the algorithm on the retrieved balances and on feedback data reflecting the outcome of an attempted payment, and then routing subsequent transaction to a settlement date selected by the retrained algorithm are not processes which can be performed using mental processes. Applicant’s argument is not persuasive. The independent claims do not claim the decision tree algorithm with booted gradient trees and therefore, was not considered in the analysis of the independent claim. With respect to Claim 3, dependent claim 3 recites generic well known machine learning techniques (see Recentive Analytic v Fox Corp) which when considered in the dependency of claim 1 is merely applying well understood technology to implement the abstract idea of methods of organizing human activity. With respect to claim 1 the limitations recite receiving “payment message”, “a transaction request”, input data associated with transaction request to the retrained scaled score to determine plurality of associated scores for …dates” and “input data and transaction amount for calculating a plurality of scores corresponding to potential settlement dates representing likelihood of settlement of payment for transaction” which through observation the human mind can comprehend and interpret and calculations using the data observed and known of potential dates, account amounts and transaction payment amounts. The limitations include “determine potential settlement dates for transaction” and “determine …scores indicate a likelihood of settlement …exceeds a threshold “ where the human mind can comprehend through analysis and understanding of dates and transaction processes and probabilities. The limitation “initiate completion of …transaction” is so broad as to include the decision to start the completion of a transaction as the human mind can make decisions. The rejection is maintained.
In the remarks applicant points to example 47 Claim 3 of the USPTO 101 guidance, where the trained algorithm for detecting traffic anomalies of networks and then acted on by dropping malicious packets and blocking future traffic from source access resulting in integration into a practical application. Applicant argues the amended claim limitations follows the same pattern by training and retraining the algorithm using account balances retrieved on candidate settlement dates that where scored together with feedback data identifying settlement dates on which settlement attempted and succeeded. The application of the retrained algorithm to receive transaction request determine score exceeds settlement likelihood threshold on corresponding settlement date and initiates completion of transaction. Applicant argues that similar to example 47, the claim does not stop at a score it acts on the model output to change how the transaction is routed. Applicant’s argument is not persuasive. In example 47 claim 3, the focus of the process is to solve a problem rooted in technology itself. Patent eligibility was not found in the machine learning algorithm training, as the problem was rooted in the network where malicious traffic was a problem in the network process. This is not the case of the current application. The claim limitations when considered individually or as a combination are directed toward analyzing payment risk on different settlement dates. Example 47 is not applicable, the rejection is maintained.
In the remarks applicant points to the Desjardins decision which directs consideration of the specification for including the technical benefits. Applicant argues the present claims recite an improvement pointing to the “closed feedback loop which real-world settlement outcomes are fed back to retrain the model that governs routing decisions with technical benefits. Applicant’s argument is not persuasive. The examiner notes that applicant does not point to the specific support within the specification where the specification describes processes for improving technology or providing a solution to a technical problem within the technology itself. Applicant’s arguments focuses on application of the calculated score representing likelihood of a successful payment on different settlement dates. Determining potential successful settlement dates which is applied to initiate completion of a transaction is not directed toward indications of patent eligibility under step 2A prong 2. Accordingly, the current application does not recite a technical solution to a technical problem because the problem disclosed in the is the need to predict potential settlement payment dates based on the analysis of account data, payment account and available settlement dates, which is business problem, not a technical one. With respect to the “training” of the model as claimed, the claimed subject matter similar to Recentive Analytics, Inc v Fox Corp, simply uses machine learning to perform a task. The court found that machine learning is now viewed as a common tool rather than a technological breakthrough. The current specification fails to clearly describe a technical problem or explain how the claimed invention improves the “functioning” of the technology, rather than the outcome of applying the technology for analysis. Furthermore, similar to Recentive, the “training”, where the specification teaches the learning model trained using training data, makes clear that any suitable machine learning technique can be applied which allows data to be inputted and changed based on changes in data. According to Recentive “The requirements that the machine learning model be “trained” or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement… See, e.g., Opposition Br. 9 (“[U]sing a machine learning technique[] . . . necessarily includes [an] iterative[] training step . . . .” (internal quotation marks and citation omitted)); Transcript at 26:21–24 (“[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input”).”. Accordingly, the examiner maintains that similar to Recentive the claimed ML limitations do not transform the claimed subject matter into patent eligibility.
In the remarks applicant argues the retrieval of account balances and retraining limitations are not insignificant extra solution activity pointing to MPEP 2106.05(d) II, MPEP 2106.05(g). The retrieval of account balances is not incidental data gathering as it is the training input on which the training operates. Removal of the retrieving step makes the retraining step not possible. The limitations therefore, necessarily predicates improvement to technology. Applicant argues the generation and output of a score initiates completion of corresponding transaction on particular settlement date identified by the retrained algorithm. The affirmation, machine-effected action on a transaction, similar to example 47 is the opposite of outputting results. The claim in which model outputs are validated against real-world data used to retrain the model and then applied to control execution of a later transaction is patent eligible. Applicant’s argument is not persuasive. The retrieval of account data is data gathering and is not part of the machine learning process. The input of data from a data source for use in analysis by an algorithm as claimed amounts to no more than mere instructions to “retrain” the algorithm. The specification does not provide any details as to a technical process or technique meets the standards of Desjardins where the specification describes processes for improving technology or providing a solution to a technical problem within the technology itself. The data retrieved is merely the data acted upon for analysis in calculating a score which is outputted and applied to initiate a transaction. The claim limitations do not recite a process where the initiation is controlled by any technology as a result of the outputted score and the initiation is not directed toward implementing a solution to a problem in technology or improvement thereof as found in example 47 or Diehr v Diamond. The rejection is maintained.
In the remarks applicant argues that the specification provides substantial technical description of the retraining claimed. Applicant points to the previous Office actions determination that the specification “lacks technical disclosure” with respect to the “retrain” limitation. Applicant argues that the level details as to technical implementation is a question arising under 112 not 101. Eligibility under 101, does not require a claim recite an algorithm of source code but requires integrate any exception into a practical application. The specification discloses retraining its inputs and mechanism enumerating feedback data on which the retraining operates (spec ¶143-145). The claimed feedback data used to retrain the algorithm for attempted payment processing on whether the attempted payment processing was successful. The regression and clustering analysis and techniques may be used to group factors or variables relied on by the score algorithm in generating scores. The passages identify what data the retraining techniques are applied and the effect on the model. This provides technical disclosure of the machine learning improvement. Applicant’s argument is not persuasive. As discussed above, in argument 3, the specification only describes the data acted upon for calculating scores. The specification merely describes what data can be applied for use in the retraining of the model and not the training process itself and not for the purpose of improving any underlying technology or machine learning technology (see ¶ 0144-0150, ¶ 0156-0161). The rejection is maintained.
In the remarks applicant argues that the claimed limitations when considered as an ordered combination improve payment routing technology. Applicant argues the derived plurality of candidate settlement dates from the data by which a transaction settles, the calculated score representing likelihood on each date and account balances retrieved on those dates and outcomes of attempted payments retraining the scoring model on the ground truth where the model applied to route and settle a later transaction on a data. Applicant’s argument is not persuasive. Applicant is arguing limitations not claimed, the claim limitations are silent with respect to a payment routing process. Instead the claim limitations recite a time for initiating a transaction based on calculated score. The rejection is maintained.
In the remarks applicant argues amended claims 1, 10 and 19 as explained by the specification routes payments according to predetermined setting of the merchant leading to optimal payment processing or failed transactions” pointing to the spec ¶ 0004 and ¶0025. The claimed system replaces static settings with a model that is corrected against observed settlement outcomes, reducing failed settlement attempts with payment networks. The improvement to operation of payment processing system not mere automation of business judgement. Applicant’s argument is not persuasive. The calculated score output based on analysis of account balances, potential payment dates and payment amounts do not impact any payment routing technology, but instead score the business judgement on what dates to perform payments, which is explicitly a business activity. The rejection is maintained.
In the remarks applicant argues based on the arguments above, the independent claims 1, 10 and 19 and corresponding dependent claims are patent eligible. The examiner respectfully disagrees. See response above.
Claim Rejections - 35 USC § 103
Applicant's arguments filed 08/03/2026 have been fully considered but they are not persuasive.
In the remarks applicant argues that the prior art reference Cataline fails to disclose any learning models or training of such models. Applicant is arguing references individually. According to MPEP 2145 IV, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., Inc., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). This is because "[T]he test for obviousness is what the combined teachings of the references would have suggested to [a PHOSITA]." In re Mouttet, 686 F.3d 1322, 1333, 103 USPQ2d 1219, 1226 (Fed. Cir. 2012). The rejection is maintained.
In the remarks applicant argues the prior art reference Cataline fail to teach “a plurality of scores…corresponding to the plurality of potential settlement dates, each …scores representing the likelihood of settlement of the …payment transaction from an account of the account holder on the corresponding …plurality of potential settlement dates”. Applicant argues that the prior art Chen forecast made at closing time for billing cycle/repayment date is for determining how much credit to extend for determining when to adjust credit limits. Applicant argues that Cateline prior art reference scoring mechanism scores people by allowing bank/entity to score people, type of account or any past problems of the people. (para 0092-0092). Applicant argues the prior art Cataline scoring of mechanism is not dates. Accordingly the prior art Cateline fails to teach plurality of scores corresponding to potential settlement dates. The examiner respectfully disagrees with the premise of applicant’s argument. The claim limitations do not recite scoring dates, the claim limitations recite “determining based on the date by which …payment transaction must settle, a plurality of potential settlement date for the …payment transaction, the …potential settlement dates being on or before the date by which the …payment transaction must settle”. The claimed limitations recite “determine …plurality of associated scores indicating likelihood of settlement on corresponding plurality of possible settlement dates”. There is no requirement in the limitations of scoring the dates, only scoring likelihood of settlement based on potential dates. The prior art Cateline teaches communicating information regarding a payment request and determining payment option to direct transmission of funds from based on payment source to an account.(abstract) and teaches the payment system allows payment initiator to select a prospective time frame in which a payment shall be made (para 0052). The analysis is performed using a risk scoring and optimization algorithm/model using information gathered and available to determine most effective settlement related to available settlement mechanism (para 0055, para 0071, para 0091, para 0139, para 0144). Although the prior art Chen does teach a process for underwriting for credit/loan extension the underwriting analysis includes scoring likelihood of available funds on a selected time frame/date in order to determine whether credit needs to be extended or not. This is not in conflict with the claimed limitations or Chen which require collecting account balance data, analyzing the account balance data, spending behavior , settlement data in order to score likelihood of available funds and initiating completion of payment. Chen simply adds an additional security in completion of payment by determining whether funds are available for settlement dates and if not extending credit. (abstract, para 0016, para 0018, para 0021, para 0023-0025). Although the prior art Chen does teach “determining based on the date by which …payment transaction must settle,…”. The claimed limitations recite “determine …plurality of associated scores indicating likelihood of settlement on corresponding … possible settlement dates”. The prior art Chen does not teach “a plurality of potential settlement dates for the …payment transaction, the …potential settlement dates being on or before the date by which the …payment transaction must settle” or teach “determine … scores indicating likelihood of settlement on corresponding plurality of possible settlement dates”. Although the plurality of possible settlement dates is suggested the prior art Chen does not explicitly state “plurality of possible settlement dates”. The combination of Chen in view of Cateline teach the limitation. The rejection is maintained.
In the remarks applicant argues the combination of prior art references proposed impermissible hindsight. The examiner respectfully disagrees. The prior art reference Cateline provides the motivation of determining available balance on different potential dates in order to determine a settlement data according to balances in order to avoid late fees. Applicant has not identified the hindsight reasoning for the argument presented and has not explained how the reason to avoid late fees is hindsight. The rejection is maintained.
In the remarks applicant argues the remaining references fail to cure the deficit of the prior art references applied in the independent claims. Applicant’s argument is moot, see response above. The rejection is maintained.
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 Claims 1-9:
STEP 1. Per Step 1 of the two-step analysis, the claims are determined to include a system, as in independent Claim 1 and the dependent claims. Such systems fall under the statutory category of "machine." Therefore, the claims are directed to a statutory eligibility category.
STEP 2A Prong 1. The claimed invention is directed to an abstract idea without significantly more. System claim 1 recites a functional process 1) receive payment message 2) establishing date transaction settlement must settle (3) determine a plurality of settlement dates 4) inputting data into an algorithm 5) generate a score [intended use not positively recited] 6) retrieving data 7) retrain algorithm (8) receive request (9) input data with request to retrain algorithm (10) determining plurality of associated scores (11) initiate transaction completion.
The specification discloses para 0004 that existing payment routing of payments in payment systems are generally according to predetermined setting of the merchant which can lead to suboptimal payment processing/failed transactions with a solution that includes payment routing according to likelihood of settlement, where transaction data is gathered, analyzed, scored and the results are outputted based on payment transaction metadata (para 0007) where the data provided for the analysis includes feedback data for payment transactions including attempted payment processing completed which is used to retrain the algorithm. (0010).
The specification discloses the “retraining” by using different data sets (¶ 0010-0011, para 0144, para 0147-0151, para 0156-0159) and applying regression/clustering analysis and techniques to group factors/variables for generating a score (para 0146- e.g. performing mathematical calculations and processes). Where the data may include transaction payment feedback data (i.e. date, payment completed, payment rails used, particular days), account balance data. Where the operation of the model is to use the data inputted to achieve an expected result. The way the model works is the inputs are applied in order to calculate scores where the retraining is merely data dependent using known generic mathematical techniques. The limitations rely on generic machine learning technology in order ty carryout the claimed methods for retraining the score algorithm using account balance data for generating a score. The focus of the claim limitations is not the technical process of retraining an algorithm but rather the data acted upon for analysis in generating the scaled score. Accordingly, the claimed limitations which under its broadest reasonable interpretation, covers performance of commercial/sales activity and mathematical concepts. 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 processors and/or transceivers, a merchant or payer device.
The claimed generically programmed processor and/or transceivers applied to perform the operations of “receive…message”, “input…data”, “retrieve…account balances…”, “receive …request” and “input data…” which according to MPEP 2106.05(d) II (see also MPEP 2106.05(g)) are directed toward extra solution activity. The courts have recognized the following computer functions are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014)
Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93
These limitations are recited at a high level of generality without details of technical implementation and thus are insignificant extra solution activity.
The claimed processors and/or transceivers applied to perform the functions “establishing a date by which…payment …just settle”, “determine …a plurality of potential settlement dates”, applied for the intended use of “generate a plurality of …scores”, “retrain…algorithm using the …account balances”, “determine …plurality of…scores” and “initiate completion of …transaction”. These limitations are recited at a high-level of generality such that it amounts to no more than applying the exception using generic processors and/or transceivers for the purpose of analyzing financial transaction data to mitigate settlement risk. The claimed functions are result oriented amounting to no more than mere instructions to perform the abstract idea. Taking the claim elements separately, the operation performed by the system at each step of the process is purely in terms of results desired and devoid of implementation of technical details. Although the claim limitations recite the operation “retrain”, the claim limitations are silent with respect to any technical implementation, instead only focusing on the data used in the model. The claim limitations do not delineate steps through which the model is retrained that could be construed as being directed toward indications of patent eligible subject matter such as improvement to machine learning or other technology or solving a problem rooted in technology, or the use of technology in a manner that imposes meaningful limits upon the judicial exception. Technology is not integral to the process as the claimed subject matter is so high level the claim limitations do no more than apply established methods of machine learning to specific transaction data merely invoking the model as a tool to analyze data in order to use feedback data for use in a model. 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 claim limitations when considered individually fail to provide any indications of patent eligible subject matter, according to MPEP guidance (see MPEP 2106.05 (a)-(c), (e )-(h).
(i) an improvement to the functioning of a computer;
(ii) an improvement to another technology or technical field;
(iii) an application of the abstract idea with, or by use of, a
particular machine;
(iv) a transformation or reduction of a particular article to a
different state or thing; or
(v) other meaningful limitations beyond generally linking the
use of the abstract idea to a particular technological environment.
When the claims are taken as a whole, as an ordered combination, the combination of limitations 1-3 and 4-5 are directed toward receiving and determining settlement dates for payment used to generate a risk score performed by a processor and/or transceiver for a risk of payment analysis process. The combination of limitations 6-7 are directed toward applying the processor and/or transceivers for use in retrieving account balance data and retraining the algorithm using the retrieved data by any known means. The combination of limitations 1-7 and 8-10 is directed toward receiving the transaction request of limitations 1-7 that is inputted for analysis and for determining a plurality of scores for possible settlement dates and initiating the completion of the transaction. The combination of limitations merely apply the processor and/or transceivers to receive and analyze data to generate a score for risk mitigation and retrain/update a model for analyzing account balance data using received request data, applied to determine likelihood scores exceeding thresholds and initiating transaction completion. The combinations of parts is not directed toward any technical process or technological technique or technological solution to a problem rooted in technology.
In addition, when the claims are taken as a whole, as an ordered combination, the combination of steps not integrate the judicial exception into a practical application as the claim process fails to impose meaningful limits upon the abstract idea. This is because the claimed subject matter fails to provide additional elements or combination or elements to apply or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The functions recited in the claims recite the concept of gathering data that is inputted into a model for analysis and then retraining a model using feedback data without claiming any processes directed toward claiming the model itself. The claim limitations and specification are silent with respect to a specific technology for performing the “retraining” of the model, instead merely focuses on the data received and acted upon. The “training” of the model as claimed, similar to Recentive Analytics, Inc v Fox Corp, simply uses machine learning to perform a task. The court found that machine learning is now viewed as a common tool rather than a technological breakthrough. The current specification fails to clearly describe a technical problem or explain how the claimed invention improves the “functioning” of the technology, rather than the outcome of applying the technology for analysis. The “training”, where the specification teaches the learning model trained using training data, makes clear that any suitable machine learning technique can be applied which allows data to be inputted and changed based on changes in data. According to Recentive “The requirements that the machine learning model be “trained” or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement… Accordingly, the examiner maintains the claimed ML limitations do not transform the claimed subject matter into patent eligibility.
The integration of elements do not improve upon technology or improve upon computer functionality or capability in how models carry out one of their basic functions. The integration of elements do not provide a process that allows the claimed model 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 model functionality in the related arts. The steps are still a combination made to apply transaction data received, inputted and retrieved that is applied to retrain a model using feedback data, using any suitable retraining technique. Thus the limitations fail to provide any of the determined indications of patent eligibility set forth in the MPEP 2106. The additional steps only add to those abstract ideas using generic functions, and the claims do not show improved ways of, for example, an particular technical function for performing the abstract idea that imposes meaningful limits upon the abstract idea. Moreover, Examiner was not able to identify any specific technological processes that goes beyond merely confining the abstract idea in a particular technological environment, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
STEP 2B; The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to concepts of the abstract idea into a practical application. The additional elements recited in the claim beyond the abstract idea include a system comprising one or more processors and/or transceivers programmed to perform the functions of receiving data, inputting data, retrieving data and retraining a model----are some of the most basic functions of system processors. Taking the claim elements separately, the function performed by the processor at each step of the process is purely conventional. When the claims are taken as a whole, as an ordered combination, the combination of steps does not add “significantly more” by virtue of considering the steps as a whole, as an ordered combination. All of these processor functions are generic, routine, conventional computer activities that are performed only for their conventional uses. This is because the machine learning limitation “retraining” is no more than a broad functionally described outcome based on data. Absent a possible narrower construction of the terms “receiving”, “inputting”, “retrieving”, “retraining”, “determining” and “initiating” ... are functions can be achieved by any general purpose system processor without special programming. None of these activities are used in some unconventional manner nor do any produce some unexpected result. In short, each step does no more than require a generic computer to perform generic computer functions.
As to the data operated upon, "even if a process of collecting and analyzing information is 'limited to particular content' or a particular 'source,' that limitation does not make the collection and analysis other than abstract." SAP America, Inc. v. Invest Pic LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018). Considered as an ordered combination, the computer components of Applicant’s claimed functions add nothing that is not already present when the steps are considered separately. The sequence of data reception-analysis modification-transmission is equally generic and conventional. See Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014) (sequence of receiving, selecting, offering for exchange, display, allowing access, and receiving payment recited as an abstraction), Inventor Holdings, LLC v. Bed Bath & Beyond, Inc., 876 F.3d 1372, 1378 (Fed. Cir. 2017) (sequence of data retrieval, analysis, modification, generation, display, and transmission), Two-Way Media Ltd. v. Comcast Cable Communications, LLC, 874 F.3d 1329, 1339 (Fed. Cir. 2017) (sequence of processing, routing, controlling, and monitoring). The ordering of the steps is therefore ordinary and conventional. The analysis concludes that the claims do not provide an inventive concept because the additional elements recited in the claims do not provide significantly more than the recited judicial exception.
According to 2106.05 well-understood and routine processes to perform the abstract idea is not sufficient to transform the claim into patent eligibility. As evidence the examiner provides:
[0028] Turning briefly to Figure 2, generally the computing device 102 may comprise tablet computers, laptop computers, desktop computers, workstation computers, smart phones, smart watches, and the like. Also, or in addition, the computing device 102 may include a plurality of copiers, printers, routers, switches, servers, and any other device that can connect to an internal or external network, and/or communication network. For example, the computing device 102 may also include a plurality of proxy servers, web servers, communications servers, routers, load balancers, and/or firewall servers, as are commonly known. Each computing device 102 may respectively include a processing element 200 and a memory element 204. Each computing device 102 may also respectively include circuitry capable of wired and/or wireless communication with the card issuer 104, merchant 106, account data storage device 108, databases 110, and/or financial institution 112, including, for example, transceiver element 202. Further, the computing device 102 may include software configured with instructions for performing and/or enabling performance of at least some of the steps set forth herein. In an embodiment, the software comprises programs stored on computer-readable media of memory elements 204.
[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 “retrain” limitation the specification lacks technical disclosure. The specification discloses the “retraining” by using different data sets (¶ 0010-0011, para 0144, para 0147-0151, para 0156-0159) and applying regression/clustering analysis and techniques to group factors/variables for generating a score (para 0146- e.g. performing mathematical calculations and processes). Where the data may include transaction payment feedback data (i.e. date, payment completed, payment rails used, particular days), account balance data. Where the operation of the model is to use the data inputted to achieve an expected result.
With respect to the “training” of the model as claimed, the claimed subject matter similar to Recentive Analytics, Inc v Fox Corp, simply uses machine learning to perform a task. The court found that machine learning is now viewed as a common tool rather than a technological breakthrough. The current specification fails to clearly describe a technical problem or explain how the claimed invention improves the “functioning” of the technology, rather than the outcome of applying the technology for analysis. Furthermore, similar to Recentive, the “training”, where the specification teaches the learning model trained using training data, makes clear that any suitable machine learning technique can be applied which allows data to be inputted and changed based on changes in data. According to Recentive “The requirements that the machine learning model be “trained” or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement… See, e.g., Opposition Br. 9 (“[U]sing a machine learning technique[] . . . necessarily includes [an] iterative[] training step . . . .” (internal quotation marks and citation omitted)); Transcript at 26:21–24 (“[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input”).”. Accordingly, the examiner maintains that similar to Recentive the claimed ML limitations do not transform the claimed subject matter into patent eligibility.
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 2-9 these dependent claim have also been reviewed with the same analysis as independent claim 1. Dependent claim(s) 2 is directed toward applying API technology to receive data and designate data for analysis- well understood application of technology for data transmission and input. Dependent claim(s) 3 and 4 are directed toward applying mathematical techniques by the scoring algorithm – mathematical concepts. Dependent claim 5 is directed toward data content used in the analysis-non-functional descriptive subject matter. Dependent claim(s) 6 and 7 are directed toward retraining an algorithm using mathematical techniques without details as to technical implementation and specific data- mathematical processes. Dependent claim(s) 8 is directed toward determine account balances and analyzing transaction data to project account balances corresponding to potential dates and analyze transactions to determine factors impacting account balances on a plurality of corresponding dates. Dependent claim 9 is directed toward financial factors used in the analysis- common business practice.
The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 1. Where all claims are directed to the same abstract idea, “addressing each claim of the asserted patents [is] unnecessary.” Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat 7 Ass ’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims 2-9 are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter.
In reference to Claims 10-18:
STEP 1. Per Step 1 of the two-step analysis, the claims are determined to include a method, as in independent Claim 10 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 10 corresponds to the functions of system claim 1. Therefore, claim 10 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 10 corresponds to the functions of system claim 1. Therefore, claim 10 has been analyzed and rejected as failing to provide limitations that are indicative of integration into a practical application, as previously discussed with respect to claim 1.
STEP 2B; The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to concepts of the abstract idea into a practical application. The additional elements beyond the abstract idea include a one or more transceivers and/or processors to perform the operations of claim 1–is purely functional and generic. Nearly every computer element application for implementing a method will include a “processor” capable of performing the basic computer functions -of “receiving message, inputting data, retrieving data, retraining model” - As a result, none of the hardware recited by the method claim offers a meaningful limitation beyond generally linking the use of the method to a particular technological environment, that is, implementation via one or more transceivers and/or processors.
Method claim 10 steps corresponds to system functions claim 1. Therefore, claim 10 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:
[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.
With respect to the “retrain” limitation the specification lacks technical disclosure. The specification discloses the “retraining” by using different data sets (¶ 0010-0011, para 0144, para 0147-0151, para 0156-0159) and applying regression/clustering analysis and techniques to group factors/variables for generating a score (para 0146- e.g. performing mathematical calculations and processes). Where the data may include transaction payment feedback data (i.e. date, payment completed, payment rails used, particular days), account balance data. Where the operation of the model is to use the data inputted to achieve an expected result. With respect to the “training” of the model as claimed, the claimed subject matter similar to Recentive Analytics, Inc v Fox Corp, simply uses machine learning to perform a task. The court found that machine learning is now viewed as a common tool rather than a technological breakthrough. The current specification fails to clearly describe a technical problem or explain how the claimed invention improves the “functioning” of the technology, rather than the outcome of applying the technology for analysis. Furthermore, similar to Recentive, the “training”, where the specification teaches the learning model trained using training data, makes clear that any suitable machine learning technique can be applied which allows data to be inputted and changed based on changes in data. According to Recentive “The requirements that the machine learning model be “trained” or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement… See, e.g., Opposition Br. 9 (“[U]sing a machine learning technique[] . . . necessarily includes [an] iterative[] training step . . . .” (internal quotation marks and citation omitted)); Transcript at 26:21–24 (“[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input”).”. Accordingly, the examiner maintains that similar to Recentive the claimed ML limitations do not transform the claimed subject matter into patent eligibility.
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 11-18 these dependent claim have also been reviewed with the same analysis as independent claim 10. The steps of method claim 11 corresponds to elements of system claim 2. Therefore, claim 2 has been analyzed and rejected as previously discussed with respect to claim 11. The steps of method claim(s) 12-13 and 15-16 corresponds to elements of system claim(s) 3-4 and 6-7. Therefore, claim(s) 12-13 and 15 -16 have been analyzed and rejected as previously discussed with respect to claim(s) 3-4 and 6-7. The steps of method claim 14 corresponds to elements of system claim 5. Therefore, claim 14 has been analyzed and rejected as previously discussed with respect to claim 5. The steps of method claim 17 corresponds to elements of system claim 8. Therefore, claim 17 has been analyzed and rejected as previously discussed with respect to claim 8. The steps of method claim 18 corresponds to elements of system claim 9. Therefore, claim 18 has been analyzed and rejected as previously discussed with respect to claim 9.
The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 10. Where all claims are directed to the same abstract idea, “addressing each claim of the asserted patents [is] unnecessary.” Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat 7 Ass ’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims 11-18 are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter.
In reference to Claims 19-20:
STEP 1. Per Step 1 of the two-step analysis, the claims are determined to include a non-transitory computer-readable storage media, as in independent Claim 16 and the dependent claims. Such mediums fall under the statutory category of "manufacture." Therefore, the claims are directed to a statutory eligibility category.
STEP 2A Prong 1. The instructions of medium claim 19 corresponds to the functions of system claim 1. Therefore, claim 19 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: The instructions of medium claim 19 corresponds to the functions of system claim 1. Therefore, claim 19 has been analyzed and rejected as failing to provide limitations that are indicative of integration into a practical application, as previously discussed with respect to claim 1.
STEP 2B; The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to concepts of the abstract idea into a practical application. The additional elements beyond the abstract idea include a non-transitory computer-readable storage media having compute executable instructions when executed by at least one or more processor the executable instructions causing the processor to perform the operations of claim 19–is purely functional and generic. Nearly every non-transitory computer readable media will include instructions executed by one or more processors for implementing the instructions corresponding to the functions of system claim 19 -of “receiving message, inputting data, retrieving data and retraining models - As a result, none of the computer software and hardware recited by the media claim offers a meaningful limitation beyond generally linking the use of the method to a particular technological environment, that is, implementation via one or more transceivers and/or processors.
The instructions of medium claim 19 corresponds to the functions of system claim 1. Therefore, claim 16 has been analyzed and rejected as failing to provide additional elements that amount to an inventive concept –i.e. significantly more than the recited judicial exception. Furthermore, as previously discussed with respect to claim 1, the limitations when considered individually, as a combination of parts or as a whole fail to provide any indication that the elements recited are unconventional or otherwise more than what is well understood, conventional, routine activity in the field.
According to 2106.05 well-understood and routine processes to perform the abstract idea is not sufficient to transform the claim into patent eligibility. As evidence the examiner provides:
[0036] The memory element 204 may include electronic hardware data storage components such as read-only memory (ROM), programmable ROM, erasable programmable ROM, random access memory (RAM) such as static RAM (SRAM) or dynamic RAM (DRAM), cache memory, hard disks, floppy disks, optical disks, flash memory, thumb drives, universal serial bus (USB) drives, or the like, or combinations thereof. In some embodiments, the memory element 204 may be embedded in, or packaged in the same package as, the processing element 200. The memory element 204 may include, or may constitute, a "computer-readable medium." The memory element 204 may store the instructions, code, code segments, software, firmware, programs, applications, apps, services, daemons, or the like that are executed by the processing element 200. In an embodiment, the memory element 204 respectively stores software applications. The memory element 204 may also store settings, data, documents, sound files, photographs, movies, images, databases, and the like.
With respect to the “retrain” limitation the specification lacks technical disclosure. The specification discloses the “retraining” by using different data sets (¶ 0010-0011, para 0144, para 0147-0151, para 0156-0159) and applying regression/clustering analysis and techniques to group factors/variables for generating a score (para 0146- e.g. performing mathematical calculations and processes). Where the data may include transaction payment feedback data (i.e. date, payment completed, payment rails used, particular days), account balance data. Where the operation of the model is to use the data inputted to achieve an expected result. With respect to the “training” of the model as claimed, the claimed subject matter similar to Recentive Analytics, Inc v Fox Corp, simply uses machine learning to perform a task. The court found that machine learning is now viewed as a common tool rather than a technological breakthrough. The current specification fails to clearly describe a technical problem or explain how the claimed invention improves the “functioning” of the technology, rather than the outcome of applying the technology for analysis. Furthermore, similar to Recentive, the “training”, where the specification teaches the learning model trained using training data, makes clear that any suitable machine learning technique can be applied which allows data to be inputted and changed based on changes in data. According to Recentive “The requirements that the machine learning model be “trained” or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement… See, e.g., Opposition Br. 9 (“[U]sing a machine learning technique[] . . . necessarily includes [an] iterative[] training step . . . .” (internal quotation marks and citation omitted)); Transcript at 26:21–24 (“[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input”).”. Accordingly, the examiner maintains that similar to Recentive the claimed ML limitations do not transform the claimed subject matter into patent eligibility.
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 claim 20, this dependent claim has also been reviewed with the same analysis as independent claim 19. Dependent claim 20 is directed toward is directed toward applying API technology to receive data and designate data for analysis- well understood application of technology for data transmission and input.
The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 19. 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 claim(s) 20 is 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, 5-8; Claims 10, 12, 14-17 and Claim(s) 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pub No. 2023/0005055 A1 by Chen et al. (Chen) , and further in view of US Pub No. 2007/0162387 A1 by Cateline et al (Cateline)
In reference to Claim 1:
Chen teaches:
(currently Amended) A system for payment routing according to a likelihood of settlement ((Chen) in at least Abstract), the system comprising
one or more processors and/or transceivers individually or collectively programmed ((Chen) in at least para 0080), to:
receive, from a merchant device or a payer device, 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 0016, para 0018 wherein the prior art teaches an entity such as a company or other organization may request credit underwriting, para 0019 wherein the prior art teaches the user may request transaction processing provided to the user (merchants [e.g. seller, payment receiver, ect), para 0027 wherein the prior art teaches receiving transaction data for payment request from the merchant acquirer generates a total for the transaction request which the user can pay where the request requires user name and financial data) and establishing a date by which the putative payment transaction must settle ((Chen) in at least Fig. 3B; para 0021-0022 wherein the prior art teaches predicting balance at a specific time or date where the dates are based on end of billing cycle or due date, para 0025 wherein the prior art teaches predicting available balance for entity for billing cycle or repayment date for paying off credit account)….
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 respectively corresponding to the plurality of potential settlement dates, each of the plurality of scaled scores representing the likelihood of settlement of the putative payment transaction from an account of the accountholder on the corresponding one of the plurality of potential settlement dates ((Chen) in at least para 0014, para 0021-0022, para 0034, para 0047-0049);
retrieve, from a financial institution corresponding to the account, actual account balances for the account on the plurality of corresponding potential settlement dates ((Chen) in at least Abstract wherein the prior art teaches retrievable balances for use in forecasting by trained model, para 0016, para 0018, para 0034, para 0074); and
retrain the scaled score algorithm using the actual account balances and using feedback data identifying which of the plurality of potential settlement dates was used to attempt payment processing of the putative payment transaction and whether the attempted payment processing was successful ((Chen) in at least para 0053, para 0069-0070, para 0072-0073 wherein the prior art teaches retraining the model using provided feedback data for decision making to does not reach sufficient levels of accuracy so the model may be trained to provide predictive output for anticipated global balances of an entity at a certain future date based on input features)
receive a transaction request for a corresponding transaction ((Chen) in at least Abstract wherein the prior art teaches retrievable balances for use in forecasting by trained model, para 0016, para 0018, para 0034, para 0074);
input data associated with the transaction request to the retrained scaled score algorithm to determine a plurality of associated scaled scores for a plurality of possible settlement dates ((Chen) in at least para 0056-0059 wherein the prior art teaches input layers using ML model training features and decision tree nodes and neural network receives input values, para 0059 wherein the prior art teaches training model using training data, where nodes are adjusted (trained) and produces output values categorizing risk and when output is incorrect the nodes may be adjusted to improve results; para 0061 wherein the prior art teaches entity balance may increase as new funds added or new round of funding, revenue, investments or expenses decrease over time; para 0063 wherein the prior art teaches using ML model receiving additional global balance data);
determine at least one of the plurality of associated scaled scores indicates a likelihood of settlement that exceeds a threshold on a corresponding one of the … settlement dates ((Chen) in at least para 0023 wherein the prior art teaches utilizing affordability ML model setting limit to determine score meets/exceeds threshold and adjusting available balance to an allowed credit limit creating less volatility at end of billing cycle payments at what date based on ne data for entity including updated cash balance; para 0070); and
Chen does not explicitly teach:
determine, based on the date by which the putative payment transaction must settle, a plurality of potential settlement dates for the putative payment transaction, the plurality of potential settlement dates being on or before the date by which the putative payment transaction must settle;
determine at least one of the plurality of associated scaled scores indicates a likelihood of settlement … on a corresponding one of the plurality of possible settlement dates
initiate completion of the corresponding transaction on the corresponding one of the plurality of possible settlement dates.
Cateline teaches:
determine, based on the date by which the putative payment transaction must settle, a plurality of potential settlement dates for the putative payment transaction, the plurality of potential settlement dates being on or before the date by which the putative payment transaction must settle ((Cateline) in at least para 0052, 0057-0060 ;
determine at least one of the plurality of associated scaled scores indicates a likelihood of settlement … on a corresponding one of the plurality of possible settlement dates ((Cateline) in at least para 0062, para 0090-0092, para 0139, para 0141)
initiate completion of the corresponding transaction on the corresponding one of the plurality of possible settlement dates. ((Cateline) in at least FIG. 2; para 0057, para 0066, para 0071, para 0097, para 0134)
Both Chen and Cateline are directed toward analyzing account balance data in order to predict balance amounts for future dates. Cateline teaches the motivation of providing in the output of the predicted balance different with the selection of payment dates prior to the due date in order to avoid late fees. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the analysis for determining future available balances for payment of future payments of Chen to include determining in the analysis a plurality of payment dates for selection as taught by Cateline since Cateline teaches the motivation of providing in the output of the predicted balance different with the selection of payment dates prior to the due date in order to avoid late fees.
In reference to Claim 3:
The combination of Chen and Cateline 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 FIG. 2A; para 0024, para 0053-0054)
In reference to Claim 5:
The combination of Chen and Cateline 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 plurality of corresponding potential settlement dates includes a last-occurring date of the plurality of corresponding potential settlement dates, and the actual account balances are retrieved on or after the last-occurring date ((Chen) in at least para 0025 wherein the prior art teaches account data for closing for payment can be inputted daily and account data can be last known available balance for use in forecasting balance for available balance, para 0026, para 0028 wherein the prior art teaches bank data can be updated in real-time, hourly, daily, ect…, para 0064 wherein the prior art teaches up-to-date reading since data closer to settlement is more accurate for forecast of balance, para 0064) .
In reference to Claim 6:
The combination of Chen and Cateline discloses the limitations of independent claim 1. Chen further discloses the limitations of dependent claim 6
(Original) The system of claim 1 (see rejection of claim 1 above), the one or more processors and/or transceivers being further individually or collectively
programmed to retrain the scaled score algorithm using regression on additional historical data reflecting credits to and debits from the account to determine periodicity. ((Chen) in at least para 0014, para 0021-0022, para 0024, para 0034, para 0047-0049, para 0053-0054, para 0069-0070, para 0072-0073)
In reference to Claim 7:
The combination of Chen and Cateline discloses the limitations of dependent claim 6. Chen further discloses the limitations of dependent claim 7
(Original) The system of claim 6 (see rejection of claim 6 above),
wherein the retraining using regression includes weighting the scaled score algorithm to emphasize the influence of the credits and the debits based on periodicity and/or dollar amount. ((Chen) in at least para 0014, para 0021-0022, para 0024, para 0034, para 0047-0049, para 0053-0055, para 0058-0059, para 0069-0070, para 0072-0073; claim 6)
In reference to Claim 8:
The combination of Chen and Cateline discloses the limitations of independent claim 1. Chen further discloses the limitations of dependent claim 8
(Original) The system of claim 1 (see rejection of claim 1 above),
wherein the scaled score algorithm includes –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 credits and debits of the historical transaction data for the account to project an account balance in the account on the corresponding one of the plurality of potential settlement dates, 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 for the account on each of the plurality of corresponding potential settlement dates ((Chen) in at least para 0014, para 0020-0022-0023, para 0026, para 0034, para 0047-0049, para 0077)
In reference to Claim 10:
The combination of Chen and Cateline discloses the limitations of independent claim 10.
The steps of method claim 10 correspond to the operations of system claim 1. Therefore, claim 10 has been analyzed and rejected as previously discussed with respect to claim 1.
In reference to Claim 12:
The combination of Chen and Cateline discloses the limitations of dependent claim 11. Chen further discloses the limitations of dependent claim 12
The steps of method claim 12 correspond to the operations of system claim 3. Therefore, claim 12 has been analyzed and rejected as previously discussed with respect to claim 3
In reference to Claim 14:
The combination of Chen and Cateline discloses the limitations of dependent claim 11. Chen further discloses the limitations of dependent claim 14
The steps of method claim 14 correspond to the operations of system claim 5. Therefore, claim 14 has been analyzed and rejected as previously discussed with respect to claim 5.
In reference to Claim 15:
The combination of Chen and Cateline discloses the limitations of dependent claim 11. Chen further discloses the limitations of dependent claim 15
The steps of method claim 15 correspond to the operations of system claim 6. Therefore, claim 15 has been analyzed and rejected as previously discussed with respect to claim 6
In reference to Claim 16:
The combination of Chen and Cateline 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 7. Therefore, claim 16 has been analyzed and rejected as previously discussed with respect to claim 7
In reference to Claim 17:
The combination of Chen and Cateline discloses the limitations of dependent claim 11. Chen further discloses the limitations of dependent claim 17
The steps of method claim 17 correspond to the operations of system claim 8. Therefore, claim 17 has been analyzed and rejected as previously discussed with respect to claim 8
In reference to Claim 19:
The combination of Chen and Cateline discloses the limitations of independent claim 19.
The instructions of medium claim 19 correspond to the operations of system claim 1. Therefore, claim 19 has been analyzed and rejected as previously discussed with respect to claim 1.
In reference to Claim 20:
The combination of Chen and Cateline discloses the limitations of independent claim 19. Chen further discloses the limitations of dependent claim 20
The instructions of medium claim 20 correspond to the operations of system claim 1. Therefore, claim 20 has been analyzed and rejected as previously discussed with respect to claim 1.
Claim(s) 2 of claim 1 above, Claim(s) 11 of claim 10 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 Pub No. 2007/0162387 A1 by Cateline et al (Cateline) and further in view of US Pub No. 2019/0272547 A1 by Coman et al. (Coman)
In reference to Claim 2:
The combination of Chen and Cateline 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), the one or more processors and/or transceivers being further individually or collectively programmed to
maintain an.. interface …configured to automatically receive the actual account balances and designate the actual account balances for the retraining. ((Chen) in at least Abstract; para 0033-0034, para 0040, para 0059-0060, para 0066, para 0072-0073)
Chen does not explicitly teach:
maintain an application programming interface (API)
Coman teaches:
maintain an application programming interface (API) configured to automatically receive the actual account balances and designate the actual account balances for the retraining ((Coman) in at least para 0073, para 0075, para 0083-0084, para 0087-0088)
According to KSR, common sense rationale, simple substitution of one known element for another to obtain predictable results is obvious. The prior art Chen contain an interface which differed from the claimed interface by the substitution of one known interface for another. The prior art Coman provides evidence that the substituted API interface (API) and their functions where known in the art. Both the interface of Chen and the API interface of Coman are essentially performing the same function. Accordingly, based on the teaching of the prior art references and the functions being performed by the interface of both the generic interface of Chan and claimed API, one of ordinary skill in the art could have substituted one known element for another, and the results of the substitution would have been predictable
Both Chen and Coman are directed toward applying machine learning models to analyze account balance data in order to analyze customer life circumstances and financial behavior. Coman teaches the motivation of applying API as technology as a tool in response to customer behavior and to use predictive analytics to create events that represent instructions to an API to lookup customer account balances to provide to the model for analysis where the model is updated based on newly received customer data. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the interface for obtaining account data of Chen to include API interface as taught by Coman since Coman teaches the motivation of applying API as technology as a tool in response to customer behavior and to use predictive analytics to create events that represent instructions to an API to lookup customer account balances to provide to the model for analysis where the model is updated based on newly received customer data.
In reference to Claim 11:
The combination of Chen and Cateline discloses the limitations of independent claim 10. Chen further discloses the limitations of dependent claim 11
The steps of method claim 11 correspond to the operations of system claim 2. Therefore, claim 11 has been analyzed and rejected as previously discussed with respect to claim 2.
Claim(s) 4 of claim 3 above, Claim(s) 13 of claim 12 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 Pub No. 2007/0162387 A1 by Cateline et al. (Cateline) and further in view of US Pub No. US Pub No. 2019/0259095 A1 by Templeton
Examiner Note: The term applied for the limitation “confusion matrix” can also be referred to as “classification matrix”, “accuracy matrix”, “true positive”, “false positive”, “true negative”, “false negative”
In reference to Claim 4:
The combination of Chen and Cateline 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), wherein the retraining includes
Chen does not explicitly teach:
incorporating the actual account balances into a confusion matrix.
Templeton teaches:
incorporating the actual account balances into a confusion matrix. ((Templeton) in at least para 0012, para 0018 wherein the prior art teaches model obtaining initial balance; para 0043 wherein the prior art teaches model using information of current banking balance via data feed, para 0092, para 0094 wherein the prior art teaches model observing cashflows such as payments and salary)
Both Chen and Templeton teach trained models performing forecast analysis on predicting future available balance on a future data. Templeton teaches the motivation of applying accuracy modeling of data inputted for analysis in order to evaluate error rates and to identifying the model over estimating likely future spending reducing volatility probabilities. . It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the analysis techniques for predictive analysis of Chen to include applying accuracy/confusion matrix analysis as taught by Templeton since Templeton teaches the motivation of applying accuracy modeling of data inputted for analysis in order to evaluate error rates and to identifying the model over estimating likely future spending reducing volatility probabilities..
In reference to Claim 13:
The combination of Chen and Cateline discloses the limitations of dependent claim 12. Chen further discloses the limitations of dependent claim 13
The steps of method claim 13 correspond to the operations of system claim 4. Therefore, claim 13 has been analyzed and rejected as previously discussed with respect to claim 4.
Claim(s) 9 of claim 8 above, Claim(s) 18 of claim 17 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 Pub No. 2007/0162387 A1 by Cateline et al. (Cateline) and further in view of US Pub No. US Pub No. 20210090161-A1 by Chen et al. (Chen161)
In reference to Claim 9:
The combination of Chen and Cateline discloses the limitations of dependent claim 8. Chen further discloses the limitations of dependent claim 9
(Original) The system of claim 8 (see rejection of claim 8 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
Chen161 teaches:
the one or more factors including a non-sufficient funds overdraft protection policy of a financial institution corresponding to the account ((Chen161) in at least para 0004, para 0015-0016, para 0021)
Both Chen and Chen161 are directed toward applying a predictive learning model in order to predict account balances based on analysis of account data and balances. Chen161 teaches the motivation of applying in the factors of account balances overdraft policies for insufficient funds in order to factor in the risk analysis the number of transactions and overdraft amounts that the financial institution may authorize for payment or tolerate in order to meet and satisfy customer payment needs and reduce cost of returned unpaid transactions in order to protect financial institution for having to charge-off negative balances after an account has been overdrawn for extended periods of time. 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 predictive analysis of account balances for determining potential factors for settlement of transactions on account balances of Chen to include the overdraft policies as taught by Chen161 since Chen161 teaches the motivation of applying in the factors of account balances overdraft policies for insufficient funds in order to factor in the risk analysis the number of transactions and overdraft amounts that the financial institution may authorize for payment or tolerate in order to meet and satisfy customer payment needs and reduce cost of returned unpaid transactions in order to protect financial institution for having to charge-off negative balances after an account has been overdrawn for extended periods of time. .
In reference to Claim 18:
The combination of Chen and Cateline 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 9. Therefore, claim 18 has been analyzed and rejected as previously discussed with respect to claim 9
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US Pub No. 2013/0226753 A1 by Haggerty et al.
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/MARY M GREGG/Examiner, Art Unit 3695
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