CTFR 18/472,043 CTFR 84463 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 February 18, 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. 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 Information Disclosure Statement The IDS submitted December 09/2025 has been reviewed and considered. Response to Amendment/Arguments Claim Rejections - 35 USC § 101 07-37 AIA Applicant's arguments filed 08/28/2025 have been fully considered but they are not persuasive. In the remarks applicant argues that the amended claims are patent eligible under 2A and 2B. Applicant discusses the guidance of MPEP 2106.04 subsection II, MPEP 2106.07(a), MPEP 2106.07(a)(1); USPTO 2014 101 guidance, Mayo, Enfish and Thales Visionix v US for determining patent eligible subject matter. The applicant discuses the abstract categories of mental concepts, mathematical concepts and methods of organizing human activity set forth in the guidance provided. In the remarks applicant points to example 47 and the Desjardins decision, arguing that the claim limitations under step 2A prong 2 integrate any alleged abstract idea into a practical application. Applicant argues the “training” limitations of the claims, similar to the “training” of example 47 and further in light of Desjardins, is patent eligible. Applicant recites the amended “retrain” process of the algorithm which includes “receive …request”, “input data associated with the transaction request to the retrain …algorithm…to determine a plurality of …scores for a plurality of settlement dates”, “determine at least one of the plurality of …possible settlement dates”, “determine at least one of the plurality of associated scores indicates likelihood of settlement exceeds a threshold…”, “initiate completion of the …transaction corresponding one of the plurality of possible settlement dates”. Applicant’s argument is not persuasive. The argued limitations do not disclose a “retaining” technical process, but instead describes the receiving of data, the analysis of the data for an expected outcome according to threshold values and outputting the result. Example 47, recites training the model using any of the selected training algorithm, which was found to be patent ineligible. Claim 3 of example 47 discloses the process using the trained algorithm for solving a problem rooted in technology by detecting the existence of the problem, identifying the malicious network packet and determining the source of the attack, dropping the malicious packet and blocking from the source further malicious packets with remedies and prevents network intrusions/attacks. The specification of example 47 claim 3, describes the enhances previous detection systems by acting in real time to proactively prevent network intrusions. This is not the case of the current application. Similar to example 47 claim 3, the current limitations apply learning technology as a tool to receive and analyze data which is not patent eligible, however the claim limitations as a whole is not to improve technology or provide a solution to a problem rooted in the technology itself or caused by the technology itself as found in example 47 claim 3. Rather as a whole the current limitations as a whole are directed toward receiving and analyzing business related data to generate a probability score of likelihood of a settlement in a transaction and outputting the result. The “retraining” is not directed toward machine learning itself, but rather determining a plurality of scores for a plurality of settlement data based on additional inputted data where based on the results meeting a threshold values initiate completing the transaction. Example 47 claim 3 is not applicable. The rejection is maintained . Claim Rejections - 35 USC § 103 In the remarks applicant argues the prior art references fail to teach “potential settlement dates “on or before” the date the transaction must be settled. 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 Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 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. 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. 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. 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 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-23-aia AIA 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. 07-21-aia AIA 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 ((Chen) in at least para 0053, para 0069-0070, para 0072-0073) 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 . 07-21-aia AIA 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 . 07-21-aia AIA 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 . 07-21-aia AIA 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 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US Patent No. 8,583,548 B1 by Goldstein et al; FR-2979449-A1 by Quillian; AU-2015100852-A4 by Urry; CA-2692677-A1 by Law 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. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christine Behncke can be reached at 571-272-8103. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /MARY M GREGG/ Examiner, Art Unit 3695 /CHRISTINE M Tran/Supervisory Patent Examiner, Art Unit 3695 Application/Control Number: 18/472,043 Page 2 Art Unit: 3695 Application/Control Number: 18/472,043 Page 3 Art Unit: 3695 Application/Control Number: 18/472,043 Page 4 Art Unit: 3695 Application/Control Number: 18/472,043 Page 5 Art Unit: 3695 Application/Control Number: 18/472,043 Page 6 Art Unit: 3695 Application/Control Number: 18/472,043 Page 7 Art Unit: 3695 Application/Control Number: 18/472,043 Page 8 Art Unit: 3695 Application/Control Number: 18/472,043 Page 9 Art Unit: 3695 Application/Control Number: 18/472,043 Page 10 Art Unit: 3695 Application/Control Number: 18/472,043 Page 11 Art Unit: 3695 Application/Control Number: 18/472,043 Page 12 Art Unit: 3695 Application/Control Number: 18/472,043 Page 13 Art Unit: 3695 Application/Control Number: 18/472,043 Page 14 Art Unit: 3695 Application/Control Number: 18/472,043 Page 15 Art Unit: 3695 Application/Control Number: 18/472,043 Page 16 Art Unit: 3695 Application/Control Number: 18/472,043 Page 17 Art Unit: 3695 Application/Control Number: 18/472,043 Page 18 Art Unit: 3695 Application/Control Number: 18/472,043 Page 19 Art Unit: 3695 Application/Control Number: 18/472,043 Page 20 Art Unit: 3695 Application/Control Number: 18/472,043 Page 21 Art Unit: 3695 Application/Control Number: 18/472,043 Page 22 Art Unit: 3695 Application/Control Number: 18/472,043 Page 23 Art Unit: 3695 Application/Control Number: 18/472,043 Page 24 Art Unit: 3695 Application/Control Number: 18/472,043 Page 25 Art Unit: 3695 Application/Control Number: 18/472,043 Page 26 Art Unit: 3695 Application/Control Number: 18/472,043 Page 27 Art Unit: 3695 Application/Control Number: 18/472,043 Page 28 Art Unit: 3695 Application/Control Number: 18/472,043 Page 29 Art Unit: 3695 Application/Control Number: 18/472,043 Page 30 Art Unit: 3695 Application/Control Number: 18/472,043 Page 31 Art Unit: 3695 Application/Control Number: 18/472,043 Page 32 Art Unit: 3695 Application/Control Number: 18/472,043 Page 33 Art Unit: 3695 Application/Control Number: 18/472,043 Page 34 Art Unit: 3695 Application/Control Number: 18/472,043 Page 35 Art Unit: 3695 Application/Control Number: 18/472,043 Page 36 Art Unit: 3695 Application/Control Number: 18/472,043 Page 37 Art Unit: 3695 Application/Control Number: 18/472,043 Page 38 Art Unit: 3695
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