DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 filed on February 2, 2026, has been entered.
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 .
Acknowledgements
This Office Action is in response to Applicant’s response filed on December 1, 2025, which includes inter alia, claims and remarks.
Claims 21-40 are currently pending and have been examined.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 21-40 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventors, at the time the application was filed, had possession of the claimed invention.
Claim 1, Claim 31, and Claim 38, each, independently recite:
“inputting…transactions into a machine learning model…to:
generate a refined data set comprising data analytics from a plurality of disparate data sets.”
However, the “refined data set” was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that it comprised “data analytics” (i.e.: “generate a refined data set comprising data analytics”). Instead, the specification explains that “[m]achine learning can ingest the user’s data, draw parallels and conclusions across disparate data sets to provide refined data. The refined data can then be abstracted further by performing operations such as categorizing, coding, transforming, interpreting, summarizing, and calculating. Further, the abstracted data can be used in the future for decision-making.” (Page 18). Next, the specification states that “[i]n an exemplary embodiment, data abstraction can be done by reviewing the user’s payment transaction information and abstracting (i.e., extracting) key data, which can be used further. As a next step, analytics can then be performed on the abstracted data to determine payment-related services to improve the user’s experience.” (Page 18). As such, the specification describes performing “analytics” on the data after it has refined and abstracted. Furthermore, the specification does not describe a scenario where the machine learning model generates “a refined set comprising data analytics.” While the specification does state “[i]n another embodiment, the machine learning module 211 may also analyze historical records of the registered users to suggest different data points and outcomes to provide timely credit rating/scores” (Page 18), this analysis is not described as being part of the refined data set that is generated. Therefore, the claim limitation “generate a refined data set comprising data analytics from a plurality of disparate data sets” does not have written description support.
Claim 1, Claim 31, and Claim 38, each, independently recite:
“wherein the dynamic report includes interactive user interface elements”
However, the dynamic report was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that it comprised “interactive” user interface elements. While the specification states at paragraph [060] that “data analytics can generate a dynamic report” and “[e]xamples of the reports may include charts, graphs…” the reports are not described as having interactive user interface elements. Furthermore, the remainder of the specification does not have any description of the dynamic report including interactive user interface elements. Therefore, the claim limitation “the dynamic report includes interactive user interface elements” does not have written description support. For purposes of applying the prior art, the limitation “wherein the dynamic report includes interactive user interface elements” will be interpreted as: “wherein the dynamic report includes user interface elements.”
Claims 22-30 depend on Claim 21; therefore, Claims 22-30 are rejected for the same reasons given above regarding Claim 21.
Claims 32-37 depend on Claim 31; therefore, Claims 32-37 are rejected for the same reasons given above regarding Claim 31.
Claims 39-40 depend on Claim 38; therefore, Claims 39-40 are rejected for the same reasons given above regarding Claim 38.
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 21-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 of the Subject Matter Eligibility Analysis for Products and Processes1 (“SME Analysis”):
Claims 21-40 are directed to one of the statutory categories.
Claims 21-30 are directed to a process.
Claims 31-37 are directed to an article of manufacture.
Claims 38-40 are directed to a system.
Step 2A- Prong One of the SME Analysis:
Claim 21 (representative of Claims 31 and 38) recites the following steps:
a method to monitor online transactions, the method comprising:
determining, via an intra-bank transaction of a financial institution, a plurality of transactions associated with a registered user;
generate a refined data set comprising data analytics from a plurality of disparate data sets;
calculate a total value of the plurality of transactions during a first pre-determined time period;
transmit an amount equivalent to the total value of the plurality of transactions to recipient accounts associated with service providers of the plurality of transactions based on the first pre-determined time period and/or based on a pre-determined total amount threshold;
calculate a total value of the transmitted amount during a second pre-determined time period, wherein the first pre-determined time period is a subset of the second pre-determined time period; and
deduct an amount equivalent to the total value of the transmitted amount from a payment account associated with the registered user during the second pre-determined time period; and
generating a report based on the refined data set and including the total value of the plurality of transactions during the first pre-determined time period and the total value of the transmitted amount.
These steps, under their broadest reasonable interpretation, describe or set-forth effecting payments for aggregated transactions, which amounts to a fundamental economic principle or practice (including hedging, insurance, mitigating risk) and or a commercial or legal interaction (including sales activities or behaviors; business relations). These limitations therefore fall within the “certain methods of organizing human activity” subject matter grouping of abstract ideas.
Additionally, these steps, under their broadest reasonable interpretation, describe or set-forth encompass a human manually (e.g., in their mind, or using paper and pen) effecting payments for aggregated transactions (i.e., one or more concepts performed in the human mind, such as one or more observations, evaluations, judgments, opinions), but for the recitation of generic computer components. If one or more claim limitations, under their broadest reasonable interpretation, covers performance of the limitation(s) in the mind but for the recitation of generic computer components, then it falls within the “mental processes” subject matter grouping of abstract ideas.
As such, Claim 21 recites an abstract idea.
Independent Claims 31 and 38 recite/describe nearly identical steps (and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and these claims are therefore determined to recite an abstract idea under the same analysis.
Each of the depending claims likewise recite/describe these steps (by incorporation - and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and these claims are therefore determined to recite an abstract idea under the same analysis. Any element(s) recited in a dependent claim that are not specifically identified/addressed by the Examiner under step 2A (prong two) or step 2B of this analysis shall be understood to be an additional part of the abstract idea recited by that particular claim.
As such, Claims 21-40 recite an abstract idea.
Step 2A- Prong Two of the SME Analysis:
The claims recite the additional elements/limitations of:
a computer-implemented method for training a machine learning,
a non-transitory computer readable medium, the non-transitory computer readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform;
a system, the system comprising: one or more processors; a non-transitory computer readable medium storing instructions that, when executed by the one or more processors, cause the one or more processors to perform;
via one or more processors of a processing system;
inputting, via the one or more processors, the plurality of transactions into a machine learning model, wherein the machine learning model has been trained based on a set of training data including pre-defined mappings defining relationships between one or more input parameters and one or more output parameters;
the report being dynamic; and
wherein the dynamic report includes interactive user interface elements.
The requirement to execute the claimed steps/functions using the computer, system, non-transitory computer-readable storage mediums, and one or more processors of a processing system, and including the characteristic of the report being “dynamic” is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations do not impose any meaningful limits on practicing the abstract idea and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Likewise, the requirement to execute or implement the claimed steps/functions for “training a machine learning model” is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer since the training, the machine learning model, and the training of the machine learning model, is recited at a high level of generality without any recited details of the training. These limitations do not impose any meaningful limits on practicing the abstract idea and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
The recited additional elements of the “inputting” simply appends insignificant extra-solution activity to the judicial exception, (e.g., mere pre-solution activity, such as data gathering, in conjunction with an abstract idea; mere post-solution activity in conjunction with an abstract idea). The term “extra-solution activity” is understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. The recited additional elements are deemed “extra-solution” because they are merely inputting data into software. These limitations do not impose any meaningful limits on practicing the abstract idea and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(h)).
The recited additional element of “a set of training data including pre-defined mappings defining relationships between one or more input parameters and one or more output parameters” serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. Specifically, it serves to limit the application of the abstract idea to the field of machine learning. This reasoning was demonstrated in Bilski, where it was determined that certain claim elements limiting the basic concept of hedging to commodities and energy markets (merely limiting an abstract idea to one field of use) did not make the concept patentable. This reasoning was demonstrated in Intellectual Ventures I LLC v. Capital One Bank (Fed. Cir. 2015), where the court determined “an abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer”). This limitation does not impose any meaningful limits on practicing the abstract idea and therefore does not integrate the abstract idea into a practical application (see MPEP 2106.05(g)).
The recited additional element of “wherein the dynamic report includes interactive user interface elements” serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. Specifically, it serves to limit the application of the abstract idea to the technological environment of user interfaces. This reasoning was demonstrated in Bilski, where it was determined that certain claim elements limiting the basic concept of hedging to commodities and energy markets (merely limiting an abstract idea to one field of use) did not make the concept patentable. This reasoning was demonstrated in Intellectual Ventures I LLC v. Capital One Bank (Fed. Cir. 2015), where the court determined “an abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer”). This limitation does not impose any meaningful limits on practicing the abstract idea and therefore does not integrate the abstract idea into a practical application (see MPEP 2106.05(g)).
Furthermore, although the claims recite a specific sequence of computer-implemented functions, and although the specification suggests certain functions may be advantageous for various reasons (e.g., business reasons), the ordered combination of claim elements (i.e., the claims as a whole) are not directed to an improvement to computer functionality/capabilities, an improvement to a computer-related technology or technological environment, and do not amount to a technology-based solution to a technology-based problem.
The remaining dependent claims fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims are further part of the abstract idea as identified for each respective dependent claim (i.e. they are part of the abstract idea recited in each respective claim).
Therefore, the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application.
Accordingly, the claims are directed to an abstract idea.
Step 2B of the SME Analysis:
As discussed above in “Step 2A – Prong 2”, the requirement to execute the claimed steps/functions using the computer, system, non-transitory computer-readable storage mediums, “training a machine learning model,” one or more processors, and the quality of being “dynamic” is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations therefore do not qualify as “significantly more” (see MPEP 2106.05(f)).
As discussed above in “Step 2A – Prong 2”, the recited additional element of “a set of training data including pre-defined mappings defining relationships between one or more input parameters and one or more output parameters” serves merely to generally link the use of the judicial exception to the technological environment of machine learning or field of machine learning, and the recited additional element of “wherein the dynamic report includes interactive user interface elements” serves merely to generally link the use of the judicial exception to the technological environment of user interfaces. These limitations therefore do not qualify as “significantly more” (see MPEP 2106.05(g)).
As discussed above in “Step 2A – Prong 2”, the recited additional elements of the “inputting” simply append insignificant extra-solution activity to the judicial exception, (e.g., mere pre-solution activity, such as data gathering, in conjunction with an abstract idea; mere post-solution activity in conjunction with an abstract idea). These additional elements, taken individually or in combination, additionally amount to well-understood, routine and conventional activities previously known to the industry, appended to the judicial exception. These additional elements, taken individually or in combination, are well-understood, routine and conventional to those in the field of electronic commerce. The determination that receiving data/messages over a network is well-understood, routine, and conventional is supported by Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014), and MPEP 2106.05(d)(II), which note the well-understood, routine, conventional nature of receiving data/messages over a network. As such, these limitations do not qualify as “significantly more”. (see MPEP 2106.05(d)). This conclusion is based on a factual determination.
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer, append the abstract idea with insignificant extra solution activity associated with the implementation of the judicial exception, (e.g., mere data gathering, post-solution activity), and append the abstract idea with well-understood, routine and conventional activities previously known to the industry.
The remaining dependent claims fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims are further part of the abstract idea identified for each respective dependent claim (i.e. they are part of the abstract idea identified by the Examiner to which each respective claim is directed).
Thus, no additional element, or combination of additional claims elements are sufficient to ensure the claims amount to significantly more than the abstract idea identified above.
For the reasons stated above, Claims 21-40 as whole do not amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
Claims 21-26, 28, 30-36, 38-40 are rejected under 35 U.S.C. 103 as being unpatentable over Dixon et al. (US 2013/0275245 A1)(“Dixon”) in view of Prabhu et al. (US 2022/0207521 A1)(“Prabhu”), in view of Butler et al. (US 11,922,426 B2)(“Butler”), and further in view of Cooper (US 2010/0005016 A1)(“Cooper”).
As to Claim 21, 31, and 38, Dixon discloses a computer-implemented method (“methods,” [0077]), a non-transitory computer readable medium (memory in “server computer,” [0040]), the non-transitory computer readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform a method, and a system (“payment processor 160” [0039]), the system comprising: one or more processors (“The payment processor 160 may include a server computer.” [0040]); a non-transitory computer readable medium memory in “server computer,” [0040]) storing instructions that, when executed by the one or more processors (“The payment processor 160 may include a server computer.” [0040]), cause the one or more processors to perform a method comprising:
determining, via one or more processors (“The payment processor 160 may include a server computer. A server computer is typically a powerful computer or cluster of computers.” [0040]) of an intra-bank transaction processing system of a financial institution (“an ‘acquirer’ is typically a business entity (e.g., a commercial bank) that has a business relationship with a particular merchant. An ‘issuer’ is typically a business entity (e.g., a bank), which issues a portable payment device such as a credit, debit, or stored value card to a consumer. Some entities may perform both issuer and acquirer functions.” [0035], “The acquirer 150, payment processor 160, and the issuer 170 make up a payment processing system 180.” [0039]), a plurality of transactions associated with a registered (“rider may set up the transit account” [0065]) user (consumer 120)(“In step 530, the aggregated set of access transactions are placed in storage, such as at the transit POS 240, at the transit central computer’s 270 database 305, or at both.” [0069]);
calculate a total value of the plurality of transactions during a first pre-determined time period (“The predetermined criteria can require that such aggregation occur over a time period,” [0069], “At step 570, the fare value for each fare within the aggregated set of fares is determined based on stored access transaction history and transit agency policy…This ordered set of transit transactions can then be used at step 550 of process 500 to determine the total transit fares to be assessed to each FIPPD 130 and its corresponding account.” “By way of example, if each of the rider’s Monday transit fares cost three dollars ($3.00 US), then the aggregated set for that month would equal twelve dollars ($12.00 US), that is four trips at three dollars each.” [0074], wherein the first preset time period is a day, such as Monday in this example);
calculate a total value of the transmitted amount during a second pre-determined time period (“e.g., an aggregation of all access transitions for one FIPPD 130 over a month’s period,” [0075], wherein the second preset time period is a month for example), wherein the first pre-determined time period is a subset of the second pre-determined time period (see [0074]-[0075], wherein the second preset time period is a month and the first preset time period is a day, as discussed above); and
deduct an amount equivalent to the total value of the transmitted amount from a payment account associated with the registered user during the second pre-determined time period (“Once the transit fare is sent to the payment processing system 180 it can be processed according to typical protocol for merchants 140…fare can be authorized, settled, and cleared through the payment processing system 180, the transit agency can be paid…” [0062], “Optionally, at step 580 the transaction fares can be communicated to the payment processing system 180 for collection. Based on the payment model being used by the transit system, this communication may consist of an aggregate total amount of fares that exceeds a predetermined threshold…or an aggregated set of fares…over a…period,” [0075]).
Dixon does not directly disclose
the method, non-transitory computer readable medium, and system for training a machine learning to monitor online transactions;
inputting, via the one or more processors of the intra-bank transaction processing system of a financial institution, the plurality of transactions into a machine learning model, wherein the machine learning model has been trained based on a set of training data including pre-defined mappings defining relationships between one or more input parameters and one or more output parameters to:
generate a refined data set comprising data analytics from a plurality of disparate data sets;
the first pre-determined time period based on the refined data set;
the second pre-determined time period based on the refined data set;
transmit an amount equivalent to the total value of the plurality of transactions to recipient accounts associated with service providers of the plurality of transactions based on the first pre-determined time period and/or based on a pre-determined total amount threshold; and
generate a dynamic report based on the refined data set and including the total value of the plurality of transactions during the first pre-determined time period and the total value of the transmitted amount, wherein the dynamic report includes interactive user interface elements.
Prabhu teaches
a computer-implemented method for a machine learning (“machine learning model,” [0076]) to monitor online transactions (“online transactions” [0018])(“each of the FI selection module 208 and the payment deferral module 206 may use a machine learning model to determine the corresponding portions of the payment arrangement.” [0076]-[0077]);
inputting, via one or more processors (Processor 914), the plurality of transactions (“… the wallet manager 202 may access transaction history data associated with the user account from the account database 136…the payment deferral module 206 may then determine the payment arrangement for the electronic transaction using the data obtained by the wallet manager 202.” [0065]) into a machine learning model (“The payment deferral module 206 may use another machine learning model that is configured to output a deferral time (e.g., 5 hours, 1 day, 21 days, etc.) for paying the electronic transaction. The input parameters for the machine learning model may include an amount associated with the electronic transaction, expected expenses and income within a period of time ( e.g., within a week, within a month, etc.).” [0077]), wherein the machine learning to:
generate a refined data set (“The input parameters for the machine learning model may include an amount associated with the electronic transaction, expected expenses and income within a period of time (e.g., within a week, within a month, etc.).” [0077]) comprising data analytics from a plurality of disparate data sets (“As such, the wallet manager 202 may access various data storages to obtain information that may be used by the FI selection module 208 and the payment deferral module 206. For example, the wallet manager 202 may access transaction history data associated with the user account from the account database 136. The wallet manager 202 may also access rewards data, balance data, and/or limit data from different servers associated with the different financial institutions. The FI selection module 208 and the payment deferral module 206 may then determine the payment arrangement for the electronic transaction using the data obtained by the wallet manager 202.” [0065], “the payment deferral module 206 may determine any recurring expenses or incomes such as bills, periodic transfer of funds, based on historic transactions associated with the user account.” [0114]);
a first pre-determined time period (“deferral time,” [0077]) based on the refined data set (“The payment deferral module 206 may use another machine learning model that is configured to output a deferral time (e.g., 5 hours, 1 day, 21 days, etc.) for paying the electronic transaction. The input parameters for the machine learning model may include an amount associated with the electronic transaction, expected expenses and income within a period of time (e.g., within a week, within a month, etc.).” [0077]);
transmit an amount equivalent to the total value of the plurality of transactions to recipient accounts (merchant accounts) associated with service providers (“The merchant server 120, in various embodiments, may be maintained by a business entity (or in some cases, by a partner of a business entity that processes transactions on behalf of business entity). Examples of business entities include merchants, resource information providers, utility providers, real estate management providers, social networking platforms, etc., which offer various items for purchase and process payments for the purchases” [0051]) of the plurality of transactions (“The receivable record indicates that the transaction has been entirely allocated to the user account of the electronic transaction system. The electronic transaction system may transmit a transaction complete message back to the merchant indicating that the electronic transaction is completed, even though no funds have been charged to any of the financial instruments associated with the user. Instead, the electronic transaction is initially paid for by the electronic transaction system or on the credit of the electronic transaction system.” [0030]) based on the first pre-determined time period and/or based on a pre-determined total amount threshold (“The payment arrangement may also specify a payment deferral arrangement, which represents a period of time (e.g., 5 hours, 1 day, 21 days) from conducting the electronic transaction for the user to pay for the electronic transaction, even when the financial instrument selected for the electronic transaction is not a credit-based financial instrument (e.g., a credit card). To enable the payment deferral arrangement, the electronic transaction system may provide the necessary credits to the user for paying the electronic transaction (e.g., a purchase) until the period of time expires (then the financial instrument of the user is charged for the amount of the electronic transaction).” [0020]); and
a second pre-determined time period (new deferral period) based on the refined data set (“After processing the electronic transaction (and/or the re-payment transactions) using the first payment arrangement, the process 600 may determine (at step 610) a second payment arrangement for the electronic transaction. For example, the wallet module 132 may use the FI selection module 208 and the payment deferral module 206 to determine an optimal payment arrangement for the electronic transaction after the electronic transaction (and/or the initial re-payment transactions) was processed. The wallet module 132 may have acquired new information that may be used to determine the optimal payment arrangement after the electronic transaction was processed.” [0106], “After determining a new payment arrangement, the wallet module 132 may modify the payment arrangement for the electronic transaction (e.g., using the financial instruments 306 and 308 instead of the financial instruments 302 and 304, having a 21 days deferral instead of no payment deferral, etc.).” [0084], “The payment deferral module 206 may use another machine learning model that is configured to output a deferral time (e.g., 5 hours, 1 day, 21 days, etc.) for paying the electronic transaction. The input parameters for the machine learning model may include an amount associated with the electronic transaction, expected expenses and income within a period of time (e.g., within a week, within a month, etc.).” [0077]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dixon by the feature of Prabhu and in particular to include in Dixon’s method, non-transitory computer readable medium, and system, a machine learning model as taught by Prabhu; to include in Dixon’s one or more processors of the intra-bank transaction processing system of a financial institution, the step of inputting, the plurality of transactions into a machine learning model, to generate a refined data set comprising data analytics from a plurality of disparate data sets, as taught by Prabhu; to include in Dixon, the feature of the first and second pre-determined time period based on the refined data set, as taught by Prabhu; and to include in Dixon, the step of transmitting an amount equivalent to the total value of the plurality of transactions to recipient accounts associated with service providers of the plurality of transactions based on the first pre-determined time period and/or based on a pre-determined total amount threshold, as taught by Prabhu.
A person having ordinary skill in the art would have been motivated to combine these features because “delay[ing] the payment of the current electronic transaction until after the future funds are available [to] improve the cash flow of the user account (especially if the balance of the user account is low such as below the amount of the electronic transaction).” (Prabhu, [0025]).
Prabhu does not directly disclose that the machine learning model is trained based on a set of training data including pre-defined mappings defining relationships between one or more input parameters and one or more output parameters.
However, Butler teaches that the machine learning model is trained based on a set of training data (“the predictive model may be developed and trained using historical data associated with user transactions and accounts.” C.7, L.58-60, “the predictive model may be trained using the provided user and account information. Once the predictive model has been trained using the training data, the predictive model may be applied to user accounts to identify future transactions and associated future account balances.” C.15, L.21-25) including pre-defined mappings defining relationships between one or more input parameters and one or more output parameters (“Predictive model 150 may be a supervised learning model with predictor variables” C.7, L.38-39, a person with ordinary skill in the art knows that supervised learning requires using a training set that has mapped/labeled inputs to outputs, “the predictive model may be developed and trained using historical data associated with user transactions and accounts.” C.7, L.58-60, “Supervisor-given target activations may be supplied for some output units at certain time steps. For example, if the input sequence is a speech signal corresponding to a spoken digit, the final target output at the end of the sequence may be a label classifying the digit.” C.8, L.60-65).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Dixon/Prabhu combination by the features of Butler and in particular to include in the machine learning model of Prabhu in the Dixon/Prabhu combination, the feature of the machine learning model being trained on a set of training data including pre-defined mappings defining relationships between one or more input parameters and one or more output parameters, as taught by Butler, because training a machine learning model including pre-defined mappings defining relationships between one or more input parameters and one or more output parameters would improve its accuracy, and because one of ordinary skill in the art would have recognized that applying the known technique of training based on a set of training data including pre-defined mappings defining relationships between one or more input parameters and one or more output parameters to a machine learning model would have yielded predictable results and resulted in an improved system because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such data processing features into similar systems.
Cooper teaches
generate a dynamic report (“The spending limit balance available is then updated 28 to reflect the purchase made.” [0028], “the statement may be made available to the consumer by electronically posting the statement information at a secure site such as an internet site accessible by the account holder’s personal computer 56” [0029]) based on a refined data set (“accumulate a billing record of all debit transactions for a billing cycle…” [0029]) and including the total value of the plurality of transactions during a first pre-determined time period (“typically one month, without debiting the consumer’s account.” “at the end of the billing cycle, the managing computer system 50 recalls the billing history and issues a statement which includes a notice itemizing and totaling the deferred debit transaction history for the consumer’s account.” [0029]) and the total value of the transmitted amount (“The card issuing financial institution's managing computer system 50 is configured so that, during the settlement phase, after the receipt of the posting file 22, and after the card issuing financial institution 10 makes payment to the credit institution 6, the managing computer system 50 makes a deferred transaction billing record or history 26 without debiting the amount of the transaction against the consumer’s account.” [0028]), wherein the dynamic report includes interactive user interface elements (“the statement may be made available to the consumer by electronically posting the statement information at a secure site such as an internet site accessible by the account holder’s personal computer 56” [0029]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Dixon/Prabhu/Butler combination by the features of Cooper and in particular to include the Dixon/Prabhu/Butler combination, the feature of generate a dynamic report based on the refined data set and including the total value of the plurality of transactions during the first pre-determined time period and the total value of the transmitted amount, wherein the dynamic report includes interactive user interface elements, as taught by Cooper, because this would aid in important information being “reported to the [debit card] account holders. The [debit card] account holders are able to review the [debit] transactions and have the opportunity to supply additional funds or alternative sources of payment other than a direct debit to the account. In this way, joint checking account holders can avoid inadvertent overdrafts” (Cooper, [0010]).
As to Claims 22, 32, and 39, the Dixon/Prabhu/Butler/Cooper combination discloses as discussed above. Butler teaches wherein the machine learning model is continuously updated via a supervised deep convolution network (“Predictive model 150 may be a supervised learning model… Predictive model 150 may include continuous learning capabilities that allow the model to adjust itself or its determinations as more information and data becomes available.” C.7, L.38-50, “In some embodiments, the machine learning algorithms employed may include… convolutional neural networks” C.7, L.51-55).
As to Claims 23, 33, and 40, the Dixon/Prabhu/Butler/Cooper combination discloses as discussed above. Butler teaches wherein the machine learning model is trained to find contextual data associated with the registered user from unstructured data, and wherein the machine learning model is further trained to combine the unstructured data with structured data to improve data accuracy (“The predictor variables of the model may be selected from the data stored in account database 180 and/or merchant database 190, or any other available data associated with user transactions and accounts. In some embodiments, the predictor variables may be a subset of the available information; the subset of data used may increase, may decrease, or may otherwise be modified over time as the development of the predictive model continues.” C.7, L.39-50, C.7, L.65-67, “transactions associated with one or more accounts may be monitored over time to determine the accuracy of the predictive model, e.g. in determining future transactions and future account balances, which may be used for revising the predictive model.” C.8, L.1-3).
As to Claims 24 and 34, the Dixon/Prabhu/Butler/Cooper combination discloses as discussed above. Butler teaches wherein the machine learning model ingests the plurality of transactions (“the predictive model may be developed and trained using historical data associated with user transactions and accounts.” C.7, L.58-60), draw parallels and conclusions across disparate data sets to provide refined data, and wherein the refined data is abstracted by categorizing, coding, transforming, interpreting, summarizing, and/or calculating the abstracted data for decision-making (“the predictor variables may be a subset of the available information; the subset of data used may increase, may decrease, or may otherwise be modified over time as the development of the predictive model continues.” C.7, L.39-50, C.7, L.65-67, “transactions associated with one or more accounts may be monitored over time to determine the accuracy of the predictive model, e.g. in determining future transactions and future account balances, which may be used for revising the predictive model.” C.8, L.1-3, “From this data, correlations, patterns and/or associations may be drawn between various users, transactions and account balances.” C.15, L.18-20, “At block 440, the prioritization engine may determine the accuracy of the results of the predictive model…At block 450, the prioritization engine may adjust the predictive model in response to the determined accuracy,,” C.15, L.31-63).
As to Claims 25 and 35, the Dixon/Prabhu/Butler/Cooper combination discloses as discussed above. Prabhu teaches integrating a payment vehicle (“physical payment card” [0017]) and the payment account associated with the registered user with the recipient accounts associated with the service providers based, at least in part, on approval from the registered user and the service providers (“The user may then confirm the use of the determined payment arrangement for the electronic transaction…” [0026], “the merchant device may accept the token and process the electronic transaction by transmitting the token to a payment network” [0027], “When the server receives the token via the payment network, the electronic transaction system may then…process the electronic transaction.” [0029]); and synchronizing, in real-time, transaction data for the plurality of transactions, the total value of the plurality of transactions, and/or the total value of the transmitted amount between the payment account and the recipient accounts (“The payment arrangement may also specify a payment deferral arrangement, which represents a period of time (e.g., 5 hours, 1 day, 21 days) from conducting the electronic transaction for the user to pay for the electronic transaction, even when the financial instrument selected for the electronic transaction is not a credit-based financial instrument (e.g., a credit card). To enable the payment deferral arrangement, the electronic transaction system may provide the necessary credits to the user for paying the electronic transaction (e.g., a purchase) until the period of time expires (then the financial instrument of the user is charged for the amount of the electronic transaction).” [0020]).
As to Claims 26 and 36, the Dixon/Prabhu/Butler/Cooper combination discloses as discussed above. Prabhu teaches processing historical transaction data associated with the registered user to predict expenses of the registered user (“In some embodiments, the electronic transaction system may determine (or suggest) a payment arrangement for the electronic transaction based on one or more factors, such as payment history of the user account, rewards associated with using the financial instruments, anticipated/expected future expenses and income, and other factors” [0023]); determining the predicted expenses for the registered user exceeds current balance of the payment account associated with the registered user (“the electronic transaction system may delay the payment of the current electronic transaction until after the future funds are available to improve the cash flow of the user account (especially if the balance of the user account is low such as below the amount of the electronic transaction).” [0025]); and determining preset rules for the registered user based, at least in part, on the determination that the predicted expenses exceeds the current balance of the payment account (“the electronic transaction system may delay the payment of the current electronic transaction until after the future funds are available to improve the cash flow of the user account (especially if the balance of the user account is low such as below the amount of the electronic transaction).” [0025]).
As to Claim 28, the Dixon/Prabhu/Butler/Cooper combination discloses as discussed above. Prabhu teaches processing the payment account of the registered user to determine a payment account balance is below a pre-determined minimum balance threshold (“the electronic transaction system may delay the payment of the current electronic transaction until after the future funds are available to improve the cash flow of the user account (especially if the balance of the user account is low such as below the amount of the electronic transaction).” [0025]); determining the total value of the plurality of transactions exceeds the payment account balance (“charge funds from the one or more financial institutions specified in the payment arrangement in order to process the re-payment transactions” [0031]); and determining to transmit the amount equivalent to the total value during the second pre-determined time period (“(e.g., charging one or more financial instruments of the user to pay back the electronic payment system. After the payment arrangement is determined, the electronic transaction system may communicate with various financial institutions (e.g., issuer banks, acquirer banks, etc.) within the payment network to charge funds from the one or more financial institutions specified in the payment arrangement in order to process the re-payment transactions” [0031]) based, at least in part, on historical transaction data of the registered user, wherein the historical transaction data includes predicted income of the registered user (“if the electronic transaction system detects an anticipated income in the near future…funds are available to improve the cash flow of the user account (especially if the balance of the user account is low such as below the amount of the electronic transaction).” [0025]), and wherein the predicted income is sufficient to settle the transmitted amount (“determine if any planned expenses and income within a predetermined time period (e.g., within a day, 5 days, 2 weeks, etc.). If a sale transaction has been conducted through the user account, the payment deferral module 206 may determine that funds may be received by the user account within a short period of time. Furthermore, the payment deferral module 206 may determine any recurring expenses or incomes such as bills, periodic transfer of funds, based on historic transactions associated with the user account.” [0114], “if the electronic transaction system detects an anticipated income in the near future…funds are available to improve the cash flow of the user account (especially if the balance of the user account is low such as below the amount of the electronic transaction).” [0025]).
As to Claim 30, the Dixon/Prabhu/Butler/Cooper combination discloses as discussed above. Dixon further discloses wherein the payment account and the recipient accounts are associated with a same financial institution (“an "acquirer" is typically a business entity (e.g., a commercial bank) that has a business relationship with a particular merchant. An "issuer" is typically a business entity (e.g., a bank), which issues a portable payment device such as a credit, debit, or stored value card to a consumer. Some entities may perform both issuer and acquirer functions.” [0035]).
Claims 27 and 37 are rejected under 35 U.S.C. 103 as being unpatentable over Dixon in view of Prabhu, in view of Butler, in view of Cooper, and further in view of Bol et al. (US 8,335,739 B1)(“Bol”).
As to Claims 27 and 37, the Dixon/Prabhu/Butler/Cooper combination discloses as discussed above.
Dixon does not directly disclose processing the historical transaction data to determine a credit ranking and a credit score for the registered user, wherein the historical transaction data includes credit history information, income information, debt-to-income ratio information, or a combination thereof; and determining the first pre-determined time period, the second pre-determined time period, the pre-determined total amount threshold, or a combination thereof based on the credit ranking and the credit score.
Bol teaches
processing historical transaction information to determine a credit ranking (“the card issuer 102 may determine whether the customer 101 is a good credit risk or a poor credit risk.” C.4, L.49-51) and a credit score for the registered user, wherein the historical transaction information includes credit history information, income information, debt-to-income ratio information, or a combination thereof (“The credit score may be based on various factors, such as an individual’s payment history for debts, amount of debt, length of credit history, and types of credit used.” C.4, L.25-29); and
determining a first preset time period, a second preset time period, a pre-determined total amount threshold, or a combination thereof based on the credit ranking and the credit score (“criteria set by the card issuer 102…may include…any time interval for performing the action(s),” C.6, L.16-21).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Dixon/Prabhu/Butler/Cooper combination by the features of Bol and in particular to include in Dixon, the features of processing historical transaction information to determine a credit ranking and a credit score for the registered user, wherein the historical transaction information includes credit history information, income information, debt-to-income ratio information, or a combination thereof; and determining the first preset time period, the second preset time period, the pre-determined total outstanding amount threshold, or a combination thereof based on the credit ranking and the credit score, as taught by Bol.
A person having ordinary skill in the art would have been motivated to combine these features because it would help gauge the appropriate level of risk to take for a given user.
Claim 29 is rejected under 35 U.S.C. 103 as being unpatentable over Dixon in view of Prabhu, in view of Butler, in view of Cooper, and further in view of the Admitted Prior Art.
As to Claim 29, the Dixon/Prabhu/Butler/Cooper combination discloses as discussed above.
Dixon further discloses determining a failure of at least one transaction from the plurality of transactions associated with the registered user (“if there are insufficient funds or credit in the consumer's account, or if the consumer's portable payment device is on a negative list (e.g., it is indicated as possibly stolen), then an electronic payment transaction may not be authorized,” [0035]).
It is noted that Applicant did not seasonably traverse the Official Notice assertion made in the last Office Action. As stated in the MPEP, “[i]f applicant does not traverse the examiner’s assertion of official notice or applicant’s traverse is not adequate, the examiner should clearly indicate in the next Office action that the common knowledge or well-known in the art statement is taken to be admitted prior art because applicant either failed to traverse the examiner’s assertion of official notice or that the traverse was inadequate. See Ahlert, 424 F.2d at 1091, 165 USPQ at 420.” As such, the limitation of “processing a failed transaction to determine a reason for the failure; and generating a presentation in a user interface of a device associated with a registered user, wherein the presentation includes an alert on the reason for the failure of the at least one transaction” is now considered Admitted Prior Art.
Dixon does not directly disclose processing at least one failed transaction to determine a reason for the failure; and generating a presentation in a user interface of a device associated with the registered user, wherein the presentation includes an alert on the reason for the failure of the at least one transaction.
The Admitted Prior Art teaches: processing a failed transaction to determine a reason for the failure; and generating a presentation in a user interface of a device associated with a registered user, wherein the presentation includes an alert on the reason for the failure of the at least one transaction, such as on a storefront checkout webpage online notifying the end-user that the credit card number is invalid.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to include in the Dixon/Prabhu/Butler/Cooper combination the features of processing at least one failed transaction to determine a reason for the failure; and generating a presentation in a user interface of a device associated with the registered user, wherein the presentation includes an alert on the reason for the failure of the at least one transaction, as taught by the Admitted Prior Art since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 29 is alternatively rejected under 35 U.S.C. 103 as being unpatentable over Dixon in view of Prabhu, in view of Butler, in view of Cooper, and further in view of Nandiraju (US 2010/0250376 A1)(“Nandiraju”).
As to Claim 29, the Dixon/Prabhu/Butler/Cooper combination discloses as discussed above.
Dixon further discloses determining a failure of at least one transaction from the plurality of transactions associated with the registered user (“if there are insufficient funds or credit in the consumer's account, or if the consumer's portable payment device is on a negative list (e.g., it is indicated as possibly stolen), then an electronic payment transaction may not be authorized,” [0035]).
Dixon does not directly disclose processing at least one failed transaction to determine a reason for the failure; and generating a presentation in a user interface of a device associated with the registered user, wherein the presentation includes an alert on the reason for the failure of the at least one transaction.
Nandiraju teaches processing a failed transaction to determine a reason for the failure (“Otherwise, if the payment cannot be processed, the payment processing application may transmit an error code to the payment acceptance application to indicate the type of processing error.” [0032]); and generating a presentation in a user interface of a device associated with a registered user, wherein the presentation includes an alert on the reason for the failure of the at least one transaction (“Upon receiving the error code, the send tab may turn the flashing yellow light to a solid red to indicate that the payment processing has failed and may also display an error message corresponding to the received error code in 214. The error message alerts the user to the type of error, such as an invalid credit card number…” [0032]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to include in the Dixon/Prabhu/Butler/Cooper combination the features of processing at least one failed transaction to determine a reason for the failure; and generating a presentation in a user interface of a device associated with the registered user, wherein the presentation includes an alert on the reason for the failure of the at least one transaction, as taught by Nandiraju “so that the user may [go back to the preview tab to] correct the error for retransmission and payment reprocessing (Nandiraju, [0032]), and since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 21-40 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over Claims 1-20 of copending Application No. 17/700,125 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because they are both directed to intra-bank transactions, using trained machine learning models to determine multiple preset time periods, and effecting payment on aggregated transaction amounts. This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Response to Arguments
Applicants’ arguments filed on December 1, 2025 have been fully considered and addressed below.
On pages 13-16, Applicant argues that the claims do not recite an abstract idea and instead the claims as a whole are directed to improving intra-bank transaction processing. Applicant argues that the steps of the claim are in a coordinated sequence of technical operations that include among various things, machine-learning, and collectively “define a technological solution.” On page 15, Applicant points to an improvement in the technology “by creating ‘efficient routing and settlement of payment transactions between bank accounts…’” Applicant goes on to say that an improvement that has been amended is the “generating a dynamic report…” The arguments presented by Applicant are not persuasive because as discussed above,
Claim 21 (representative of Claims 31 and 38) recites the following steps:
a method to monitor online transactions, the method comprising:
determining, via an intra-bank transaction of a financial institution, a plurality of transactions associated with a registered user;
generate a refined data set comprising data analytics from a plurality of disparate data sets;
calculate a total value of the plurality of transactions during a first pre-determined time period;
transmit an amount equivalent to the total value of the plurality of transactions to recipient accounts associated with service providers of the plurality of transactions based on the first pre-determined time period and/or based on a pre-determined total amount threshold;
calculate a total value of the transmitted amount during a second pre-determined time period, wherein the first pre-determined time period is a subset of the second pre-determined time period; and
deduct an amount equivalent to the total value of the transmitted amount from a payment account associated with the registered user during the second pre-determined time period; and
generating a report based on the refined data set and including the total value of the plurality of transactions during the first pre-determined time period and the total value of the transmitted amount.
These steps, under their broadest reasonable interpretation, describe or set-forth effecting payments for aggregated transactions, which amounts to a fundamental economic principle or practice (including hedging, insurance, mitigating risk) and or a commercial or legal interaction (including sales activities or behaviors; business relations). These limitations therefore fall within the “certain methods of organizing human activity” subject matter grouping of abstract ideas. As such, Claims 21-40 recite an abstract idea.
The claims recite the additional elements/limitations of the report being dynamic; and wherein the dynamic report includes interactive user interface elements. However, the characteristic of the report being “dynamic” is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations do not impose any meaningful limits on practicing the abstract idea and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
The recited additional element of “wherein the dynamic report includes interactive user interface elements” serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. Specifically, it serves to limit the application of the abstract idea to the technological environment of user interfaces. This reasoning was demonstrated in Bilski, where it was determined that certain claim elements limiting the basic concept of hedging to commodities and energy markets (merely limiting an abstract idea to one field of use) did not make the concept patentable. This reasoning was demonstrated in Intellectual Ventures I LLC v. Capital One Bank (Fed. Cir. 2015), where the court determined “an abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer”). This limitation does not impose any meaningful limits on practicing the abstract idea and therefore does not integrate the abstract idea into a practical application (see MPEP 2106.05(g)).
On page 18, Applicant argues that the combination of Dixon, Prabhu, and Butler do not disclose various limitations of the claims including the “generate a refined data set…,” “calculate…refined date,” “calculate…time period,” and “generating a dynamic report…” limitations. In particular, Applicant argues that Dixon does not disclose the “generate a refined data set…” and “generating a dynamic report.” The Examiner agrees. However, as discussed above in the respective rejection,
Prabhu teaches
generate a refined data set (“The input parameters for the machine learning model may include an amount associated with the electronic transaction, expected expenses and income within a period of time (e.g., within a week, within a month, etc.).” [0077]) comprising data analytics from a plurality of disparate data sets (“As such, the wallet manager 202 may access various data storages to obtain information that may be used by the FI selection module 208 and the payment deferral module 206. For example, the wallet manager 202 may access transaction history data associated with the user account from the account database 136. The wallet manager 202 may also access rewards data, balance data, and/or limit data from different servers associated with the different financial institutions. The FI selection module 208 and the payment deferral module 206 may then determine the payment arrangement for the electronic transaction using the data obtained by the wallet manager 202.” [0065], “the payment deferral module 206 may determine any recurring expenses or incomes such as bills, periodic transfer of funds, based on historic transactions associated with the user account.” [0114]);
a first pre-determined time period (“deferral time,” [0077]) based on the refined data set (“The payment deferral module 206 may use another machine learning model that is configured to output a deferral time (e.g., 5 hours, 1 day, 21 days, etc.) for paying the electronic transaction. The input parameters for the machine learning model may include an amount associated with the electronic transaction, expected expenses and income within a period of time (e.g., within a week, within a month, etc.).” [0077]);
a second pre-determined time period (new deferral period) based on the refined data set (“After processing the electronic transaction (and/or the re-payment transactions) using the first payment arrangement, the process 600 may determine (at step 610) a second payment arrangement for the electronic transaction. For example, the wallet module 132 may use the FI selection module 208 and the payment deferral module 206 to determine an optimal payment arrangement for the electronic transaction after the electronic transaction (and/or the initial re-payment transactions) was processed. The wallet module 132 may have acquired new information that may be used to determine the optimal payment arrangement after the electronic transaction was processed.” [0106], “After determining a new payment arrangement, the wallet module 132 may modify the payment arrangement for the electronic transaction (e.g., using the financial instruments 306 and 308 instead of the financial instruments 302 and 304, having a 21 days deferral instead of no payment deferral, etc.).” [0084], “The payment deferral module 206 may use another machine learning model that is configured to output a deferral time (e.g., 5 hours, 1 day, 21 days, etc.) for paying the electronic transaction. The input parameters for the machine learning model may include an amount associated with the electronic transaction, expected expenses and income within a period of time (e.g., within a week, within a month, etc.).” [0077]).
Furthermore, as discussed above in the respective rejection,
Cooper teaches
generate a dynamic report (“The spending limit balance available is then updated 28 to reflect the purchase made.” [0028], “the statement may be made available to the consumer by electronically posting the statement information at a secure site such as an internet site accessible by the account holder’s personal computer 56” [0029]) based on a refined data set (“accumulate a billing record of all debit transactions for a billing cycle…” [0029]) and including the total value of the plurality of transactions during a first pre-determined time period (“typically one month, without debiting the consumer’s account.” “at the end of the billing cycle, the managing computer system 50 recalls the billing history and issues a statement which includes a notice itemizing and totaling the deferred debit transaction history for the consumer’s account.” [0029]) and the total value of the transmitted amount (“The card issuing financial institution's managing computer system 50 is configured so that, during the settlement phase, after the receipt of the posting file 22, and after the card issuing financial institution 10 makes payment to the credit institution 6, the managing computer system 50 makes a deferred transaction billing record or history 26 without debiting the amount of the transaction against the consumer’s account.” [0028]), wherein the dynamic report includes interactive user interface elements (“the statement may be made available to the consumer by electronically posting the statement information at a secure site such as an internet site accessible by the account holder’s personal computer 56” [0029]).
Therefore, Applicant’s argument that Prabhu and Butler fail to remedy the deficiencies of Dixon is unpersuasive. Furthermore, Cooper- a new reference, is currently applied to teach the new limitation of generating of the dynamic report.
The Examiner respectfully disagrees with Applicant’s that the double patenting rejection of the instant application as being unpatentable over application 17/700,125 should be withdrawn. As discussed above, Claims 21-40 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over Claims 1-20 of copending Application No. 17/700,125 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because they are both directed to intra-bank transactions, using trained machine learning models to determine multiple preset time periods, and effecting payment on aggregated transaction amounts. This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Conclusion
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/M.A.M/Examiner, Art Unit 3622
/ILANA L SPAR/Supervisory Patent Examiner, Art Unit 3622
1 See Subject Matter Eligibility Analysis for Products and Processes in MPEP §2106 III.
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