DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Acknowledgements
This Office Action is in response to Applicant’s response filed on December 1, 2025 (“December 2025 Response”) which includes inter alia, claim amendments (“December 2025 Claims”) and “REMARKS” (“December 2025 Remarks”).
Claims 1-8 and 11-22 are currently pending and have been examined.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8 and 11-22 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 1-8 and 11-22 are directed to one of the statutory categories.
Claims 1-8 and 21-22 are directed to a process.
Claims 11-16 are directed to a system.
Claims 17-20 are directed to an article of manufacture.
Step 2A- Prong One of the SME Analysis:
Claim 1 (representative of Claims 11 and 17) recites the following steps:
receiving, by an intra-bank transaction part of a financial institution, in real-time, a plurality of transactions for a payment account associated with a payment vehicle of a registered user;
inputting, by the intra-bank transaction part of a financial institution, the plurality of transactions and data associated with the registered user into a model to generate a first preset time period and a second preset time period during which at least one of the plurality of transactions occurred;
aggregating, by the intra-bank transaction part of a financial institution, outstanding amount associated with the plurality of transactions of the payment account during the first preset time period;
transmitting, by the intra-bank transaction part of a financial institution, payments for the aggregated outstanding amount to a plurality of recipient accounts associated with merchants of the plurality of transactions during the first preset time period and/or based on a pre-determined total outstanding amount threshold, wherein the plurality of recipient accounts and payment account of the registered user are associated with the financial institution such that the intra-bank transaction processing system processes the plurality of transactions on behalf of the payment account and plurality of recipient accounts such that the financial institution acts as a payor and a payee;
aggregating, by the intra-bank transaction part of a financial institution, the transmitted payments by combining the aggregated outstanding amounts transmitted during the second preset time period, wherein the first preset time period is a subset of the second preset time period;
deducting, by intra-bank transaction part of a financial institution, an amount that equals the combined aggregated outstanding amounts of the aggregated transmitted payments from the payment account during the second preset time period; and
generating, by the intra-bank transaction part of a financial institution, associated with the registered user, a notification on the deduction of the aggregated transmitted payments from the payment account; and
generating, by the intra-bank transaction part of the financial institution, an alert on the aggregated outstanding amount during the first preset time period, and a notification that the aggregated outstanding amount is debited from the payment account during the second preset time period.
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 1 recites an abstract idea.
Independent Claims 11 and 17 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 1-8 and 11-22 recite an abstract idea.
Step 2A- Prong Two of the SME Analysis:
The claims recite the additional elements/limitations of:
“processing system;”
“configured to;”
“by one or more processors;”
“a trained machine learning model;”
“an apparatus for a payment-related service comprising: at least one processor; and at least one memory including computer program code for one or more programs, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform operations;”
“a non-transitory computer-readable storage medium carrying one or more sequences of one or more instructions for a payment-related service which, when executed by one or more processors, cause an apparatus to perform operations;”
“a first user interface element in a user interface of a device” that includes the “notification on the deduction of the aggregated transmitted payments from the payment account” (and similar interface elements in the user interface as recited in Claims 7 and 10);
“a second user interface element in the user interface” that includes the “alert on the aggregated outstanding amount during the first preset time period” and the “notification that the aggregated outstanding amount is debited from the payment account during the second preset time period.”
“training, by the one or more processors of the intra-bank transaction processing system of the financial institution, the machine learning model using input data including structured data and unstructured data” (as recited in Claim 21); and
“retraining, by the one or more processors of the intra-bank transaction processing system of the financial institution, the machine learning model using a supervised deep convolutional network, wherein the retraining includes validating the input data and enhance predictive accuracy of the machine learning model” (as recited in Claim 22).
The requirement to execute the claimed steps/functions using the apparatus, the non-transitory computer-readable storage medium, one or more processors, device, processing system, and the computer as implied by the phrase “configured to,” 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 elements of a “trained machine learning model,” the “training” step as recited in Claim 21, and the “retraining” step recited in Claim 22, serve 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 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 elements of the “user interface(s)” 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 outputting information that has already been determined and this is incidental to the primary process of aggregating and effecting payments. 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)).
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 not specifically addressed above 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 apparatus, the non-transitory computer-readable storage medium, one or more processors, processing system, device, and the computer as implied by the phrase “configured to,” 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 “trained machine learning model,” the “training” step as recited in Claim 21, and the “retraining” step recited in Claim 22 serve merely to generally link the use of the judicial exception to a particular technological environment or field of use. This limitation therefore does not qualify as “significantly more” (see MPEP 2106.05(g)).
As discussed above in “Step 2A – Prong 2”, the recited additional elements of the “user interface(s)” 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 not specifically mentioned above, 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 1-8 and 11-22 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 1-2, 6, 8, 11-12, and 16-18 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”) and further in view of Wilkes (US 2014/0304155 A9)(“Wilkes”).
Claims 1, 11, and 17
As to Claim 1 (representative of Claims 11 and 17), Claim 11, and Claim 17, Dixon discloses a method comprising:/an apparatus (“payment processor 160” [0039]) comprising: at least one processor (“The payment processor 160 may include a server computer.” [0040]); and at least one memory (memory in “server computer,” [0040]) including computer program code for one or more programs, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform operations comprising:/a non-transitory computer-readable storage medium (memory in “server computer,” [0040]) carrying one or more sequences of one or more instructions which, when executed by one or more processors, cause an apparatus to perform operations comprising:
receiving, by one or more processors (system 200) of an intra-bank transaction processing system of a financial institution (“‘acquirer’ is typically a business entity (e.g., a commercial bank)…merchant” [0035], “ ‘issuer’ is typically…a bank…consumer.” “Some entities may perform both issuer and acquirer functions.” [0035], “The payment system 100 includes, a plurality of merchants 140 associated with one or more acquirers 150, and issuers 170.” [0037], “…the transaction can be processed on-line in communication with the payment processing system 180, as would a retail transaction with the merchant 140. The fare value can be transmitted to the payment processing system 180 via the on-line financial institution network 310.” [0051]), in real-time, a plurality of transactions for a payment account associated with a payment vehicle (“a FIPPD is intended to be broadly understood as being a portable payment device” [0034]) of 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]);
aggregating, by the one or more processors of the intra-bank transaction processing system of the financial institution, outstanding amount associated with the plurality of transactions of the payment account during the first preset time period during which at least one of the plurality of transactions occurred (“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);
transmitting, by the one or more processors of the intra-bank transaction processing system of the financial institution, payments for the aggregated outstanding amount to a plurality of recipient accounts associated with merchants (merchants 140) of the plurality of transactions during the first preset time period and/or based on a pre-determined total outstanding amount threshold (“Once the transit fare is sent to the payment processing system 180 it can be processed according to typical protocol for merchants 140. For example, each $2.00 transit fare can be authorized, settled, and cleared through the payment processing system 180, the transit agency can be paid…” [0063], “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]), wherein the plurality of recipient accounts and payment account of the registered user are associated with the financial institution (“commercial bank,” [0035], “a bank,” “Some entities may perform both issuer and acquirer functions.” [0035]) such that the intra-bank transaction processing system processes the plurality of transactions on behalf of the payment account and plurality of recipient accounts such that the financial institution acts as a payor and payee (as noted above, “‘acquirer’ is typically a business entity (e.g., a commercial bank)…merchant” [0035], “ ‘issuer’ is typically…a bank…consumer.” “Some entities may perform both issuer and acquirer functions.” [0035], therefore, since some banks/financial institutions may perform both issuer and acquirer functions they act as both payor and payee, “The payment system 100 includes, a plurality of merchants 140 associated with one or more acquirers 150, and issuers 170.” [0037], “…the transaction can be processed on-line in communication with the payment processing system 180, as would a retail transaction with the merchant 140. The fare value can be transmitted to the payment processing system 180 via the on-line financial institution network 310.” [0051]);
aggregating, by the one or more processors of the intra-bank transaction processing system of the financial institution, the payments during the second preset time period during which at least one of the plurality of transactions occurred (“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 preset time period is a subset of the second preset 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).
Dixon does not directly disclose
inputting, by the one or more processors of the intra-bank transaction processing system of the financial institution, the plurality of transactions and data associated with the registered user into a trained machine learning model configured to generate a first preset time period and a second preset time period;
the aggregating is of the transmitted payments by combining the aggregated outstanding amounts transmitted during the second preset time period;
deducting, by the one or more processors of the intra-bank transaction processing system of the financial institution, an amount that equals the combined aggregated outstanding amounts of the aggregated transmitted payments from the payment account during the second preset time period; and
generating, by the one or more processors of the intra-bank transaction processing system of the financial institution, a first user interface element in a user interface of a device associated with the registered user, wherein the first user interface element includes a notification on the deduction of the aggregated transmitted payments from the payment account; and
generating, by the one or more processors of the intra-bank transaction processing system of the financial institution, a second user interface element in the user interface, wherein the second user interface element includes an alert on the aggregated outstanding amount during the first preset time period, and wherein the second user interface element includes a notification that the aggregated outstanding amount is debited from the payment account during the second preset time period.
Prabhu teaches
inputting, by one or more processors (Processor 914), transaction data and data associated with the registered user into a trained machine learning model configured to generate preset time periods (“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]);
aggregating, by the one or more processors, the transmitted payments by combining the aggregated outstanding amounts transmitted (“the electronic transaction system may create a receivable record in association with the transaction. 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.” [0030]) during a second preset time period (“The user interface 410 presents the payment deferral arrangement that was determined initially for the electronic transaction when the electronic transaction was conducted. Via the user interface 410, the user may adjust the payment deferral time period” [0089], “The wallet application 116 may present the user interface 502 in response to an indication that the user 140 wants to pay multiple electronic transactions as a group. As shown, the user interface 502 presents a list of recently conducted electronic transactions. The list of recently conducted electronic transactions may include electronic transactions that have been conducted but have not yet paid off by the user 140, such that the wallet module 132 may still modify the payment arrangement(s) of the electronic transactions.” [0091], see Fig.5);
deducting, by the one or more processors, an amount that equals the combined aggregated outstanding amounts of the aggregated transmitted payments from the payment account (“The wallet application 116 may present the user interface 502 in response to an indication that the user 140 wants to pay multiple electronic transactions as a group. As shown, the user interface 502 presents a list of recently conducted electronic transactions. The list of recently conducted electronic transactions may include electronic transactions that have been conducted but have not yet paid off by the user 140, such that the wallet module 132 may still modify the payment arrangement(s) of the electronic transactions.” [0091], see Fig.5, “the process 800 automatically transfers (at step 825) funds from the user account to pay for the electronic transaction.” [0116]) during the second preset time period (“the user interface 406 indicates that the checking account having account number that ends with ‘8832’ was used to pay for the electronic transaction, and a 21 day payment deferral arrangement was used for the electronic transaction” [0088]); and
generating, by the one or more processors, a second user interface element in the user interface, wherein the second user interface element includes an alert on the aggregated outstanding amount during a time period (see Fig.4A, see also associated text “The user interface 402 prompts the user 140 for a payment deferral period. Via the user interface 402, the user 140 may provide inputs indicating a payment deferral arrangement. For example, the user 140 may select to pay immediately, or at a later time (e.g., within 5 hours, 1 day, 21 days, through an installment plan, etc.).” [0086]), and wherein the second user interface element includes a notification that the aggregated outstanding amount is debited from the payment account during the second preset time period (“the user interface 406 indicates that the checking account having account number that ends with ‘8832’ was used to pay for the electronic transaction, and a 21 day payment deferral arrangement was used for the electronic transaction” [0088]).
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 features of Prabhu and in particular to:
include in Dixon’s one or more processors of the intra-bank transaction processing system of the financial institution, the feature of inputting, the transaction data of Dixon (i.e.: the plurality of transactions) and data associated with the registered user into a trained machine learning model configured to generate time periods, as applied to Dixon’s first preset time period and second preset time period, as taught by Prabhu, because it would help in “providing assistance…conducting electronic transactions” (Prabhu, [0004]);
include in Dixon’s aggregating feature, the feature of aggregating, the transmitted payments by combining aggregated outstanding amounts transmitted during the second preset time period (as applied to Dixon’s second preset time period), as taught by Prabhu, since “[t]he feature of grouping different electronic transactions that have been previously conducted improves how cash flow can be managed…while maintaining data security” ([0094]);
include in Dixon’s one or more processors of the intra-bank transaction processing system of the financial institution, the feature of deducting an amount that equals the combined aggregated outstanding amounts of the aggregated transmitted payments from the payment account during the second preset time period (as applied to Dixon’s second preset time period), as taught by Prabhu, because this would help to fulfill the payment owed by the user of the transactions; and
include in Dixon’s one or more processors of the intra-bank transaction processing system of the financial institution, the feature of generating, by the one or more processors, a second user interface element in the user interface, wherein the second user interface element includes an alert on the aggregated outstanding amount during a time period (as applied to Dixon’s first preset time period), and wherein the second user interface element includes a notification that the aggregated outstanding amount is debited from the payment account during the second preset time period, as taught by Prabhu, because it would help the user plan and account for available funds.
Wilkes teaches generating, by one or more processors, a user interface element in a user interface (see interface depicted in Fig.2) of a device (client device 110) associated with a registered user, wherein the user interface element includes a notification (“transaction alert message,” [0022], see Fig.2) on the deduction (“Other examples of conditions that may be submitted by an account holder to trigger alerts may include instances where…notification of a bill payment that has either cleared” [0029]) of the aggregated (“one of the conditions submitted by the account holder may include a predetermined cumulative transaction amount that is exceeded within a predetermined time period. For example, the account holder may wish to be alerted whenever more than $1,000.00 worth of purchases have been made within a two day period” [0028]) transmitted payments from the payment account (“sending of an alert message to the account holder to notify the account holder that one or more of the conditions have been met” [0025], “If one or more of the conditions have been met, the method 400 generates 406 account information that is descriptive of the one or more conditions that have been met. The account information is then transmitted 408 to an electronic device associated with the account holder in the form of an alert message.” [0036]).
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 Wilkes and in particular to include in Dixon’s one or more processors of the intra-bank transaction processing system of the financial institution in the Dixon/Prabhu combination, the feature of generating a user interface element in a user interface of a device associated with the registered user, wherein the user interface element includes a notification on the deduction of the aggregated transmitted payments from the payment account, as taught by Wilkes, since “current information relating to available balances …can enable users to know whether sufficient funds are available to initiate specific transactions, or whether more funds need to be added” (Wilkes, [0006]);
Claims 2, 12, and 18
As to Claims 2, 12, and 18, the Dixon/Prabhu/Wilkes combination discloses as discussed above.
Dixon further discloses
receiving, over a communication network, access credentials of the registered user, wherein the access credentials include predefined values, a preset username and password, international mobile equipment identity (IMEI), an electronic serial number, a mobile equipment identity (MEID), one or more identifiers unique to the device, or a combination thereof (“card identification number” [0044], [0052], “At step 420, optional authentication evaluations can be conducted at the transit POS 240 including rudimentary checks on the status of the FIPPD 130” [0053], “The white list or black list can be hosted at the database 305 in communication with the transit central computer 270, yet be accessible at the transit POS 240 instead of maintaining a white list or a black list at the transit POS 240 itself.” [0056]); and
authenticating the access credentials to authorize the registered user to access a service (“At step 420, optional authentication evaluations can be conducted at the transit POS 240 including rudimentary checks on the status of the FIPPD 130” [0053], “The white list or black list can be hosted at the database 305 in communication with the transit central computer 270, yet be accessible at the transit POS 240 instead of maintaining a white list or a black list at the transit POS 240 itself.” [0056]).
Claims 6 and 16
As to Claims 6 and 16, the Dixon/Prabhu/Wilkes combination discloses as discussed above.
Dixon further discloses
processing the payment account of the registered user to determine a payment account balance is below a pre-determined minimum balance threshold (“The rider may set up the transit account such that the account is "topped off' at predetermined intervals-such as when the end of the month arrives or when the transit account has reached a threshold lowest value such as $5.00 (U.S. dollars), whereby a predetermined amount is charged to the account that is associated with the FIPPD 130 in the payment processing system 180. Therefore” [0065]);
determining the aggregated outstanding amount exceeds the payment account balance (“The rider may set up the transit account such that the account is "topped off' at predetermined intervals-such as when the end of the month arrives or when the transit account has reached a threshold lowest value such as $5.00 (U.S. dollars), whereby a predetermined amount is charged to the account that is associated with the FIPPD 130 in the payment processing system 180. Therefore” [0065]); and
determining to transmit payments for the aggregated outstanding amount during the second preset time period based on historical transaction information of the registered user (“Therefore, the transit system may conduct an on-line transaction, for example for $50.00 (U.S. dollars) with the payment processing system 180 once the predetermined interval is reached.” [0065], wherein the rider’s preset top off settings of amount to input into card is the historical transaction information), wherein the historical transaction information includes a predicted income of the registered user (e.g. “$50.00” [0065]), and wherein the predicted income is sufficient to settle the aggregated transmitted payments ([0065]).
Claim 8
As to Claim 8, the Dixon/Prabhu/Wilkes combination discloses as discussed above.
Dixon further discloses
processing historical transaction information associated with the registered user, wherein the historical transaction information includes past payment transactions, shopping basket contents, or a combination thereof (“step 450, the transit transaction history stored in step 430 may be accessed to calculate off-line (e.g.; not in real time) the value of a fare using the stored transaction data and the transit agency policies.” [0059]); and
determining a benefit program (“frequent ridership” [0059]) for the registered user based, at least in part, on the processing, wherein the benefit program includes a loyalty program, a coupon redemption program, a lottery program, or a combination thereof (“a classification of the rider corresponding to the FIPPD 130 (e.g., concessions based on age, student status, or frequent ridership);” [0059]).
Claims 3, 13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Dixon in view of Prabhu, in view of Wilkes, and further in view of Kerolos (US 2022/0147956 A1)(“Kerolos”).
Claims 3, 13, and 19
As to Claims 3, 13, and 19, the Dixon/Prabhu/Wilkes combination discloses as discussed above.
Dixon further discloses synchronizing, in real-time, transaction information for the plurality of transactions, the aggregated outstanding amount, the aggregated transmitted payments, or a combination thereof between the payment account, the plurality of recipient accounts, and the service (“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 aggregated set of fares for the FIPPD 130 (e.g., an aggregation of all access transitions for one FIPPD 130 over a month's period).” [0075]).
Dixon does not directly disclose integrating the payment vehicle, the payment account, and the plurality of recipient accounts associated with the service, wherein the integration is based on a consent from the registered user and the merchants.
Kerolos teaches integrating the payment vehicle, the payment account, and the plurality of recipient accounts associated with a service, wherein the integration is based on a consent from the registered user and the merchants (“Consumers obtain the payment transaction facilitation computer program from the transaction processing entity… The consumer then provides access to their bank account or accounts (which will likely be at one of the banks the transaction process entity has an account with) as part of obtaining the program.” “The merchant also obtains the payment transaction facilitation computer program from the transaction processing entity… The merchant then provides the necessary information to allow the transaction processing entity to deposit funds into their bank account (which is likely to be at one of the banks the transaction process entity has an account with) as part of obtaining the program.” [0035]).
One of ordinary skill in the art of field of endeavor before the effective filing date of the invention would have found it obvious to update the system that includes the payment vehicle, the payment account, the plurality of recipient accounts, and the service, of Dixon in the Dixon/Prabhu/Wilkes combination using modern payment transaction facilitation programs to integrate them, as found in Kerolos, in order to gain the commonly understood benefits of such adaptation, such as increased reliability, simplified operation, and reduced cost. All this would be accomplished with no unpredictable results.
Claims 4, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Dixon in view of Prabhu, in view of Wilkes, and further in view of Sadlier et al. (US 2014/0244503 A1)(“Sadlier”).
Claims 4, 14, and 20
As to Claims 4, 14, and 20, the Dixon/Prabhu/Wilkes combination discloses as discussed above.
Dixon does not directly disclose
processing historical transaction information associated with the registered user to determine a probability of expenses in a next instance of time, wherein the historical transaction information includes past payment transactions;
determining the expenses in the next instance of time exceeds payment account balance; and
determining preset rules for the registered user based on the determination that the expenses in the next instance of time exceeds the payment account balance.
Sadlier teaches
processing historical transaction information associated with the registered user to determine a probability of expenses in a next instance of time, wherein the historical transaction information includes past payment transactions (“historical data may include any transaction data suitable for use in the determination of payment card use control thresholds as will be apparent to persons having skill in the relevant art, such as transaction amounts, merchant information, geographic location of the transactions, time and/or date of the transactions, etc.” [0029], “if the transaction frequency for the payment card exceeds the mean value 404, such as those days included in the region 408, then the account owner may be notified.” [0048]);
determining the expenses in the next instance of time exceeds payment account balance (“indicating that the financial transaction exceeding an automatically identified alert threshold.” [0045]); and
determining preset rules for the registered user based on the determination that the expenses in the next instance of time exceeds the payment account balance (“In step 332, the processing server 114 may update the historical transaction data stored in the transaction database 202 with the transaction data in the financial transaction. Then, in step 334, the processing server 114 may update the alert and/or use thresholds for the payment card 104 based on the updated historical data.” [0046]).
One of ordinary skill in the art before the effective filing date of the invention would have recognized that applying the known technique of looking at historical spending behaviors to determine limits and formulate transaction rules based on those limits (processing historical transaction information associated with the registered user to determine a probability of expenses in a next instance of time, wherein the historical transaction information includes past payment transactions; determining the expenses in the next instance of time exceeds payment account balance; and determining preset rules for the registered user based on the determination that the expenses in the next instance of time exceeds the payment account balance) as taught by Sadlier, would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of using historical spending behaviors to determine limits and formulate transaction rules based on those limits as taught by Sadlier to the teachings of Dixon in the Dixon/Prabhu/Wilkes combination would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such secure data processing features into similar systems.
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Dixon in view of Prabhu, in view of Wilkes, and further in view of Bol et al. (US 8,335,739 B1)(“Bol”).
Claims 5 and 15
As to Claims 5 and 15, the Dixon/Prabhu/Wilkes combination discloses as discussed above.
Dixon does not directly disclose
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.
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 outstanding 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/Wilkes 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 7 is rejected under 35 U.S.C. 103 as being unpatentable over Dixon in view of Prabhu, in view of Wilkes, and further in view of the Admitted Prior Art.
Since Applicant did not seasonably traverse the findings asserted as Official Notice as stated in a previous Office Action (Notification Date: 03/31/2023), the findings asserted as Official Notice are taken to be Admitted Prior Art.
Claim 7
As to Claim 7, the Dixon/Prabhu/Wilkes combination discloses as discussed above.
Dixon does not directly disclose
determining a failure of at least one transaction from the plurality of transactions of the registered user;
processing the at least one transaction to determine a reason for the failure; and
generating a third user interface element in the user interface, wherein the third user interface element includes an alert on the reason for the failure of the at least one transaction.
It is now Admitted Prior Art that the features of: determining a failure of at least one transaction from the plurality of transactions of the registered user; processing the at least one transaction to determine a reason for the failure; and generating a second [or another, such as a third] user interface element in the user interface, wherein the second [or another, such as a third] user interface element includes an alert on the reason for the failure of the at least one transaction, such as for example: sending to a credit card holder a “decline” transaction message to their smartphone when their transaction was determined to be declined, were old and well-known before the effective filing date of the invention.
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/Wilkes combination the features of “determining…; processing …; and generating…” 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 21 is rejected under 35 U.S.C. 103 as being unpatentable over Dixon in view of Prabhu, in view of Wilkes, and further in view of Dickie et al. (US 2022/0207430 A1)(“Dickie”).
Claim 21
As to Claim 21, the Dixon/Prabhu/Wilkes combination discloses as discussed above.
Dixon does not directly disclose training, by the one or more processors of the intra-bank transaction processing system of the financial institution, the machine learning model using input data including structured data and unstructured data.
Dickie teaches training a machine learning model using input data (“machine-learning or artificial-intelligence process may be adaptively trained” [0019]) including structured data and unstructured data (“the training and validation data may include, but are not limited to, elements of profile, account, or reporting data characterizing corresponding ones of the customers of the financial institution, along with elements of delinquency data identifying and characterizing prior occurrences of default events associated with, or involving, the corresponding customers.” [0019], “certain of these exemplary processes, which generate training, validation, and input datasets that include feature values obtained from, or derived from, elements of contextual data that characterize purchase transactions initiated by customers of the financial institution, may enable the one or more of the FI computing systems to adaptively train” [0023], “transaction data records 114 may include any additional, or alternate, number of discrete, structured or unstructured data that” [0050]).
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/Wilkes combination by the features of Dickie, and in particular, to include in Prabhu’s machine learning model and Dixon’s one or more processors of the intra-bank transaction processing system of the financial institution, in the Dixon/Prabhu/Wilkes combination, the features of training the machine learning model using input data including structured data and unstructured data, as taught by Dickie.
A person having ordinary skill in the art would have been motivated to combine these features because it would help to better “characterize a determined or detected change in the spending and purchase habits of the customers across one or more temporal intervals” (Dickie, [0020]).
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Dixon in view of Prabhu, in view of Wilkes, in view of Dickie, and further in view of Olsen et al. (US 2022/0327100 A1)(“Olsen”).
Claim 22
As to Claim 22, the Dixon/Prabhu/Wilkes/Dickie combination discloses as discussed above.
Dickie teaches retraining, by the one or more processors of the intra-bank transaction processing system of the financial institution (Dixon discloses the processors in the Dixon/Prabhu/Wilkes/Dickie combination), the machine learning model using a neural network (“one or more artificial intelligence models, such as, but not limited to, an artificial neural network model, a recurrent neural network model, a Bayesian network model, or a Markov model” [0058]), wherein the retraining includes validating the input data and enhance predictive accuracy of the machine learning model (“Certain of these exemplary NLP algorithms or models (e.g., the machine-learning processes or artificial intelligence models described herein) can be trained against, and adaptively improved using, training data having a specified composition, which may be extracted from portions of aggregated data store 132 or consolidated data store 144, and can be deemed successfully trained and ready for deployment when a model accuracy (e.g., as established based on a comparison with the outcome data) exceeds a threshold value.” [0058]).
Dickie does not directly disclose retraining a machine learning model using a supervised deep convolutional network.
Olsen teaches retraining a machine learning model using a supervised deep convolutional network (“At step 316, the entity classification server may generate a score indicating a likelihood that the recommended entity code correctly identifies the entity in the transaction data. The entity classification server may use a machine classifier to generate the score based on the entity location. The machine classifier may be a supervised machine learning classifier and/or an unsupervised machine learning classifier. The machine classifier may use the merchant location, merchant names, the recommended MCC, and the like as inputs to the machine classifier. The machine classifier may use additional input, such as one or more products associated with the transaction. It should be readily apparent to one having ordinary skill in the art that a variety of machine classifier architectures can be utilized including (but not limited to) decision trees, k-nearest neighbors, support vector machines (SVM), neural networks (NN), recurrent neural networks (RNN), convolutional neural networks (CNN), probabilistic neural networks (PNN), transformer models, and the like. RNNs can further include (but are not limited to) fully recurrent networks, Hopfield networks, Boltzmann machines, self-organizing maps, learning vector quantization, simple recurrent networks, echo state networks, long short-term memory networks, bi-directional RNNs, hierarchical RNNs, stochastic neural networks, and/or genetic scale RNNs. In a number of embodiments, a combination of machine classifiers can be utilized, more specific machine classifiers when available, and general machine classifiers at other times can further increase the accuracy of predictions.” [0046]).
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/Wilkes/Dickie combination by the features of Olsen, and in particular, to include in Prabhu’s machine learning model and Dixon’s one or more processors of the intra-bank transaction processing system of the financial institution, in the Dixon/Prabhu/Wilkes/Dickie combination, the features of retraining a machine learning model using a supervised deep convolutional network, as taught by Olsen.
A person having ordinary skill in the art would have been motivated to combine these features because it would help to “further increase the accuracy of predictions” (Olsen, [0046]).
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 1-20 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 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.
Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over Claims 21-40 of copending Application No. 18/056,396 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because they are both directed to 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
Applicant’s arguments filed on the December 2025 Remarks have been fully considered and addressed below.
§101 Arguments
On pages 16-18, Applicant argues that the claim as amended are not directed to organizing human activity or a mental process. In particular, Applicant states that “these steps define a technological solution implemented by a specialized transaction-processing system to address technical challenges in processing high-volume intra-bank transactions with reduced latency and improved transparency. Accordingly, the foregoing features are not a method of organizing activity between people. Instead, the claim is rooted in computer technology and provides a practical application of computer-based intra-bank settlement automation, which falls outside the judicial exception of organizing human activity” and that “[c]ollectively, these steps define a technological solution implemented by a specialized transaction-processing system to address technical challenges in processing high-volume intra-bank transactions with reduced latency and improved transparency. Accordingly, the foregoing features are not a method of organizing activity between people. Instead, the claim is rooted in computer technology and provides a practical application of computer-based intra-bank settlement automation, which falls outside the judicial exception of organizing human activity.” The Examiner respectfully disagrees. While the claim is performed on a computer an improvement to the computer, the examiner finds that there is no “specialized transaction-processing system” as asserted by Applicant recited in the specification or the claims. Instead, the Examiner finds that the transaction totals are aggregated during two time periods and paid during those two time periods.
On pages 18-20, Applicant argues that the claims integrate the abstract idea into a practical application that improve the functioning of the intra-bank transaction system. Some of the things Applicant mentions is: “computerized receipt of transaction data, machine-learning-based derivation of preset settlement periods, automated intra-bank aggregation and transmission of outstanding amounts, system executed debit operations, and the automatic generation of user interface elements.” Applicant argues that “[t]hese operations are implemented by the computer system to reduce latency, automate multi-period settlement, and eliminate manual reconciliation steps, thereby enhancing the computer's ability to process, segment, and communicate high-volume intra-bank transactions.” However, as discussed in the respective rejection, the requirement to execute the claimed steps/functions using the apparatus, the non-transitory computer-readable storage medium, one or more processors, device, processing system, and the computer as implied by the phrase “configured to,” 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 elements of a “trained machine learning model,” the “training” step as recited in Claim 21, and the “retraining” step recited in Claim 22, serve 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 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 elements of the “user interface(s)” 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 outputting information that has already been determined and this is incidental to the primary process of aggregating and effecting payments. 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)). 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.
On pages 20-22, Applicant argues that the specification discloses a technical solution for automated intra-bank transaction processing. However, while the specification generally explains certain advantages of the invention, the portions the specification Applicant points to appears to be based on the exclusion of intermediaries in a transaction. The claims do not set forth the exclusion of intermediaries, any other disclosed improvement, or the technology used to effect such improvement. Instead, the Examiner finds that the transaction totals are aggregated during two time periods and paid during those two time periods. Therefore, the argument is unpersuasive.
On pages 22-24, Applicant argues that the Examiner did not comply with the Berkheimer Memo because “Instead, the Office Action contains an insufficiently supported allegation that the claimed limitations are mere instructions to implement an abstract idea on a generic computer. (Office Action at p. 8). In contrast, the present claim is directed to a computer-implemented intra-bank transaction processing system that automatically receives and preprocesses transaction data, uses a trained machine-learning model to determine multiple preset time periods…Applicant asserts that the additional elements recited in the claims are not ‘well-understood, routine, or conventional,’ and accordingly respectfully requests reconsideration and allowance of the claims.” However, the rejection does not state that the additional elements to the abstract idea are all “well-understood, routine, or conventional.” The Examiner instead stated that: as discussed above in “Step 2A – Prong 2”, the recited additional elements of the “user interface(s)” 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. Therefore, the argument is unpersuasive.
§103 Arguments
On pages 24-25, Applicant argues that Prabhu at paragraph 77 does not disclose the “inputting…” step. Specifically, Applicant argues that “the cited paragraph simply describes a machine-learning model that outputs a single deferral time (a future delay interval, such as 5 hours or 21 days) for postponing payment of an individual electronic transaction. This deferral time is not a preset time period tied to when any transaction occurred, nor is it derived from analyzing multiple transactions. The cited paragraph does not teach generating two distinct preset time periods, nor does it disclose using transaction-occurrence data to identify or segment historical time window. The paragraph discloses the machine-learning model outputting a single forward-looking delay interval unrelated to transaction occurrence. Accordingly, it does not disclose the claimed feature ‘a trained machine learning model configured to generate a first preset time period and a second preset time period during which at least one of the plurality of transactions occurred.’” However, as discussed in the respective rejection of this limitation, it relies on the combination of Dixon and Prabhu. To summarize, Dixon discloses two separate time periods during which transactions occurred, and Prabhu employs a machine learning model to determine preset time periods of the transactions based on transaction data. As discussed in the respective rejection, 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 features of Prabhu and in particular to: include in Dixon’s one or more processors of the intra-bank transaction processing system of the financial institution, the feature of inputting, the transaction data of Dixon (i.e.: the plurality of transactions) and data associated with the registered user into a trained machine learning model configured to generate time periods, as applied to Dixon’s first preset time period and second preset time period, as taught by Prabhu, because it would help in “providing assistance…conducting electronic transactions” (Prabhu, [0004]). Therefore, the argument is not found persuasive.
On pages 25-26, Applicant argues that the time periods for the aggregating are generated by a machine learning model and that neither Prabhu nor Dixon teach that. However, as similarly discussed above, and as discussed in the respective rejection of this limitation, it relies on the combination of Dixon and Prabhu. Dixon discloses aggregating, by the one or more processors of the intra-bank transaction processing system of the financial institution, outstanding amount associated with the plurality of transactions of the payment account during the first preset time period during which at least one of the plurality of transactions occurred (“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). 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 features of Prabhu and in particular to: include in Dixon’s one or more processors of the intra-bank transaction processing system of the financial institution, the feature of inputting, the transaction data of Dixon (i.e.: the plurality of transactions) and data associated with the registered user into a trained machine learning model configured to generate time periods, as applied to Dixon’s first preset time period and second preset time period, as taught by Prabhu, because it would help in “providing assistance…conducting electronic transactions” (Prabhu, [0004]). Therefore, the argument is unpersuasive.
On page 26, Applicant argues that Prabhu does not disclose the “aggregating…by combining…during the second preset time period….” limitation of the claim by focusing on the fact that in Prabhu the user selects a time period. However, Prabhu also teaches that time periods can likewise be generated by 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]). Furthermore, this limitation is taught by the combination of Dixon and Prabhu’s features as set forth in the respective rejection. Dixon discloses aggregating, by the one or more processors of the intra-bank transaction processing system of the financial institution, the payments during the second preset time period during which at least one of the plurality of transactions occurred (“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 preset time period is a subset of the second preset 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). Prabhu teaches aggregating, by the one or more processors, the transmitted payments by combining the aggregated outstanding amounts transmitted (“the electronic transaction system may create a receivable record in association with the transaction. 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.” [0030]) during a second preset time period (“The user interface 410 presents the payment deferral arrangement that was determined initially for the electronic transaction when the electronic transaction was conducted. Via the user interface 410, the user may adjust the payment deferral time period” [0089], “The wallet application 116 may present the user interface 502 in response to an indication that the user 140 wants to pay multiple electronic transactions as a group. As shown, the user interface 502 presents a list of recently conducted electronic transactions. The list of recently conducted electronic transactions may include electronic transactions that have been conducted but have not yet paid off by the user 140, such that the wallet module 132 may still modify the payment arrangement(s) of the electronic transactions.” [0091], see Fig.5). 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 features of Prabhu and in particular to include in Dixon’s aggregating feature, the feature of aggregating, the transmitted payments by combining aggregated outstanding amounts transmitted during the second preset time period (as applied to Dixon’s second preset time period), as taught by Prabhu, since “[t]he feature of grouping different electronic transactions that have been previously conducted improves how cash flow can be managed…while maintaining data security” ([0094]). Therefore, the argument is unpersuasive.
On pages 26-27 Applicant argues that Prabhu does not disclose the second interface element because they do not disclose “any alert on an aggregated outstanding amount during a first preset time period, nor do they provide any notification that such an aggregated amount has been or will be debited during a second preset time period”. The Examiner respectfully disagrees. It is noted that the limitation in question relies on the combination of Dixon and Prabhu. As discussed in the respective rejection, Prabhu teaches generating, by the one or more processors, a second user interface element in the user interface, wherein the second user interface element includes an alert on the aggregated outstanding amount during a time period (see Fig.4A, see also associated text “The user interface 402 prompts the user 140 for a payment deferral period. Via the user interface 402, the user 140 may provide inputs indicating a payment deferral arrangement. For example, the user 140 may select to pay immediately, or at a later time (e.g., within 5 hours, 1 day, 21 days, through an installment plan, etc.).” [0086]), and wherein the second user interface element includes a notification that the aggregated outstanding amount is debited from the payment account during the second preset time period (“the user interface 406 indicates that the checking account having account number that ends with ‘8832’ was used to pay for the electronic transaction, and a 21 day payment deferral arrangement was used for the electronic transaction” [0088]). 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 features of Prabhu and in particular to include in Dixon’s one or more processors of the intra-bank transaction processing system of the financial institution, the feature of generating, by the one or more processors, a second user interface element in the user interface, wherein the second user interface element includes an alert on the aggregated outstanding amount during a time period (as applied to Dixon’s first preset time period), and wherein the second user interface element includes a notification that the aggregated outstanding amount is debited from the payment account during the second preset time period, as taught by Prabhu, because it would help the user plan and account for available funds. Therefore, the argument is unpersuasive.
Double Patenting Arguments
On pages 27-28, Applicant argues the Double Patenting rejections and requests that the rejections be held in abeyance. The argument is not persuasive.
Conclusion
Applicant’s amendment filed on December 1, 2025 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 MONICA A MANDEL whose telephone number is (571)270-7046. The examiner can normally be reached Monday and Thursday 10:00 AM-6:00 PM.
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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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