DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, 19/008,287, was filed on Jan. 2, 2025, and does not claim foreign priority or domestic benefit to any other application.
The effective filing date is after the AIA date of March 16, 2013, and so the application is being examined under the “first inventor to file” provisions of the AIA .
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.
Status of the Application
This Final Office Action is in response to Applicant’s communication of 05/19/2026.
Claims 1, 3-9, 11-17, and 19-23 are pending, of which claims 1, 9, and 17 are independent.
Claims 1, 3, 4, 9, 11, 12, and 17 are amended, claims 2, 10, and 18 are newly cancelled, and claims 21-23 are newly added.
All pending claims have been examined on the merits.
Allowable Subject Matter
Claims 21-23 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
21. (New) The computer-implemented method of claim 1, wherein the at least one interactable hyperlink is configured to instantiate a recurring digital payment transaction for the first user.
22. (New) The computer-implemented method of claim 9, wherein the at least one interactable hyperlink is configured to instantiate a recurring digital payment transaction for a second user.
23. (New) The system of claim 17, wherein the at least one interactable hyperlink is configured to instantiate a recurring digital payment transaction for the first user.
In regards to the 35 USC § 101 rejection, the Examiner holds that claims 21-23 recite a practical application of the abstract idea, because the “at least one interactable hyperlink” is configured to “instantiate a recurring digital payment transaction for the [first/second] user”.
In regards to the 35 USC § 103 rejection, the Examiner holds that none of references cited in the 35 USC § 103 rejection (US 2014/0129428 A1 to Tyler et al. in view of US 2024/0045890 A1 to Nguyen et al., and further in view of US 12,646,059) disclose the features claimed in claims 21-23.
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, 3-9, 11-17, and 19-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to non-statutory subject matter. The claimed invention is directed to an abstract idea, without “significantly more”.
Based on the flowchart in MPEP § 2106, Step 1 of the Alice/Mayo analysis is: “Is the claim to a process, machine, manufacture or composition of matter?”
In regards to Step 1 of the Alice/Mayo analysis, independent claims 1 and 9 are method claims, and claim 17 is an apparatus claim.
For the sake of compact prosecution, we continue with the Alice/Mayo “abstract idea” analysis.
Step 2A, prong 1 of the Alice/Mayo analysis is: “Does the claim recite a law of nature, a natural phenomenon (product of nature), or an abstract idea?”
In regards to Step 2A, prongs 1 and 2 of the Alice/Mayo analysis, the abstract idea elements recited in independent claim 17 are shown in italic font. (The “additional elements” and “extra solution steps” are shown in italic and underlined font):
17. (Currently Amended) A system comprising:
communications hardware configured to receive a first digital payment request from a first user device of a first user;
digital payment management (DPM) circuitry configured to:
generate a first digital payment object based on the first digital payment request, wherein the first digital payment object comprises sender identification data associated with the first user and recipient identification data associated with a target recipient indicated by the first digital payment request, and
determine a first memo string associated with a memo field of the first digital payment object, wherein the first memo string comprises data related to a payment description indicated by the first digital payment request; and
a DPM model configured to:
determine a first similarity score associated with the first memo string and a first previous memo string based on comparing the first memo string to a set of previous memo strings related to a set of previous incoming digital payment objects associated with the target recipient;
determine, based on the first similarity score associated with the first memo string, a first payment type associated with the first digital payment object; and
generate, by the DPM model and based on the first similarity score and the first payment type, a first set of recommendations for the target recipient, wherein the communications hardware is configured to provide the first set of recommendations to a second user device associated with the target recipient, wherein providing the first set of recommendations includes causing a user interface associated with the second user device to display at least one interactable hyperlink.
More specifically, claims 1, 3-9, 11-17, and 19-20 recite an abstract idea: “Certain Methods of Organizing Human Activity", specifically “Commercial or Legal Interactions (Including Agreements in the form of Contracts; Legal Obligations; Advertising, Marketing, or Sales Activities or Behaviors; Business Relations)”, as discussed in MPEP §2106(a)(2) Parts (I) and (II), and in the 2019 Revised Patent Subject Matter Eligibility Guidance.
The “Commercial or Legal Interactions” elements include:
“generate a first digital payment object based on the first digital payment request, wherein the first digital payment object comprises sender identification data associated with the first user and recipient identification data associated with a target recipient indicated by the first digital payment request”.
“determine a first memo string associated with a memo field of the first digital payment object, wherein the first memo string comprises data related to a payment description indicated by the first digital payment request”.
“determine a first similarity score associated with the first memo string and a first previous memo string based on comparing the first memo string to a set of previous memo strings related to a set of previous incoming digital payment objects associated with the target recipient”.
“determine, based on the first similarity score associated with the first memo string, a first payment type associated with the first digital payment object”.
Moreover, claims 1, 3-9, 11-17, and 19-20 recite “Mathematical Concepts", specifically “Mathematical Relationships”, “Mathematical Formulas or Equations”, and “Mathematical Calculations”, as discussed in MPEP §2106.04(a)(2) Part (IV), and in the 2019 Revised Patent Subject Matter Eligibility Guidance.
The mathematic elements include:
“determine a first similarity score associated with the first memo string and a first previous memo string based on comparing the first memo string to a set of previous memo strings related to a set of previous incoming digital payment objects associated with the target recipient”.
“determine, based on the first similarity score associated with the first memo string, a first payment type associated with the first digital payment object”.
The “additional elements” include: “communications hardware”, and “digital payment management (DPM) circuitry”.
Moreover, “additional extra-solution elements” include: “receive a first digital payment request from a first user device of a first user”, and “provide the first set of recommendations to a second user device associated with the target recipient”, and “wherein providing the first set of recommendations includes causing a user interface associated with the second user device to display at least one interactable hyperlink”.
Step 2A, prong 2 of the Alice/Mayo analysis is “Does the claim recite additional elements that integrate elements that integrate the judicial exception into a practical application?”
In regards to Step 2A, prong 2 of the Alice/Mayo analysis, this abstract idea is not integrated into a practical application, because:
The claim is directed to an abstract idea with additional generic computer elements. The generically recited computer elements (“communications hardware”, and “digital payment management (DPM) circuitry”) do not add a meaningful limitation to the abstract idea, because they amount to simply implementing the abstract idea on a computer. The claim amounts to adding the words "apply it" (or an equivalent) with the abstract idea, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea.
The extra-solution activities (“receive a first digital payment request from a first user device of a first user”, and “provide the first set of recommendations to a second user device associated with the target recipient”, and “wherein providing the first set of recommendations includes causing a user interface associated with the second user device to display at least one interactable hyperlink”) do not add a meaningful limitation to the method, as they are insignificant extra-solution activity;
The combination of the abstract idea with the additional elements (generically recited computer elements), and/or with the extra-solution activities, does not integrate the abstract idea into a practical application.
Step 2B of the Alice/Mayo analysis is: “Does the claim recite additional elements that amount to significantly more than the judicial exception?”
In regards to Step 2B of the Alice/Mayo analysis, the claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea, because:
When considering the elements "alone and in combination" (“communications hardware”, and “digital payment management (DPM) circuitry”), they do not add significantly more (also known as an "inventive concept") to the exception, because they amount to simply implementing the abstract idea on a computer. Instead, they merely add the words "apply it" (or an equivalent) with the abstract idea, or mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea.
In regards to the extra solution activities (“receive a first digital payment request from a first user device of a first user” and “provide the first set of recommendations to a second user device associated with the target recipient”, and “wherein providing the first set of recommendations includes causing a user interface associated with the second user device to display at least one interactable hyperlink”), these are recognized as such by the court decisions listed in MPEP § 2106.05(d).
More specifically, in regards to the “receiving” and “providing” steps, see the court cases OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network) and (presenting offers and gathering statistics), OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93; buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Moreover, in regards to “apply it”, according to MPEP § 2106.05(f)(2):
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
The Examiner holds that the independent claims “use a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data)” or “simply add a general purpose computer or computer components after the fact to an abstract idea”.
Independent claims 1 and 9 are rejected on the same grounds as independent claim 17.
All dependent claims are also rejected, because they merely further define the abstract idea.
Claim Rejections - 35 USC § 103
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 7-9, 11, 14-17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over US 2014/0129428 A1 to Tyler et al. (“Tyler”. Eff. Filed on Nov. 5, 2012. Published on May 8, 2014) in view of US 2024/0045890 A1 to Nguyen et al. (“Nguyen”. Filed Aug. 4, 2022. Published on Feb. 8, 2024), and further in view of US 12,646,059 B2 to Jones et al. (“Jones”. Eff. Filed May 28, 2021).
In regards to claim 1,
1. A computer-implemented method comprising:
receiving, by communications hardware, a first digital payment request from a first user device of a first user;
(See Tyler, para. [0038]: “Payment management server 150 may provide funds transfers from a financial account associated with a user of sender mobile device 110 to a financial account associated with a user of recipient mobile device 112. For example, in response to a request from sender mobile device 110, payment management server 150 may transmit data comprising a QR code to sender mobile device 110. The data comprising the QR code may represent a payment or a funds transfer. Sender mobile device 110 may display the QR code and recipient mobile device 112 may scan or otherwise read the OR code. Recipient mobile device 112 may read the QR code that is displayed by sender mobile device 110. Further, payment management server 150 may receive data from recipient mobile device 112 indicating that recipient mobile device 112 read the QR code. Accordingly, payment management server 150 may initiate a transfer of funds from a financial account associated with a user of sender mobile device 110 to a financial account associated with a user of recipient mobile device 112.
generating, by digital payment management (DPM) circuitry, a first digital payment object based on the first digital payment request, wherein the first digital payment object comprises sender identification data associated with the first user and recipient identification data associated with a target recipient indicated by the first digital payment request;
(See Tyler, para. [0059]: “As shown in FIG. 5, QR code 500 may comprise various data and information, such as a user identifier 502, a transaction token 504, an amount 506, an application identifier 508, and a memo field 510. In some embodiments, one or more of the elements may be omitted from QR code 500. In other embodiments, more than one version or copy of each element may be incorporated into OR code 500. ”)
(See Tyler, para. [0064]: “QR code 500 may further comprise a memo field 510 to provide for the entry of a note or memo associated with a particular transaction. Memo field 510 may, for example, comprise information that would be associated with the memo field on a check. For example, a sender may send QR code 500 as a payment with, for example, the following memo: "Payment for sports tickets." In other embodiments, memo field 510 may comprise information including, but not limited to, the identity of the sender or the recipient, the identity of a particular financial account hosted on one or more of sender financial account server 130 or recipient financial account server 140, a date, purpose, or any other information that may be associated with a financial transaction. In some embodiments, payment application 300 and/or QR generator module 302 may be operable to amend memo field 510 after the user initially generates information within it. ”)
(See Tyler, para. [0092]: “QR generator module 302 may display the generated OR code 500 on display 206 of sender mobile device 110 (Step 1270). Display of the code may occur substantially similarly to the manner illustrated in FIG. 9, described above. Display of QR code 500 may be accompanied with additional information configured and displayed by OR generator module 302, such as the amount 506 contained within the code, a memo field 510 associated with the code, sender or recipient identification information such as user identifier 502, etc. It is understood that OR code 500 may be displayed with any other information deemed necessary, based on configurations of sender mobile device 110, display 206, payment application 300, sender financial account server 130, or payment management server 150.”)
However, under a conservative interpretation of Tyler, it could be argued that Tyler does not explicitly teach the italicized portions below, that are taught by Nguyen:
determining, by a DPM circuitry, a first memo string associated with a memo field of the first digital payment object, wherein the first memo string comprises data related to a payment description indicated by the first digital payment request;
(See Nguyen, para. [0060]: “In further detail, CashApp (e.g., one of the applications 612, 614, 616) sends the historical data 640 to the cloud service 604. The historical data 640 includes, for example and in the example context, bank statement (query) records, invoice (target) records, and ground truth bank statement-invoice matches. The bank statement records include features of different data types (e.g. memo line (string), posting date (date), country key (categorical)). The invoice records share some similar features to the bank statements (e.g., company code) and also include features of different data types. The ground truth data includes matching pairs of query and target keys and their matching types. The historical data 640 is used by the training infrastructure 620 to train both the GLIM model and the embedding model.
(See Nguyen, para. [0065]: “Historical data is received (702). For example, and as described herein by way of non-limiting example with reference to FIG. 6 , CashApp (e.g., one of the applications 612, 614, 616) sends the historical data 640 to the cloud service 604. The historical data 640 includes, for example and in the example context, bank statement (query) records, invoice (target) records, and ground truth bank statement-invoice matches. The bank statement records include features of different data types (e.g. memo line (string), posting date (date), country key (categorical)). The invoice records share some similar features to the bank statements (e.g., company code) and also include features of different data types. The ground truth data includes matching pairs of query and target keys and their matching types (e.g., single, multi).”)
determining, by the DPM circuitry, a first similarity score associated with the first memo string and a first previous memo string based on comparing the first memo string to a set of previous memo strings related to a set of previous outgoing digital payment objects associated with the first user;
(See Nguyen, para. [0002]: “Implementations of the present disclosure are directed to a machine learning (ML) system for matching a query entity to one or more target entities. More particularly, implementations of the present disclosure are directed to a ML system that reduces a number of target entities from consideration as potential matches to a query entity using learned embeddings.”)
(See Nguyen, para. [0003]: “In some implementations, actions include receiving historical data including a set of ground truth query-target entity pairs, determining a filtering threshold based on similarity scores of a validation set of ground truth query-target entity pairs of the historical data, the validation set of ground truth query-target entity pairs being a sub-set of the set of ground truth query-target entity pairs of the historical data, receiving inference data comprising a set of query entities and a set of target entities, each query entity in the set of query entities to be matched to one or more target entities of the set of target entities, providing, by an embedding module, a set of query entity embeddings and a set of target entity embeddings, defining a set of query-target entity pairs, each query-target entity pair including a query entity of the set of query entities and a target entity of the set of target entities, for each query-target entity pair in the set of query-target entity pairs, determining a similarity score, filtering query-target entity pairs from the set of query-target entity pairs based on respective similarity scores to provide a set of filtered query-target entity pairs, the set of filtered query-target entity pairs having fewer query-target entity pairs than the set of query-target entity pairs, and executing, by a ML model, inference on each filtered query-target entity pair in the set of filtered query-target entity pairs, during inference, the ML model assigning a label to each filtered query-target entity pair. Other implementations of this aspect include corresponding systems, apparatus, and computer programs, configured to perform the actions of the methods, encoded on computer storage devices.”)
It would have been obvious to a person having ordinary skill in the art (PHOSITA), before the effective filing date of the claimed invention, to include in the “QR code-enabled p2p payment systems and methods”, as taught by Tyler above, with “Scalable entity matching with filtering using learned embeddings and approximate nearest neighborhood search”, as further taught by Nguyen above, because Tyler teaches QR code-enabled payment systems, and Nguyen enables “a machine learning (ML) system for matching a query entity to one or more target entities” (see Nguyen’s Abstract), which can be applied to match QR payments and to train a machine learning system to match QR Payments via testing for a “correlation score”. (See Nguyen para. [0025]).
However, under a conservative interpretation of Tyler in view of Nguyen, it could be argued that Tyler in view of Nguyen does not explicitly teach the italicized portions below, that are taught by Jones:
determining, by the DPM circuitry and based on the first similarity score associated with the first memo string, a first payment type associated with the first digital payment object;
(See Jones, Abstract: “Computer-implemented systems and processes may provision, in real-time, targeted, event-specific digital content based on decomposed, structured message data and peer data. For example, an apparatus receives a message associated with an exchange of data involving a first counterparty and a second counterparty. The message may include elements of message data characterizing a real-time payment from the second counterparty to the first counterparty. Based on elements of message data, the apparatus may determine a value of at least one parameter characterizing the first counterparty and use a trained machine-learning or artificial-intelligence process, and may obtain elements of peer data consistent with at least one parameter value. The apparatus may transmit notification data including digital content associated with at least one of the elements of peer data or the at least one parameter value to a device operable by the first counterparty, which may present the digital content within a digital interface.”)
generating, by the DPM circuitry and based on the first similarity score and the first payment type, a first set of recommendations for the first user; and
providing, by the communications hardware, the first set of recommendations to the first user device associated with the first user,
(See Jones, Abstract: “Computer-implemented systems and processes may provision, in real-time, targeted, event-specific digital content based on decomposed, structured message data and peer data. For example, an apparatus receives a message associated with an exchange of data involving a first counterparty and a second counterparty. The message may include elements of message data characterizing a real-time payment from the second counterparty to the first counterparty. Based on elements of message data, the apparatus may determine a value of at least one parameter characterizing the first counterparty and use a trained machine-learning or artificial-intelligence process, and may obtain elements of peer data consistent with at least one parameter value. The apparatus may transmit notification data including digital content associated with at least one of the elements of peer data or the at least one parameter value to a device operable by the first counterparty, which may present the digital content within a digital interface.”)
wherein providing the first set of recommendations includes causing a user interface associated with the first user device to display at least one interactable hyperlink.
(See Jones, col. 4, lines 9-27: “The generated and transmitted RFP message may, for example, be formatted in accordance with the ISO 20022 data-exchange format, and may include message fields populated with information that includes, but is not limited to, information identifying the customer and the employer, and information identifying the payroll event initiated by the employer and involving the customer (e.g., a requested payment amount, a requested payment date, an identifier of the employer account selected by the employer to fund the requested, real-time payment, or an identifier of the customer account capable of receiving the funds from the requested, real-time payment, etc.). Further, the ISO-20022-compliant RFP message may also include a link within a structured or unstructured message field to information, such as formatted paystub data, associated with the requested, real-time payment for the wages earned by the customer (e.g., a long- or shortened Uniform Resource Location (URL) pointing to a paystub in PDF or HTML form that includes any of the information described herein).”)
It would have been obvious to a person having ordinary skill in the art (PHOSITA), before the effective filing date of the claimed invention, to include in the “QR code-enabled p2p payment systems and methods”, as taught by Tyler above, with “Scalable entity matching with filtering using learned embeddings and approximate nearest neighborhood search”, as further taught by Nguyen above, because Tyler teaches QR code-enabled payment systems, and Nguyen enables “a machine learning (ML) system for matching a query entity to one or more target entities” (see Nguyen’s Abstract), which can be applied to match QR payments and to train a machine learning system to match QR Payments via testing for a “correlation score”. (See Nguyen para. [0025]), and it would have been obvious to further amend Tyler and Nguyen with the features of Jones, because Jones expressly teaches the following:
(See Jones, col. 4, line 50 to col. 5, line 3: “Further, when intercepted and processed by the one or more computing systems of the customer's financial institution, these elements of structured or unstructured RFP message data may also enable the one or more computing systems to generate dynamically elements of recommendation data that identify and characterize recommended financial products, financial services, or purchase transactions of potential interest to the customer based on demographic-, payroll-, account- or transaction-based similarities between the customer and the additional customers of the customer peer group, identify one or more targeted offers or incentives that are available to the customer and that are associated with one or more of the recommended financial products, financial services, or purchase transactions, and provision, to a device of the customer in real-time and contemporaneously with the receipt of the RFP message, elements of digital content of that not only confirm an execution of the requested, real-time payment of the funds to the customer and an availability of the funds within the customer account, but that also prompt the customer to accept, or reject, each or a selected subset of the targeted offers of incentives.”)
In regards to claim 7,
7. (Original) The computer-implemented method of claim 1, further comprising:
receiving, by the communications hardware and from the first user device, a recommendation execution indication associated with a first recommendation of the first set of recommendations; and
executing, by the DPM circuitry and based on the recommendation execution indication, a set of operations related to the first recommendation.
(See Nguyen para. [0001]: “Enterprises continuously seek to improve and gain efficiencies in their operations. To this end, enterprises employ software systems to support execution of operations. Recently, enterprises have embarked on the journey of so-called intelligent enterprise, which includes automating tasks executed in support of enterprise operations using machine learning (ML) systems. For example, one or more ML models are each trained to perform some task based on training data. Trained ML models are deployed, each receiving input (e.g., a computer-readable document) and providing output (e.g., classification of the computer-readable document) in execution of a task (e.g., document classification task). ML systems can be used in a variety of problem spaces. An example problem space includes autonomous systems that are tasked with matching items of one entity to items of another entity. Examples include, without limitation, matching questions to answers, people to products, bank statements to invoices, and bank statements to customer accounts.”)
The Examiner interprets that “determining, by the DPM model and based on the first similarity score associated with the first memo string, a first payment type associated with the first digital payment object” is equivalent to Nguyen’s “matching” (for example, of a payment object to a payment type).
In regards to claim 8,
8. (Original) The computer-implemented method of claim 7, further comprising:
causing transfer, by payment transfer circuitry and based on the recommendation execution indication, the sender identification data, and the recipient identification data, of a first portion of a total amount of funds indicated by the first digital payment object from a first financial account associated with the first user to a second financial account associated with the target recipient.
(See Tyler, para. [0031]: “Disclosed embodiments provide systems and methods for peer-to-peer payment transactions. Specifically, the disclosed systems and methods enable a QR code-enabled P2P payment transaction between a sender and a recipient. The parties may conduct the transaction via a communication between a first mobile device (i.e., a device of the sender) and a second mobile device (i.e., a device of the recipient). For example, the first mobile device may display a OR code representing the payment transaction and the communication may include the second mobile device reading the OR code displayed on the first mobile device. Accordingly, the reading of the QR code may result in the transfer of the payment from a financial services account of the sender to a financial services account of the recipient.”)
In regards to claim 9, it is rejected on the same grounds as claim 1.
In regards to claim 11,
11. (Currently Amended) The computer-implemented method of claim 9, further comprising:
determining, by the DPM circuitry and based on the first similarity score associated with the first memo string, a first payment receipt pattern of the target recipient.
(See Nguyen para. [0001]: “Enterprises continuously seek to improve and gain efficiencies in their operations. To this end, enterprises employ software systems to support execution of operations. Recently, enterprises have embarked on the journey of so-called intelligent enterprise, which includes automating tasks executed in support of enterprise operations using machine learning (ML) systems. For example, one or more ML models are each trained to perform some task based on training data. Trained ML models are deployed, each receiving input (e.g., a computer-readable document) and providing output (e.g., classification of the computer-readable document) in execution of a task (e.g., document classification task). ML systems can be used in a variety of problem spaces. An example problem space includes autonomous systems that are tasked with matching items of one entity to items of another entity. Examples include, without limitation, matching questions to answers, people to products, bank statements to invoices, and bank statements to customer accounts.”)
The Examiner interprets that “determining, by the DPM model and based on the first similarity score associated with the first memo string, a first payment type associated with the first digital payment object” is equivalent to Nguyen’s “matching” (for example, of a payment object to a customer account).
In regards to claim 14, it is rejected on the same grounds as claim 7.
In regards to claim 15, it is rejected on the same grounds as claim 8.
In regards to claim 16,
16. (Original) The computer-implemented method of claim 15, further comprising:
causing transfer, by the payment transfer circuitry and based on the recommendation execution indication, of a second portion of the total amount of funds indicated by the first digital payment object from the first financial account associated with the first user to a third financial account associated with the target recipient.
(See Nguyen para. [0001]: “Enterprises continuously seek to improve and gain efficiencies in their operations. To this end, enterprises employ software systems to support execution of operations. Recently, enterprises have embarked on the journey of so-called intelligent enterprise, which includes automating tasks executed in support of enterprise operations using machine learning (ML) systems. For example, one or more ML models are each trained to perform some task based on training data. Trained ML models are deployed, each receiving input (e.g., a computer-readable document) and providing output (e.g., classification of the computer-readable document) in execution of a task (e.g., document classification task). ML systems can be used in a variety of problem spaces. An example problem space includes autonomous systems that are tasked with matching items of one entity to items of another entity. Examples include, without limitation, matching questions to answers, people to products, bank statements to invoices, and bank statements to customer accounts.”)
The Examiner interprets that “determining, by the DPM model and based on the first similarity score associated with the first memo string, a first payment type associated with the first digital payment object” is equivalent to Nguyen’s “matching” (for example, of a payment object to a customer account).
In regards to claim 17, it is rejected on the same grounds as claim 1.
In regards to claim 19, it is rejected on the same grounds as claim 11.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications should be directed to Examiner Ayal Sharon, whose telephone number is (571) 272-5614, and fax number is (571) 273-1794. The Examiner can normally be reached from Monday to Friday between 9 AM and 6 PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SPE Christine Behncke can be reached at (571) 272-8103 or at christine.behncke@uspto.gov. The fax number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
Sincerely,
/Ayal I. Sharon/
Examiner, Art Unit 3695
July 21, 2026
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