Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
2. The Amendment filed March 18, 2026 has been entered. Claims 2-21 are pending and are rejected for the reasons set forth below.
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 2-21 are rejected under 35 U.S.C. §101 because the claimed invention recites and is directed to a judicial exception to patentability (i.e., a law of nature, a natural phenomenon, or an abstract idea) and does not include an inventive concept that is “significantly more” than the judicial exception under the January 2019 and October 2019 patentable subject matter eligibility guidance (2019 PEG) analysis which follows.
Step 1
5. Under the 2019 PEG step 1 analysis, it must first be determined whether the claims are directed to one of the four statutory categories of invention (i.e., process, machine, manufacture, or composition of matter). Applying step 1 of the analysis for patentable subject matter to the claims, it is determined that the claims are directed to the statutory category of a process (claims 9-15), a machine (claims 2-8) and a manufacture (claims 16-21). Therefore, we proceed to step 2A, Prong 1.
Step 2A, Prong 1
6. Under the 2019 PEG step 2A, Prong 1 analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories of patent ineligible subject matter (i.e., organizing human activity, mathematical concepts, and mental processes) that amount to a judicial exception to patentability.
Claim 2 recites the abstract idea of:
determine that a user initiated, [[via a merchant interface displayed on a user device]], a transaction with a merchant, [[wherein the merchant interface is provided by a merchant server of the merchant]];
determine, [[using a machine learning model]] and based on the first interaction data, a first likelihood of an event associated with the transaction, wherein [[the machine learning model]] is configured to identify one or more modalities of the first interactions with [[the network resource]] based on the first interaction data and determine the first likelihood based on the one or more modalities of the first interactions;
in response to determining that the first likelihood does not satisfy a threshold, suspend a settlement process in association with the transaction;
determine, [[using the machine learning model]] and based on the second interaction data, a second likelihood of the event associated with the transaction; and
in response to determining that the second likelihood satisfies the threshold, resume the settlement process in association with the transaction.
Here, the recited abstract idea falls within one or more of the three enumerated 2019 PEG categories of patent ineligible subject matter, to wit: certain methods of organizing human activity, which includes fundamental economic practices or principles and/or commercial interactions (e.g., facilitating the settlement of a transaction).
Step 2A, Prong 2
7. Under the 2019 PEG step 2A, Prong 2 analysis, the identified abstract idea to which claim 2 is directed does not include limitations or additional elements that integrate the abstract idea into a practical application.
Besides reciting the abstract idea, the limitations of claim 2 also recite generic computer components (e.g., a non-transitory memory storing instructions, one or more hardware processors, a merchant interface provided by a merchant server of the merchant, a user device, an application of the user device, a network, and a machine learning model). In particular, the recited features of the abstract idea are merely being applied on a computer or computing device or via software programming that is simply being used as a tool (“apply it”) to implement the abstract idea. (See e.g., MPEP §2106.05(f)). Therefore, these additional elements are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components. In other words, the additional elements are simply used as tools to perform the abstract idea.
Claim 2 also recites the following limitations:
establish a connection with an application of the user device over a network, wherein the application is configured to monitor first interactions of the user with network resources via the user device during a first time period and to communicate the monitored first interactions to the system, wherein the network resources comprise one or more online platforms different from the merchant interface and accessed by the user device;
obtain first interaction data associated with the first interactions from the application via the connection;
instruct the application to monitor second interactions of the user with the network resources via the user device during a second time period; and
obtain second interaction data associated with the second interactions from the application via the connection;
These limitations simply state that the system monitors/gathers information corresponding to interactions between the user and network resources. However, the claims do not provide significant technical data regarding how the information is gathered. Rather, the claims simply state that the information is gathered over a period of time. Therefore, these limitations amount to no more than mere data gathering, which is a form of insignificant extra-solution activity (See MPEP 2106.05(g): OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015) and buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2024)).
Thus, claim 2 does not include any limitations or additional elements that integrate the abstract idea into a practical application. As a result, claim 2 is directed to an abstract idea.
Step 2B
8. Under the 2019 PEG step 2B analysis, the additional elements of claim 2 are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea. (i.e., an innovative concept). Here, the recited additional elements (e.g., a non-transitory memory storing instructions, one or more hardware processors, a merchant interface provided by a merchant server of the merchant, a user device, an application of the user device, a network, and a machine learning model), do not amount to an innovative concept since, as stated above in the Step 2A, Prong 2 analysis, the claims are simply using the additional elements as a tool to carry out the abstract idea (i.e., “apply it”) on a computer or computing device and/or via software programming (See e.g., MPEP §2106.05(f)). The additional elements are specified at a high level of generality such that they are being used in the claims to simply implement the abstract idea and are not themselves being technologically improved (See e.g., MPEP 2106.05(I)(A)); (See also applicant’s Specification at least Paragraphs 77-85).
Additionally, the following limitation identified above as insignificant extra-solution activity (mere data gathering) has been revaluated in Step 2B:
establish a connection with an application of the user device over a network, wherein the application is configured to monitor first interactions of the user with network resources via the user device during a first time period and to communicate the monitored first interactions to the system, wherein the network resources comprise one or more online platforms different from the merchant interface and accessed by the user device;
obtain first interaction data associated with the first interactions from the application via the connection;
instruct the application to monitor second interactions of the user with the network resources via the user device during a second time period; and
obtain second interaction data associated with the second interactions from the application via the connection;
As stated in MPEP 2106.05(d), a factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity (Berkheimer v. HP, Inc., 881 F.3d 1360, 1368 (Fed. Cir. 2018)). In view of this requirement set forth by Berkheimer, this limitation does not integrate the abstract idea into a practical application, or amount to significantly more than the abstract idea, because the courts have found the concept of mere data gathering to be well-understood, routine, and conventional activity (See MPEP 2106.05(d): OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015); and buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, (Fed. Cir. 2014)).
Thus, claim 2 does not recite any additional elements that amount to “significantly more” than the abstract idea.
Additional Independent Claims
9. Independent claims 9 and 16 are similarly rejected under 35 U.S.C. 101 for the reasons described below:
Claim 9 recites limitations that are substantially similar to those recited in claim 2. However, the primary difference between claims 9 and 2 is that claim 9 is drafted as a method rather than as a system. Similarly, as described above regarding claim 2, claim 9 recites generic computer components (e.g., a computer system, a merchant interface, an application of a user device, and a machine learning model) that are simply being used as a tool (“apply it”) to implement the abstract idea. Therefore, since the same analysis should be used for claims 2 and 9, claim 9 is not patent eligible (See Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 2354 (2014)).
Claim 16 recites limitations that are substantially similar to those recited in claim 1. However, the primary difference between claims 16 and 2 is that claim 16 is drafted as a computer-readable medium rather than as a system. Similarly, as described above regarding claim 2, claim 16 recites generic computer components (e.g., a non-transitory machine-readable medium, a machine, a merchant interface, an application of a user device, and a machine learning model) that are simply being used as a tool (“apply it”) to implement the abstract idea. Therefore, since the same analysis should be used for claims 2 and 16, claim 16 is not patent eligible (See Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 2354 (2014)).
Dependent Claims
10. Dependent claims 3-8, 10-15, and 17-21 are also rejected under 35 U.S.C. 101 for the reasons described below:
Claims 3 and 19 simply state that the system trains the machine learning model based on a determination regarding whether the event associated with the transaction has occurred. However, these claims do not provide significant technical detail regarding how the machine learning model is trained. Simply stating that the model is trained based on whether the event has occurred does not provide any indication of an improvement to machine learning technology, or any other technology or technological field. Rather, this amounts to no more than applying generic machine learning techniques to implement the abstract idea on a computer.
Claims 4, 5, 17, and 18 simply provide further definition to the “first interactions” recited in claims 2 and 16. Simply stating that the first interactions comprise accessing a webpage does not provide an indication of an improvement to any technology or technological field. Rather, this merely defines the type of data collected by the system. Additionally, simply stating that content within the website is input into a machine learning model does not provide any indication of an improvement to machine learning technology, or any other technology or technological field. Rather, this amounts to no more than applying generic machine learning techniques to implement the abstract idea on a computer.
Claim 6 simply provide further definition to the “webpage” recited in claim 2. Simply stating that the webpage is associated with a merchant does not provide an indication of an improvement to any technology or technological field. Rather, this merely defines the entity associated with the webpage.
Claims 7, 13, and 20 recite the limitation, “obtain, from the machine learning model, output data indicating the first likelihood and one or more remedial actions for reducing the first likelihood.” This limitation simply refines the abstract idea because it recites a process step (e.g., receiving an output regarding a remedial action to be performed regarding the transaction) that falls under the category of organizing human activity, as described above regarding claim 2. Additionally, merely stating that this process is performed by the machine learning model amounts to no more than merely applying generic machine learning technology to implement the abstract idea on a computer.
Additionally, claims 7, 13, and 20 recite the limitations, “generate an interactive user interface for the merchant based on the output data, wherein the interactive user interface comprises one or more selectable elements corresponding to the one or more remedial actions; and provide the interactive user interface on a device associated with the merchant.” These limitations simply state that the system generates and outputs an interface via a device associated with the merchant. However, the claims do not provide significant technical detail regarding how the user interface function. Simply stating that the interface comprises selectable elements does not amount to an improvement to user interface technology. Rather, this amounts to no more than outputting/displaying data, which is a form of insignificant extra-solution activity (See MPEP 2016.05(g): OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015)). In view of the requirement set forth by Berkheimer, this limitation does not integrate the abstract idea into a practical application, or amount to significantly more than the abstract idea, because the courts have found the concept of merely outputting/displaying data to be well-understood, routine, and conventional activity (See MPEP 2106.05(d): OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015)).
Claims 8, 14, and 21 simply refine the abstract idea because they recite a process step (e.g., selecting a remedial action to perform) that falls under the category of organizing human activity, namely facilitating a transaction, as described above regarding claim 2. Additionally, merely stating that this process is performed via a user interface does not amount to an improvement to any technology or technological field. The claims do not provide significant technical detail regarding how the user interface is structured and/or how the user interacts with the selectable elements. Therefore, such limitations amount to no more than merely applying a generic user interface to implement the abstract idea on a computer.
Claims 10 and 11 simply provide further description to the process of determining the first likelihood recited in claim 9. Simply stating the determining the first likelihood is based on “device attributes” does not provide an indication of an improvement to any technology or technological field. Rather, this simply defines the type of data used to determine the first likelihood.
Claim 12 simply provides further description to the “one or more interaction modalities” recited in claim 9. Simply stating the determining the one or modalities comprise, “at least one of selecting a user interface element, accessing a particular portion of a user interface, or conducting an online chat session with an online platform” does not provide an indication of an improvement to any technology or technological field. Rather, this simply defines the type of interaction modalities that may be identified by the system.
Claim 15 simply states that the user interface is updated based on determining that the second likelihood satisfies the threshold. However, the claims do not provide any technical detail regarding how the interface is updated. Therefore, this limitation amounts to no more than outputting/displaying data, which is a form of insignificant extra-solution activity (See MPEP 2016.05(g): OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015)). In view of the requirement set forth by Berkheimer, this limitation does not integrate the abstract idea into a practical application, or amount to significantly more than the abstract idea, because the courts have found the concept of merely outputting/displaying data to be well-understood, routine, and conventional activity (See MPEP 2106.05(d): OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015)).
Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claims being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Claim Rejections - 35 USC § 103
11. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
12. Claims 16, 17, 20, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Bhandari (U.S. Pre-Grant Publication No. 20170124631) in view of Eldon (U.S. Pre-Grant Publication No. 20090307028) and Kursun (U.S. Pre-Grant Publication No. 20200167784).
Claim 16
Regarding Claim 16, Bhandari teaches:
A non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising: (See at least Paragraphs 75-77: Describes a system for identifying orders that are likely to end in an unintended fulfillment outcome. The system comprises a processor and a computer-readable medium)
receiving an indication of a transaction between a user and a merchant [[via a merchant interface]] (See at least Paragraph 39: The system may receive an order for a product. Examiner’s Note: Bhandari does not explicitly state that the transaction is initiated via a user interface associated with a merchant. However, this limitation is disclosed by Eldon as described below);
determining, using a machine learning model and based on [[the first interactions]], that a first likelihood of an event associated with the transaction does not satisfy a threshold (See at least Paragraph 39: The ordering system applies a predictive model [i.e., a machine learning model; see Paragraphs 69 and 70] to the order details to identify likelihoods that the order will cause one or more unintended order fulfillment outcomes, such as the probability of the order being disputed [i.e., an event]. Examiner’s Note: Bhandari does not explicitly teach that the first likelihood is determined based on “monitored interactions.” However, this limitation is disclosed by Eldon as described below),
suspending a settlement process associated with the transaction (See at least Paragraph 29: If the system identifies an elevated likelihood of an unintended order fulfillment outcome, the system may require the user to implement a remedial action regarding the order [also see Paragraph 43]. The system may require that the user implement the remedial action before submitting the order for processing. In other words, the settlement of the order is "suspended" until the remedial action is performed);
determining, using the machine learning model [[and based on second interactions with the network resources monitored by the application of the user device during a second time period]], that a second likelihood of the event associated with the transaction satisfies the threshold (See at least Paragraph 44: The action identifier identifies actions from the action database and applies them to the order details. The prediction processor may then reapply the predictive model to the revised order details to determine whether the new likelihoods [i.e., a second likelihood] for an unintended fulfillment outcomes satisfy the prediction thresholds. Examiner’s Note: Bhandari does not explicitly teach that the second likelihood is based on “second monitored interaction.” However, this limitation is disclosed by Eldon as described below); and
subsequent to the determining that the second likelihood of the event satisfies the threshold, resuming the settlement process associated with the transaction (See at least Paragraphs 44-46: The billing system receives order details from the ordering system after the user of the ordering system has performed the remedial actions identified by the action identifier. In other words, the process of completing/settling the order is "resumed" when the user performs the remedial action, and the remedial action decreases the likelihoods below a corresponding prediction threshold).
Regarding Claim 16, Bhandari does not explicitly teach, but Eldon, however, does teach:
receiving an indication of a transaction between a user and a merchant via a merchant interface (See at least Paragraphs 80 and 84: Describes a system for monitoring user interactions on a merchant website [i.e., a merchant interface]. The website is provided to the customer's computer by a merchant who maintains and runs the website via a web server. The customer may initiate a transaction via the merchant website);
establishing a connection with an application of a user device of the user, wherein the application is configured to monitor one or more first interactions of the user with network resources via the user device during a first time period (See at least Paragraph 104: The system may monitor the user interactions over a given time interval [e.g., right before authorizing a credit card; Also see Paragraph 108: the time period may be before the transaction is settled]. The system may be used to detect fraud [i.e., an event] associated with the transaction. The system may track customers via a connection between the merchant’s website and the customer’s telecommunication device [See Paragraph 59 and 77]); and
determining, using the machine learning model and based on second interactions with the network resources monitored by the application of the user device during a second time period, that a second likelihood of the event associated with the transaction satisfies the threshold (See at least Paragraph 104: The system may intermittently monitor the customer to determine a behavior profile for the customer. In other words, the system may monitor the customer's activity at a second time to update the fraud risk potential).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the application, to combine the teachings of Bhandari and Eldon in order to reduce the impact of adverse events, such as fraud, resulting from the completion of payments using personal computers and other devices via the internet (Eldon: Paragraphs 1-4).
Regarding Claim 16, the combination of Bhandari and Eldon does not explicitly teach, but Kursun, however, does teach:
wherein the machine learning model is configured to identify one or more modalities of the first interactions with the network resource and determine the first likelihood based on the one or more modalities of the first interactions (See at least Paragraph 113: Describes a system for determining the likelihood that a transaction is associated with a malfeasance [i.e., an event]. The system may compare the type of transaction device [i.e., a modality of communication associated with the transaction] being utilized to initiate, authorize, execute, and/or approve the transaction to a database of known transaction device types and their associated likelihoods of being associated with a malfeasance. The disclosed processes may be performed by a machine learning system [See Paragraph 74]).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the application, to combine the teachings of Bhandari, Eldon, and Kursun in order to provide a system that is specifically designed to analyze and detect particular anomalous trends, patterns, and characteristics across complex networks. Such a system can improve the identification success rate and provide an opportunity to address potentially malfeasant interactions in real time (Kursun: Paragraph 1).
Claim 17
Regarding Claim 17, Bhandari does not explicitly teach, but Eldon, however, does teach:
wherein the first interactions comprise accessing a webpage (See at least Paragraph 104: The system monitors user interactions on a merchant website).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the application, to combine the teachings of Bhandari and Eldon in order to reduce the impact of adverse events, such as fraud, resulting from the completion of payments using personal computers and other devices via the internet (Eldon: Paragraphs 1-4).
Claim 20
Regarding Claim 20, Bhandari teaches:
obtaining, from the machine learning model and based on the first interactions, output data indicating the first likelihood and one or more remedial actions for reducing the first likelihood (See at least Paragraph 29: If the system identifies an elevated likelihood of an unintended order fulfillment outcome, the system may require the user to implement a remedial action regarding the order [also see Paragraph 43]);
generating a user interface based on the output data, wherein the user interface comprises one or more selectable elements corresponding to the one or more remedial actions (See at least Paragraph 54: The system may display a user interface comprising information regarding a plurality of unintended fulfilment outcomes, and remedial actions that may be taken to reduce the likelihood of the corresponding unintended fulfillment outcome [also see Figure 4]. The interface may comprise action buttons [i.e., selectable elements] that facilitate the performance of the plurality of remedial actions); and
providing the user interface on a device associated with the merchant (See at least Paragraph 54: The interface is presented on a client computer associated with the user [i.e., the user is merchant who operates the user interface; See Paragraphs 27 and 91]).
Claim 21
Regarding Claim 21, Bhandari teaches:
receiving, via the user interface, a selection of a particular selectable element from the one or more selectable elements (See at least Paragraph 54: The user can select an action button displayed on the user interface); and
performing a particular remedial action from the one or more remedial actions that corresponds to the particular selectable element (See at least Paragraph 54: Selecting an action button begins the process of performing an action to reduce the likelihood of the corresponding unintended fulfillment outcome).
13. Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Bhandari (U.S. Pre-Grant Publication No. 20170124631) in view of Eldon (U.S. Pre-Grant Publication No. 20090307028) and Kursun (U.S. Pre-Grant Publication No. 20200167784), and in further view of Kramme (U.S. Pre-Grant Publication No. 20210374753).
Claim 18
Regarding Claim 18, the combination of Bhandari, Eldon, and Kursun does not explicitly teach, but Kramme, however, does teach:
determining content within the webpage; and providing the content as an input to the machine learning model (See at least Paragraphs 69-72: Describes system for identifying a potential chargeback scenario using a trained machine learning program. The system may determine online activity data for a customer, such as data indicating actions that the customers took, and/or web sites visited by the customers. This includes content within web sites visited by customers. The online activity data may be used as an input within a machine learning program to detect fraud).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the application, to combine the teachings of Bhandari, Eldon, Kursun, and Kramme in order to reduce the time and cost associated with determining whether an adverse event, such as fraud, may be associated with a transaction (Kramme: Paragraphs 3-5).
14. Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Bhandari (U.S. Pre-Grant Publication No. 20170124631) in view of Eldon (U.S. Pre-Grant Publication No. 20090307028) and Kursun (U.S. Pre-Grant Publication No. 20200167784), and in further view of Higgins (U.S. Patent No. 10915900).
Claim 19
Regarding Claim 19, the combination of Bhandari, Eldon, and Kursun does not explicitly teach, but Higgins, however, does teach:
detecting that the event associated with the transaction has not occurred within the second time period, wherein the machine learning model is trained based on the event having not occurred within the second time period (See at least Col. 21, Lines 23-36: Describes techniques for leveraging a likelihood that a transaction will be associated with a refund request to determine whether and/or how long to delay sending, to a payment service, a request for processing the transaction. The data model may be iteratively updated based on newly received data. For example, the training module may receive updated training data and may re-train the data model based at least partly on the updated training data. The data may include data indicating whether any portion of the new transaction was associated with a refund request).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the application, to combine the teachings of Bhandari, Eldon, Kursun, and Higgins in order to reduce the costs associated with delayed and cancelled transaction, and reduce the number of communications exchanged between parties involved in the transaction (Higgins: Col. 6, line 24 – Col. 7, Line 54).
Response to Arguments
15. Applicant’s arguments filed March 18, 2026 have been fully considered.
Arguments Regarding Double Patenting
16. The double patenting rejection applied to claims 2-21 in the Non-Final Rejection has been withdrawn in response to the applicant’s claim amendments. The claims of the instant application have been sufficiently differentiated from the claims of the parent applications.
Arguments Regarding 35 U.S.C. 101
17. Applicant’s arguments (Amendment, Pgs. 10-13) concerning the prior rejection of the claims under 35 USC §101, including supposed deficiencies in the rejection, are not persuasive for the following reasons. Under the prior and current 101 analysis under 2019 PEG, the amended claims recite and are directed to a patent ineligible abstract idea, without something significantly more, for the reasons given above after consideration of the claimed features and elements. The abstract idea has been restated herein in line with the 2019 PEG guidance and the amended claims. Applicant is directed to the above full Alice/Mayo analysis in the 101 rejection.
Additionally, on pages 11 and 12 of their remarks, the applicant argues, “As such, the claimed solution provides an improvement to the technical field of electronic transaction processing. Specifically, the claimed solution reduces the computer resources required to process unnecessary transactions (e.g., reversal of the transactions such as chargebacks, etc.), thereby improving the computer resource efficiency in processing electronic transactions.” The examiner respectfully disagrees. Specifically, the examiner notes that the claims do not provide significant technical detail regarding how the system monitors the user interactions and/or how the monitored interactions are used to adjust the settlement process. While the examiner recognizes that there may be benefits to monitoring user interactions, as described in the applicant’s arguments, the claims do not provide any indication that these benefits are achieved through an improvement to any technology or technological field. Rather, the claims simply utilize generic data gathering processes to receive the user interaction data and determine the likelihood of an event associated with the transaction. Similarly, simply applying a generic machine learning model to analyze the gathered data and determine the likelihood of an event associated with a transaction does not amount to an improvement in transaction processing technology. The claims do not provide any technical detail regarding how the machine learning model is trained and or implemented to provide the desired output. Therefore, such limitations amount to no more than merely applying generic machine learning technology to implement the abstract idea.
Additionally, on pages 12 and 13 of their remarks, the applicant argues, “Similar to Claim 1 in Example 42, the additional elements recited in amended claim 2 herein enables a payment processing system to dynamically throttle the settlement process in electronic payment transactions based on the corresponding delay event likelihoods predicted by a machine learning model framework… Thus, the elements as a whole provides a specific improvement over prior payment transaction processing systems.” The examiner respectfully disagrees. Specifically, the examiner disagrees that the claims of the instant application are analogous to claim 1 of example 42. Specifically, claim 1 of Example 42 was found to be eligible because it recites a specific, technical improvement regarding how information is shared between users in real time. A similar improvement to transaction processing technology is not recited in the claims of the instant application. As described above, the claims do not provide sufficient technical detail regarding how the claimed processes are performed such that the claims amount to an improvement to transaction processing technology itself.
Therefore, for these reasons and the reasons given above, the rejection of these claims under 35 U.S.C. 101 is maintained.
Arguments Regarding 35 U.S.C. 102/103
18. The rejection of claims 2-15 under 35 U.S.C. 103 has been withdrawn in response to the applicant’s claim amendments. However, the rejection of claims 16-21 under 35 U.S.C. has been maintained. This rejection has been updated to address the newly added claim limitations, as described above.
Additionally, on pages 13 and 14 of their remarks, the applicant argues, “Independent claim 2 has been amended based on what was discussed during the Examiner Interview, which the Examiner has indicated would likely overcome the current § 103 rejections. As discussed, the cited references, alone or in combination, fail to disclose the limitations of "wherein the network resources comprise one or more online platforms different from the merchant interface and accessed by the user device... wherein the machine learning model is configured to identify one or more modalities of the first interactions with the network resource based on the first interaction data and determine the first likelihood based on the one or more modalities of the first interactions" as recited in amended claim 2.” The examiner respectfully agrees with this argument. As noted by the applicant, the combination of Bhandari and Eldon does not teach that the network resources comprise one or more online platforms different from the merchant interface. Rather, Eldon teaches monitoring interactions that occur on the merchant’s website. The combination of Bhandari and Eldon also does not teach a process for determining a modality of the determined interactions, and utilizing the modality of the interactions to determine the first likelihood. A sufficient combination of prior art references could not be identified to reasonably teach the specific combination of limitations recited in amended claim 2. Therefore, the rejection of claim 2 under 35 U.S.C 103 has been withdrawn. A similar argument can be made for independent claim 9.
However, on page 14 of their remarks, the applicant argues, “Independent claims 9 and 16 also include limitations similar to those described above by reference to claim 2. As such, the cited references also do not render claims 9 and 16 obvious for similar reasons discussed above by reference to claim 2.” The examiner respectfully disagrees. Specifically, the examiner notes that claim 16 was not amended to include all of the claim amendments recited in amended claim 2. Rather, claim 16 does not state that the network resources comprise one or more online platforms different from the merchant interface. Therefore, the rejection of claim 16 has been maintained and updated to address the newly added claim limitations. Specifically, claim 16 was amended to recite a process for determining a modality of the determined interactions, and utilizing the modality of the interactions to determine the first likelihood. However, this limitation is disclosed by Kursun, as described in the rejection above. Therefore, the sole addition of this particular limitation does not render claim 16 allowable over the prior art.
Citation of Pertinent Prior Art
19. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Ahmad (U.S. Pre-Grant Publication No. 20110258117): Describes a system that relates to the area of financial transaction processing, and more specifically relates to the use of multiple sources of data to modify real-time transaction decision making and behavior.
Mullins (U.S. Pre-Grant Publication No. 20220391989): Describes a payment processing service that may receive transaction data associated with a payment transaction between the merchant and a customer. Based on a predictive model, the server(s) can determine a level of risk associated with the merchant and/or the payment transaction and can determine a portion of the transaction to withhold from a settlement amount of the payment transaction based at least in part on the level of risk.
Conclusion
THIS ACTION IS MADE FINAL. 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 WILLIAM D NEWLON whose telephone number is (571)272-4407. The examiner can normally be reached Mon - Fri 8:30 - 4:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Gart can be reached at (571) 272-3955. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/WILLIAM D NEWLON/Examiner, Art Unit 3696
/MATTHEW S GART/Supervisory Patent Examiner, Art Unit 3696
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