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 .
The following is a Final Office Action in response to communications received March 24, 2026. Claim(s) 5-6, 16-17 have been canceled. Claim(s) 1, 3, 8, 14-15, 18 and 20 have been amended. No new claims have been added. Therefore, claims 1-4, 7-15 and 18-20 are pending and addressed below.
Priority
Applicant No. 18,654,102 filing date: 05/03/2024.
Applicant name/Assignee: Affirm, Inc
Inventor(s): Kondrat, Dominik; Qian, Joshua; Lin, Allan; Fisher, Duncan
Response to Arguments/Amendments
Affidavit
The affidavit under 37 CFR 1.132 filed March 24, 2026 is insufficient to overcome the rejection of claim 1-4, 7-15 and 18-20 based upon 101 rejections as set forth in the last Office action because: The affidavit discusses the experience and goals of the inventor in making decisions in behavioral economics in financial systems in designing evaluation for enabling decisions in economic performance. The Affidavit describes the affiant’s occupation in data driven decision making, risk assessment, portfolio forecasting and enabled decision frameworks using business models, artificial intelligence technology and complex modeling strategies. In particular how the current application aggregates data streams and the application of loan matching algorithms to infer relationships among transactions. The process is directed toward multiple transactions belong to common latent purchase events for modifying the system state by restructuring the associated loan. Therefore, the claimed architecture transforms static transaction processing into a stateful inference engine operating over streaming data, enabling continuous context aware decision making. Applicant’s affidavit argument of “transforming” a transaction into a stateful inference engine focuses on the transaction activity and what the analysis of the data focuses on rather that the “state” of the system and its operation processing. The specification and the claims are silent and have no support for any processes which are directed toward the “state” of the system or its processing. The affidavit merely discloses that the computer components are integrated into the solution defined for loan decision making based data streams and analysis of relationships between the transactions analyzed and loan decisions and does not explain how the technology recited in the claims provide indications of patent eligibility under step 2A prong 2 going beyond generally linking technology to the abstract idea to perform the abstract idea. The affidavit provides opinions on the computer arrangements as being unconventional without explanation as to what or why the computer arrangements rather than the abstract idea process is unconventional.
The affidavit further argues that the providing of a technical means to give customers the ability to use virtual cards to support multiple transaction and multiple corresponding loans into a single loans provides a technical solution to the problem in transactions by providing processing that matches transaction to a corresponding loan to allow customer to use a persistent PAN. The affidavit argues that the employing of ML modules to select and update matching models for displaying to user feedback elements is advantageous for use in loan matching algorithms due large influx of different types of transaction information that have multiple loans for different items allowing for continuous updating to enable loan matching processes. This argument merely supports the previous Office analysis that the focus of the claims is the abstract idea where technology is merely applied as a tool to apply the abstract idea.
The affidavit further provides the opinion that the limitations provide significantly more than applying technology as a field of use to implement the identified abstract idea. The recitation of the affidavit that the combination of the technical tool (ML model) and the concepts (loan matching process) without explaining why the recited limitations provide significantly more than the identified abstract idea is not sufficient.
Accordingly the affidavit fails to set forth the reasons for the patent ineligibility by failing to set forth facts that are commiserate with patent eligibility according to the law. The opinions presented are not germane to the rejection at issue. It include(s) statements which amount to an affirmation that the claimed subject matter for the analysis of data streams for decision making for loan matching where the analysis is performed in a computer environment using machine learning algorithms for the analysis. This is not relevant to the issue of patent eligibility of the claimed subject matter and provides no objective evidence thereof. See MPEP § 716.
Claim Rejections - 35 USC § 101
Applicant's arguments filed March 24, 2026 have been fully considered but they are not persuasive.
In the remarks applicant points to MPEP 2106.05(f) arguing that the claimed limitations integrate any alleged abstract idea into a practical application. Applicant argues the claimed functions in a defined computing platform of providing persistent PAN in multiple transactions in combination with machine learning tools that is trained. Applicant points to the relation is executing processing circuitry to provide loan matching process through use of the same card in multiple transactions where the ML model defines in relation to how the model is utilized and trained in ways that are not conventional. Applicant is arguing limitations not claimed. The limitations do not recite any processes for “training” ML models. Rather the limitations recites updating the matching model using the ML model by providing feedback data to enhance the ML module. The specification is equally silent with respect to a training of ML modules. The specification discloses the learning module employing known learning techniques at a high level, applying training data and the use for the model in the analysis of transaction data.
[0072] The machine learning module 190 may employ one or more instances of a neural network (e.g., a CNN), a support vector machine (SVM), Bayesian network, logistic regression, logistic classification, decision tree, ensemble classifier or other machine learning model to process inputs received by the machine learning module 190 to generate outputs as described herein. The machine learning module 190 may be supervised (identifying patterns in raw data upon which inference processes are desired to be performed via training examples) or unsupervised (identifying patterns in raw data upon which inference processes are desired to be performed without training examples). In an example embodiment, the machine learning module 190 may include a neural network of nodes where each node includes input values, a set of weights, and an activation function. The neural network node may calculate the activation function on the input values to produce an output value. The activation function may be a nonlinear function computed on the weighted sum of the input values plus an optional constant. Neural network nodes may be connected to each other such that the output of one node is the input of another node. Moreover, neural network nodes may be organized into layers, each layer
comprising one or more nodes. The neural network may be trained and update its internal parameters via backpropagation during training. A CNN may be a type of neural network that further adds one or more convolutional filters (e.g., kernels) that operate on the outputs of the preceding neural network layer to produce and output to then next layer. The convolutional filters may have a window in which they operate, which is spatially local. A node of a preceding layer may be connected to a node in the current layer if the node of the preceding layer is within the window. If not within the window, then the nodes are not connected.
[0073] In an example embodiment, training may occur via the provision of training data along with target data that includes desired output data associated achieved from the training data via respective models stored in the memory 104 and accessible to the machine learning module 190. Thereafter, when inferences are to be drawn with respect to a new set of data including new information to provide an output that is indicative of options for output, training backpropagation may be provided to update the learning. The information provided to the machine learning module 190, and the corresponding outputs to be gained therefrom, may vary. Thus, for example, the machine learning module 190 may, in some cases, be employed by the security module 140 to enhance security performance. In such cases, for example, massive amounts of real time account activity across a multitude of instances of the user account 152 may be simultaneously monitored. Specific potential instances of fraud may be detectable in real time, whereas others may only be detectable by monitoring patterns that play out over longer periods of time. The machine learning module 190 may assist in load balancing between real time fraud detection resources and post hoc fraud detection resources of the security module 140. As such, the load balancing function may effectively triage massive amounts of data into respective camps that dictate how quickly fraud detection resources are to be employed for detecting suspicious
activity. In such capacity, the machine learning module may not only conduct load balancing, but may schedule individual fraud monitoring activities at specific future times during which resources for fraud detection are expected to be available for the corresponding type or priority of data being analyzed for fraudulent activity.
[0076] The training data used to train the machine learning module 190 may be selected by the facilitator ahead of time to include merchants, products, and categories thereof that are known by the facilitator through past experience to follow various known patterns. The known patterns may be used to build models that can infer relationships based on pieces of data that suggest the potential existence of the known patterns. However, every time a match is made, whether correctly and automatically by the machine learning module 190, or through input by the facilitator, merchants, or customers, the machine learning module 190 and its respective models (e.g., category specific models) may also be updated. Failed matches may also train models, as noted above. Moreover, the machine learning module 190 may also be trained to address other interactions with the customer that may enhance the customer experience so that, over time, the customer experience is continuously enhanced.
Applicant’s argument is not persuasive.
Claim Rejections - 35 USC § 103
Applicant's arguments filed March 24, 2026 have been fully considered but they are not persuasive.
In the remarks applicant argues the prior art references fail to teach the amended limitations “the loan matching algorithm comprises: selecting a matching model based on the transaction notification, wherein the loan matching algorithm employs a machine learning module, the machine learning module being configured to select and update the matching model, and wherein the machine learning module updates the matching model via providing a live feedback element for display at a user device of the customer, the feedback element, responsive to interface with the customer, providing data for user enhanced learning to the machine learning module”. Applicant argues that the combination of Wheeler and Zhu fails to teach the updates provided as a live feedback element for display at a device of a customer providing data to the ML module. Applicant’s argument is not persuasive, the prior art combination Wheeler and Zhu teaches-para 0023 wherein the prior art teaches receiving communications from user device in chat program, instant text messaging, sms message, email, where information may be updated and provided to risk score generation system, para 0051 wherein the prior art teaches prompting user via GUI for additional information and updating model in response to user inputting additional information and displaying loan options to the user. The rejection is maintained.
In the remarks applicant argues that dependent claims 2-5, 8-13, 15 and 18-20 are allowable over the prior art references based on the allowable subject matter of independent claims 1 and 14. The examiner respectfully disagrees, see response above, the rejection is maintained.
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 1-5, 8-15 and 18-20 are rejected under 35 U.S.C. § 101 because the instant application is directed to non-patentable subject matter. Specifically, the claims are directed toward at least one judicial exception without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of USPTO, applies to all statutory categories, and is explained in detail below.
In reference to claims 1-5 and 8-13:
STEP 1. Per Step 1 of the two-step analysis, the claims are determined to include a method, as in independent Claim 1 and the dependent claims. Such methods fall under the statutory category of "process." Therefore, the claims are directed to a statutory eligibility category.
STEP 2A Prong 1. The claimed invention is directed to an abstract idea without significantly more. Method claim 1 recites a method steps 1) receiving notification 2) facilitating payment of merchant 3) executing loan matching algorithm to determine whether transaction associated with existing loan 4) determining transaction is one of plurality of transaction of common purchase event associated with existing loan 5) modifying loan to include transaction 6) issuing new loan for customer if determining transaction not one of plurality of transactions of common purchase event 7) facilitating servicing new loan/modifying loan 8) selecting a matching model, 9) employes learning module to select and update matching model, 10) providing feedback elements for display 11) providing data. The specification discloses that the focus of the invention is to enable customers to associate a loan with purchase events using a virtual card, allowing customers to access credit in a buy now, pay later format (see para 0006). The claimed limitations in light of the specification, which under its broadest reasonable interpretation, covers performance of sales activity.
These concepts are enumerated in Section I of the 2019 revised patent subject matter eligibility guidance published in the federal register (84 FR 50) on January 7, 2019) is directed toward abstract category of methods of organizing human activity.
STEP 2A Prong 2: The identified judicial exception is not integrated into a practical application because the claims fail to provide indications of patent eligible subject matter that integrate the alleged abstract idea into a practical application. The additional elements recited in the claim beyond the abstract idea include a “loan matching algorithm” applied to determine whether to associate a transaction with an existing loan and determining transaction is one of a plurality of transactions of a common purchase event associated with an existing loan. The recited “loan matching algorithm” amounts to no more than mere instructions to implement the identified abstract idea. This is because the claimed “algorithm” is merely applied/used for performing the “determining” steps lacking technical disclosure or being tied to any structural elements and therefore is software per se. (see MPEP 2106.03). The steps “receiving…notification” lacks technical disclosure and is not tied to any structural elements.
The receiving step, which according to MPEP 2106.05(d) II (see also MPEP 2106.05(g)) are directed toward extra solution activity. The courts have recognized the following computer functions are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014)
The claim limitations “facilitating payment”, “modifying the matched loan”, “issuing a new loan” based on a determined condition and “facilitating servicing the …loan or modified loan” are not tied to any structural elements, instead the limitations are the process of the abstract idea of a sales activity/commercial interaction.
The additional element machine learning module applied to select and update matching model.
The additional element “user device” applied to display feedback element and “user interface” applied for providing data for machine learning module.
The claim limitations when considered individually fail to provide any indications of patent eligible subject matter, according to MPEP guidance (see MPEP 2106.05 (a)-(c), (e )-(h).
(i) an improvement to the functioning of a computer;
(ii) an improvement to another technology or technical field;
(iii) an application of the abstract idea with, or by use of, a
particular machine;
(iv) a transformation or reduction of a particular article to a
different state or thing; or
(v) other meaningful limitations beyond generally linking the
use of the abstract idea to a particular technological environment.
When the claims are taken as an ordered combination or as a whole, the combination of limitations, the combination of limitations 1-2 are directed toward receiving transaction notifications and facilitating payments without any structural elements and there a transaction process. The combination of limitations 3-7 lack any technical structural merely reciting issuing loans associated with transactions based on determining loan matching process based on a condition and facilitating servicing the loan issued or modifying associated transaction loans. When considered as a whole or ordered combination the combination of limitations 1-7 are directed toward a sales and commercial activity. The combination of limitations 1-7 and 8-11 of selecting matching and updating matching models and providing feedback elements and providing data for user to learning module.
The combinations of parts is not directed toward any of the indications of patent eligible subject matter under step 2A prong 2, but instead a transaction activity and loans associated with the transaction. MPEP guidance (see MPEP 2106.05 (a)-(c), (e )-(h). The claim limitations as a whole, as an ordered combination and the combination of steps not integrate the judicial exception into a practical application as the claim process fails to impose meaningful limits upon the abstract idea. This is because the claimed subject matter fails to provide additional elements or combination or elements except for the recitation of a loan algorithm lacking any structural elements as a tool to perform the identified abstract idea. The claim limitations and specification lacks technical disclosure on what the technical problem was and how the claimed limitations provide a technical solution to a technical problem rather than a solution to a problem found in the abstract idea. Taking the claim elements separately, or as a combination, at each step of the process is purely in terms of results desired and devoid of implementation of details. Technology is not integral to the process as the claimed subject matter where the recited steps could be performed by any known means. Furthermore, the claimed functions do not provide an operation that could be considered as sufficient to provide a technological implementation or application of/or improvement to this concept (i.e. integrated into a practical application).
The integration of elements do not improve upon technology or improve upon computer functionality or capability in how computers carry out one of their basic functions. The integration of elements do not provide a process that allows computers to perform functions that previously could not be performed. The integration of elements do not provide a process which applies a relationship to apply a new way of using an application. The limitations do not recite a specific use machine or the transformation of an article to a different state or thing. The limitations do not provide other meaningful limits beyond generally linking the use of the abstract idea to a particular technological environment. The steps are still a combination made to perform a financial activity and does not provide any of the determined indications of patent eligibility set forth in the 2019 USPTO 101 guidance. The additional steps only add to those abstract ideas using generic functions, and the claims do not show improved ways of, for example, an particular technical function for performing the abstract idea that imposes meaningful limits upon the abstract idea. Moreover, Examiner was not able to identify any specific technological processes, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified. The claim provides no technical details regarding how the steps are performed. Instead, similar to the claims at issue in Intellectual Ventures I LLC v. Capital One Financial Corp., 850 F.3d 1332 (Fed. Cir. 2017), “the claim language . . . provides only a result-oriented solution with insufficient detail for how a computer accomplishes it. Our law demands more.” Intellectual Ventures, 850 F.3d at 1342 (citing Elec. Power Grp. LLC vy. Alstom, S.A., 830 F.3d 1350, 1356 (Fed. Cir. 2016)). The claim is directed to an abstract idea
STEP 2B; The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to concepts of the abstract idea into a practical application. This is because the claim limitations fail to provide any technical process only nominally mentioning the use of an loan algorithm to match loans and the employing (applying) a machine learning module to select and update the matching model and providing user interface and device for displaying data.
As to the data operated upon, "even if a process of collecting and analyzing information is 'limited to particular content' or a particular 'source,' that limitation does not make the collection and analysis other than abstract." SAP America, Inc. v. Invest Pic LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018). Considered as an ordered combination, the computer components of Applicant’s claimed functions add nothing that is not already present when the steps are considered separately.
As evidence of well understood and conventional process.
[0072] The machine learning module 190 may employ one or more instances of a neural network (e.g., a CNN), a support vector machine (SVM), Bayesian network, logistic regression, logistic classification, decision tree, ensemble classifier or other machine learning model to process inputs received by the machine learning module 190 to generate outputs as described herein. The machine learning module 190 may be supervised (identifying patterns in raw data upon which inference processes are desired to be performed via training examples) or unsupervised (identifying patterns in raw data upon which inference processes are desired to be performed without training examples). In an example embodiment, the machine learning module 190 may include a neural network of nodes where each node includes input values, a set of weights, and an activation function. The neural network node may calculate the activation function on the input values to produce an output value. The activation function may be a nonlinear function computed on the weighted sum of the input values plus an optional constant. Neural network nodes may be connected to each other such that the output of one node is the input of another node. Moreover, neural network nodes may be organized into layers, each layer
comprising one or more nodes. The neural network may be trained and update its internal parameters via backpropagation during training. A CNN may be a type of neural network that further adds one or more convolutional filters (e.g., kernels) that operate on the outputs of the preceding neural network layer to produce and output to then next layer. The convolutional filters may have a window in which they operate, which is spatially local. A node of a preceding layer may be connected to a node in the current layer if the node of the preceding layer is within the window. If not within the window, then the nodes are not connected.
[0073] In an example embodiment, training may occur via the provision of training data along with target data that includes desired output data associated achieved from the training data via respective models stored in the memory 104 and accessible to the machine learning module 190. Thereafter, when inferences are to be drawn with respect to a new set of data including new information to provide an output that is indicative of options for output, training backpropagation may be provided to update the learning. The information provided to the machine learning module 190, and the corresponding outputs to be gained therefrom, may vary. Thus, for example, the machine learning module 190 may, in some cases, be employed by the security module 140 to enhance security performance. In such cases, for example, massive amounts of real time account activity across a multitude of instances of the user account 152 may be simultaneously monitored. Specific potential instances of fraud may be detectable in real time, whereas others may only be detectable by monitoring patterns that play out over longer periods of time. The machine learning module 190 may assist in load balancing between real time fraud detection resources and post hoc fraud detection resources of the security module 140. As such, the load balancing function may effectively triage massive amounts of data into respective camps that dictate how quickly fraud detection resources are to be employed for detecting suspicious
activity. In such capacity, the machine learning module may not only conduct load balancing, but may schedule individual fraud monitoring activities at specific future times during which resources for fraud detection are expected to be available for the corresponding type or priority of data being analyzed for fraudulent activity.
[0076] The training data used to train the machine learning module 190 may be selected by the facilitator ahead of time to include merchants, products, and categories thereof that are known by the facilitator through past experience to follow various known patterns. The known patterns may be used to build models that can infer relationships based on pieces of data that suggest the potential existence of the known patterns. However, every time a match is made, whether correctly and automatically by the machine learning module 190, or through input by the facilitator, merchants, or customers, the machine learning module 190 and its respective models (e.g., category specific models) may also be updated. Failed matches may also train models, as noted above. Moreover, the machine learning module 190 may also be trained to address other interactions with the customer that may enhance the customer experience so that, over time, the customer experience is continuously enhanced.
The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is generic components and functions in the related arts. The claim is not patent eligible.
The remaining dependent claims—which impose additional limitations—also fail to claim patent-eligible subject matter because the limitations cannot be considered statutory. In reference to claims 2-5 and 8-13 these dependent claim have also been reviewed with the same analysis as independent claim 1. Dependent claim 2 is directed toward receiving a request, setting up a customer account and issuing a payment instrument with technical details for a transaction activity. Dependent claim 3 is directed toward accessing list of loans, applying matching model and loans of the list, ranking loans according to matching score model results – applying a model nominally to analyze data for a loan process- commercial activity. Dependent claim 4 is directed toward selecting highest ranked loan having matching score- a commercial activity. Dependent claim 8 is directed toward matching models with different category of purchase events- a commercial activity without any technical structure. Dependent 9 is directed toward non-functional descriptive subject matter and the selection of models based on category codes sharing common purchase events- a commercial activity with nominal mention of applying models. Dependent claim 10 is directed toward models with sets of transaction patterns that identify common purchase events for different category of purchase events- commercial activity without technical disclosure. Dependent claim 11 is directed toward list of loan initiated/modified within a time period prior to transaction notification- commercial activity. Dependent claim 12 is directed toward receiving indication of refund, employing machine learning module to determine loan corresponding to return product and applying refund corresponding to loan – a commercial activity where the use of the ML model amounts to no more than mere instructions to apply the abstract idea without technical disclosure. Dependent claim 13 is directed toward receiving indication of second refund, employing machine learning algorithm to determine second refund is part of common purchase event with first refund and applying second refund to corresponding loan- a commercial activity where the use of the ML model amounts to no more than mere instructions to apply the abstract idea without technical disclosure.
The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 1. Where all claims are directed to the same abstract idea, “addressing each claim of the asserted patents [is] unnecessary.” Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat 7 Ass ’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims 2-5 and 8-13 are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter.
In reference to claims 14-15 and 18-20:
STEP 1. Per Step 1 of the two-step analysis, the claims are determined to include an apparatus, as in independent Claim 14 and the dependent claims. Such apparatus fall under the statutory category of "machine." Therefore, the claims are directed to a statutory eligibility category.
STEP 2A Prong 1. The claimed invention is directed to an abstract idea without significantly more. Apparatus claim 14 recites functional operations 1) receive notification 2) facilitate payment of merchant 3) execute loan matching algorithm to determine whether transaction associated with existing loan 4) determine transaction is one of plurality of transaction of common purchase event associated with existing loan 5) modify loan to include transaction 6) issue new loan for customer if determining transaction not one of plurality of transactions of common purchase event 7) facilitate servicing new loan/modifying loan 8) selecting a matching model, 9) employes learning module to select and update matching model, 10) providing feedback elements for display 11) providing data. The specification discloses that the focus of the invention is to enable customers to associate a loan with purchase events using a virtual card, allowing customers to access credit in a buy now, pay later format (see para 0006). The claimed limitations in light of the specification, which under its broadest reasonable interpretation, covers performance of sales activity.
These concepts are enumerated in Section I of the 2019 revised patent subject matter eligibility guidance published in the federal register (84 FR 50) on January 7, 2019) is directed toward abstract category of methods of organizing human activity.
STEP 2A Prong 2: The identified judicial exception is not integrated into a practical application because the claims fail to provide indications of patent eligible subject matter that integrate the alleged abstract idea into a practical application. The additional elements recited in the claim beyond the abstract idea include an apparatus comprising processing circuitry to perform the operations corresponding to the limitations of claim 14. The claimed processing circuitry is merely applied at a high level to perform the insignificant activity of “receiving…notifications” and to perform the transaction activity facilitating a payment and matching a transaction to an existing loan, modify the loan to include the transaction or issue a new loan and facilitating the new loan/modify the loan which is a transaction and commercial activity. The processing circuitry operations of the apparatus merely recite functions with expected outcomes and can be performed by any known technical means. The additional elements recited in the claim beyond the abstract idea include loan matching algorithm and machine learning module applied to select and update the matching model. A user interface and user device applied for display data.
When the claims are taken as an ordered combination or as a whole, the combination of limitations, the combination of limitations 1-2 are directed toward receiving transaction notifications and facilitating payments without any structural elements and there a transaction process. The combination of limitations 3-7 lack any technical structural merely reciting issuing loans associated with transactions based on determining loan matching process based on a condition and facilitating servicing the loan issued or modifying associated transaction loans.
When considered as a whole or ordered combination the combination of limitations 1-7 and 8-11 of selecting matching and updating matching models and providing feedback elements and providing data for user to learning module which as a whole are directed toward a sales and commercial activity.
The combinations of parts is not directed toward any of the indications of patent eligible subject matter under step 2A prong 2, but instead a transaction activity and loans associated with the transaction. MPEP guidance (see MPEP 2106.05 (a)-(c), (e )-(h). The claim limitations as a whole, as an ordered combination and the combination of steps not integrate the judicial exception into a practical application as the claim process fails to impose meaningful limits upon the abstract idea. This is because the claimed subject matter fails to provide additional elements or combination or elements except for the recitation of a loan algorithm lacking any structural elements as a tool to perform the identified abstract idea. The claim limitations and specification lacks technical disclosure on what the technical problem was and how the claimed limitations provide a technical solution to a technical problem rather than a solution to a problem found in the abstract idea. Taking the claim elements separately, or as a combination, at each step of the process is purely in terms of results desired and devoid of implementation of details. Technology is not integral to the process as the claimed subject matter where the recited steps could be performed by any known means. Furthermore, the claimed functions do not provide an operation that could be considered as sufficient to provide a technological implementation or application of/or improvement to this concept (i.e. integrated into a practical application).
The integration of elements do not improve upon technology or improve upon computer functionality or capability in how computers carry out one of their basic functions. The integration of elements do not provide a process that allows computers to perform functions that previously could not be performed. The integration of elements do not provide a process which applies a relationship to apply a new way of using an application. The limitations do not recite a specific use machine or the transformation of an article to a different state or thing. The limitations do not provide other meaningful limits beyond generally linking the use of the abstract idea to a particular technological environment. The steps are still a combination made to perform a financial activity and does not provide any of the determined indications of patent eligibility set forth in the 2019 USPTO 101 guidance. The additional steps only add to those abstract ideas using generic functions, and the claims do not show improved ways of, for example, an particular technical function for performing the abstract idea that imposes meaningful limits upon the abstract idea. Moreover, Examiner was not able to identify any specific technological processes, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified. The claim provides no technical details regarding how the steps are performed. Instead, similar to the claims at issue in Intellectual Ventures I LLC v. Capital One Financial Corp., 850 F.3d 1332 (Fed. Cir. 2017), “the claim language . . . provides only a result-oriented solution with insufficient detail for how a computer accomplishes it. Our law demands more.” Intellectual Ventures, 850 F.3d at 1342 (citing Elec. Power Grp. LLC vy. Alstom, S.A., 830 F.3d 1350, 1356 (Fed. Cir. 2016)). The claim is directed to an abstract idea
STEP 2B; The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to concepts of the abstract idea into a practical application. The additional elements recited in the claim beyond the abstract idea include an apparatus comprising a processing circuitry to perform the operations of receive…notification, facilitate payment, “execute loan matching algorithm …to determine whether to associated …transaction with existing loan”, “consider the existing loan to be a matched loan”, “modify…matched loan to include the transaction”, “issue a new loan …based on transaction”, “facilitate servicing the new loan…modified loan” - are some of the most basic functions of a computer. This is because the claim limitations fail to provide any technical process only nominally mentioning the use of an loan algorithm to match loans and the employing (applying) a machine learning module to select and update the matching model and providing user interface and device for displaying data.
Taking the claim elements separately, the function performed by the processing circuitry of the apparatus at each step of the process is purely conventional. The claimed machine learning model is high level without details as to technical implementation and is not enough to qualify as “significantly more” include “apply it” (or an equivalent) with an abstract idea, mere instructions to implement the abstract idea on a computer or requiring no more than a generic compute to perform generic computer functions that are well understood activities known to the industry. As a result, none of the hardware recited by the apparatus claims offers a meaningful limitation beyond generally linking the use of the method to a particular technological environment, that is, implementation via computers.... The claim limitations do not recite that any of the “devices” perform more than a high level generic function .... None of the limitations recite technological implementation details for any of these steps, but instead recite only results desired to be achieved by any and all possible means.... Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
When the claims are taken as a whole, as an ordered combination, the combination of steps does not add “significantly more” by virtue of considering the steps as a whole, as an ordered combination. All of these apparatus functions are generic, routine, conventional computer activities that are performed only for their conventional uses. See Elec. Power Grp. v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016). Also see In re Katz Interactive Call Processing Patent Litigation, 639 F.3d 1303, 1316 (Fed. Cir. 2011) ("Absent a possible narrower construction of the terms “generating”, “transmitting”, “intercepting”, identifying”, “determining”, “replacing” and “routing' ... are functions can be achieved by any general purpose computer without special programming"). None of these activities are used in some unconventional manner nor do any produce some unexpected result. Applicants do not contend they invented any of these activities. In short, each step does no more than require a generic computer to perform generic computer functions.
As to the data operated upon, "even if a process of collecting and analyzing information is 'limited to particular content' or a particular 'source,' that limitation does not make the collection and analysis other than abstract." SAP America, Inc. v. Invest Pic LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018). Considered as an ordered combination, the computer components of Applicant’s claimed functions add nothing that is not already present when the steps are considered separately. The sequence of data reception-analysis modification-transmission is equally generic and conventional. See Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014) (sequence of receiving, selecting, offering for exchange, display, allowing access, and receiving payment recited as an abstraction), Inventor Holdings, LLC v. Bed Bath & Beyond, Inc., 876 F.3d 1372, 1378 (Fed. Cir. 2017) (sequence of data retrieval, analysis, modification, generation, display, and transmission), Two-Way Media Ltd. v. Comcast Cable Communications, LLC, 874 F.3d 1329, 1339 (Fed. Cir. 2017) (sequence of processing, routing, controlling, and monitoring). The ordering of the steps is therefore ordinary and conventional. The analysis concludes that the claims do not provide an inventive concept because the additional elements recited in the claims do not provide significantly more than the recited judicial exception.
According to 2106.05 well-understood and routine processes to perform the abstract idea is not sufficient to transform the claim into patent eligibility. As evidence the examiner provides:
Specification describes:
[0008] In another example embodiment, an apparatus for matching separate transactions to a single loan may be provided. The apparatus may include processing circuitry configured for receiving a transaction notification associated with a customer use of a virtual card in association with a transaction with a merchant, where the virtual card has a persistent PAN that enables the virtual card to be used for obtaining financing for multiple transactions without changing the persistent PAN. The processing circuitry may also be configured for facilitating payment of the merchant on behalf of the customer in response to the transaction notification, and executing a loan matching algorithm with respect to the transaction notification to determine whether to
associate the transaction with an existing loan. The processing circuitry may also be configured for, responsive to the loan matching algorithm determining that the transaction is one of a plurality of transactions of a common purchase event associated with the existing loan, considering the existing loan to be a matched loan, and modifying the matched loan to include the transaction. The processing circuitry may also be configured for, responsive to the loan matching algorithm not determining that the transaction is one among the plurality of transactions of the common purchase event, issuing a new loan for the customer based on the transaction, and facilitating servicing the new loan or the modified loan.
[0069] The selection of the matching model is accomplished by selecting a best or most appropriate model to a given situation or set of circumstances. Thus, the matching model that is selected may be one of a plurality of candidate models, where each of the candidate models is associated with a different category of purchase events. In some cases, the transaction notification or any portion thereof may be used, at least in part, to select the matching model from among the candidate models. The selected matching model may be selected based on an association of one or more category codes to a corresponding one of the candidate models sharing the common
purchase event. In other words, if the common purchase event is in the category of travel, the candidate model for travel purchase events may be selected. Thus, it may be understood that the candidate models may each include a distinct set of transaction patterns that identify the common purchase event distinctly for different categories of purchase events. However, models may be even more finely tuned to specific situations, merchants, products, brands, geographic regions, shopping seasons, or any other features that may have statistical propensities to have common patterns that can be recognized using the machine learning module 190.
[0072] The machine learning module 190 may employ one or more instances of a neural network (e.g., a CNN), a support vector machine (SVM), Bayesian network, logistic regression, logistic classification, decision tree, ensemble classifier or other machine learning model to process inputs received by the machine learning module 190 to generate outputs as described herein. The machine learning module 190 may be supervised (identifying patterns in raw data upon which inference processes are desired to be performed via training examples) or
unsupervised (identifying patterns in raw data upon which inference processes are desired to be performed without training examples). In an example embodiment, the machine learning module 190 may include a neural network of nodes where each node includes input values, a set of weights, and an activation function. The neural network node may calculate the activation function on the input values to produce an output value. The activation function may be a nonlinear function computed on the weighted sum of the input values plus an optional constant. Neural network nodes may be connected to each other such that the output of one node is the input of another node. Moreover, neural network nodes may be organized into layers, each layer comprising one or more nodes. The neural network may be trained and update its internal parameters via backpropagation during training. A CNN may be a type of neural network that further adds one or more convolutional filters (e.g., kernels) that operate on the outputs of the preceding neural network layer to produce and output to then next layer. The convolutional filters may have a window in which they operate, which is spatially local. A node of a preceding layer may be connected to a node in the current layer if the node of the preceding layer is within the window. If not within the window, then the nodes are not connected.
[0074] In some embodiments, the account management module 150 and/or the
transaction management module 160 may leverage resources of the machine learning module
190 for enhanced performance as well. In this regard, for example, the machine learning module
190 may employ the model selected to determine the presence of situational factors that indicate
a high likelihood that a current transaction is actually part of a common purchase even with a
transaction (or transactions) that have previously been processed into an existing loan. To do so,
matching data and matching efforts are run on a huge scale simultaneously or nearly so all over
the country (or indeed all over the world), and the machine learning module 190 may rapidly
apply the model to each individual situation at volume in order to make rapid matching
determinations. The patterns (and models) may be specific to certain categories of activity or
merchants, as noted above. Thus, for example, sets of transactions that are often part of a
common purchase event may be quickly and accurately identified. For example, one merchant
may have a habit of breaking a common purchase event of a specific type (e.g., a cruise ship
booking) into multiple separate transactions (e.g., a charge at check-in, additional incremental
increases as customer spending meets thresholds, and then a final charge at checkout). The
machine learning module 190 may employ a model for cruise activity, and the model may enable
the machine learning module 190 to anticipate, for a given cruise line, the specific timing and/or
names/identifiers of the entities initiating transactions at various times in association with what
the customer would prefer to consider a common purchase event, and associate with a single
loan. Specific payment services (e.g., PayPal, Square, etc.) may follow distinctive patterns for
transaction handling, which may also be considered and handled by the respective models and
the machine learning module 190. Large, nation-wide merchant chains may have different store
identifiers, and different processing characteristics that apply to specific stores, specific products,
specific geographic locations, etc., and those patterns may be learned and modified over time by
the machine learning module 190.
[0076] The training data used to train the machine learning module 190 may be selected
by the facilitator ahead of time to include merchants, products, and categories thereof that are
known by the facilitator through past experience to follow various known patterns. The known
patterns may be used to build models that can infer relationships based on pieces of data that
suggest the potential existence of the known patterns. However, every time a match is made,
whether correctly and automatically by the machine learning module 190, or through input by
the facilitator, merchants, or customers, the machine learning module 190 and its respective
models (e.g., category specific models) may also be updated. Failed matches may also train
models, as noted above. Moreover, the machine learning module 190 may also be trained to
address other interactions with the customer that may enhance the customer experience so that,
over time, the customer experience is continuously enhanced.
The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is generic components and functions in the related arts. The claim is not patent eligible.
The remaining dependent claims—which impose additional limitations—also fail to claim patent-eligible subject matter because the limitations cannot be considered statutory. In reference to claims 15 and 18-20 these dependent claim have also been reviewed with the same analysis as independent claim 14. Dependent claim 15 is directed toward accessing list of loans, selecting a model based on notification, applying model to notification and loan list, ranking loans according to score- directed toward a commercial activity. Dependent claim 19 is directed toward content of notification- non-functional descriptive subject matter, the module to select matching model based on association of category codes to corresponding one of models sharing common purchase events, models include set of transaction patterns that identify common purchase event for different category of purchase events- applying technology to analyze commercial activity. Dependent claim 20 is directed toward receiving indications of first refund, employing learning modules to determine a loan to a returned product, applying first refund, receiving an indication of second refund from different merchants, employing learning module to determine second refund part of common purchase event with first refund, apply second refund to loan-applying technology to analyze commercial activity
The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 14. Where all claims are directed to the same abstract idea, “addressing each claim of the asserted patents [is] unnecessary.” Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat 7 Ass ’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims 15-16 and 18-20 are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter.
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 (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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-3, 6-7, 11; Claim(s) 14-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over WO 02/13435 A1 by Wheeler et al (Wheeler), in view of US Pub. No. 2013/0346302 A1 by Purves et al (Purves) and further in view of US Pub No. 2023/0222575 A1 by Zhu et al. (Zhu)
In reference to Claim 1:
Wheeler teaches:
A method of matching separate transactions to a single loan ((Wheeler) in at least page 16 lines 10-15, page 19 lines 35-36), the method comprising:
receiving a transaction notification associated with a customer [purchaser] use of a payment instrument in association with a transaction with a merchant [supply company], the payment instrument [PO}…((Wheeler) in at least page 17 lines 29-page 18 lines 1-27, page 23 lines 23-page 24 lines 1- 7, page 25 lines 2-9, page 44 lines 27-34);
facilitating payment of the merchant on behalf of the customer in response to the transaction notification …((Wheeler) in at least page 23 lines 23-page 24 lines 1- 7, page 25 lines 10-18, page 44 lines 27-34);
… with respect to the transaction notification to determine whether to associate the transaction with an existing loan ((Wheeler) in at least page 17 lines 10-15, page 21 lines 34-page 22 lines 1-3, page 23 lines 23-34);
responsive to the … determining that the transaction is one of a plurality of transactions of a common purchase event associated with the existing loan,… ((Wheeler) in at least page 17 lines 10-15, page 21 lines 34-page 22 lines 1-3, page 23 lines 23-34);
modifying the matched loan to include the transaction ((Wheeler) in at least page 21 lines 12-30, page 22 lines 22-27, page 23 lines 23-34, page 63 lines 23-31); …
facilitating servicing the new loan or the modified loan.((Wheeler) in at least page 22 lines 22-27 wherein the prior art teaches administrating the loan payments and account balances)
Wheeler does not explicitly teach:
…the payment instrument having a persistent personal account number (PAN) that enables the payment instrument to be used for obtaining financing for multiple transactions without changing the persistent PAN
executing a loan matching algorithm… considering the existing loan to be a matched loan
responsive to the loan matching algorithm not determining that the transaction is one among the plurality of transactions of the common purchase event, issuing a new loan for the customer based on the transaction; and
wherein the loan matching algorithm comprises:
selecting a matching model based on the transaction notification, wherein the loan matching algorithm employs a machine learning module, the machine learning module being configured to select and update the matching model, and wherein the machine learning module updates the matching model via providing a live feedback element for display at a user device of the customer, the feedback element, responsive to interface with the customer, providing data for user enhanced learning to the machine learning module.
Purves teaches:
…the payment instrument having a persistent personal account number (PAN) that enables the payment instrument to be used for obtaining financing for multiple transactions without changing the persistent PAN ((Purves) in at least pan 0135, para 0159, para 0161, para 0257)
… issuing a new loan for the customer based on the transaction ((Purves) in at least para 0139, para 0153) ; and
facilitating servicing the new loan or the modified loan ((Purves) in at least para 0157-0158)
According to KSR, common sense rationale, simple substitution of one known element for another to obtain predictable results is common sense obvious rationale. The prior art Wheeler contained a payment instrument (purchase order) and account identifier (high level) which differed from the claimed device by the substitution of some components (generic account identifier) with other components (PAN account identifier). The prior art Purves provides evidence that the substituted components (account identifiers) for payment instruments (purchase orders, virtual cards – see Purves) and their function were known in the art. Purves teaches that such payment instruments can be options according to user selections and the corresponding account identifiers can include PAN’s. Accordingly one of ordinary skill in the art could have substituted one known element for another (generic account identifier for PAN based on payment instrument selected), and the results of the substitution would have been predictable.
Both Wheeler and Purves teach receiving in a purchase request account identifying data which can include the creation of different payment instruments. Purves teaches that in a purchasing process that the user has available multiple payment instrument options for bill pay which include the selection of a wallet application which provides as the account identifier PAN data existing or generated for a new account in a credit response for a transaction. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the payment instrument and account identifiers as taught by Wheeler to include virtual cards with PAN’s representing account identifiers as taught by Purves since Purves teaches that in a purchasing process that the user has available multiple payment instrument options for bill pay which include the selection of a wallet application which provides as the account identifier PAN data existing or generated for a new account in a credit response for a transaction.
Zhu teaches:
executing a loan matching algorithm… considering the existing loan to be a matched loan ((Zhu) in at least para 0046, para 0049, para 0051-0052, para 0072, para 0077, para 0086)
responsive to the loan matching algorithm not determining that the transaction is one among the plurality of transactions of the common purchase event, issuing a new loan for the customer based on the transaction ((Zhu) in at least para 0054-0058, para 0070, para 0076); and
wherein the loan matching algorithm comprises; selecting a matching model based on the transaction notification, wherein the loan matching algorithm employs a machine learning module, the machine learning module being configured to select and update the matching model, and wherein the machine learning module updates the matching model via providing a live feedback element for display at a user device of the customer, the feedback element, responsive to interface with the customer, providing data for user enhanced learning to the machine learning module ((Zhu) in at least para 0003-0004, para 0014, para 0023 wherein the prior art teaches receiving communications from user device in chat program, instant text messaging, sms message, email, where information may be updated and provided to risk score generation system, para 0027, para 0047, para 0051 wherein the prior art teaches prompting user via GUI for additional information and updating model in response to user inputting additional information and displaying loan options to the user, para 0052-0053, para 0056, para 0058-0059, para 0065-0067, para 0069-0070, para 0084 wherein the prior art teaches updating model based on user selection of options; para 0091);
Both Wheeler and Zhu are directed toward applying credit for purchases from merchants in response to transaction request where the credit available at the purchases is analyzed for use in the transaction in order to determine funding for the purchase and teaches applying algorithms for business processes. Zhu teaches the motivation of applying machine learning algorithm so that the system may learn their own parameters achieving higher accuracy and to perform analysis of purchaser historical and financial data in order to determine the number of existing loans of the purchaser in order to generate a risk score of user likelihood to repay a loan and based on results match loan options for selection in order to offer to the purchaser loans for use in transaction when financing is needed for a transaction. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the credit lines analysis for use in purchases from merchants of Wheeler to expand the analysis of customer financial data for existing credit lines used for purchases in a computer environment to include using algorithms which analyze and match existing loan data as taught by Zhu since Zhu teaches the motivation of applying machine learning algorithm so that the system may learn their own parameters achieving higher accuracy and to perform analysis of purchaser historical and financial data in order to determine the number of existing loans of the purchaser in order to generate a risk score of user likelihood to repay a loan and based on results match loan options for selection in order to offer to the purchaser loans for use in transaction when financing is needed for a transaction.
Both Wheeler and Zhu teach receiving and analyzing purchaser financial data in a transaction process for determining available credit and credit sufficient. Zhu teaches the motivation of providing credit options when credit is not available for a transaction where credit options are determined using ML model approach in order to complete task, such as determining whether certain aspects of customer transactional and financial patterns indicate likelihood of repayment for loan products offered and teaches the motivation of updating the learning model in response to customer selection of loan options. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify computer analysis of the credit lines for transactions of Wheeler to include machine learning model algorithms which are updated as taught by Zhu since Zhu teaches the motivation of providing credit options when credit is not available for a transaction where credit options are determined using ML model approach in order to complete task, such as determining whether certain aspects of customer transactional and financial patterns indicate likelihood of repayment for loan products offered and teaches the motivation of updating the learning model in response to customer selection of loan options.
In reference to Claim 2:
The combination of Wheeler, Purves and Zhu discloses the limitations of independent claim 1. Wheeler further discloses the limitations of dependent claim 2
(Previously Presented) The method of claim 1(see rejection of claim 1 above), further comprising:
receiving a …[payment instrument] request from the customer for issuance of the payment instrument ((Wheeler) in at least page 14 lines 11-22, page 23 lines 23-34);
setting up a customer account for the payment instrument based on the …[payment instrument] request ((Wheeler) in at least page 23 lines 23-34); and
issuing the payment instrument with the persistent …[instrument identifier] to a client device of the customer ((Wheeler) in at least page 23 lines 23-page 24 lines 1-7, page 25 lines 10-13).
Wheeler does not explicitly teach:
receiving a card request …
setting up a customer account for the payment instrument based on the card request; and
issuing the payment instrument with the persistent PAN to a client device of the customer.
Purves teaches:
receiving a card request from the customer for issuance of the payment instrument ((Purves) in at least FIG. 4B; Fig. 2019; para 0104, para 0126-0127, para 0139 wherein the prior art teaches issuance of temporary line credit on card BIN, para 0154, para 0164 wherein the prior art teaches user apply for new credit card;
setting up a customer account for the payment instrument based on the card request ((Purves) in at least para 0135, para 0158-0159, para 0257, para 0262); and
issuing the payment instrument with the persistent PAN to a client device of the customer ((Purves) in at least para 0135, para 0158-0159, para 0257, para 0262).
According to KSR, common sense rationale, simple substitution of one known element for another to obtain predictable results is common sense obvious rationale. The prior art Wheeler contained a payment instrument (purchase order) and account identifier (high level) which differed from the claimed device by the substitution of some components (generic account identifier) with other components (PAN account identifier). The prior art Purves provides evidence that the substituted components (account identifiers) for payment instruments (purchase orders, virtual cards – see Purves) and their function were known in the art. Purves teaches that such payment instruments can be options according to user selections and the corresponding account identifiers can include PAN’s. Accordingly one of ordinary skill in the art could have substituted one known element for another (generic account identifier for PAN based on payment instrument selected), and the results of the substitution would have been predictable.
Both Wheeler and Purves teach receiving in a purchase request account identifying data which can include the creation of different payment instruments. Purves teaches that in a purchasing process that the user has available multiple payment instrument options for bill pay which include the selection of a wallet application which provides as the account identifier PAN data existing or generated for a new account in a credit response for a transaction. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the payment instrument and account identifiers as taught by Wheeler to include virtual cards with PAN’s representing account identifiers as taught by Purves since Purves teaches that in a purchasing process that the user has available multiple payment instrument options for bill pay which include the selection of a wallet application which provides as the account identifier PAN data existing or generated for a new account in a credit response for a transaction.
In reference to claim 3:
The combination of Wheeler, Purves and Zhu discloses the limitations of independent claim 1. Wheeler further discloses the limitations of dependent claim 3.
(Currently Amended) The method of claim 1 (see rejection of claim 1 above), wherein the loan matching algorithm comprises;
Wheeler does not explicitly teach:
accessing a list of candidate loans;
applying the matching model to the transaction notification and the candidate loans of the list of candidate loans; and
ranking each of the candidate loans according to a matching score resulting from applying the matching model.
Zhu teaches:
accessing a list of candidate loans ((Zhu) in at least Abstract; Fig. 3-5; para 0004, para 0055, para 0066-0067);
applying the matching model to the transaction notification and the candidate loans of the list of candidate loans ((Zhu) in at least para 0045-0047, para 0051, para 0070); and
ranking each of the candidate loans according to a matching score resulting from applying the matching model ((Zhu) in at least para 0043).
Both Wheeler and Zhu are directed toward applying credit for purchases from merchants in response to transaction request where the credit available at the purchases is analyzed for use in the transaction in order to determine funding for the purchase. Zhu teaches the motivation of applying an algorithm to analyze purchaser historical and financial data in order to determine the number of existing loans of the purchaser in order to generate a risk score of user likelihood to repay a loan and rank loans according to likelihood to repay results and based on results match loan options for selection in order to offer to the purchaser loans for use in transaction when financing is needed for a transaction. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the credit lines analysis for use in purchases from merchants of Wheeler to expand the analysis of customer financial data for existing credit lines used for purchases in a computer environment to include using algorithms which analyze and match existing loan data as taught by Zhu since Zhu teaches the motivation of applying an algorithm to analyze purchaser historical and financial data in order to determine the number of existing loans of the purchaser in order to generate a risk score of user likelihood to repay a loan and rank loans according to likelihood to repay results and based on results match loan options for selection in order to offer to the purchaser loans for use in transaction when financing is needed for a transaction.
In reference to claim 11:
The combination of Wheeler, Purves and Zhu discloses the limitations of dependent claim 3. Wheeler further discloses the limitations of dependent claim 11.
(Previously Presented) The method of claim 3 (see rejection of claim 3 above),
Wheeler does not explicitly teach:
wherein the list of candidate loans comprises a list of loans initiated or modified within a predetermined period of time prior to the transaction notification.
Zhu teaches:
wherein the list of candidate loans comprises a list of loans initiated or modified within a predetermined period of time prior to the transaction notification ((Zhu) in at least para 0051).
Both Wheeler and Zhu teach receiving and analyzing purchaser financial data in a transaction process for determining available credit and credit sufficient. Zhu teaches the motivation of providing credit options when credit is not available for a transaction where when additional information for loans are provided loan options may be updated and additional time to match loan options is needed in response to the inputted additional information where the user has agreed to wait for a certain amount of time. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify computer analysis of the credit lines for transactions of Wheeler to include a loan analysis process for offering additional financing for a transaction request as taught by Zhu since Zhu teaches the motivation of providing credit options when credit is not available for a transaction where when additional information for loans are provided loan options may be updated and additional time to match loan options is needed in response to the inputted additional information where the user has agreed to wait for a certain amount of time.
In reference to Claim 14:
The combination of Wheeler, Purves and Zhu discloses the limitations of dependent claim 14.
The functional processes of apparatus of claim 14 correspond to the steps of method claim 1. The additional limitations recited in claim 14 that go beyond the limitations of claim 1 include an apparatus ((Wheeler) in at least FIG. 1, FIG. 3, FIG. 5, FIG. 8) comprising processing circuitry ((Wheeler) in at least page 7 lines 2-4, page 16 lines 23-28) to perform the operation that correspond to claim 1
Therefore, claim 14 has been analyzed and rejected as previously discussed with respect to claim 1.
In reference to Claim 15:
The combination of Wheeler, Purves and Zhu discloses the limitations of independent claim 14. Wheeler further discloses the limitations of dependent claim 15.
The operations of apparatus claim 15 corresponds to steps method claim 3. Therefore, claim 15 has been analyzed and rejected as previously discussed with respect to claim 3
Claim(s) 4-5 is/are rejected under 35 U.S.C. 103 as being unpatentable over WO 02/13435 A1 by Wheeler et al (Wheeler), in view of US Pub. No. 2013/0346302 A1 by Purves et al (Purves) in view of US Pub No. 2023/0222575 A1 by Zhu et al. (Zhu) as applied to claim 3 above, and further in view of US Patent No. 11,138,657 B1 by Boeder et al (Boeder)
In reference to claim 4:
The combination of Wheeler, Purves and Zhu discloses the limitations of dependent claim 3. Wheeler further discloses the limitations of dependent claim 4
(Previously Presented) The method of claim 3 (see rejection of claim 3 above), wherein the loan matching algorithm further comprises
Wheeler does not explicitly teach:
automatically selecting a highest ranked one of the candidate loans as the matched loan in response to the highest ranked one of the candidate loans having the matching score above a threshold value.
Boeder teaches:
automatically selecting a highest ranked one of the candidate loans as the matched loan in response to the highest ranked one of the candidate loans having the matching score above a threshold value. ((Boeder) in at least Col 12 lines 20-40, Col 15 lines 7-41)
Both Wheeler and Boeder are directed toward providing credit for transactions with sufficient funds for the transaction. Boeder teaches the motivation of providing a rating process in order to analyze lending devices for rating that meets a specified threshold in order to rank the ratings associated with the lending devices so that the lending offers can be automatically selected according to the highest overall composite rating determined based on lending and customer factors if threshold is satisfied. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify computer analysis of the credit lines for transactions of Wheeler to include in the loan analysis process for offering additional financing rating the plurality of lending offers above a threshold as taught by Boeder since Boeder teaches the motivation of providing a rating process in order to analyze lending devices for rating that meets a specified threshold in order to rank the ratings associated with the lending devices so that the lending offers can be automatically selected according to the highest overall composite rating determined based on lending and customer factors if threshold is satisfied.
In reference to claim 5:
The combination of Wheeler, Purves and Zhu discloses the limitations of dependent claim 3. Wheeler further discloses the limitations of dependent claim 5.
(Previously Presented) The method of claim 3 (see rejection of claim 3 above), wherein the loan matching algorithm further comprises
Wheeler does not explicitly teach:
generating a display element for communication to the customer identifying a highest ranked one of the candidate loans as a potential matched loan and, responsive to selection of the display element by the customer, considering the highest ranked one of the candidate loans as the matched loan
Boeder teaches:
generating a display element for communication to the customer identifying a highest ranked one of the candidate loans as a potential matched loan and, responsive to selection of the display element by the customer, considering the highest ranked one of the candidate loans as the matched loan. ((Boeder) in at least Col 12 lines 20-40, Col 14 lines 50-Col 15 lines 1-31, Col 24 lines 37-47)
Both Wheeler and Boeder are directed toward providing credit for transactions with sufficient funds for the transaction. Boeder teaches the motivation of outputting/providing rating information for selection of lending devices that are ranked for loan request needed by the user device where output options include displays on a screen. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify analysis process of the credit lines for transactions of Wheeler to include outputting the results of the analysis as taught by Boeder since Boeder teaches the motivation of outputting/providing rating information for selection of lending devices that are ranked for loan request needed by the user device where output options include displays on a screen.
Claim(s) 8-10 as applied to claim 1 above, Claim 18-19 as applied to claim 14 above is/are rejected under 35 U.S.C. 103 as being unpatentable over WO 02/13435 A1 by Wheeler et al (Wheeler), in view of US Pub. No. 2013/0346302 A1 by Purves et al (Purves) in view of US Pub No. 2023/0222575 A1 by Zhu et al. (Zhu)and further in view of WO 2012/012777 A9 by 2016/0086222 A1 by Falkenborg et al (Falkenborg)
In reference to claim 8:
The combination of Wheeler, Purves and Zhu discloses the limitations of independent claim 1. Wheeler further discloses the limitations of dependent claim 8.
(Currently Amended) The method of claim 1 (see rejection of claim 1),
Wheeler does not explicitly teach:
wherein the matching model is one of a plurality of candidate models, each of the candidate models being associated with a different category of purchase events.
Falkenborg teaches:
wherein the matching model is one of a plurality of candidate models, each of the candidate models being associated with a different category of purchase events ((Falkenborg) in at least para 0094, para 00200, para 00212-00214, para 00376, para 00402)
Both Wheeler and Falkenborg are directed toward analyzing transaction made via credit accounts. Falkenborg teaches the motivation of applying developing and selecting propensity segmentation models for marketing campaigns for profitability which can provide intelligence information on behavior patterns, preference, propensity and trends in making purchases. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify analysis for transactions of Wheeler to include applying selected models for categorizing different purchase events as taught by Falkenborg since Falkenborg teaches the motivation of applying developing and selecting propensity segmentation models for marketing campaigns for profitability which can provide intelligence information on behavior patterns, preference, propensity and trends in making purchases..
In reference to claim 9:
The combination of Wheeler, Purves, Zhu and Falkenborg discloses the limitations of dependent claim 8. Wheeler further discloses the limitations of dependent claim 9
The method of claim 8 (see rejection of claim 8 above),
Wheeler does not explicitly teach:
wherein the transaction notification includes a billing descriptor, a merchant identifier, and category code, and wherein the machine learning module employs the category code to select the matching model based on an association of one or more category codes to a corresponding one of the candidate models sharing the common purchase event.
Falkenborg teaches:
wherein the transaction notification includes a billing descriptor, a merchant identifier, and category code, and wherein the machine learning module employs the category code to select the matching model based on an association of one or more category codes to a corresponding one of the candidate models sharing the common purchase event. ((Falkenborg) in at least para 0068-0069, para 0095, para 00181, para 00187, para 00201, para 00211-00212, para 00214, para 00376-00377, para 00396, para 00444)
Both Wheeler and Falkenborg are directed toward analyzing transaction made via credit accounts. Falkenborg teaches the motivation of applying developing and selecting specific to user profiles propensity segmentation models for marketing campaigns for profitability which can provide intelligence information on behavior patterns, preference, propensity and trends in making purchases. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify analysis for transactions of Wheeler to include applying selected models for categorizing different purchase events as taught by Falkenborg since Falkenborg teaches the motivation of applying developing and selecting specific to user profiles propensity segmentation models for marketing campaigns for profitability which can provide intelligence information on behavior patterns, preference, propensity and trends in making purchases..
In reference to claim 10:
The combination of Wheeler, Purves, Zhu and Falkenborg discloses the limitations of dependent claim 8. Wheeler further discloses the limitations of dependent claim 10.
(Previously Presented) The method of claim 8 (see rejection of claim 8 above),
Wheeler does not explicitly teach:
wherein the candidate models each include a distinct set of transaction patterns that identify the common purchase event distinctly for the different category of purchase events.
Falkenborg teaches:
wherein the candidate models each include a distinct set of transaction patterns that identify the common purchase event distinctly for the different category of purchase events. ((Falkenborg) in at least para 0066, para 0068, para 00238-00241, para 00259-00271)
Both Wheeler and Falkenborg are directed toward analyzing transaction made via credit accounts. Falkenborg teaches the motivation of applying developing and selecting specific to user profiles propensity segmentation models for marketing campaigns for profitability which can provide intelligence information on behavior patterns, preference, propensity and trends in making purchases. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify analysis for transactions of Wheeler to include applying selected models for categorizing different purchase events as taught by Falkenborg since Falkenborg teaches the motivation of applying developing and selecting specific to user profiles propensity segmentation models for marketing campaigns for profitability which can provide intelligence information on behavior patterns, preference, propensity and trends in making purchases..
In reference to Claim 18:
The combination of Wheeler, Purves, Zhu and Falkenborg discloses the limitations of independent claim 14. Wheeler further discloses the limitations of dependent claim 18.
The operations of apparatus claim 14 corresponds to steps method claim 10. Therefore, claim 14 has been analyzed and rejected as previously discussed with respect to claim 10
In reference to claim 19:
The combination of Wheeler, Purves and Zhu discloses the limitations of dependent claim 18. Wheeler further discloses the limitations of dependent claim 19.
(Previously Presented) The apparatus of claim 18 (see rejection of claim 18 above),
Wheeler does not explicitly teach:
wherein the transaction notification includes a billing descriptor, a merchant identifier, and category code, and wherein the machine learning module employs the category code to select the matching model based on an association of one or more category codes to a corresponding one of the candidate models sharing the common purchase event, and wherein the candidate models each include a distinct set of transaction patterns that identify the common purchase event distinctly for the different category of purchase events.
Falkenborg teaches:
wherein the transaction notification includes a billing descriptor, a merchant identifier, and category code, and wherein the machine learning module employs the category code to select the matching model based on an association of one or more category codes to a corresponding one of the candidate models sharing the common purchase event ((Falkenborg) in at least para 0068-0069, para 0095, para 00181, para 00187, para 00201, para 00211-00212, para 00214, para 00376-00377, para 00396, para 00444), and wherein the candidate models each include a distinct set of transaction patterns that identify the common purchase event distinctly for the different category of purchase events. ((Falkenborg) in at least para 0066, para 0068, para 00238-00241, para 00259-00271)
Both Wheeler and Falkenborg are directed toward analyzing transaction made via credit accounts. Falkenborg teaches the motivation of applying developing and selecting specific to user profiles propensity segmentation models for marketing campaigns for profitability which can provide intelligence information on behavior patterns, preference, propensity and trends in making purchases. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify analysis for transactions of Wheeler to include applying selected models for categorizing different purchase events as taught by Falkenborg since Falkenborg teaches the motivation of applying developing and selecting specific to user profiles propensity segmentation models for marketing campaigns for profitability which can provide intelligence information on behavior patterns, preference, propensity and trends in making purchases..
Claim(s) 12-13 as applied to claim 6 above; claim(s) 20 as applied to claim 16 above is/are rejected under 35 U.S.C. 103 as being unpatentable over WO 02/13435 A1 by Wheeler et al (Wheeler), in view of US Pub. No. 2013/0346302 A1 by Purves et al (Purves) in view of US Pub No. 2023/0222575 A1 by Zhu et al. (Zhu), and further in view of US Patent No. 8,001,578 B2 by Bjoraker et al (Bjoraker) and CN 106156977 A by Zeng (Zeng) with translation as annotated by the examiner
In reference to claim 12:
The combination of Wheeler, Purves and Zhu discloses the limitations of independent claim 1. Wheeler further discloses the limitations of dependent claim 12.
(Previously Presented) The method of claim 1 (see rejection of claim 1 above), wherein facilitating servicing of the new loan or the modified loan comprises:
Wheeler does not explicitly teach:
receiving an indication of a first refund for a returned product;
employing the machine learning module to determine a corresponding loan to the returned product; and applying the first refund to the corresponding loan.
Zeng teaches:
receiving an indication of a first refund for a returned product ((Zeng) in at least para 0024-0025);
employing the … module determine a corresponding loan to the returned product ((Zeng) in at least para 0016-0017, para 0019, para 0023-0024, para 0034-0036)
applying the first refund to the corresponding loan ((Zeng) in at least para 0023-0024)
Although Zeng does not teach “employing the machine learning module” the prior art does teach employing computer module to perform the claimed limitations and teaches that the software for contributing to the claimed process can be modified to include existing technology in the form of a software product (see para 0036). The claimed limitation does not require the “machine learning model” to perform operations beyond generic software analysis that is cited in the art. Accordingly the term “machine learning model” as claimed is merely a descriptor without technical details that go beyond the application of software modules for performing the process claimed. According to MPEP 2114, the prior art modules can functionally perform the functional limitation as claimed. The prior art teaches a combination of hardware and software capable of performing the claim limitations which can be modified according to embodiments. Furthermore, implementing a known function on a computer has been deemed obvious to one of ordinary skill in the art if the automation of the known function on a general purpose computer is nothing more than the predictable use of prior art elements according to their established functions. KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 417, 82 USPQ2d 1385, 1396 (2007). According to KSR, known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces. The scope and content of the prior art included a similar or analogous combination of hardware and software and provided in the prior art an explanation that the software could be modified based on design incentives or market forces. The application of machine learning model or other generic computer software modules for high level data analysis is a known variations and principle in the art. In view of the identified design incentives or other market forces, one could have implemented the claimed variation of the prior art, and the claimed variation would have been predictable to one of ordinary skill in the art.
Both Wheeler and Zeng teach applying a transaction instrument in performing a purchase as a designated merchant. Zeng teaches the motivation that it is needed to provide a process when an item/object purchased using the transaction instrument tied to a loan is returned so the corresponding amount of the return item can be refunded back to the loan. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the management of the credit line modified when credit is applied for transactions on a specific payment instrument to include the return item process as taught by Zeng since Zeng teaches the motivation that it is needed to provide a process when an item/object purchased using the transaction instrument tied to a loan is returned so the corresponding amount of the return item can be refunded back to the loan.
In reference to claim 13:
The combination of Wheeler, Purves, Zhu and Zeng discloses the limitations of dependent claim 12. Wheeler further discloses the limitations of dependent claim 13.
(Previously Presented) The method of claim 12 (see rejection of claim 12 above), wherein facilitating servicing of the new loan or the modified loan further comprises:
Wheeler does not explicitly teach:
receiving an indication of a second refund from a different merchant;
employing the machine learning module to determine that the second refund is part of the common purchase event with the first refund; and
applying the second refund to the corresponding loan.
Zeng teaches:
receiving an indication of a second refund from a different merchant ((Zeng) in at least para 0013-0015, para 0017, para 0019-0020);
employing the machine … module to determine that the second refund is part of the common purchase event with the first refund ((Zeng) in at least para 0031-0036); and
applying the second refund to the corresponding loan ((Zeng) in at least para 0033-0034).
Although Zeng does not teach “employing the machine learning module” the prior art does teach employing computer module to perform the claimed limitations and teaches that the software for contributing to the claimed process can be modified to include existing technology in the form of a software product (see para 0036). The claimed limitation does not require the “machine learning model” to perform operations beyond generic software analysis that is cited in the art. Accordingly the term “machine learning model” as claimed is merely a descriptor without technical details that go beyond the application of software modules for performing the process claimed. According to MPEP 2114, the prior art modules can functionally perform the functional limitation as claimed. The prior art teaches a combination of hardware and software capable of performing the claim limitations that can be modified in response to implemented embodiments. Furthermore, implementing a known function on a computer has been deemed obvious to one of ordinary skill in the art if the automation of the known function on a general purpose computer is nothing more than the predictable use of prior art elements according to their established functions. KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 417, 82 USPQ2d 1385, 1396 (2007). According to KSR, known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces. The scope and content of the prior art included a similar or analogous combination of hardware and software and provided in the prior art an explanation that the software could be modified based on design incentives or market forces. The application of machine learning model or other generic computer software modules for high level data analysis is a known variations and principle in the art. In view of the identified design incentives or other market forces, one could have implemented the claimed variation of the prior art, and the claimed variation would have been predictable to one of ordinary skill in the art.
Both Wheeler and Zeng teach applying a transaction instrument in performing a purchase as a designated merchant. Zeng teaches the motivation that it is needed to provide a process when an item/object purchased at a first or second store using the transaction instrument tied to a loan is returned so the corresponding amount of the return item can be refunded back to the loan. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the management of the credit line modified when credit is applied for transactions on a specific payment instrument to include the return item process as taught by Zeng since Zeng teaches the motivation that it is needed to provide a process when an item/object purchased at a first or second store using the transaction instrument tied to a loan is returned so the corresponding amount of the return item can be refunded back to the loan.
In reference to claim 20:
The combination of Wheeler, Purves and Zhu discloses the limitations of dependent claim 16. Wheeler further discloses the limitations of dependent claim 20.
(Currently Amended) The apparatus of claim 14(see rejection of claim 14 above), wherein facilitating servicing of the new loan or the modified loan comprises:
Wheeler does not explicitly teach:
receiving an indication of a first refund for a returned product;
employing the machine learning module to determine a corresponding loan to the returned product; applying the first refund to the corresponding loan;
receiving an indication of a second refund from a different merchant;
employing the machine learning module to determine that the second refund is part of the common purchase event with the first refund; and
applying the second refund to the corresponding loan.
Zeng teaches:
receiving an indication of a first refund for a returned product ((Zeng) in at least para 0024-0025);
employing the … module determine a corresponding loan to the returned product ((Zeng) in at least para 0016-0017, para 0019, para 0023-0024, para 0034-0036)
applying the first refund to the corresponding loan ((Zeng) in at least para 0023-0024)
receiving an indication of a second refund from a different merchant ((Zeng) in at least para 0013-0015, para 0017, para 0019-0020);
employing the machine … module to determine that the second refund is part of the common purchase event with the first refund ((Zeng) in at least para 0031-0036); and
applying the second refund to the corresponding loan ((Zeng) in at least para 0033-0034).
Although Zeng does not teach “employing the machine learning module” the prior art does teach employing computer module to perform the claimed limitations and teaches that the software for contributing to the claimed process can be modified to include existing technology in the form of a software product (see para 0036). The claimed limitation does not require the “machine learning model” to perform operations beyond generic software analysis that is cited in the art. Accordingly the term “machine learning model” as claimed is merely a descriptor without technical details that go beyond the application of software modules for performing the process claimed. According to MPEP 2114, the prior art modules can functionally perform the functional limitation as claimed. The prior art teaches a combination of hardware and software capable of performing the claim limitations. Furthermore, implementing a known function on a computer has been deemed obvious to one of ordinary skill in the art if the automation of the known function on a general purpose computer is nothing more than the predictable use of prior art elements according to their established functions. KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 417, 82 USPQ2d 1385, 1396 (2007). According to KSR, known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces. The scope and content of the prior art included a similar or analogous combination of hardware and software and provided in the prior art an explanation that the software could be modified based on design incentives or market forces. The application of machine learning model or other generic computer software modules for high level data analysis is a known variations and principle in the art. In view of the identified design incentives or other market forces, one could have implemented the claimed variation of the prior art, and the claimed variation would have been predictable to one of ordinary skill in the art.
Both Wheeler and Zeng teach applying a transaction instrument in performing a purchase as a designated merchant. Zeng teaches the motivation that it is needed to provide a process when an item/object purchased at a first or second store using the transaction instrument tied to a loan is returned so the corresponding amount of the return item can be refunded back to the loan. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the management of the credit line modified when credit is applied for transactions on a specific payment instrument to include the return item process as taught by Zeng since Zeng teaches the motivation that it is needed to provide a process when an item/object purchased at a first or second store using the transaction instrument tied to a loan is returned so the corresponding amount of the return item can be refunded back to the loan.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US Patent No. 12462239 B2 by Goyal et al; US Pub No. 2023/0342754 A1 by Haider et al; US Patent No. 11,138657 B1 by Boeder et al; US Patent No. 11,379,862 B1 by Harbour et al wherein the prior art teaches “selecting a matching model based on the transaction notification, wherein the loan matching algorithm employs a machine learning module, the machine learning module being configured to select and update the matching model, and wherein the machine learning module updates the matching model via providing a live feedback element for display at a user device of the customer, the feedback element, responsive to interface with the customer, providing data for user enhanced learning to the machine learning module- Fig. 2B; Col 5 lines 26-38, Col 6 lines 20-23, lines 27-64, Col 8 lines 63-66.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARY M GREGG whose telephone number is (571)270-5050. The examiner can normally be reached M-F 9am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christine Behncke can be reached at 571-272-8103. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MARY M GREGG/ Examiner, Art Unit 3695
/CHRISTINE M Tran/Supervisory Patent Examiner, Art Unit 3695
Prosecution Insights