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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/02/2025 has been entered.
Response to Amendments / Arguments
Regarding the rejection(s) of claims under 35 USC 101:
Applicant's arguments, filed 08/26/2025, regarding the 35 U.S.C. 101 rejection have been fully considered and are persuasive, therefore the 101 rejections have been withdrawn.
Applicants arguments, filed 08/26/2025, regarding the 35 U.S.C 112 rejection has been fully considered and is persuasive, therefore the rejection has been withdrawn.
Applicants arguments, filed 08/26/2025, regarding the 35 U.S.C 102/103 Rejection has been fully considered and are not persuasive.
Applicant argues that Goldfield does not teach using a "cluster model" for classifying user action history data or assigning "probability scores indicating a likelihood one or more of the financial transactions are to be assigned to one or more clusters of data." For instance, Applicant contends that "Goldfield fails to uncover any teaching or suggestion that Goldfield predicts a metric using a cluster model on how likely an electronic process is recurring" and that Goldfield only "utilizes generalized ML techniques" that "do not predict the same output as Applicant's claimed invention."
In response, it is noted that Goldfield explicitly teaches classifying transaction data into categories. Specifically, paragraph [0058] recites "The computing server 110 classifies 310 a plurality of named entities to a category of transacting named entities," and paragraph [0061] further recites "The computing server 110 determines 330 that a target transacting named entity extracted from the transaction data belongs to the category." Additionally, paragraphs [0051-0052] describe applying a "recurrent event identification model 232 to determine the recurring frequency of the series" based on transaction analysis. The examiner find that this classification process determines the likelihood of category membership, which constitutes the claimed probability score assignment, even if the determined probabilities are only either 1 or 0 (assigned to the category or not). The semantic distinction between Goldfield's "classification into categories" and Applicant's "cluster model" does not create a patentable difference when the underlying functionality is identical.
Applicant further argues that their system serves a different purpose - "facilitating easy payments to multiple digital platforms" versus Goldfield's "dashboard for a company with information about vendors." However, both systems fundamentally analyze financial transaction data to predict recurring electronic processes. This intended use does not affect the structure or functionality claimed.
Applicant also contends that Goldfield lacks teaching of "iteratively train[ing], using training data, a neural network" with specific weight adjustment processes. In response, paragraphs [0091-0099] of Goldfield extensively describe machine learning models including "neural networks, including convolutional neural networks (CNN), recurrent neural networks (RNN), and long short-term memory networks (LSTM)" with "iterations of forward propagation and backpropagation" where "coefficients (e.g. weights and kernel coefficients) that are adjustable during training." Paragraph [0099] specifically recites that "the neural network performs backpropagation by using gradient descent such as stochastic gradient descent (SGD) to adjust the coefficients in various functions to improve the value of the objective function." This clearly teaches the claimed iterative training with weight adjustment to reduce prediction error.
Therefore, the identified claim limitations are considered to be taught by the Goldfield reference, and the rejection is maintained. Further, since Applicant has not presented additional arguments concerning the dependent claims, their rejections are likewise maintained.
DETAILED ACTION
This is a reply to the arguments filed on 10/02/2025, in which, claims 1-4, 6-19 and 21-22 are pending. Claims 1, 11, and 16 are independent. Claims 5 and 20 are cancelled.
When making claim amendments, the applicant is encouraged to consider the references in their entireties, including those portions that have not been cited by the examiner and their equivalents as they may most broadly and appropriately apply to any particular anticipated claim amendments.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 01/05/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim 11-14 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by Goldfield et al. (US 20230031874 A1, referred to as Goldfield).
In reference to claim 11, A computing system for data aggregation, the system comprising: a memory; one or more processors in communication with the memory; and program instructions executable by the one or more processors via the memory (Goldfield: [0022] and [0111] Provides for a computing system for transaction management (data aggregation incorporating authentication data) with explicit mention of components like servers, data stores, and devices. Goldfield [0035] Provides for an architecture with memory, processors, and executable program instructions for data processing.)
perform predictive analytics using one or more analytical tools on an aggregation of user action history data that incorporates financial transactions previously performed by a user, the financial transactions including payments made by the user, the predictive analytics comprising classifying, using a cluster model, the user action history data including the financial transactions previously performed by the user, the classifying comprising assigning a probability score indicating a likelihood one or more of the financial transactions are to be assigned to one or more clusters of data (Goldfield: [0058], [0061] Provides for classifying named entities and transaction data into categories of transacting named entities, where the computing server determines that target transacting named entities belong to specific categories. [0051]-[0052] Provides for applying recurrent event identification models that inherently involve probability assessments when determining likelihood of transactions belonging to recurring series (clusters).)
predicting, based on the classifying and the user action history data of the financial transactions, a metric indicating how likely an electronic process is a recurring electronic process as indicated by the aggregation of user action history data (Goldfield: [0062]-[0063]] Provides for determining that transaction data includes a transaction series and applying models to predict recurring frequency of the transaction series based on classification of named entities and transaction analysis.)
performing an evaluation that includes automatically generating, based on the classifying and the user action history data, a prediction of the metric when the user initiated a transaction (Goldfield: [0053]-[0060] Provides for the transaction prediction engine that automatically generates predictions of upcoming transaction timing based on transaction analysis and classification, including predicting when recurring events will occur in transaction series.)
Iteratively train, using training data, a neural network incorporating a machine learning program to predict whether process data is indicative of a recurring electronic process, the training including (Goldfield: [0091]-[0092] Provides for iterative training of neural networks to predict recurring processes.)
Inserting the training data into an iterative training and testing loop to predict a target variable (Goldfield: [0098]-[0099] Provides for iterative training loops with target variable prediction (recurring frequency as the target).)
Repeatedly predicting the target variable during each iteration of the training and testing loop, wherein each iteration of the training and testing loop has differing weights applied to one or more nodes of the neural network, each of the differing weights being updated with each iteration of the training and testing loop to reduce error in predicting the target variable, which improves predictability of the target variable and functionality of the neural network (Goldfield: [0099] Provides for weight updates during each iteration to reduce error and improve prediction accuracy.)
Deploy the trained neural network (Goldfield: [0100] Provides for deploying trained models for operational use in the system.)
Predict, using the trained and deployed neural network and based on the authentication data associated with the action initiation request, one or more recurring electronic processes associated with the authentication data, the predicting of the one or more recurring electronic processes being based on stored user data that is associated with the authentication data and that includes action data of prior actions that are recurring, the stored user data being associated with a plurality of different types of financial accounts of the user that are associated with a financial institution (Goldfield: [0053]-[0054] Provides for prediction of recurring processes (transactions) using trained models and stored user transaction data.)
Store transaction data of the recurring electronic processes predicted to an aggregation table of optional user interactions for performance via a digital platform (Goldfield: [0036] Provides for storing the transaction data.)
performing at least one action of the optional user interactions, wherein performing the at least one action comprises using stored interaction-based authentication data to access the digital platform (Goldfield: [0027]-[0030], [0055] Provides for client devices accessing the computing server platform through applications and interfaces, where clients communicate with the server to perform transaction management tasks. [0038] Provides for account management with card credentials and authentication information associated with transactions.)
In reference to claim 12, The computing system for data aggregation a of claim 11, wherein the recurring electronic processes require authentication of the user and wherein the stored transaction data includes user authentication information of the user (Goldfield: [0037]-[0038] Provides for creating and managing payment accounts, requiring user authentication for transactions.)
In reference to claim 13, The computing system for data aggregation of claim 11, wherein the stored transaction data includes payment details necessary to effectuate a payment to a third party (Goldfield: [0037] Provides for storing various payment details that are necessary to effectuate payments to third parties.)
In reference to claim 14, The computing system for data aggregation of claim 11, wherein the program instructions further receive, from a computing device, a request to perform the recurring electronic processes(Goldfield: [0027]-[0032] Provides for a system where users can interact with the computing server through a client device to perform various transaction-related tasks.)
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.
Claims 1-4, 6-7, 9-10 and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Goldfield et al. (US 20230031874 A1, referred to as Goldfield), in view of Oh et al. (US 20230120160 A1, referred to as Oh).
In reference to claim 1, A computing system for data aggregation, the system comprising: a memory; one or more processors in communication with the memory; and program instructions executable by the one or more processors via the memory (Goldfield: [0022] and [0111] Provides for a computing system for transaction management (data aggregation incorporating authentication data) with explicit mention of components like servers, data stores, and devices. Goldfield [0035] Provides for an architecture with memory, processors, and executable program instructions for data processing.)
perform predictive analytics using one or more analytical tools on an aggregation of user action history data that incorporates financial transactions previously performed by a user, the financial transactions including payments made by the user, the predictive analytics comprising classifying, using a cluster model, the user action history data including the financial transactions previously performed by the user, the classifying comprising assigning a probability score indicating a likelihood one or more of the financial transactions are to be assigned to one or more clusters of data (Goldfield: [0058], [0061] Provides for classifying named entities and transaction data into categories of transacting named entities, where the computing server determines that target transacting named entities belong to specific categories. [0051]-[0052] Provides for applying recurrent event identification models that inherently involve probability assessments when determining likelihood of transactions belonging to recurring series (clusters).)
predicting, based on the classifying and the user action history data of the financial transactions, a metric indicating how likely an electronic process is a recurring electronic process as indicated by the aggregation of user action history data (Goldfield: [0062]-[0063]] Provides for determining that transaction data includes a transaction series and applying models to predict recurring frequency of the transaction series based on classification of named entities and transaction analysis.)
performing an evaluation that includes automatically generating, based on the classifying and the user action history data, a prediction of the metric when the user initiated a transaction (Goldfield: [0053]-[0060] Provides for the transaction prediction engine that automatically generates predictions of upcoming transaction timing based on transaction analysis and classification, including predicting when recurring events will occur in transaction series.)
Iteratively train, using training data, a neural network incorporating a machine learning program to predict whether process data is indicative of a recurring electronic process, the training including (Goldfield: [0091]-[0092] Provides for iterative training of neural networks to predict recurring processes.)
Inserting the training data into an iterative training and testing loop to predict a target variable (Goldfield: [0098]-[0099] Provides for iterative training loops with target variable prediction (recurring frequency as the target).)
Repeatedly predicting the target variable during each iteration of the training and testing loop, wherein each iteration of the training and testing loop has differing weights applied to one or more nodes of the neural network, each of the differing weights being updated with each iteration of the training and testing loop to reduce error in predicting the target variable, which improves predictability of the target variable and functionality of the neural network (Goldfield: [0099] Provides for weight updates during each iteration to reduce error and improve prediction accuracy.)
Deploy the trained neural network (Goldfield: [0100] Provides for deploying trained models for operational use in the system.) Receive an access initiation request from a computing device, to enable the computing device to access a user interaction aggregator of a digital platform to perform one or more actions across a network, the action initiation request being associated with authentication data of a user (Goldfield: [0027]-[0030] Provides for users accessing a system via a device to perform various transaction-related tasks.)
Predict, using the trained and deployed neural network and based on the authentication data associated with the action initiation request, one or more recurring electronic processes associated with the authentication data, the predicting of the one or more recurring electronic processes being based on stored user data that is associated with the authentication data and that includes action data of prior actions that are recurring, the stored user data being associated with a plurality of different types of financial accounts of the user that are associated with a financial institution (Goldfield: [0053]-[0054] Provides for prediction of recurring processes (transactions) using trained models and stored user transaction data.) Generate a virtual aggregation table wherein the virtual aggregation table includes stored recurring electronic processes identified from the predicted one or more recurring electronic processes associated with the authentication data (Goldfield: [0036], [0062] and [0101]-[0102] Provides for storing and managing transaction data and named entities including recurring payments as well as generating tables/aggregations of recurring processes with predicted information..)
initiate display, via a user interface of the computing device, an aggregation of optional actions for performance via the digital platform, the optional actions being displayed as a list, each optional action being selected from the virtual aggregation table of stored recurring electronic processes and each optional action being prioritized within the list in accordance with the metric assigned such that the prioritized optional actions are most likely to align with the likely future outcome the neural network is trained to predict (Goldfield: [0101]-[0104] and [0107] Provides for a user interface that displays recurring transactions with optional interactions. Goldfield: [0065], [0101]-[0102], [0106], [0113] Provides information in list/table format with associated actions available. Goldfield: [0065], [0102], [0053], [0100], [0091] Provides for neural network predictions, displayed lists and sorting capability.)
performing at least one action of the optional user interactions, wherein performing the at least one action comprises using stored interaction-based authentication data to access the digital platform (Goldfield: [0027]-[0030], [0055] Provides for client devices accessing the computing server platform through applications and interfaces, where clients communicate with the server to perform transaction management tasks. [0038] Provides for account management with card credentials and authentication information associated with transactions.) Although Goldfield describes accounts and cards associated with specific users, there is no explicit mention of “interaction-based authentication data”. However, Oh discloses: wherein each stored recurring electronic process has associated therewith, stored interaction-based authentication data (Oh: [0018]-[0021], [0032]-[0036], [0056]-[0058] and [0060]-[0068] Provides for each stored recurring electronic process having associated therewith stored interaction-based authentication data for performing interactions with third-party services. )
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Goldfield, which teaches a system for managing and displaying recurring transactions and payments, with the teachings of Oh, which introduces specific interaction-based authentication data for each user interaction with third-party services. One of ordinary skill in the art would recognize the ability to incorporate Oh's authentication data into Goldfield's recurring transaction management system. One of ordinary skill in the art would be motivated to make this modification in order to enhance the security of the recurring transactions by ensuring that each interaction is properly authenticated.
In reference to claim 2, The computing system for data aggregation of claim 1, wherein the one or more recurring electronic processes are predicted based on: accessing a transaction ledger of a plurality of transactions associated with the user (Goldfield: [0036] Provides for storing and accessing transaction data for clients.) Determining, via the trained and deployed neural network that at least one transaction of the plurality of transactions from the transaction ledger is a repeated transaction requiring associated interaction-based authentication data (Goldfield: [0032] and [0062] Provides for identifying recurring transactions (subscription series) from transaction data.)
In reference to claim 3, The computing system for data aggregation of claim 2, wherein the plurality of transactions include financial transactions and the at least one transaction of the plurality of transactions is identified from recurring payments associated with outstanding financial obligations (Goldfield: [0032] Provides for various types of financial transactions, including recurring payments for financial obligations. It mentions credit card payments, electronic bill payments, and other forms of electronic fund transfers, which teaches financial transactions and recurring payments for outstanding financial obligations.)
In reference to claim 4, The computing system for data aggregation of claim 1, wherein the program instructions further: determine whether one or more of the optional user interactions are associated with respective due dates (Goldfield: [0063] Provides for identifying recurring events and their frequency in transaction series.)
Prioritize, within the virtual aggregation table, the optional user interactions based on the respective due dates (Goldfield: [0064] Provides for predicting the timing of upcoming transactions.) Wherein the displaying the aggregation of optional user interactions includes providing recommended user interactions to the user, the recommended user interactions being based on the respective due dates (Goldfield: [0064] and [0102] Provides for displaying a table of recurring transactions with predicted next payment dates.)
In reference to claim 6, The computing system for data aggregation of claim 4, wherein the generating the virtual aggregation table of stored recurring electronic processes includes sorting the recommended user interactions according to the respective due dates(Goldfield: [0100]-[0102] Provides for a table of recurring transactions that includes predicted next payment dates including the ability to sort these transactions by the dates of upcoming transactions, allowing users to view them in chronological order.)
In reference to claim 7, The computing system for data aggregation of claim 1, wherein the digital platform includes an online entity platform of the financial institution for processing financial transactions (Goldfield: [0032] Provides for a digital platform (the computing server and its associated interfaces) that processes various types of financial transactions. It teaches managing transactions, vendors, and merchants, as well as providing interfaces for clients to manage these transactions. The art also specifically lists various types of electronic financial transactions that can be processed, including online transfers and electronic bill payments.)
In reference to claim 9, The computing system for data aggregation of claim 8, wherein the one or more financial accounts include one or more loan accounts (Goldfield: [0030]-[0033] and [0103] Provides for the systems capabilities of handling and processing loan-related transactions.)
In reference to claim 10, The computing system for data aggregation of claim 8, wherein the one or more financial accounts include one or more credit card accounts (Goldfield: [0037] Provides for credit card accounts as part of the accounts managed by the system.)
In reference to claim 16, A computing-implemented method for data aggregation (Goldfield: [0022] and [0111] Provides for a computing system for transaction management (data aggregation incorporating authentication data) with explicit mention of components like servers, data stores, and devices. Goldfield [0035] Provides for an architecture with memory, processors, and executable program instructions for data processing.)
perform predictive analytics using one or more analytical tools on an aggregation of user action history data that incorporates financial transactions previously performed by a user, the financial transactions including payments made by the user, the predictive analytics comprising classifying, using a cluster model, the user action history data including the financial transactions previously performed by the user, the classifying comprising assigning a probability score indicating a likelihood one or more of the financial transactions are to be assigned to one or more clusters of data (Goldfield: [0058], [0061] Provides for classifying named entities and transaction data into categories of transacting named entities, where the computing server determines that target transacting named entities belong to specific categories. [0051]-[0052] Provides for applying recurrent event identification models that inherently involve probability assessments when determining likelihood of transactions belonging to recurring series (clusters).)
predicting, based on the classifying and the user action history data of the financial transactions, a metric indicating how likely an electronic process is a recurring electronic process as indicated by the aggregation of user action history data (Goldfield: [0062]-[0063]] Provides for determining that transaction data includes a transaction series and applying models to predict recurring frequency of the transaction series based on classification of named entities and transaction analysis.)
performing an evaluation that includes automatically generating, based on the classifying and the user action history data, a prediction of the metric when the user initiated a transaction (Goldfield: [0053]-[0060] Provides for the transaction prediction engine that automatically generates predictions of upcoming transaction timing based on transaction analysis and classification, including predicting when recurring events will occur in transaction series.)
Iteratively training, using training data, a neural network incorporating a machine learning program to predict whether process data is indicative of a recurring electronic process, the training including (Goldfield: [0091]-[0092] Provides for iterative training of neural networks to predict recurring processes.)
Inserting the training data into an iterative training and testing loop to predict a target variable (Goldfield: [0098]-[0099] Provides for iterative training loops with target variable prediction (recurring frequency as the target).)
Repeatedly predicting the target variable during each iteration of the training and testing loop, wherein each iteration of the training and testing loop has differing weights applied to one or more nodes of the neural network, each of the differing weights being updated with each iteration of the training and testing loop to reduce error in predicting the target variable, which improves predictability of the target variable and functionality of the neural network (Goldfield: [0099] Provides for weight updates during each iteration to reduce error and improve prediction accuracy.)
Deploying the trained neural network (Goldfield: [0100] Provides for deploying trained models for operational use in the system.) Receiving an access initiation request from a computing device, to enable the computing device to access a user interaction aggregator of a digital platform to perform one or more actions across a network, the action initiation request being associated with authentication data of a user (Goldfield: [0027]-[0030] Provides for users accessing a system via a device to perform various transaction-related tasks.)
Predicting, using the trained and deployed neural network and based on the authentication data associated with the action initiation request, one or more recurring electronic processes associated with the authentication data, the predicting of the one or more recurring electronic processes being based on stored user data that is associated with the authentication data and that includes action data of prior actions that are recurring, the stored user data being associated with a plurality of different types of financial accounts of the user that are associated with a financial institution (Goldfield: [0053]-[0054] Provides for prediction of recurring processes (transactions) using trained models and stored user transaction data.) Generating a virtual aggregation table wherein the virtual aggregation table includes stored recurring electronic processes identified from the predicted one or more recurring electronic processes associated with the authentication data (Goldfield: [0036], [0062] and [0101]-[0102] Provides for storing and managing transaction data and named entities including recurring payments as well as generating tables/aggregations of recurring processes with predicted information..)
initiating display, via a user interface of the computing device, an aggregation of optional actions for performance via the digital platform, the optional actions being displayed as a list, each optional action being selected from the virtual aggregation table of stored recurring electronic processes and each optional action being prioritized within the list in accordance with the metric assigned such that the prioritized optional actions are most likely to align with the likely future outcome the neural network is trained to predict (Goldfield: [0101]-[0104] and [0107] Provides for a user interface that displays recurring transactions with optional interactions. Goldfield: [0065], [0101]-[0102], [0106], [0113] Provides information in list/table format with associated actions available. Goldfield: [0065], [0102], [0053], [0100], [0091] Provides for neural network predictions, displayed lists and sorting capability.)
performing at least one action of the optional user interactions, wherein performing the at least one action comprises using stored interaction-based authentication data to access the digital platform (Goldfield: [0027]-[0030], [0055] Provides for client devices accessing the computing server platform through applications and interfaces, where clients communicate with the server to perform transaction management tasks. [0038] Provides for account management with card credentials and authentication information associated with transactions.) Although Goldfield describes accounts and cards associated with specific users, there is no explicit mention of “interaction-based authentication data”. However, Oh discloses: wherein each stored recurring electronic process has associated therewith, stored interaction-based authentication data (Oh: [0018]-[0021], [0032]-[0036], [0056]-[0058] and [0060]-[0068] Provides for each stored recurring electronic process having associated therewith stored interaction-based authentication data for performing interactions with third-party services. )
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Goldfield, which teaches a system for managing and displaying recurring transactions and payments, with the teachings of Oh, which introduces specific interaction-based authentication data for each user interaction with third-party services. One of ordinary skill in the art would recognize the ability to incorporate Oh's authentication data into Goldfield's recurring transaction management system. One of ordinary skill in the art would be motivated to make this modification in order to enhance the security of the recurring transactions by ensuring that each interaction is properly authenticated.
In reference to claim 17, The computer-implemented method for data aggregation of claim 16, wherein the one or more recurring electronic processes are predicted based on: accessing a transaction ledger of a plurality of transactions associated with the user (Goldfield: [0036] Provides for storing and accessing transaction data for clients.) Determining, via the trained and deployed neural network, that at least one transaction of the plurality of transactions from the transaction ledger is a repeated transaction requiring associated interaction-based authentication data (Goldfield: [0032] and [0062] Provides for identifying recurring transactions (subscription series) from transaction data.)
In reference to claim 18, The computer-implemented method for data aggregation of claim 16, wherein the plurality of transactions include financial transactions and the at least one transaction of the plurality of transactions is identified from recurring payments associated with outstanding financial obligations. (Goldfield: [0032] Provides for various types of financial transactions, including recurring payments for financial obligations. It mentions credit card payments, electronic bill payments, and other forms of electronic fund transfers, which teaches financial transactions and recurring payments for outstanding financial obligations.)
In reference to claim 19, The computer-implemented method for data aggregation of claim 16, further comprising: determining whether one or more of the optional user interactions are associated with respective due dates (Goldfield: [0063] Provides for identifying recurring events and their frequency in transaction series.)
Prioritizing, within the virtual aggregation table, the optional user interactions based on the respective due dates (Goldfield: [0064] Provides for predicting the timing of upcoming transactions.) Wherein the displaying the aggregation of optional user interactions includes providing recommended user interactions to the user, the recommended user interactions being based on the respective due dates (Goldfield: [0064] and [0102] Provides for displaying a table of recurring transactions with predicted next payment dates.)
In reference to claim 21, The computer-implemented method for data aggregation of claim 16, wherein the digital platform includes an online entity platform of the financial institution for processing financial transactions (Goldfield: [0023]-[0026], [0032]-[0037] and [0055] Provides for online digital platform that processes financial transactions and integrates with financial institutions.)
In reference to claim 22, The computer-implemented method for data aggregation of claim 16, further including: transmitting, to a receiver of a server that is connected to the computing system via the network, a request to access stored financial credit information of the user, the stored financial credit information including a credit report of the user; identifying from the credit report of the user one or more financial accounts of the user; and storing the one or more financial accounts to the virtual aggregation table of stored recurring electronic processes (Oh: [0036], [0081] and [0095] Provides for Retrieving financial account information (brokerage accounts, holdings, buying power), verifying account funds and financial data, storing account-related information in databases and network transmission to servers for account verification.)
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.
Claims 8 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Goldfield et al. (US 20230031874 A1, referred to as Goldfield), in view of Oh et al. (US 20230120160 A1, referred to as Oh) in further view of Brown (US 20180232740 A1, referred to as Brown).
In reference to claim 8, The computing system for data aggregation of claim 1, wherein the program instructions further: transmit, to a receiver of a server that is connected to the computing system via the network, a request to access stored financial credit information of the user, the stored financial credit information of the user; identify from the report of the user one or more financial accounts of the user; and store the one or more financial accounts to the virtual aggregation table of stored recurring electronic processes (Goldfield: [0036] and [0047] Provides for identifying financial accounts and transactions and storing and managing transaction data and associated entities.) However it does not explicitly mention doing this method with credit reports. However, Brown discloses (Brown: [0131], [0152] and [0180] Provides for credit rating agencies and accessing various financial information from these entities.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Goldfield in view of Oh, which teaches a system for managing recurring transactions with associated authentication data, with the teachings of Brown, which introduces the use of credit reports and information from credit rating agencies. One of ordinary skill in the art would recognize the ability to incorporate Brown's use of credit report information into the combined system of Goldfield and Oh to identify and verify user transactions and financial information. One of ordinary skill in the art would be motivated to make this modification in order to provide a more comprehensive view of a user's financial situation, improve the accuracy of identifying recurring transactions, and enhance the system's ability to assess the user's creditworthiness and financial obligations.
In reference to claim 15, The computing system for data aggregation of claim 11, wherein the recurring electronic processes are identified from stored financial credit information of the user, the stored financial credit information (Goldfield: [0036] and [0047] Provides for identifying financial accounts and transactions and storing and managing transaction data and associated entities.) However it does not explicitly mention doing this method with credit reports. However, Brown discloses (Brown: [0131], [0152] and [0180] Provides for credit rating agencies and accessing various financial information from these entities.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Goldfield in view of Oh, which teaches a system for managing recurring transactions with associated authentication data, with the teachings of Brown, which introduces the use of credit reports and information from credit rating agencies. One of ordinary skill in the art would recognize the ability to incorporate Brown's use of credit report information into the combined system of Goldfield and Oh to identify and verify user transactions and financial information. One of ordinary skill in the art would be motivated to make this modification in order to provide a more comprehensive view of a user's financial situation, improve the accuracy of identifying recurring transactions, and enhance the system's ability to assess the user's creditworthiness and financial obligations.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AIDAN EDWARD SHAUGHNESSY whose telephone number is (703)756-1423. The examiner can normally be reached on Monday-Friday from 7:30am to 5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey Nickerson, can be reached at telephone number (469) 295-9235. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from Patent Center and the Private Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from Patent Center or Private PAIR. Status information for unpublished applications is available through Patent Center and Private PAIR for authorized users only. Should you have questions about access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
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) Form at https://www.uspto.gov/patents/usptoautomated-interview-request-air-form.
/A.E.S./Examiner, Art Unit 2432
/Jeffrey Nickerson/Supervisory Patent Examiner, Art Unit 2432