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 .
Response to Amendments / Arguments
Applicants arguments, filed 05/19/2026 regarding the 35 U.S.C 103 Rejection has been fully considered and are not persuasive.
Applicant argues that Goldfield does not teach classifying an entity to distinguish "a transaction that is merely repetitive at the point of sale" from "a predictive remotely executable recurring process," contending Goldfield classifies only "for the purpose of displaying transaction information." In response, it is noted that paragraph [0062] of Goldfield recites that where an entity is identified as "a software service provider merchant," the server determines the data "may include a subscription transaction series," while distinguishing entries that are "one-time charges or other side products." Paragraphs [0044] and [0047] further recite the server automatically adjusting subscription plans and switching payment channels, i.e., executing actions on recurring processes away from a point of sale. Applicant's "purpose of displaying" contention is directed to intended use and does not distinguish the recited classifying step, which Goldfield performs.
Applicant further argues that the conditional "store" and "cease analysis without storing" limitations constitute a gating mechanism absent from Goldfield. In response, it is noted that paragraph [0062] recites excluding entries that "do not actually belong to the series," and paragraphs [0003] and [0086] recite reducing "the processing resources expended to detect recurring transactions." Paragraphs [0036] and [0101]-[0102] recite storing the identified recurring transactions in a vendor table. Accordingly, it would have been obvious that Goldfield's classify-then-store pipeline stores data determined recurring while declining to further process and store data classified non-recurring, consistent with Goldfield's stated resource-conservation goal.
Applicant further argues that Goldfield's paragraph [0038] does not teach authentication data specific to a recurring process used to authenticate to a third-party billing entity to effectuate payment, and that Oh does not cure this. In response, it is noted that the rejection relies on Oh, not Goldfield, for this feature. Paragraphs [0040], [0048], and [0058] of Oh recite storing service-specific authentication data tied to each third-party service, and paragraphs [0043] and [0060]-[0068] recite authenticating the user to a third-party service and executing secure payment through it. Applicant's assertion that "Oh does not cure the deficiencies" is conclusory and does not address the cited teachings of Oh.
Therefore, the identified claim language is considered to be taught by the combination of Goldfield and Oh, and the rejection is maintained. Further, since Applicant has not presented additional arguments concerning the dependent claims, their rejections are likewise maintained.
It is further noted that the amendments have raised 112a and 112(b) issues along with objections to the drawing. These are addressed below and appropriate correction is required.
DETAILED ACTION
This is a reply to the arguments filed on 05/19/2026, 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 02/04/2026 and 06/05/2026 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 § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-4, 6-19 and 21-22 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 11 and 16 recite "cease analysis of the data associated with the financial transaction without storing the data to the virtual aggregation table." The negative limitation constitutes new matter. The originally filed specification does not describe, expressly or inherently, “without storing the data to the virtual aggregation table”. The closest disclosure is paragraph [0112] and the corresponding "N" branch of step 704 of Fig. 7, which state only that if the data is determined not to be associated with a predictive remotely executable recurring process, the analysis of that data is stopped and the analysis is repeated for any new data received. Neither describes what is or is not stored. Silence with respect to a particular act does not provide written description support for a negative limitation excluding that act; a negative limitation requires basis in the original disclosure, such as an express description of the excluded alternative or a stated reason to exclude it.
Dependent claims are likewise rejected by inherency.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-4, 6-19 and 21-22 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1, 11 and 16 recites "classify... an entity associated with data received for a financial transaction based on one or more attributes of the entity to determine whether the financial transaction is a predictive remotely executable recurring process..., the classifying distinguishing, based on a classification of the entity..., between a transaction that is merely repetitive at the point of sale and a transaction that is a predictive remotely executable recurring process...," and thereafter recites two limitations "responsive to determining that the financial transaction is a predictive remotely executable recurring process." These limitations are indefinite for several reasons. First, the object of the classifying is an entity, but the clause defining that same classifying describes it as distinguishing between two transactions, and no relationship between the entity classification and any transaction characterization is recited. Second, the classifying is recited as being performed "based on a classification of the entity" (on its own output) and if a second, distinct classification is instead intended, there is insufficient antecedent basis for it, as no such classification appears elsewhere in the claim. Third, "to determine whether" states the purpose or intended result of the positively recited classifying and does not require that any determination occur. Claim 1 therefore requires only that an entity be classified, and never requires that the transaction be determined to be either alternative. Fourth, there is insufficient antecedent basis for the two "responsive to determining" limitations, as no such determining is positively recited, and it cannot be ascertained when or on what basis either conditional operation is triggered.
To overcome, amend to recite the classifying and the determining as two distinct, positively recited limitations, a classifying of the entity based on one or more attributes of the entity, followed by a separate determining, based on the classification of the entity, of whether the financial transaction is merely repetitive at the point of sale or is a predictive remotely executable recurring process, and conform the later references accordingly, "the classification of the entity" and "responsive to the determining," so that they have proper antecedent basis.
For purposes of further examination, the limitation will be interpreted as requiring: classifying, using the machine learning program, an entity associated with data received for a financial transaction based on one or more attributes of the entity; and determining, based on the classification of the entity, whether the financial transaction is (i) a transaction that is merely repetitive at the point of sale, or (ii) a predictive remotely executable recurring process associated with regular billing processes to which the user owes payments and capable of being executed away from a point of sale. Explicit 112a written description support has not been verified for this interpretation.
Appropriate correction is required.
Dependent claims inherit the rejection of independent claims.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.83(a) as inconsistent with the specification. In Fig. 7, the "N" branch of step 704 does not return to the receiving of new data, and the flow as drawn instead loops indefinitely on the same data. This is inconsistent with paragraph [0112], which states that when the data is determined not to be associated with a predictive remotely executable recurring process, the analysis of that data is stopped and the computing system repeats the analysis for any new data received. The "N" branch should be redirected to step 702, or to a point preceding it at which new data is received. Corrected drawings must not introduce new matter.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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, 9-14 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.)
classify, using the machine learning program, an entity associated with data received for a financial transaction based on one or more attributes of the entity to determine whether the financial transaction is a predictive remotely executable recurring process that is capable of being executed by the computing system away from a point of sale, the classifying distinguishing, based on a classification of the entity associated with the financial transaction, between a transaction that is merely repetitive at the point of sale and a transaction that is a predictive remotely executable recurring process associated with regular billing processes to which the user owes payments (Goldfield: [0058], [0061] Provides for classifying an entity associated with a transaction based on attributes of the entity into a category and sub-category of transacting named entity (e.g., identifying that a merchant is a software service provider). Goldfield [0062] Provides for distinguishing, based on the entity classification, between transactions belonging to a recurring subscription transaction series (e.g., where the entity is a SaaS/software service provider) and transactions that are one-time charges or side products that do not belong to the series. Goldfield [0044] and [0047] Provides for the computing server remotely executing actions on such recurring processes away from a point of sale, including automatically adjusting subscription plans and switching payment channels for regular billing processes to which the user owes payments.)
responsive to determining that the financial transaction is a predictive remotely executable recurring process, store data associated with the financial transaction and interaction-based authentication data associated with the financial transaction to a virtual aggregation table (Goldfield: [0036], [0062], [0101]-[0102] Provides for storing transaction data of identified recurring processes and generating a vendor table/aggregation populated with the recurring transactions and their associated account and owner information.)
responsive to determining that the financial transaction is not a predictive remotely executable recurring process, cease analysis of the data associated with the financial transaction without storing the data to the virtual aggregation table (Goldfield: [0062] Provides for excluding data entries that do not belong to the recurring series from the series. Goldfield [0003], [0086] Provides for reducing the processing resources expended in detecting recurring transactions by not further processing entries determined not to be recurring.) 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 specific to the recurring electronic process 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. Goldfield [0044], [0103] and [0106] provides for performing an automated action on a selected recurring transaction upon user approval/selection.) 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. )
the stored interaction-based authentication data comprising authentication data specific to the recurring electronic process, the authentication data including at least one of an account number, a bank identification number, a user identification, or a password associated with a third-party billing entity corresponding to the recurring electronic process (Oh: [0040], [0048] and [0058] Provides for storing service-specific authentication data (credentials, keys, identifiers) tied to each third-party secure service.)
authenticate the user to a third-party billing entity to effectuate a payment to the third-party billing entity for an outstanding financial obligation owed by the user (Oh: [0043], [0035], [0060]-[0068] and [0071] authenticating the user to a third-party service and executing secure payment processing/transactions through it.)
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 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.)
Goldfield does not explicitly teach authenticate the user to a third-party billing entity to effectuate a payment to the third-party billing entity for an outstanding financial obligation owed by the user. However, Oh discloses:
authenticate the user to a third-party billing entity to effectuate a payment to the third-party billing entity for an outstanding financial obligation owed by the user (Oh: [0043], [0035], [0060]-[0068] and [0071] authenticating the user to a third-party service and executing secure payment processing/transactions through it.)
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 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.)
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.)
classify, using the machine learning program, an entity associated with data received for a financial transaction based on one or more attributes of the entity to determine whether the financial transaction is a predictive remotely executable recurring process that is capable of being executed by the computing system away from a point of sale, the classifying distinguishing, based on a classification of the entity associated with the financial transaction, between a transaction that is merely repetitive at the point of sale and a transaction that is a predictive remotely executable recurring process associated with regular billing processes to which the user owes payments (Goldfield: [0058], [0061] Provides for classifying an entity associated with a transaction based on attributes of the entity into a category and sub-category of transacting named entity (e.g., identifying that a merchant is a software service provider). Goldfield [0062] Provides for distinguishing, based on the entity classification, between transactions belonging to a recurring subscription transaction series (e.g., where the entity is a SaaS/software service provider) and transactions that are one-time charges or side products that do not belong to the series. Goldfield [0044] and [0047] Provides for the computing server remotely executing actions on such recurring processes away from a point of sale, including automatically adjusting subscription plans and switching payment channels for regular billing processes to which the user owes payments.)
responsive to determining that the financial transaction is a predictive remotely executable recurring process, store data associated with the financial transaction and interaction-based authentication data associated with the financial transaction to a virtual aggregation table (Goldfield: [0036], [0062], [0101]-[0102] Provides for storing transaction data of identified recurring processes and generating a vendor table/aggregation populated with the recurring transactions and their associated account and owner information.)
responsive to determining that the financial transaction is not a predictive remotely executable recurring process, cease analysis of the data associated with the financial transaction without storing the data to the virtual aggregation table (Goldfield: [0062] Provides for excluding data entries that do not belong to the recurring series from the series. Goldfield [0003], [0086] Provides for reducing the processing resources expended in detecting recurring transactions by not further processing entries determined not to be recurring.) 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 specific to the recurring electronic process 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. Goldfield [0044], [0103] and [0106] provides for performing an automated action on a selected recurring transaction upon user approval/selection.) 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. )
the stored interaction-based authentication data comprising authentication data specific to the recurring electronic process, the authentication data including at least one of an account number, a bank identification number, a user identification, or a password associated with a third-party billing entity corresponding to the recurring electronic process (Oh: [0040], [0048] and [0058] Provides for storing service-specific authentication data (credentials, keys, identifiers) tied to each third-party secure service.)
authenticate the user to a third-party billing entity to effectuate a payment to the third-party billing entity for an outstanding financial obligation owed by the user (Oh: [0043], [0035], [0060]-[0068] and [0071] authenticating the user to a third-party service and executing secure payment processing/transactions through it.)
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.
Applicant’s amendment necessitated the new ground(s) of rejection presented in this office action. Accordingly, THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/A.E.S./Examiner, Art Unit 2432
/Jeffrey Nickerson/Supervisory Patent Examiner, Art Unit 2432
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