DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Applicant filed a response dated April 27, 2026 in which claims 1-3, 8-10, 15-17, and 22-24 have been amended and claims 6, 13, and 20 have been canceled. Therefore, claims 1-5, 7-12, 14-19, and 21-24 are currently pending in the application.
Priority
Application 18/753,243 was filed on 06/25/2024 and claims benefit of 63/510,404 06/27/2023.
Examiner Request
The Applicant is requested to indicate where in the specification there is support for amendments to claims should Applicant amend. The purpose of this is to reduce potential 35 U.S.C. § 112(a) or § 112 1st paragraph issues that can arise when claims are amended without support in the specification. The Examiner thanks the Applicant in advance.
Claim Rejections - 35 USC § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5, 7-12, 14-19, and 21-24 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. (MPEP 2106). The claims are directed to a method, system, and apparatus which is one of the statutory categories of invention (Step 1: YES). The recitation of the claimed invention is analyzed as follows, in which the abstract elements are boldfaced.
Claim 1 recites the limitations of:
A computer-implemented method, comprising: automatically monitoring transactions associated with a secured payment instrument as the transactions occur, wherein the secured payment instrument is secured by an initial security deposit, and wherein the secured payment instrument is associated with a user;
training a machine learning algorithm to adjust security deposits and credit limits associated with different secured payment instruments, wherein the machine learning algorithm is trained by classifying sample credit performance data and corresponding sample adjustments associated with sample secured payment instruments into a set of clusters according to one or more vectors of similarity;
processing the transactions, credit performance data corresponding to the user, and historical data corresponding to the secured payment instrument through the machine learning algorithm to identify a cluster from the set of clusters according to the one or more vectors of similarity;
generating an adjustment to the initial security deposit and a credit limit associated with the secured payment instrument according to the identified cluster;
automatically monitoring new transactions associated with the secured payment instrument and other secured payment instruments associated with other users;
processing the new transactions and ongoing credit performance data corresponding to the adjustment and other adjustments associated with the other users to retrain the machine learning algorithm;
processing the new transactions and the ongoing credit performance data through the machine learning algorithm to identify a new cluster from the set of clusters according to the one or more vectors of similarity,
wherein the new cluster is used to generate a new adjustment to the initial security deposit and to the credit limit;
detecting that the new adjustment results in a determination that the secured payment instrument is eligible for graduation to an unsecured payment instrument; and
graduating the secured payment instrument to the unsecured payment instrument as a result of the determination; and
continuously updating the machine learning algorithm based on new ongoing credit performance data corresponding to new adjustments associated with the other users and new credit performance data associated with the unsecured payment instrument and the other secured payment instruments.
The claim as a whole recites a method that, under its broadest reasonable interpretation, covers collecting, analyzing, and transmitting data to facilitate adjusting security deposits and credit limits related to financial accounts. This is a fundamental economic practice of a financial transaction; a commercial interaction, such as for business relations; and managing personal behavior or relationships or interactions between people, which are certain methods of organizing human activity.
Finally, the claims also recite the use of a trained machine learning algorithm. This is a mathematical calculation or concept.
Thus, the claims recite an abstract idea. (Step 2A, prong 1: YES).
Moreover, the judicial exception is not integrated into a practical application. Other than reciting a “A computer-implemented method, comprising:”, “training a machine learning algorithm”, and “updating the machine learning algorithm” to perform the steps of “monitoring”, “processing”, “updating”, “detecting”, and “graduating”, nothing in the claim elements preclude the steps from practically being a certain method for organizing human activity or mathematical calculation or concept. The claim as a whole does not integrate the judicial exception into a practical application. The claim merely describes how to generally “apply” the concept of collecting, analyzing, and transmitting data to facilitate adjusting security deposits and credit limits related to financial accounts in a computer environment. The additional computer elements recited in the claim limitations are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception utilizing generic computer components.
For example, the Specification discloses “[0134] This disclosure contemplates the computer system taking any suitable physical form. As example and not by way of limitation, the computer system can be an embedded computer system, a system-on-chip (SOC), a single-board computer system (SBC) (such as, for example, a computer-on-module (COM) or system-on-module (SOM)), a desktop computer system, a laptop or notebook computer system, a tablet computer system, a wearable computer system or interface, an interactive kiosk, a mainframe, a mesh of computer systems, a mobile telephone, a personal digital assistant (PDA), a server, or a combination of two or more of these. Where appropriate, the computer system may include one or more computer systems; be unitary or distributed; span multiple locations; span multiple machines; and/or reside in a cloud computing system which may include one or more cloud components in one or more networks as described herein in association with the computing resources provider 928. Where appropriate, one or more computer systems may perform without substantial spatial or temporal limitation one or more steps of one or more methods described or illustrated herein. As an example and not by way of limitation, one or more computer systems may perform in real time or in batch mode one or more steps of one or more methods described or illustrated herein. One or more computer systems may perform at different times or at different locations one or more steps of one or more methods described or illustrated herein, where appropriate.”
Furthermore, the Specification discloses “[0164] In some embodiments, one or more implementations of an algorithm such as those described herein may be implemented using a machine learning or artificial intelligence algorithm. Such a machine learning or artificial intelligence algorithm may be trained using supervised, unsupervised, reinforcement, or other such training techniques. For example, a set of data may be analyzed using one of a variety of machine learning algorithms to identify correlations between different elements of the set of data without supervision and feedback (e.g., an unsupervised training technique). A machine learning data analysis algorithm may also be trained using sample or live data to identify potential correlations. Such algorithms may include k-means clustering algorithms, fuzzy c-means (FCM) algorithms, expectation-maximization (EM) algorithms, hierarchical clustering algorithms, density-based spatial clustering of applications with noise (DBSCAN) algorithms, and the like. Other examples of machine learning or artificial intelligence algorithms include, but are not limited to, genetic algorithms, backpropagation, reinforcement learning, decision trees, liner classification, artificial neural networks, anomaly detection, and such. More generally, machine learning or artificial intelligence methods may include regression analysis, dimensionality reduction, metalearning, reinforcement learning, deep learning, and other such algorithms and/or methods. As may be contemplated, the terms machine learning and artificial intelligence are frequently used interchangeably due to the degree of overlap between these fields and many of the disclosed techniques and algorithms have similar approaches.”
Thus, the specification supports that general purpose computers or computer components are utilized to implement the steps of the abstract idea.
Merely implementing the abstract idea on a generic computer is not a practical application of the abstract idea. The claim as a whole, in viewing the additional elements both individually and in combination, does not integrate the judicial exception into a practical application. Additionally, with respect to the machine learning algorithms, patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101. (See Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. (Step 2A prong two: No)
The claim does not include additional elements, when considered both individually and as an ordered combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using “A computer-implemented method, comprising:”, “training a machine learning algorithm”, and “updating the machine learning algorithm” to perform the steps of “monitoring”, “processing”, “updating”, “detecting”, and “graduating”, amounts to no more than mere instructions to apply the exception using generic computer component. The claim merely describes how to generally “apply” the concept of collecting, analyzing, and transmitting data to facilitate adjusting security deposits and credit limits related to financial accounts in a computer environment. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea. Such additional elements are determined to not contain an inventive concept according to MPEP 2106.05(f). It should be noted that (1) the “recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not provide significantly more because this type of recitation is equivalent to the words “apply it”, and (2) “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice, commercial interaction, or managing personal behavior or relationships or interactions between people, mental process, or mathematical calculation or concept) does not integrate a judicial exception into a practical application or provide significantly more”.
Claims 8 and 15 are substantially similar to claim 1, thus, they are rejected on similar grounds.
Claim 8 recites that additional elements of “A system, comprising: one or more processors; and memory storing thereon instructions that, as a result of being executed by the one or more processors, cause the system to:”.
Claim 15 recites the additional elements of “A non-transitory, computer-readable storage medium storing thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to:”.
For similar reasons as explained above with regard to claim 1, under Step 2A, prong two, these additional elements are merely applying generic computer components to implement the abstract idea. Under Step 2B, when viewing the additional elements individually and in combination, the additional elements do not amount to an inventive concept amounting to significantly more than the judicial exception itself as the claimed computer-related technologies are mere tools for implementing the abstract idea as explained with regard to claim 1.
Dependent claims 2-5, 7, 9-12, 14, 16-19, and 21-24 merely limit the abstract idea and do not recite any further additional elements beyond the cited abstract idea and the elements addressed above, thus, they do not amount to significantly more. The dependent claims are abstract for the reasons presented above because there are no additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Thus, the dependent claims are directed to an abstract idea. (Step 2B: No)
Therefore, claims 1-5, 7-12, 14-19, and 21-24 are not patent-eligible.
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 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 1-2, 4, 7-9, 11, 14-16, 18, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Orman, U.S. Patent Application Publication Number 2006/0031158; in view of Li, U.S. Patent Application Publication Number 2024/0303551; in view of Locar, U.S. Patent Application Publication Number 2024/0015180; in view of Black, U.S. Patent Application Publication Number 2013/0325862.
As per claim 1,
Orman explicitly teaches:
A computer-implemented method, comprising: automatically monitoring transactions associated with a secured payment instrument as the transactions occur, wherein the secured payment instrument is secured by an initial security deposit, and wherein the secured payment instrument is associated with a user;
(Orman US20060031158 at paras. 19-26) ("[0019] Periodically, the credit card system 100 may monitor the consumer's credit score. The period of monitoring may be established in the terms and conditions of the consumer account. In an embodiment of the invention, the monitor and adjustment module 116 may automatically request an updated credit score based on a pre-determined monitoring period. For example, the monitoring period could be every month; every three months, every six months, or every year. In an embodiment of the invention, a monitor and adjustment module 116 may be notified by the account creation and maintenance module 112 that the monitoring period has elapsed. In this embodiment, the monitor and adjustment module 116 may transmit a request (electronically, telephonically, or via mail) to the credit bureau(s) 120 for an updated credit score for the consumer. In response, the credit bureaus 120 may respond with only an updated credit score or may respond with the updated credit score and an updated credit report. [0020] The monitor and adjustment module 116 may receive the updated credit score and other information from the credit bureau(s) 120. The monitor and adjustment module 116 may compare the updated credit score to the originally stored credit score. If there is a difference between the updated credit score and the originally stored credit score, the monitor and adjustment module 116 may adjust a parameter of the consumer credit card account based on the change in the consumer's credit score. [0021] The monitoring and adjustment module 116 performs the monitoring of the consumer credit card account on a routine basis. The credit card issuer and the consumer can agree to a specific monitoring period, e.g., every 3 to 6 months, where the credit score is automatically updated.")
to adjust security deposits and credit limits associated with different secured payment instruments,
(Orman US20060031158 at paras. 19-26, 41-45) ("[0025] In an embodiment of the invention where the credit card is a secured credit card, the monitor and adjustment module 116 may identify that a certain credit score improvement has been achieved by the consumer. If the specified credit score improvement has been achieved, the monitor and adjustment module 116 may change the terms and conditions of the consumer credit card account so that the consumer credit card account becomes an unsecured credit card, i.e., the status is changed from secured to unsecured. Illustratively, a secured credit card may utilize collateral of the consumer, i.e., personal property or real property, to back up the money lent to the consumer. The collateral may be a cash balance maintained in an account with the bank or financial institution extending the credit to the consumer, or an ownership document for some of the consumer's property, e.g., a deed of trust that the consumer has execute. If the consumer achieves a 50 point increase in a credit score in a six-month timeframe, the monitor and adjustment module 116 may instruct an individual working for the credit card issuer to return the ownership document (and thus the claim to ownership in the property) and to remove any assignment or other ownership claims that the credit card issuer may have recorded with a government agency. In addition, the monitor and adjustment module 116 instructs the account creation and maintenance module 112 to change the status of the consumer account from a secured credit card account to an unsecured credit card account. In an embodiment of the invention, the monitor and adjustment module 116 may utilize the notification module 118 to notify the consumer of the change in credit card status." "[0041] The credit card issuer may utilize the information received from the credit bureau and/or the applicant to establish 310 an initial interest rate. In an embodiment of the invention, the initial interest rate may be based solely on the credit score or may be based on a combination of factors and parameters, which include the credit score. After the initial interest rate, the credit limit, the billing cycle, and the terms and conditions are established for the consumer account, the consumer account may be activated 314. In this embodiment of the invention, the terms and conditions may outline information such as foreclosure procedures on the secured collateral property in case the credit card consumer defaults on the credit card. In addition, the terms and conditions may outline the conditions that may allow the consumer to move from a secured credit instrument to an unsecured credit instrument. The consumer may be notified by the credit card issuer of the initial interest rate, the credit limit, the billing cycle, and the terms and conditions. The consumer may begin utilizing the credit card." "[0042] The credit card issuer may establish an automatic monitoring period in the terms and conditions of the credit card. In this embodiment of the invention, the applicant or consumer may wish to make the secured credit card an unsecured credit card, i.e., meaning that no collateral would be needed for the credit card. In most cases, the credit card issuer would not change the status from a secured credit card to an unsecured credit card unless a significant increase in credit score was achieved by the consumer.")
credit performance data and corresponding adjustments associated with secured payment instruments…
(Orman US20060031158 at paras. 19-26, 41-45) ("[0025] In an embodiment of the invention where the credit card is a secured credit card, the monitor and adjustment module 116 may identify that a certain credit score improvement has been achieved by the consumer. If the specified credit score improvement has been achieved, the monitor and adjustment module 116 may change the terms and conditions of the consumer credit card account so that the consumer credit card account becomes an unsecured credit card, i.e., the status is changed from secured to unsecured. Illustratively, a secured credit card may utilize collateral of the consumer, i.e., personal property or real property, to back up the money lent to the consumer. The collateral may be a cash balance maintained in an account with the bank or financial institution extending the credit to the consumer, or an ownership document for some of the consumer's property, e.g., a deed of trust that the consumer has execute. If the consumer achieves a 50 point increase in a credit score in a six-month timeframe, the monitor and adjustment module 116 may instruct an individual working for the credit card issuer to return the ownership document (and thus the claim to ownership in the property) and to remove any assignment or other ownership claims that the credit card issuer may have recorded with a government agency. In addition, the monitor and adjustment module 116 instructs the account creation and maintenance module 112 to change the status of the consumer account from a secured credit card account to an unsecured credit card account. In an embodiment of the invention, the monitor and adjustment module 116 may utilize the notification module 118 to notify the consumer of the change in credit card status." "[0041] The credit card issuer may utilize the information received from the credit bureau and/or the applicant to establish 310 an initial interest rate. In an embodiment of the invention, the initial interest rate may be based solely on the credit score or may be based on a combination of factors and parameters, which include the credit score. After the initial interest rate, the credit limit, the billing cycle, and the terms and conditions are established for the consumer account, the consumer account may be activated 314. In this embodiment of the invention, the terms and conditions may outline information such as foreclosure procedures on the secured collateral property in case the credit card consumer defaults on the credit card. In addition, the terms and conditions may outline the conditions that may allow the consumer to move from a secured credit instrument to an unsecured credit instrument. The consumer may be notified by the credit card issuer of the initial interest rate, the credit limit, the billing cycle, and the terms and conditions. The consumer may begin utilizing the credit card." "[0042] The credit card issuer may establish an automatic monitoring period in the terms and conditions of the credit card. In this embodiment of the invention, the applicant or consumer may wish to make the secured credit card an unsecured credit card, i.e., meaning that no collateral would be needed for the credit card. In most cases, the credit card issuer would not change the status from a secured credit card to an unsecured credit card unless a significant increase in credit score was achieved by the consumer.")
processing the transactions, credit performance data corresponding to the user, and historical data corresponding to the secured payment instrument
(Orman US20060031158 at paras. 19-26) ("[0019] Periodically, the credit card system 100 may monitor the consumer's credit score. The period of monitoring may be established in the terms and conditions of the consumer account. In an embodiment of the invention, the monitor and adjustment module 116 may automatically request an updated credit score based on a pre-determined monitoring period. For example, the monitoring period could be every month; every three months, every six months, or every year. In an embodiment of the invention, a monitor and adjustment module 116 may be notified by the account creation and maintenance module 112 that the monitoring period has elapsed. In this embodiment, the monitor and adjustment module 116 may transmit a request (electronically, telephonically, or via mail) to the credit bureau(s) 120 for an updated credit score for the consumer. In response, the credit bureaus 120 may respond with only an updated credit score or may respond with the updated credit score and an updated credit report. [0020] The monitor and adjustment module 116 may receive the updated credit score and other information from the credit bureau(s) 120. The monitor and adjustment module 116 may compare the updated credit score to the originally stored credit score. If there is a difference between the updated credit score and the originally stored credit score, the monitor and adjustment module 116 may adjust a parameter of the consumer credit card account based on the change in the consumer's credit score. [0021] The monitoring and adjustment module 116 performs the monitoring of the consumer credit card account on a routine basis. The credit card issuer and the consumer can agree to a specific monitoring period, e.g., every 3 to 6 months, where the credit score is automatically updated.")
generating an adjustment to the initial security deposit and a credit limit associated with the secured payment instrument according to the identified cluster;
(Orman US20060031158 at paras. 19-26, 41-45) ("[0025] In an embodiment of the invention where the credit card is a secured credit card, the monitor and adjustment module 116 may identify that a certain credit score improvement has been achieved by the consumer. If the specified credit score improvement has been achieved, the monitor and adjustment module 116 may change the terms and conditions of the consumer credit card account so that the consumer credit card account becomes an unsecured credit card, i.e., the status is changed from secured to unsecured. Illustratively, a secured credit card may utilize collateral of the consumer, i.e., personal property or real property, to back up the money lent to the consumer. The collateral may be a cash balance maintained in an account with the bank or financial institution extending the credit to the consumer, or an ownership document for some of the consumer's property, e.g., a deed of trust that the consumer has execute. If the consumer achieves a 50 point increase in a credit score in a six-month timeframe, the monitor and adjustment module 116 may instruct an individual working for the credit card issuer to return the ownership document (and thus the claim to ownership in the property) and to remove any assignment or other ownership claims that the credit card issuer may have recorded with a government agency. In addition, the monitor and adjustment module 116 instructs the account creation and maintenance module 112 to change the status of the consumer account from a secured credit card account to an unsecured credit card account. In an embodiment of the invention, the monitor and adjustment module 116 may utilize the notification module 118 to notify the consumer of the change in credit card status." "[0041] The credit card issuer may utilize the information received from the credit bureau and/or the applicant to establish 310 an initial interest rate. In an embodiment of the invention, the initial interest rate may be based solely on the credit score or may be based on a combination of factors and parameters, which include the credit score. After the initial interest rate, the credit limit, the billing cycle, and the terms and conditions are established for the consumer account, the consumer account may be activated 314. In this embodiment of the invention, the terms and conditions may outline information such as foreclosure procedures on the secured collateral property in case the credit card consumer defaults on the credit card. In addition, the terms and conditions may outline the conditions that may allow the consumer to move from a secured credit instrument to an unsecured credit instrument. The consumer may be notified by the credit card issuer of the initial interest rate, the credit limit, the billing cycle, and the terms and conditions. The consumer may begin utilizing the credit card." "[0042] The credit card issuer may establish an automatic monitoring period in the terms and conditions of the credit card. In this embodiment of the invention, the applicant or consumer may wish to make the secured credit card an unsecured credit card, i.e., meaning that no collateral would be needed for the credit card. In most cases, the credit card issuer would not change the status from a secured credit card to an unsecured credit card unless a significant increase in credit score was achieved by the consumer.")
to generate a new adjustment to the initial security deposit and to the credit limit;
(Orman US20060031158 at paras. 19-26, 41-45) ("[0025] In an embodiment of the invention where the credit card is a secured credit card, the monitor and adjustment module 116 may identify that a certain credit score improvement has been achieved by the consumer. If the specified credit score improvement has been achieved, the monitor and adjustment module 116 may change the terms and conditions of the consumer credit card account so that the consumer credit card account becomes an unsecured credit card, i.e., the status is changed from secured to unsecured. Illustratively, a secured credit card may utilize collateral of the consumer, i.e., personal property or real property, to back up the money lent to the consumer. The collateral may be a cash balance maintained in an account with the bank or financial institution extending the credit to the consumer, or an ownership document for some of the consumer's property, e.g., a deed of trust that the consumer has execute. If the consumer achieves a 50 point increase in a credit score in a six-month timeframe, the monitor and adjustment module 116 may instruct an individual working for the credit card issuer to return the ownership document (and thus the claim to ownership in the property) and to remove any assignment or other ownership claims that the credit card issuer may have recorded with a government agency. In addition, the monitor and adjustment module 116 instructs the account creation and maintenance module 112 to change the status of the consumer account from a secured credit card account to an unsecured credit card account. In an embodiment of the invention, the monitor and adjustment module 116 may utilize the notification module 118 to notify the consumer of the change in credit card status." "[0041] The credit card issuer may utilize the information received from the credit bureau and/or the applicant to establish 310 an initial interest rate. In an embodiment of the invention, the initial interest rate may be based solely on the credit score or may be based on a combination of factors and parameters, which include the credit score. After the initial interest rate, the credit limit, the billing cycle, and the terms and conditions are established for the consumer account, the consumer account may be activated 314. In this embodiment of the invention, the terms and conditions may outline information such as foreclosure procedures on the secured collateral property in case the credit card consumer defaults on the credit card. In addition, the terms and conditions may outline the conditions that may allow the consumer to move from a secured credit instrument to an unsecured credit instrument. The consumer may be notified by the credit card issuer of the initial interest rate, the credit limit, the billing cycle, and the terms and conditions. The consumer may begin utilizing the credit card." "[0042] The credit card issuer may establish an automatic monitoring period in the terms and conditions of the credit card. In this embodiment of the invention, the applicant or consumer may wish to make the secured credit card an unsecured credit card, i.e., meaning that no collateral would be needed for the credit card. In most cases, the credit card issuer would not change the status from a secured credit card to an unsecured credit card unless a significant increase in credit score was achieved by the consumer.")
detecting that the new adjustment results in a determination that the secured payment instrument is eligible for graduation to an unsecured payment instrument; and
(Orman US20060031158 at paras. 19-26) ("[0025] In an embodiment of the invention where the credit card is a secured credit card, the monitor and adjustment module 116 may identify that a certain credit score improvement has been achieved by the consumer. If the specified credit score improvement has been achieved, the monitor and adjustment module 116 may change the terms and conditions of the consumer credit card account so that the consumer credit card account becomes an unsecured credit card, i.e., the status is changed from secured to unsecured. Illustratively, a secured credit card may utilize collateral of the consumer, i.e., personal property or real property, to back up the money lent to the consumer. The collateral may be a cash balance maintained in an account with the bank or financial institution extending the credit to the consumer, or an ownership document for some of the consumer's property, e.g., a deed of trust that the consumer has execute. If the consumer achieves a 50 point increase in a credit score in a six-month timeframe, the monitor and adjustment module 116 may instruct an individual working for the credit card issuer to return the ownership document (and thus the claim to ownership in the property) and to remove any assignment or other ownership claims that the credit card issuer may have recorded with a government agency. In addition, the monitor and adjustment module 116 instructs the account creation and maintenance module 112 to change the status of the consumer account from a secured credit card account to an unsecured credit card account. In an embodiment of the invention, the monitor and adjustment module 116 may utilize the notification module 118 to notify the consumer of the change in credit card status.")
graduating the secured payment instrument to the unsecured payment instrument as a result of the determination; and
(Orman US20060031158 at paras. 19-26) ("[0025] In an embodiment of the invention where the credit card is a secured credit card, the monitor and adjustment module 116 may identify that a certain credit score improvement has been achieved by the consumer. If the specified credit score improvement has been achieved, the monitor and adjustment module 116 may change the terms and conditions of the consumer credit card account so that the consumer credit card account becomes an unsecured credit card, i.e., the status is changed from secured to unsecured. Illustratively, a secured credit card may utilize collateral of the consumer, i.e., personal property or real property, to back up the money lent to the consumer. The collateral may be a cash balance maintained in an account with the bank or financial institution extending the credit to the consumer, or an ownership document for some of the consumer's property, e.g., a deed of trust that the consumer has execute. If the consumer achieves a 50 point increase in a credit score in a six-month timeframe, the monitor and adjustment module 116 may instruct an individual working for the credit card issuer to return the ownership document (and thus the claim to ownership in the property) and to remove any assignment or other ownership claims that the credit card issuer may have recorded with a government agency. In addition, the monitor and adjustment module 116 instructs the account creation and maintenance module 112 to change the status of the consumer account from a secured credit card account to an unsecured credit card account. In an embodiment of the invention, the monitor and adjustment module 116 may utilize the notification module 118 to notify the consumer of the change in credit card status.")
Orman does not explicitly teach, however, Li does teach:
sample [credit performance] data and corresponding sample adjustments associated with sample secured payment instruments
(Li US20240303551 at paras. 30-33, 106-113) ("[0031] In some instances, the elements of account data 112 may identify and characterize one or more financial products or financial instruments issued by the financial institution to corresponding ones of the existing customers. For example, the elements of account data 112 may include, for each of the financial products issued to corresponding ones of the existing customers, one or more identifiers of the financial product (e.g., an alphanumeric product, an account number, expiration data, card-security-code, etc.), one or more unique customer identifiers (e.g., an alphanumeric identifiers, an alphanumeric character string, such as a login credential or a customer name, etc.), and additional information characterizing a balance or current status of the financial product or instrument (e.g., payment due dates or amounts, delinquent accounts statuses, etc.). Examples of these financial products may include, but are not limited to, a deposit account (e.g., a savings account, a checking account, etc.), a brokerage or retirements account, and a secured or unsecured credit or lending products (e.g., a real-estate secured lending product, an auto loan, a credit-card account, a personal loan, or an unsecured line-of-credit)." "[0106] Executed training input module 208 may provide initial training datasets 306, and corresponding ones of ground-truth labels 218 and inferred ground-truth labels 252, as inputs to executed adaptive training module 230, which may perform any of the exemplary processes described herein to train adaptively the machine-learning or artificial-intelligence process (e.g., the gradient-boosted, decision-tree process described herein) against additional elements of training data included within each of initial training datasets 306 and against corresponding ones of assigned ground-truth labels 218 and inferred ground-truth labels 252. For example, executed adaptive training module 230 may perform operations that establish a plurality of nodes and a plurality of decision trees for the gradient-boosted, decision-tree process (i.e., in accordance with an initial set of process parameters), which may ingest and process the additional elements of training data maintained within each of the plurality of initial training datasets 306. Further, and based on the execution of adaptive training module 230, and on the ingestion of each of initial training datasets 306 by the established nodes of the gradient-boosted, decision-tree process, FI computing system 130 the exemplary processes described herein to train adaptively the gradient-boosted, decision-tree process against the elements of training data included within each of initial training datasets 306 and corresponding ones of assigned ground-truth labels 218 and inferred ground-truth labels 252. Executed training input module 208 may perform any of the exemplary processes described herein to compute a value of one or more metrics, such as a Shapley value, that characterize a relative importance of each of the combined sequential features within one or more of initial training datasets 306 to a predictive output of the trained machine-learning or artificial-intelligence, e.g., the trained gradient-boosted, decision-tree process described herein." "[0107] Through the performance of these adaptive training processes, executed adaptive training module 230 may perform operations that iteratively add, subtract, or combine discrete features from initial training datasets 306 based on the corresponding Shapley values, and that generate one or more intermediate training datasets reflecting the iterative addition, subtraction, or combination of discrete features from corresponding ones of initial training datasets 306, and in some instances, an intermediate set of process parameters for the gradient-boosted, decision-tree process (e.g., to correct errors, etc.). For example, executed adaptive training module 230 may exclude, from the one or more intermediate training datasets, a corresponding one of the combined sequential features associated with a minimum of the computed Shapley values, a predetermined number of the combined sequential features characterized by the lowest of computed Shapley values, or any of the combined sequential features characterized by a corresponding one of the computed Shapley values disposed below a corresponding threshold value.")
automatically monitoring new transactions associated with the secured payment instrument and other secured payment instruments associated with other users;
(Li US20240303551 at paras. 18-20, 30-33, 144) ("[0030] Further, as illustrated in FIG. 1, source system 102B may also be associated with, or operated by, the financial institution, and may establish, within the one or more tangible, non-transitory memories, a data repository 109 that maintains elements of data identifying or characterizing one or more existing customers of the financial institution and interactions between these customers and the financial institution, such as, but not limited to, elements of customer profile data 110, elements of account data 112, and elements of transaction data 114. In some instances, the elements of customer profile data 110 may include, but are not limited to, one or more unique customer identifiers (e.g., an alphanumeric identifier, an alphanumeric character string, such as a login credential or a customer name, etc.), residence data (e.g., a street address, a city or town of residence, etc.), other elements of contact information (e.g., a mobile number, an email address, etc.), values of demographic parameters that characterize the particular customer (e.g., ages, occupations, marital status, etc.), and other data characterizing the relationship between the particular customer and the financial institution (e.g., a customer tenure at the financial institution, etc.). [0031] In some instances, the elements of account data 112 may identify and characterize one or more financial products or financial instruments issued by the financial institution to corresponding ones of the existing customers. For example, the elements of account data 112 may include, for each of the financial products issued to corresponding ones of the existing customers, one or more identifiers of the financial product (e.g., an alphanumeric product, an account number, expiration data, card-security-code, etc.), one or more unique customer identifiers (e.g., an alphanumeric identifiers, an alphanumeric character string, such as a login credential or a customer name, etc.), and additional information characterizing a balance or current status of the financial product or instrument (e.g., payment due dates or amounts, delinquent accounts statuses, etc.). Examples of these financial products may include, but are not limited to, a deposit account (e.g., a savings account, a checking account, etc.), a brokerage or retirements account, and a secured or unsecured credit or lending products (e.g., a real-estate secured lending product, an auto loan, a credit-card account, a personal loan, or an unsecured line-of-credit). [0032] Further, the elements of transaction data 114 may identify and characterize initiated, settled, or cleared transactions involving respective ones of the existing customers and corresponding ones of the issued financial products. Examples of these transactions include, but are not limited to, purchase transactions, bill-payment transactions, electronic funds transfers, currency conversions, purchases of securities, derivatives, or other tradeable instruments, electronic funds transfer (EFT) transactions, peer-to-peer (P2P) transfers or transactions, or real-time payment (RTP) transactions. For instance, and for a particular transaction involving a corresponding customer and corresponding financial product, the elements of transaction data 114 may include, but are limited to, the customer identifier of the corresponding customer (e.g., the alphanumeric character string described herein, etc.), a counterparty identifier (e.g., an alphanumeric character string, a counterparty name, etc.), an identifier of the corresponding financial product (e.g., a tokenized account number, expiration data, card-security-code, etc.), and values of one or more parameters of the particular transaction (e.g., a transaction amount, a transaction date, etc.).")
processing the new transactions and
(Li US20240303551 at paras. 18-20, 30-33) ("[0019] Further, and based on an application of the trained machine-learning or artificial-intelligence process to an input dataset associated with a corresponding application for an unsecured lending product, certain of the exemplary processes described herein may facilitate a prediction, in real-time, of a likelihood of an occurrence, or a non-occurrence, of one or more targeted events involving the unsecured lending product during the future temporal interval, which may corresponding application with an expected positive outcome (e.g., a predicted non-occurrence of any of the targeted events during the future interval) or alternatively, with an expected negative outcome (e.g., a predicted occurrence of at least one of the targeted events during the future interval)." "[0020] Certain of these exemplary processes, which adaptively train a machine-learning or artificial-intelligence process using datasets associated with respective training, validation, and testing periods and using corresponding assigned and inferred ground-truth labels, and which apply the trained and validated gradient-boosted, decision-tree process to an input dataset associated with a received application for unsecured lending product, may enable the one or more computing systems of the financial institution to provision a decision to approve, or alternatively, reject, the received application to a corresponding device in real-time and contemporaneously with both an initiation of the application by the corresponding device and a receipt of the corresponding application by the one or more computing systems of the financial institution. These exemplary processes may, for example, be implemented in addition to, or as alternative to, existing processes adaptive processes that introduce a bias towards an adjudication strategy currently or previously applied by the financial institution to the applications for the unsecured lending products." "[0033] The disclosed embodiments are, however, not limited to these exemplary elements of customer profile data 110, account data 112, or transaction data 114 and in other instances, the elements of customer profile data 110, account data 112, and transaction data 114 may include, respectively, any additional or alternate elements of data that identify and characterize the customers of the financial institution and their relationships or interactions with the financial institution, financial products issued to these customers by the financial institution, and transactions involving corresponding ones of the customers and the issued financial products. Further, although stored in FIG. 1 within data repositories maintained by source system 102B, the exemplary elements of customer profile data 110, account data 112, or transaction data 114 may be maintained by any additional or alternate computing system associated with the financial institution, including, but not limited to, within one or more tangible, non-transitory memories of FI computing system 130.")
ongoing credit performance data corresponding to the adjustment and
(Li US20240303551 at paras. 18-20, 30-33, 144) ("[0030] Further, as illustrated in FIG. 1, source system 102B may also be associated with, or operated by, the financial institution, and may establish, within the one or more tangible, non-transitory memories, a data repository 109 that maintains elements of data identifying or characterizing one or more existing customers of the financial institution and interactions between these customers and the financial institution, such as, but not limited to, elements of customer profile data 110, elements of account data 112, and elements of transaction data 114. In some instances, the elements of customer profile data 110 may include, but are not limited to, one or more unique customer identifiers (e.g., an alphanumeric identifier, an alphanumeric character string, such as a login credential or a customer name, etc.), residence data (e.g., a street address, a city or town of residence, etc.), other elements of contact information (e.g., a mobile number, an email address, etc.), values of demographic parameters that characterize the particular customer (e.g., ages, occupations, marital status, etc.), and other data characterizing the relationship between the particular customer and the financial institution (e.g., a customer tenure at the financial institution, etc.). [0031] In some instances, the elements of account data 112 may identify and characterize one or more financial products or financial instruments issued by the financial institution to corresponding ones of the existing customers. For example, the elements of account data 112 may include, for each of the financial products issued to corresponding ones of the existing customers, one or more identifiers of the financial product (e.g., an alphanumeric product, an account number, expiration data, card-security-code, etc.), one or more unique customer identifiers (e.g., an alphanumeric identifiers, an alphanumeric character string, such as a login credential or a customer name, etc.), and additional information characterizing a balance or current status of the financial product or instrument (e.g., payment due dates or amounts, delinquent accounts statuses, etc.). Examples of these financial products may include, but are not limited to, a deposit account (e.g., a savings account, a checking account, etc.), a brokerage or retirements account, and a secured or unsecured credit or lending products (e.g., a real-estate secured lending product, an auto loan, a credit-card account, a personal loan, or an unsecured line-of-credit). [0032] Further, the elements of transaction data 114 may identify and characterize initiated, settled, or cleared transactions involving respective ones of the existing customers and corresponding ones of the issued financial products. Examples of these transactions include, but are not limited to, purchase transactions, bill-payment transactions, electronic funds transfers, currency conversions, purchases of securities, derivatives, or other tradeable instruments, electronic funds transfer (EFT) transactions, peer-to-peer (P2P) transfers or transactions, or real-time payment (RTP) transactions. For instance, and for a particular transaction involving a corresponding customer and corresponding financial product, the elements of transaction data 114 may include, but are limited to, the customer identifier of the corresponding customer (e.g., the alphanumeric character string described herein, etc.), a counterparty identifier (e.g., an alphanumeric character string, a counterparty name, etc.), an identifier of the corresponding financial product (e.g., a tokenized account number, expiration data, card-security-code, etc.), and values of one or more parameters of the particular transaction (e.g., a transaction amount, a transaction date, etc.).")
other adjustments associated with the other users to retrain the machine learning algorithm;
(Li US20240303551 at paras. 18-20, 30-33) ("[0019] Further, and based on an application of the trained machine-learning or artificial-intelligence process to an input dataset associated with a corresponding application for an unsecured lending product, certain of the exemplary processes described herein may facilitate a prediction, in real-time, of a likelihood of an occurrence, or a non-occurrence, of one or more targeted events involving the unsecured lending product during the future temporal interval, which may corresponding application with an expected positive outcome (e.g., a predicted non-occurrence of any of the targeted events during the future interval) or alternatively, with an expected negative outcome (e.g., a predicted occurrence of at least one of the targeted events during the future interval)." "[0020] Certain of these exemplary processes, which adaptively train a machine-learning or artificial-intelligence process using datasets associated with respective training, validation, and testing periods and using corresponding assigned and inferred ground-truth labels, and which apply the trained and validated gradient-boosted, decision-tree process to an input dataset associated with a received application for unsecured lending product, may enable the one or more computing systems of the financial institution to provision a decision to approve, or alternatively, reject, the received application to a corresponding device in real-time and contemporaneously with both an initiation of the application by the corresponding device and a receipt of the corresponding application by the one or more computing systems of the financial institution. These exemplary processes may, for example, be implemented in addition to, or as alternative to, existing processes adaptive processes that introduce a bias towards an adjudication strategy currently or previously applied by the financial institution to the applications for the unsecured lending products." "[0033] The disclosed embodiments are, however, not limited to these exemplary elements of customer profile data 110, account data 112, or transaction data 114 and in other instances, the elements of customer profile data 110, account data 112, and transaction data 114 may include, respectively, any additional or alternate elements of data that identify and characterize the customers of the financial institution and their relationships or interactions with the financial institution, financial products issued to these customers by the financial institution, and transactions involving corresponding ones of the customers and the issued financial products. Further, although stored in FIG. 1 within data repositories maintained by source system 102B, the exemplary elements of customer profile data 110, account data 112, or transaction data 114 may be maintained by any additional or alternate computing system associated with the financial institution, including, but not limited to, within one or more tangible, non-transitory memories of FI computing system 130.")
processing the new transactions and the ongoing credit performance data through the machine learning algorithm
(Li US20240303551 at paras. 18-20, 30-33) ("[0019] Further, and based on an application of the trained machine-learning or artificial-intelligence process to an input dataset associated with a corresponding application for an unsecured lending product, certain of the exemplary processes described herein may facilitate a prediction, in real-time, of a likelihood of an occurrence, or a non-occurrence, of one or more targeted events involving the unsecured lending product during the future temporal interval, which may corresponding application with an expected positive outcome (e.g., a predicted non-occurrence of any of the targeted events during the future interval) or alternatively, with an expected negative outcome (e.g., a predicted occurrence of at least one of the targeted events during the future interval)." "[0020] Certain of these exemplary processes, which adaptively train a machine-learning or artificial-intelligence process using datasets associated with respective training, validation, and testing periods and using corresponding assigned and inferred ground-truth labels, and which apply the trained and validated gradient-boosted, decision-tree process to an input dataset associated with a received application for unsecured lending product, may enable the one or more computing systems of the financial institution to provision a decision to approve, or alternatively, reject, the received application to a corresponding device in real-time and contemporaneously with both an initiation of the application by the corresponding device and a receipt of the corresponding application by the one or more computing systems of the financial institution. These exemplary processes may, for example, be implemented in addition to, or as alternative to, existing processes adaptive processes that introduce a bias towards an adjudication strategy currently or previously applied by the financial institution to the applications for the unsecured lending products." "[0033] The disclosed embodiments are, however, not limited to these exemplary elements of customer profile data 110, account data 112, or transaction data 114 and in other instances, the elements of customer profile data 110, account data 112, and transaction data 114 may include, respectively, any additional or alternate elements of data that identify and characterize the customers of the financial institution and their relationships or interactions with the financial institution, financial products issued to these customers by the financial institution, and transactions involving corresponding ones of the customers and the issued financial products. Further, although stored in FIG. 1 within data repositories maintained by source system 102B, the exemplary elements of customer profile data 110, account data 112, or transaction data 114 may be maintained by any additional or alternate computing system associated with the financial institution, including, but not limited to, within one or more tangible, non-transitory memories of FI computing system 130.")
continuously updating the machine learning algorithm based on new ongoing credit performance data corresponding to new adjustments associated with the other users and new credit performance data associated with the unsecured payment instrument and the other secured payment instruments.
(Li US20240303551 at paras. 18-20, 30-33, 144) ("[0019] Further, and based on an application of the trained machine-learning or artificial-intelligence process to an input dataset associated with a corresponding application for an unsecured lending product, certain of the exemplary processes described herein may facilitate a prediction, in real-time, of a likelihood of an occurrence, or a non-occurrence, of one or more targeted events involving the unsecured lending product during the future temporal interval, which may corresponding application with an expected positive outcome (e.g., a predicted non-occurrence of any of the targeted events during the future interval) or alternatively, with an expected negative outcome (e.g., a predicted occurrence of at least one of the targeted events during the future interval)." "[0020] Certain of these exemplary processes, which adaptively train a machine-learning or artificial-intelligence process using datasets associated with respective training, validation, and testing periods and using corresponding assigned and inferred ground-truth labels, and which apply the trained and validated gradient-boosted, decision-tree process to an input dataset associated with a received application for unsecured lending product, may enable the one or more computing systems of the financial institution to provision a decision to approve, or alternatively, reject, the received application to a corresponding device in real-time and contemporaneously with both an initiation of the application by the corresponding device and a receipt of the corresponding application by the one or more computing systems of the financial institution. These exemplary processes may, for example, be implemented in addition to, or as alternative to, existing processes adaptive processes that introduce a bias towards an adjudication strategy currently or previously applied by the financial institution to the applications for the unsecured lending products." "[0031] In some instances, the elements of account data 112 may identify and characterize one or more financial products or financial instruments issued by the financial institution to corresponding ones of the existing customers. For example, the elements of account data 112 may include, for each of the financial products issued to corresponding ones of the existing customers, one or more identifiers of the financial product (e.g., an alphanumeric product, an account number, expiration data, card-security-code, etc.), one or more unique customer identifiers (e.g., an alphanumeric identifiers, an alphanumeric character string, such as a login credential or a customer name, etc.), and additional information characterizing a balance or current status of the financial product or instrument (e.g., payment due dates or amounts, delinquent accounts statuses, etc.). Examples of these financial products may include, but are not limited to, a deposit account (e.g., a savings account, a checking account, etc.), a brokerage or retirements account, and a secured or unsecured credit or lending products (e.g., a real-estate secured lending product, an auto loan, a credit-card account, a personal loan, or an unsecured line-of-credit). [0032] Further, the elements of transaction data 114 may identify and characterize initiated, settled, or cleared transactions involving respective ones of the existing customers and corresponding ones of the issued financial products. Examples of these transactions include, but are not limited to, purchase transactions, bill-payment transactions, electronic funds transfers, currency conversions, purchases of securities, derivatives, or other tradeable instruments, electronic funds transfer (EFT) transactions, peer-to-peer (P2P) transfers or transactions, or real-time payment (RTP) transactions. For instance, and for a particular transaction involving a corresponding customer and corresponding financial product, the elements of transaction data 114 may include, but are limited to, the customer identifier of the corresponding customer (e.g., the alphanumeric character string described herein, etc.), a counterparty identifier (e.g., an alphanumeric character string, a counterparty name, etc.), an identifier of the corresponding financial product (e.g., a tokenized account number, expiration data, card-security-code, etc.), and values of one or more parameters of the particular transaction (e.g., a transaction amount, a transaction date, etc.). [0033] The disclosed embodiments are, however, not limited to these exemplary elements of customer profile data 110, account data 112, or transaction data 114 and in other instances, the elements of customer profile data 110, account data 112, and transaction data 114 may include, respectively, any additional or alternate elements of data that identify and characterize the customers of the financial institution and their relationships or interactions with the financial institution, financial products issued to these customers by the financial institution, and transactions involving corresponding ones of the customers and the issued financial products. Further, although stored in FIG. 1 within data repositories maintained by source system 102B, the exemplary elements of customer profile data 110, account data 112, or transaction data 114 may be maintained by any additional or alternate computing system associated with the financial institution, including, but not limited to, within one or more tangible, non-transitory memories of FI computing system 130." "[0144] FI computing system 130 may also perform operations, such as those described herein, that store (or ingest) the obtained elements of application, customer profile, account, transaction and credit-bureau data within one or more accessible data repositories, such as aggregated data store 132, in conjunction with temporal data characterizing corresponding ingestion dates (e.g., also in step 502 of FIG. 5). In some instances, FI computing system 130 may perform operations, such as those describe herein, to obtain and ingest the elements of application, customer profile, account, transaction and credit-bureau data in accordance with a predetermined temporal schedule (e.g., on a monthly basis at a predetermined date or time, etc.), or a continuous streaming basis, across the secure, programmatic channel of communication.")
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Orman and Li, because it allows for an improved method to facilitate a real-time prediction of future events using trained artificial-intelligence processes and inferred ground-truth labelling in multiple data populations. (Li at Abstract and paras. 2-8).
Orman and Li do not explicitly teach, however, Locar does teach:
training a machine learning algorithm
(Locar US20240015180 at paras. 20-40) ("[0020] Neural network 150, in the illustrated embodiment, receives a feature vector 142 from feature module 140 and generates a signature vector 152 for the unknown website 120 based on feature vector 142. For example, neural network 150 may be a trained Siamese network, contrastive loss network, etc. The neural network 150 is a trained neural network (trained by server computer system 110 or another computer system) that is executed by decision module 130 to determine signature vectors for various websites based on feature vectors output by feature module 140. A signature vector 152 output by network 150 for the unknown website 120 may be similar to a signature vector output by network 150 for another, different website that has similar features to the unknown website 120." "[0029] Machine learning classifier 270, in the illustrated embodiment, receives one or more cluster scores 242 from scoring module 240 and generates a classification 272 for the website based on the one or more scores. In some situations, classifier 270 determines whether unknown website 220 is suspicious based on a silhouette score for the website. In other situations, classifier 270 may be trained to generate a classification for the unknown website 220 based on multiple different cluster scores 242.")
wherein the machine learning algorithm is trained by classifying
(Locar US20240015180 at paras. 20-40) ("[0021] Clustering module 160, in the illustrated embodiment, receives signature vector 152 from neural network 150 generated for unknown website 120 and generates clustering results 162 for signature vector 152 and a plurality of other signature vectors (not shown) output by neural network 150 for a plurality of other, known websites (e.g., known to be suspicious or not suspicious based on prior classification). Clustering module 160 may implement any of various clustering algorithms on signature vectors output by neural network 150 to provide clusters of websites that have similar (e.g., structural) features. For example, clustering module 160 may execute one or more of the following types of clustering algorithms: a density-based spatial clustering of applications with noise (DBSCAN), a k-means clustering, a mean shift, a gaussian mixture model, etc." "[0029] Machine learning classifier 270, in the illustrated embodiment, receives one or more cluster scores 242 from scoring module 240 and generates a classification 272 for the website based on the one or more scores. In some situations, classifier 270 determines whether unknown website 220 is suspicious based on a silhouette score for the website. In other situations, classifier 270 may be trained to generate a classification for the unknown website 220 based on multiple different cluster scores 242.")
into a set of clusters according to one or more vectors of similarity;
(Locar US20240015180 at paras. 20-40) ("[0020] Neural network 150, in the illustrated embodiment, receives a feature vector 142 from feature module 140 and generates a signature vector 152 for the unknown website 120 based on feature vector 142. For example, neural network 150 may be a trained Siamese network, contrastive loss network, etc. The neural network 150 is a trained neural network (trained by server computer system 110 or another computer system) that is executed by decision module 130 to determine signature vectors for various websites based on feature vectors output by feature module 140. A signature vector 152 output by network 150 for the unknown website 120 may be similar to a signature vector output by network 150 for another, different website that has similar features to the unknown website 120. [0021] Clustering module 160, in the illustrated embodiment, receives signature vector 152 from neural network 150 generated for unknown website 120 and generates clustering results 162 for signature vector 152 and a plurality of other signature vectors (not shown) output by neural network 150 for a plurality of other, known websites (e.g., known to be suspicious or not suspicious based on prior classification). Clustering module 160 may implement any of various clustering algorithms on signature vectors output by neural network 150 to provide clusters of websites that have similar (e.g., structural) features. For example, clustering module 160 may execute one or more of the following types of clustering algorithms: a density-based spatial clustering of applications with noise (DBSCAN), a k-means clustering, a mean shift, a gaussian mixture model, etc." "[0027] Feature module 140, in the illustrated embodiment, generates a vector 250 of structural features for website 220, which is then used by neural network 150 to generate signature vector 252. Clustering module 160 performs a clustering algorithm on signature vector 252 and signature vectors of various other websites. Clustering module 160 outputs clustering results 262 to scoring module 240. [0028] In the illustrated embodiment, based on the clustering results 262, scoring module 240 generates one or more cluster scores 242. For example, scoring module 240 generates silhouette scores for various clusters generated by clustering module 160. A silhouette score output by module 240 for a cluster in which the unknown website 220 is included may indicate the quality of that cluster. A high-quality cluster, for example, may be one with a high silhouette score, indicating that this is a dense cluster with signature vectors being very similar (the signature vectors are densely packed within the cluster). As discussed in further detail below with reference to FIG. 5, if unknown website 220 is included in a high-quality cluster (one having a high silhouette score), this may be indicative that the unknown website 220 is suspicious based on one or more other signature vectors for other websites being known suspicious websites. Scoring module 240 may generate various other types of cluster scores 242. The different cluster scores 242 generated by cluster module 240 may be included in a set of features for the cluster. For example, a set of features for a given cluster may include one or more of the following features: a percentage of known suspicious websites in the cluster, a silhouette score for the cluster, a size of the cluster, distances between signature vectors within the cluster, distances between the cluster and other, different clusters, etc. [0029] Machine learning classifier 270, in the illustrated embodiment, receives one or more cluster scores 242 from scoring module 240 and generates a classification 272 for the website based on the one or more scores. In some situations, classifier 270 determines whether unknown website 220 is suspicious based on a silhouette score for the website. In other situations, classifier 270 may be trained to generate a classification for the unknown website 220 based on multiple different cluster scores 242.")
through the machine learning algorithm to identify a cluster from the set of clusters according to the one or more vectors of similarity;
(Locar US20240015180 at paras. 20-40) ("[0020] Neural network 150, in the illustrated embodiment, receives a feature vector 142 from feature module 140 and generates a signature vector 152 for the unknown website 120 based on feature vector 142. For example, neural network 150 may be a trained Siamese network, contrastive loss network, etc. The neural network 150 is a trained neural network (trained by server computer system 110 or another computer system) that is executed by decision module 130 to determine signature vectors for various websites based on feature vectors output by feature module 140. A signature vector 152 output by network 150 for the unknown website 120 may be similar to a signature vector output by network 150 for another, different website that has similar features to the unknown website 120. [0021] Clustering module 160, in the illustrated embodiment, receives signature vector 152 from neural network 150 generated for unknown website 120 and generates clustering results 162 for signature vector 152 and a plurality of other signature vectors (not shown) output by neural network 150 for a plurality of other, known websites (e.g., known to be suspicious or not suspicious based on prior classification). Clustering module 160 may implement any of various clustering algorithms on signature vectors output by neural network 150 to provide clusters of websites that have similar (e.g., structural) features. For example, clustering module 160 may execute one or more of the following types of clustering algorithms: a density-based spatial clustering of applications with noise (DBSCAN), a k-means clustering, a mean shift, a gaussian mixture model, etc." "[0027] Feature module 140, in the illustrated embodiment, generates a vector 250 of structural features for website 220, which is then used by neural network 150 to generate signature vector 252. Clustering module 160 performs a clustering algorithm on signature vector 252 and signature vectors of various other websites. Clustering module 160 outputs clustering results 262 to scoring module 240. [0028] In the illustrated embodiment, based on the clustering results 262, scoring module 240 generates one or more cluster scores 242. For example, scoring module 240 generates silhouette scores for various clusters generated by clustering module 160. A silhouette score output by module 240 for a cluster in which the unknown website 220 is included may indicate the quality of that cluster. A high-quality cluster, for example, may be one with a high silhouette score, indicating that this is a dense cluster with signature vectors being very similar (the signature vectors are densely packed within the cluster). As discussed in further detail below with reference to FIG. 5, if unknown website 220 is included in a high-quality cluster (one having a high silhouette score), this may be indicative that the unknown website 220 is suspicious based on one or more other signature vectors for other websites being known suspicious websites. Scoring module 240 may generate various other types of cluster scores 242. The different cluster scores 242 generated by cluster module 240 may be included in a set of features for the cluster. For example, a set of features for a given cluster may include one or more of the following features: a percentage of known suspicious websites in the cluster, a silhouette score for the cluster, a size of the cluster, distances between signature vectors within the cluster, distances between the cluster and other, different clusters, etc. [0029] Machine learning classifier 270, in the illustrated embodiment, receives one or more cluster scores 242 from scoring module 240 and generates a classification 272 for the website based on the one or more scores. In some situations, classifier 270 determines whether unknown website 220 is suspicious based on a silhouette score for the website. In other situations, classifier 270 may be trained to generate a classification for the unknown website 220 based on multiple different cluster scores 242.")
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Orman, Li, and Locar, because it allows for an improved system that employs a clustering algorithm to organize similar payment instrument data into clusters prior to classification, thereby improving the quality of the classification used for lending decisions. (Locar at Abstract and paras. 20-40).
Orman, Li, and Locar do not explicitly teach, however, Black does teach:
to identify a new cluster from the set of clusters according to the one or more vectors of similarity, wherein the new cluster is used
(Black US20130325862 at paras. 3-5) ("[0003] In accordance with an aspect of the present invention, a system is provided for large-scale, incrementing clustering. A plurality of processing nodes each include a processor and a non-transitory computer readable medium. The non-transitory computer readable medium stores a plurality of clusters of feature vectors and machine executable instructions for determining a plurality of values for a distance metric relating each of the plurality of clusters to an input feature vector and selecting a cluster having a best value for the distance metric. An arbitrator is configured to receive the selected cluster and best value for the distance metric from each of the plurality of processing nodes and determine a winning cluster as one of the selected clusters and a new cluster. A multiplexer is configured to receive the winning cluster and provide the winning cluster and a new input feature vector to each of the plurality of processing nodes. [0004] In accordance with another aspect of the present invention, a method is provided for large-scale, incremental clustering. An input feature vector is distributed to each of a plurality of processing nodes from a multiplexer. Each processing node includes a processor and a non-transitory computer readable medium. At each of the plurality of processing nodes, a plurality of values for a distance metric are determined. Each value for the distance metric represents the similarity of the input feature vector to a cluster stored at the processing node. A best cluster is selected at each of the plurality of processing nodes according to the plurality of values for the distance metric determined at the processing node. An overall best cluster from the selected best clusters is selected according to their associated values for the distance metric. The overall best cluster is selected as a winning cluster if the value for the distance metric associated with the overall best cluster meets a threshold value. A new cluster is created as the winning cluster if the value for the distance metric associated with the overall best cluster does not meet a threshold value. The identity of the winning cluster is provided to the multiplexer, and the identity of the winning cluster associated with the input feature vector is distributed to the plurality of processing nodes along with a new input feature vector for analysis.")
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Orman, Li, Locar, and Black, because it allows for an improved system for large-scale, incrementing clustering. (Black at Abstract and paras. 1-10).
As per claim 2,
Orman explicitly teaches:
wherein the adjustment includes automatically reducing the initial security deposit associated with the secured payment instrument.
(Orman US20060031158 at paras. 19-26, 41-45) ("[0025] In an embodiment of the invention where the credit card is a secured credit card, the monitor and adjustment module 116 may identify that a certain credit score improvement has been achieved by the consumer. If the specified credit score improvement has been achieved, the monitor and adjustment module 116 may change the terms and conditions of the consumer credit card account so that the consumer credit card account becomes an unsecured credit card, i.e., the status is changed from secured to unsecured. Illustratively, a secured credit card may utilize collateral of the consumer, i.e., personal property or real property, to back up the money lent to the consumer. The collateral may be a cash balance maintained in an account with the bank or financial institution extending the credit to the consumer, or an ownership document for some of the consumer's property, e.g., a deed of trust that the consumer has execute. If the consumer achieves a 50 point increase in a credit score in a six-month timeframe, the monitor and adjustment module 116 may instruct an individual working for the credit card issuer to return the ownership document (and thus the claim to ownership in the property) and to remove any assignment or other ownership claims that the credit card issuer may have recorded with a government agency. In addition, the monitor and adjustment module 116 instructs the account creation and maintenance module 112 to change the status of the consumer account from a secured credit card account to an unsecured credit card account. In an embodiment of the invention, the monitor and adjustment module 116 may utilize the notification module 118 to notify the consumer of the change in credit card status." "[0041] The credit card issuer may utilize the information received from the credit bureau and/or the applicant to establish 310 an initial interest rate. In an embodiment of the invention, the initial interest rate may be based solely on the credit score or may be based on a combination of factors and parameters, which include the credit score. After the initial interest rate, the credit limit, the billing cycle, and the terms and conditions are established for the consumer account, the consumer account may be activated 314. In this embodiment of the invention, the terms and conditions may outline information such as foreclosure procedures on the secured collateral property in case the credit card consumer defaults on the credit card. In addition, the terms and conditions may outline the conditions that may allow the consumer to move from a secured credit instrument to an unsecured credit instrument. The consumer may be notified by the credit card issuer of the initial interest rate, the credit limit, the billing cycle, and the terms and conditions. The consumer may begin utilizing the credit card." "[0042] The credit card issuer may establish an automatic monitoring period in the terms and conditions of the credit card. In this embodiment of the invention, the applicant or consumer may wish to make the secured credit card an unsecured credit card, i.e., meaning that no collateral would be needed for the credit card. In most cases, the credit card issuer would not change the status from a secured credit card to an unsecured credit card unless a significant increase in credit score was achieved by the consumer.")
As per claim 4,
Orman explicitly teaches:
wherein the determination is generated as a result of the initial security deposit no longer being required as a result of the new adjustment.
(Orman US20060031158 at paras. 19-26, 41-45) ("[0042] The credit card issuer may establish an automatic monitoring period in the terms and conditions of the credit card. In this embodiment of the invention, the applicant or consumer may wish to make the secured credit card an unsecured credit card, i.e., meaning that no collateral would be needed for the credit card. In most cases, the credit card issuer would not change the status from a secured credit card to an unsecured credit card unless a significant increase in credit score was achieved by the consumer.")
As per claim 7,
Orman explicitly teaches:
further comprising: automatically disbursing the initial security deposit as a result of the new adjustment.
(Orman US20060031158 at paras. 19-26, 41-45) ("[0043] After receiving the credit score, the credit card issuer may compare the received credit score, e.g., the updated credit score, to the stored initial credit score. The difference in the received credit score and the updated credit score is calculated and an increase or decrease is noted. In an embodiment of the invention, the credit card issuer may decide 322 to change the status of the consumer's credit card account, and hence the credit card, from a secured credit card to an unsecured credit card. Illustratively, if after nine months, the consumer's credit score has increased by 75 points, the credit card issuer may decide that collateral is no longer required to secure the credit card account, and may remove this condition from the consumer accounts terms and conditions. If the credit score has not changed, or if the change in credit score has decreased, then the consumer credit card account may maintain its secured status. In this embodiment of the invention, the credit card issuer may also return the collateral or ownership document to the consumer.")
Claims 8 and 15 are substantially similar to claim 1, thus, they are rejected on similar grounds.
Claims 9 and 16 are substantially similar to claim 16, thus, they are rejected on similar grounds.
Claims 11 and 18 are substantially similar to claim 18, thus, they are rejected on similar grounds.
Claims 14 and 21 are substantially similar to claim 21, thus, they are rejected on similar grounds.
Claims 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Orman, U.S. Patent Application Publication Number 2006/0031158; in view of Li, U.S. Patent Application Publication Number 2024/0303551; in view of Locar, U.S. Patent Application Publication Number 2024/0015180; in view of Black, U.S. Patent Application Publication Number 2013/0325862; in view of Vosseller, U.S. Patent Application Publication Number 2023/0103398.
As per claim 3,
Orman, Li, Locar, and Black do not explicitly teach, however, Vosseller does teach:
wherein the adjustment includes automatically increasing the credit limit associated with the secured payment instrument.
(Vosseller US20230103398 at paras. 27-31) ("[0027] The service provider system transmits a response specifying a security deposit to be transferred as a prerequisite to the service provider account being provided access to the transaction. For instance, a response is transmitted to a client device of the service provider account. By way of example, the service provider account can receive the response as a digital message, text message, notification, popup, email, digital content, or so forth. In one example, a response specifies an amount of reputation tokens to be transferred as a security deposit. The service provider system calculates the security deposit based on the reputation score affiliated with the service provider account. By way of example, a higher security deposit can be calculated based on a low tokenized reputation score. In contrast, a lower amount of reputation tokens (as the security deposit) can be calculated based on a high tokenized reputation score. In this example, the service provider system calculates a lower amount of reputation tokens because a service provider account that is predicted by a tokenized reputation score to be more trustworthy, predicts a higher likelihood of a transaction being successfully completed by the service provider account.")
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Orman, Li, Locar, Black, and Vosseller, because it allows for an improved system to control access to transactions by using security deposits based on tokenized reputation scores. (Vosseller at Abstract and paras. 1-5).
Claims 10 and 17 are substantially similar to claim 3, thus, they are rejected on similar grounds.
Claims 5, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Orman, U.S. Patent Application Publication Number 2006/0031158; in view of Li, U.S. Patent Application Publication Number 2024/0303551; in view of Locar, U.S. Patent Application Publication Number 2024/0015180; in view of Black, U.S. Patent Application Publication Number 2013/0325862; in view of Smith, U.S. Patent Application Publication Number 2008/0005014.
As per claim 5,
Orman, Li, Locar, and Black do not explicitly teach, however, Smith does teach:
wherein the determination is generated as a result of a ratio between the credit limit and the initial security deposit being greater than a threshold amount as a result of the new adjustment.
(Smith US20080005014 at paras. 25-27) ("At step 11, the card issuer identifies high-risk customers for whom unsecured credit cards are not appropriate. This step is described in more detail in connection with FIG. 2 below. At step 12, the card issuer notifies the high-risk customers about secured credit card options available through the card issuer. The options may include traditional secured credit card products such as ratio products and fixed-line products, together with the property-secured credit card product of the present invention. If a customer requests a ratio product, the process moves from step 12 to step 13 where the card issuer offers a ratio product having a variable credit limit based on a ratio or multiple of a monetary security deposit. The security deposit typically falls in a range between a minimum amount and a maximum amount. At step 14, the customer pays the monetary security deposit as selected between the minimum and maximum amounts to achieve a desired credit limit. The process then moves to step 22 where the card issuer issues the secured credit card.")
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Orman, Li, Locar, Black, and Smith, because it allows for an improved system and method for offering and providing secured credit card products, for a secured credit card product that minimizes the risk to credit card issuers while attracting a larger number of potential customers having a poor or bad credit history, and for offering secured credit cards to people who do not have cash available for a security deposit. (Smith at Abstract and paras. 4-11).
Claims 12 and 19 are substantially similar to claim 5, thus, they are rejected on similar grounds.
Claims 22, 23, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Orman, U.S. Patent Application Publication Number 2006/0031158; in view of Li, U.S. Patent Application Publication Number 2024/0303551; in view of Locar, U.S. Patent Application Publication Number 2024/0015180; in view of Black, U.S. Patent Application Publication Number 2013/0325862; in view of Kurani, U.S. Patent Application Publication Number 2023/0410089.
As per claim 22,
Orman explicitly teaches:
wherein graduating the secured payment instrument to the unsecured payment instrument further comprises: generating a graduation [offer] for graduating the secured payment instrument to the unsecured payment instrument, wherein the graduation [offer] includes a set of terms and other information corresponding to the unsecured payment instrument; and
(Orman US20060031158 at paras. 19-26, 41-45) ("[0025] In an embodiment of the invention where the credit card is a secured credit card, the monitor and adjustment module 116 may identify that a certain credit score improvement has been achieved by the consumer. If the specified credit score improvement has been achieved, the monitor and adjustment module 116 may change the terms and conditions of the consumer credit card account so that the consumer credit card account becomes an unsecured credit card, i.e., the status is changed from secured to unsecured. Illustratively, a secured credit card may utilize collateral of the consumer, i.e., personal property or real property, to back up the money lent to the consumer. The collateral may be a cash balance maintained in an account with the bank or financial institution extending the credit to the consumer, or an ownership document for some of the consumer's property, e.g., a deed of trust that the consumer has execute. If the consumer achieves a 50 point increase in a credit score in a six-month timeframe, the monitor and adjustment module 116 may instruct an individual working for the credit card issuer to return the ownership document (and thus the claim to ownership in the property) and to remove any assignment or other ownership claims that the credit card issuer may have recorded with a government agency. In addition, the monitor and adjustment module 116 instructs the account creation and maintenance module 112 to change the status of the consumer account from a secured credit card account to an unsecured credit card account. In an embodiment of the invention, the monitor and adjustment module 116 may utilize the notification module 118 to notify the consumer of the change in credit card status.")
Orman, Li, Locar, and Black do not explicitly teach, however, Kurani does teach:
generating a offer; and
updating an application interface to present the [graduation] offer, wherein when the graduation offer is presented, the [graduation] offer is accepted.
(Kurani US20230410089 at paras. 213-215) ("[0214] At process 1104, an indication of a user interaction with the presented credit card offer is received. For example, the user may view a mobile wallet interface (such as the interface 1200 discussed below in relation to FIG. 12) that includes the credit card offer. The first field of this mobile wallet interface may enable the user to indicate a preference as to the credit card offer (e.g., accept or reject the offer). The user may interact with the interface to indicate such a preference and, by so doing, provide inputs to the program logic of the mobile wallet client application 112 being executed by the processor of the user mobile device 110. The inputs may cause the user mobile device 110 to communicate the indicated preference to the financial institution computing system 130 over the network 170 (e.g., via an API) or the mobile wallet computing system 150. The mobile wallet computing system 150 may in turn communicate the user's indicated preference to the financial institution computing system 130.")
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Orman, Li, Locar, Black, and Kurani, because it improves the efficiency with which users can perform various transactions. This is done through unique pairings of user interactions with the mobile wallet and various other functionalities. Such pairings enable users to simultaneously communicate multiple financial preferences through a single interaction with a mobile wallet. (Kurani at Abstract and paras. 2-26, 45-46).
Claims 23 and 24 are substantially similar to claim 22, thus, they are rejected on similar grounds.
Response to Arguments
Applicant’s arguments filed on April 27, 2026 have been fully considered but are not persuasive for the following reasons:
With respect to Applicant’s arguments as to the § 101 rejections for now pending claims 1-5, 7-12, 14-19, and 21-24, Examiner notes that the arguments are moot in light of the new grounds for rejection.
With respect to Applicant’s arguments as to the § 103 rejections for now pending claims 1-5, 7-12, 14-19, and 21-24, Examiner notes that the arguments are moot in light of the new grounds for rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and is available for review on Form PTO-892 Notice of References Cited.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MERRITT J HASBROUCK whose telephone number is (571)272-3109. The examiner can normally be reached M-F 9:00-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christine Tran can be reached on 571-272-8103. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MERRITT J HASBROUCK/Examiner, Art Unit 3695
/CHRISTINE M Tran/Supervisory Patent Examiner, Art Unit 3695
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