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 .
Status of Claims
The following is Office Action on the merits in response to the communication received on 10/29/24.
Claim status:
Amended claims: none
Canceled claims: none
Added New claims: None
Pending claims: 1-20
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 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is not directed to statutory subject matter. Specifically, the invention of claims 1-20 is directed to an abstract idea without significantly more.
Independent claims 1, 11 and 20 are directed to a system (claim 1), a method (claim 11) and a machine-readable non-transitory storage media (claim 20),. Therefore on its face, each of claims 1, 11 and 20 is directed to a statutory category of invention under Step 1 of the 2019 PEG. However each of claims 1, 11 and 20 is also directed to an abstract idea without significantly more, under Step 2A (Prong One and Prong Two) and Step 2B of the 2019 PEG, which is a judicial exception to 35 U.S.C. 101, as detailed below. Using the language of independent claim 1 to illustrate the claim recites the limitations of, (i) storing instructions, (ii) responsive to receiving a request by a first merchant hosting one or more shops on an e-commerce platform for a loan, obtain first data from a public data source and second data from the e-commerce platform; (iii) provide the first data and the second data as inputs to a trained predictor and execute the trained predictor to output a distribution of estimated future revenue for the first merchant; (iv) calculate, based on the distribution of estimated revenue and a cash in a payment account associated with the first merchant, a default probability (PD) value and a loss-given-default (LGD) value associated with potential loan amounts and loan interest rates; (v) determine, using the calculated PD and LGD values, one or more feasible contracts including a target loan amount and a target loan interest rate; (vi) and generate at least one loan contract for providing a loan to the first merchant with the target loan amount and the target loan interest rate under the broadest reasonable interpretation covers methods of organizing human activity – fundamental economic principles or practices, risk mitigation or a “commercial or legal interaction”. (Independent claims 11 and 20 recite similar limitations and the analysis is the same).
That is, other than reciting one or more processing devices and one or more storage devices nothing in the claim precludes the steps from being directed to methods of organizing human activity – fundamental economic principles or practices, risk mitigation or a “commercial or legal interaction”. If a claim limitation under its BRI, covers methods of organizing human activity but for the recitation of generic computer components, then the limitations fall within the “methods of organizing human activity” grouping of abstract ideas. Therefore, claim 1 recites an abstract idea under Step 2A Prong One of the Revised Patent Subject Matter Eligibility Guidance 84 Fed.Reg 50 (“2019 PEG”).
This “methods of organizing human activity” is not integrated into a practical application under Step 2A prong Two of the 2019 PEG. In particular claim 1 recites the following additional elements of, one or more processing devices and one or more storage devices. This judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements – one or more processing devices and one or more storage devices.
The one or more processing devices and one or more storage devices are recited at a high-level or generality (i.e. as a generic computer performing generic computer functions) such that, they amount to no more than instructions to apply the abstract idea with a computer (see MPEP 2106.05(h). Accordingly these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Under Step 2B of the 2019 PEG independent claim 1 does not include additional elements that are sufficient to amount to significantly more than the abstract idea. The claim(s) do not include additional elements 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 one or more processing devices and one or more storage devices, storing instructions, responsive to receiving a request by a first merchant hosting one or more shops on an e-commerce platform for a loan, obtain first data from a public data source and second data from the e-commerce platform; provide the first data and the second data as inputs to a trained predictor and execute the trained predictor to output a distribution of estimated future revenue for the first merchant; calculate, based on the distribution of estimated revenue and a cash in a payment account associated with the first merchant, a default probability (PD) value and a loss-given-default (LGD) value associated with potential loan amounts and loan interest rates; determine, using the calculated PD and LGD values, one or more feasible contracts including a target loan amount and a target loan interest rate; and generate at least one loan contract for providing a loan to the first merchant with the target loan amount and the target loan interest rate amount to instructions to apply the abstract idea with a computer. The claims are not patent eligible.
The dependent claims have been given the full two part analysis including analyzing the additional limitations individually. The Dependent claim(s) when analyzed individually are also held to be patent ineligible under 35 U.S.C. 101 because for the same reasoning as above and the additional recited limitation(s) fail to establish that the claim(s) are not directed to an abstract idea. The additional limitations of the dependent claim(s) when considered individually do not amount to significantly more than the abstract idea. Claims 2-10 and 12-19 merely further explain the abstract idea.
When viewed individually the additional limitations do not amount to a claim as a whole that is significantly more than the abstract idea. Accordingly claims 1-20 are ineligible.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 6-8 and 16-18 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 6 and 16 recite the limitation “wherein the trained predictor comprises a trained transformer neural network module.” However, there is no description in the specification of an algorithm associated with the trained predictor. Therefore, the inventor has not shown possession of the claimed invention. Claims 7-8 depend from claim 6, and claims 17-18 depend from claim 16, and are rejected for this reason.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2-10 and 12-19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2 recites the limitations "the repayment term" at line 5 and “the recalculated PD value” at line 6. There is insufficient antecedent basis for each of these limitations in the claim. Claim 3 depends from Claim 2 and is rejected for this reason.
Further, claim 3 recites the limitation “the financial institution” at page 2 line 4. There is insufficient antecedent basis for this limitation in the claim.
Claim 4 recites the limitation “the trained transformer neural network module” at lines 9-10. There is insufficient antecedent basis for this limitation in the claim. Claims 5-8 depend from claim 4 and are rejected for this reason.
Further, claim 6 recites the limitations “the transformer neural network module” at line 2 and “the plurality of attention heads.” There is insufficient antecedent basis for each of these limitations in the claim. Claims 7-8 depend from claim 6 and are rejected for this reason.
Further, claim 7 recites the limitations “the collateral value” at line 6 and “the repayment term” at the last line. There is insufficient antecedent basis for each of these limitations in the claim.
Further, claim 8 recites the limitation “the correct revenue bin” at line 8. There is insufficient antecedent basis for this limitation in the claim.
Claim 9 recites the limitation “the structural bond model” at the first line. There is insufficient antecedent basis for this limitation in the claim.
Claim 10 recites the limitation “the collateral” at the last line. There is insufficient antecedent basis for this limitation in the claim.
Claim 12 recites the limitations "the repayment term" at line 5 and “the recalculated PD value” at line 6. There is insufficient antecedent basis for each of these limitations in the claim. Claim 13 depends from Claim 12 and is rejected for this reason.
Further, claim 13 recites the limitation “the financial institution” at line 6. There is insufficient antecedent basis for this limitation in the claim.
Claim 14 recites the limitation “the trained transformer neural network module” at lines 9-10. There is insufficient antecedent basis for this limitation in the claim. Claims 15-18 depend from claim 14 and are rejected for this reason.
Further, claim 16 recites the limitations “the transformer neural network module” at line 2 and “the plurality of attention heads” at line 4. There is insufficient antecedent basis for each of these limitations in the claim. Claims 17-18 depend from claim 16 and are rejected for this reason.
Further, claim 17 recites the limitations “the collateral value” at the next to the last line and “the repayment term” at the last line. There is insufficient antecedent basis for each of these limitations in the claim.
Further, claim 18 recites the limitation “the correct revenue bin” at line 8. There is insufficient antecedent basis for this limitation in the claim.
Claim 19 recites the limitation “the collateral” at line 4. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 11-13 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over, Wen (U.S. Pub. No. 2023/0237569), in view of Savinon (U.S. Pub. No. 2021/0174324) and Santa Cruz Masoni (U.S. Pub. No. 11,144,990).
With respect to claims 1, 11 and 20:
Wen teaches:
A system comprising one or more processing devices and one or more storage devices for storing instructions that when executed by the one or more processing devices cause the one or more processing devices to: responsive to {…..} one or more shops on an e-commerce platform for a loan, obtain first data from a public data source and second data from the e-commerce platform (“FIG. 7 is a more detailed flow diagram of one embodiment of a process for performing an advance that is paid down when processing transactions with a payment processing system. In one embodiment, the processes are performed by processing logic that may comprise hardware (circuitry, dedicated logic, etc.), software (e.g., software running on a chip), firmware, or a combination of the three. In one embodiment, the process is performed by a payment processing system of a payment processor. An example of a payment processing system is described in more detail below” (Wen Pgh. [0095]) and “In one embodiment, the predictions are made using a machine learning (ML) framework described herein. The machine learning framework is utilized to create models for prediction and analytics based on data that is collected and obtained. In one embodiment, the data corresponds to one or more of transaction data related to payment processing information, other data internal to a payment processing system, and one or more external signals. That is, the payment processing information includes transaction data corresponding to transactions for which payment processing is performed. In one embodiment, the ML framework uses an interpretable machine learning model to make the predictions. More detail regarding the interpretable machine learning model is provided below” (Wen Pgh. [0019]) and “In one embodiment, the model uses features based on one or more external market signals to calculate scores. In one embodiment, the model uses a feature based on an external Option Adjusted Spread (OAS) signal. The OAS is a spread between a computed OAS index of bonds in a given rating category and a spot treasury curve. The widened spread is a leading indicator of economic downturns and future defaults in non-investment grade bonds. The use of OAS calibration in the model enables the model to react to the changes in industry and market risk, which can be leveraged to inform policy and pricing adjustments in different economic conditions (e.g., COVID-19 pandemic, etc.” (Wen Pgh. [0066]) and “In one embodiment, processing logic also receives one or more external signals (processing block 402) and receives internal proprietary data associated with the plurality of merchants (processing block 403). In one embodiment, the one or more external signals comprise a market signal. In one embodiment, the market signal comprise an option-adjusted spread (OAS) signal. In one embodiment, the market signal is obtained by setting up a communication channel (e.g., FTP communication channel) to obtain the market signal on a predetermined time interval from a remote location and receiving the market signal according to the predetermined time interval over the communication channel. In one embodiment, the internal proprietary data comprises dispute information associated with one or more of the plurality of merchants, information related to processing volumes, dispute rates, growth rates, churn rates, payment and charge information, negative balances, refunds, etc.” Wen Pgh. [0073]);
provide the first data and the second data as inputs to a trained predictor and execute the trained predictor to output a distribution of estimated future revenue for the first merchant (“Techniques are disclosed herein for making predictions regarding merchant transaction data are disclosed. In one embodiment, the predictions are regarding a merchant’s future processing volume in a commerce platform. In one embodiment, the predictions are regarding decreases and/or increases in a merchant’s future processing volume, which may be indicative of future revenue of the merchant. In one embodiment, the future processing volume is associated with handling transactions involving payment flows in a payment processing infrastructure” (Wen Pgh. [0018]) and “In one embodiment, the predictions are made using a machine learning (ML) framework described herein. The machine learning framework is utilized to create models for prediction and analytics based on data that is collected and obtained. In one embodiment, the data corresponds to one or more of transaction data related to payment processing information, other data internal to a payment processing system, and one or more external signals. That is, the payment processing information includes transaction data corresponding to transactions for which payment processing is performed. In one embodiment, the ML framework uses an interpretable machine learning model to make the predictions. More detail regarding the interpretable machine learning model is provided below” (Wen Pgh. [0019]) and “In one embodiment, the predictions are used to determine whether to provide loan financing to one or more merchants. The loan financing may be related to a cash advance or other type of loan that is provided by a payment processing infrastructure. That is, the techniques disclosed herein enable a payment processing infrastructure to incorporate the processing of advances to merchants and other service providers and their associated paydowns while handling the settlement process. In one embodiment, the cash advances are in the form of flex loans that have minimum payments. In one embodiment, the minimum payments are calculated according to the cash advance amount” (Wen Pgh. [0020]) and “Merchant - A merchant, as used herein, is an entity that is associated with selling or licensing products and/or services over electronic systems such as the Internet and other computer networks. The merchant may be the direct seller/licensor, or the merchant may be an agent for a direct seller/licensor. For example, entities such as Amazon® sometimes act as the direct seller/licensor, and sometimes act as an agent for a direct seller/licensor” (Wen Pgh. [0028]) and “Merchant Site - The merchant site is the e-commerce site (e.g., website) of the merchant. The merchant (100) and merchant server (120) in the figures are associated with the merchant site. The merchant site is associated with a client-side (client side) application and a server-side (server side) application. In one embodiment, the merchant site includes Merchant Server (120), and the server-side application executes on the Merchant Server (120)” (Wen Pgh. [0029]) and “1. The model can be explained by model-agnostic methods such as partial dependence plots (PDP), local surrogates (LIME), and SHAP values. These methods are data dependent, i.e., they need background data to approximate the distributions of features. For example, when a PDP is made to visualize the interactions of two features, assumptions need to be made on the distributions of all other features” (Wen Pgh. [0061]) and “Processing logic transforms information associated with one or more of the transaction data, the one or more external signals, and the internal proprietary data into features that are input into the model (processing block 404). In some embodiments, six types of feature engineering and transformations are performed before training the model. In some embodiments, these may include one or more of the following: ratio, missing treatment, split, one-hot dummy on continuous feature, clip, and log” Wen Pgh. [0074]);
calculate, based on the distribution of estimated revenue {…..} potential loan amounts and loan interest rates (“In one embodiment, the payment processor offers merchant cash advances (MCAs) in the form of flex loans and other loans to eligible merchants. If an offer is accepted, a merchant pays down the MCA/flex loan through a percentage of its future processing volumes. In one embodiment, the models employed by the ML framework predict the likelihood of a significant decrease in a merchant’s future processing volume. In one embodiment, this is accomplished by leveraging information derived from the merchant’s transaction activities to quantify its risk level. In one embodiment, the model is used in an underwriting process both for loan generation and loan financing terms (e.g., pricing). In one embodiment, a model score threshold is used as part of the eligibility criteria to determine whether a merchant will be eligible for a loan financing product (e.g., cash advance). Thus, the model produces scores that are used in underwriting process for both loan generation and loan financing terms (e.g., pricing)” (Wen Pgh. [0053]) and “In one embodiment, the model uses scores calibrated to (simulated) loss rates” (Wen Pgh. [0057]) and “Referring to FIG. 5 , the process begins by processing logic sending an offer for an advance (e.g., a cash advance) electronically, via a network (e.g., the Internet), to a merchant (e.g., Merchant 100 of FIG. 1 ) that specifies the advance amount and terms for receiving and paying down the advance with a percentage or fraction of payments that are processed on behalf of the merchant by the payment processor (processing block 501). For example, the terms might specify that the cost of the advance is a number from 10-20% of the advance amount and that the withhold rate of the payments that are processed daily by the payment processing system is a percentage from 1-20%. These are only examples of numbers and others may be used. In one embodiment, this offer is sent by the payment processing system and its processing logic (e.g., Stripe 300 and Processor 400 of FIG. 1)” Wen Pgh. [0089]);
determine, {…..}, one or more feasible contracts including a target loan amount and a target loan interest rate (“In one embodiment, the payment processor offers merchant cash advances (MCAs) in the form of flex loans and other loans to eligible merchants. If an offer is accepted, a merchant pays down the MCA/flex loan through a percentage of its future processing volumes. In one embodiment, the models employed by the ML framework predict the likelihood of a significant decrease in a merchant’s future processing volume. In one embodiment, this is accomplished by leveraging information derived from the merchant’s transaction activities to quantify its risk level. In one embodiment, the model is used in an underwriting process both for loan generation and loan financing terms (e.g., pricing). In one embodiment, a model score threshold is used as part of the eligibility criteria to determine whether a merchant will be eligible for a loan financing product (e.g., cash advance). Thus, the model produces scores that are used in underwriting process for both loan generation and loan financing terms (e.g., pricing)” (Wen Pgh. [0053]) and “In one embodiment, the model uses scores calibrated to (simulated) loss rates” (Wen Pgh. [0057]) and “Referring to FIG. 5 , the process begins by processing logic sending an offer for an advance (e.g., a cash advance) electronically, via a network (e.g., the Internet), to a merchant (e.g., Merchant 100 of FIG. 1 ) that specifies the advance amount and terms for receiving and paying down the advance with a percentage or fraction of payments that are processed on behalf of the merchant by the payment processor (processing block 501). For example, the terms might specify that the cost of the advance is a number from 10-20% of the advance amount and that the withhold rate of the payments that are processed daily by the payment processing system is a percentage from 1-20%. These are only examples of numbers and others may be used. In one embodiment, this offer is sent by the payment processing system and its processing logic (e.g., Stripe 300 and Processor 400 of FIG. 1)” Wen Pgh. [0089]); and
generate at least one loan contract for providing a loan to the first merchant with the target loan amount and the target loan interest rate (“Referring to FIG. 5 , the process begins by processing logic sending an offer for an advance (e.g., a cash advance) electronically, via a network (e.g., the Internet), to a merchant (e.g., Merchant 100 of FIG. 1 ) that specifies the advance amount and terms for receiving and paying down the advance with a percentage or fraction of payments that are processed on behalf of the merchant by the payment processor (processing block 501). For example, the terms might specify that the cost of the advance is a number from 10-20% of the advance amount and that the withhold rate of the payments that are processed daily by the payment processing system is a percentage from 1-20%. These are only examples of numbers and others may be used. In one embodiment, this offer is sent by the payment processing system and its processing logic (e.g., Stripe 300 and Processor 400 of FIG. 1)” Wen Pgh. [0089]).
Wen further teaches a machine-readable non-transitory storage media encoded with instructions that are executed by one or more processing devices at paragraph 134.
Wen does not teach but Savinon teaches:
responsive to receiving a request by a first merchant hosting one or more shops on an e-commerce platform for a loan (“FIG. 2 illustrates an example of a user interface 200 for configuring loan repayment in accordance with an example embodiment. In addition, the user interface 200 may educate the merchant by providing information about how the daily percentage affects the loan repayment. The merchant (e.g., a user with authority to transact on behalf of the merchant) may request a loan from a lender. As part of the services provided with the loan, the lender may also provide a portal, dashboard, etc., which includes the user interface 200. Here, the lender system (e.g., web server, cloud platform, etc.) may have installed therein software such as an application or other program which includes an API according to various embodiments through which software running on the merchant system may interact to view the user interface 200. During the loan process (or at a subsequent time) the merchant user may enroll in the repayment solution described herein” Savinon Pgh. [0032]).
It would have been obvious to one of ordinary skill of the art to have modified Wen’s teachings to incorporate Savinon’s teachings, in order to “automatically route money from every transaction to the lender” Savinon Pgh. [0031]).
Wen does not teach but Santa Cruz Masoni teaches:
calculate, based on {…..} cash in a payment account associated with the first merchant, a default probability (PD) value and a loss-given-default (LGD) value associated with potential loan amounts and loan interest rates (“At step 323, the payment service system may identify a plurality of second transactions associated with income of the merchant from second transaction data retrieved from one or more of the third-party systems. In particular embodiments, the payment service system may gain access to the third-party systems using security credentials received from the merchant. The third-party systems may make available a plurality of types of data. At least part of the data may represent a plurality of second transactions associated with the merchant (e.g., a bank account statement containing a summary of transactions). The payment service system may retrieve information associated with the transactions and analyze the transactions to identify those that provide evidence as to the merchant's income with some required level of reliability. In particular embodiments, the payment service system may select at least one of the second transactions for analysis and calculation in subsequent steps based on information associated with the second transactions. The payment service system may use one or more text recognition techniques (e.g., fuzzy matching) to identify relevant second transactions. As an example and not by way of limitation, the payment service system may select one or more second transactions each corresponding to a deposit from one of a plurality of chosen sources to an account of the merchant associated with one of the third-party systems. For example, the merchant may provide its goods or services for sale on one or more e-commerce sites. The e-commerce sites may collect payments for the merchant and periodically deposit any money it holds for the merchant to a bank account associated with the merchant. The bank may record these deposits as well as their sources. The payment service system may recognize the deposits from particular e-commerce sites as a reliable indication of the merchant's income and select the corresponding transactions” (Santa Cruz Masoni Column 10 Lines 33-67) and “At step 324, the payment service system may calculate, using the risk model, a second financing factor based on at least one of the second transactions. In particular embodiments, the second financing factor may represent a measure of additional income of the merchant as determined based on transactions recorded by third-party systems. The risk model may take as input a plurality of income factors (e.g., average monthly amount of deposits, standard deviation). It may have been trained using supervised learning based on inputs from one or more human agents on existing transactions. As an example and not by way of limitation, the second financing factor may correspond to a GPV associated with the merchant as recorded by the third-party systems. The second financing factor may correspond to a particular time period (e.g., one month, one week, one year). The second financing factor may be calculated as an average value of multiple time periods. The second financing factor may also be calculated as a prediction of a future value using regression analysis based on past values. In particular embodiments, the payment service system may consider one or more other factors in calculating the second financing factor. As an example and not by way of limitation, it may access information associated with one or more loans or savings accounts associated with the merchant from the second transaction data and calculate the second financing factor based further one the information associated with the loans or savings. For example, the payment service system may reduce a credit limit for a merchant if the merchant has taken significant loans from another entity. The payment service system may increase a credit limit for the merchant if the merchant has substantial savings that is likely available for repaying loans offered by the payment service” Santa Cruz Masoni Column 11 Lines 31-63).
It would have been obvious to one of ordinary skill of the art to have modified Wen’s teachings to incorporate Santa Cruz Masoni’s teachings, in order “to determine the merchant's eligibility for a credit offer as well as a proper credit limit for the merchant” Santa Cruz Masoni Column 1 Lines 62-64.
With respect to claims 2 and 12:
Wen does not teach but Santa Cruz Masoni teaches:
wherein the one or more processing devices are further to: obtain an update of the second data from the e-commerce platform; provide the updated second data as inputs to the trained predictor and execute the trained predictor to output an updated distribution of estimated revenue of the first merchant over the repayment term; determine, based on the recalculated PD value and LGD value, an action to be taken against the first merchant; and issue an instruction to take the action to a payment control circuit (“For example, the risk model may comprise a machine-learning model for calculating a probability of default for each of a plurality of merchants, a machine-learning model for calculating a financing factor based on transactions processed by the payment service system, a machine-learning model for calculating a financing factor based on transactions recorded by one or more third-party systems, or a machine-learning model for calculating a financing factor based on data not related to transactions. The payment service system may process transactions for a plurality of merchants. The transactions may include, for example, payments by customers to the merchants in exchange for goods or services. The payment service system may obtain insights into a merchant (e.g., monthly revenue of the merchant) based on such transactions. Based on such insights, the payment service may make one or more credit offers to the merchant or decide to not extend credit to the merchant. However, the merchant may have additional revenue from transactions other than those processed by the payment service. The payment service may obtain authorization from the merchant to access data associated with one or more third-party systems and update its decisions related to credit offers for the merchant based on the data. Furthermore, the payment service provider may offer one or more additional services to a merchant (e.g., appointment management, payroll, inventory management, marketing, customer information management). The payment service system may further derive knowledge as to the merchant's eligibility for a loan and ability to repay based on the merchant's use of these additional services and, accordingly, update its decisions related to credit offers for the merchant” (Santa Cruz Masoni Column 2 Lines 1-31) and “At step 323, the payment service system may identify a plurality of second transactions associated with income of the merchant from second transaction data retrieved from one or more of the third-party systems. In particular embodiments, the payment service system may gain access to the third-party systems using security credentials received from the merchant. The third-party systems may make available a plurality of types of data. At least part of the data may represent a plurality of second transactions associated with the merchant (e.g., a bank account statement containing a summary of transactions). The payment service system may retrieve information associated with the transactions and analyze the transactions to identify those that provide evidence as to the merchant's income with some required level of reliability. In particular embodiments, the payment service system may select at least one of the second transactions for analysis and calculation in subsequent steps based on information associated with the second transactions. The payment service system may use one or more text recognition techniques (e.g., fuzzy matching) to identify relevant second transactions. As an example and not by way of limitation, the payment service system may select one or more second transactions each corresponding to a deposit from one of a plurality of chosen sources to an account of the merchant associated with one of the third-party systems. For example, the merchant may provide its goods or services for sale on one or more e-commerce sites. The e-commerce sites may collect payments for the merchant and periodically deposit any money it holds for the merchant to a bank account associated with the merchant. The bank may record these deposits as well as their sources. The payment service system may recognize the deposits from particular e-commerce sites as a reliable indication of the merchant's income and select the corresponding transactions” (Santa Cruz Masoni Column 10 Lines 33-67) and “At step 324, the payment service system may calculate, using the risk model, a second financing factor based on at least one of the second transactions. In particular embodiments, the second financing factor may represent a measure of additional income of the merchant as determined based on transactions recorded by third-party systems. The risk model may take as input a plurality of income factors (e.g., average monthly amount of deposits, standard deviation). It may have been trained using supervised learning based on inputs from one or more human agents on existing transactions. As an example and not by way of limitation, the second financing factor may correspond to a GPV associated with the merchant as recorded by the third-party systems. The second financing factor may correspond to a particular time period (e.g., one month, one week, one year). The second financing factor may be calculated as an average value of multiple time periods. The second financing factor may also be calculated as a prediction of a future value using regression analysis based on past values. In particular embodiments, the payment service system may consider one or more other factors in calculating the second financing factor. As an example and not by way of limitation, it may access information associated with one or more loans or savings accounts associated with the merchant from the second transaction data and calculate the second financing factor based further one the information associated with the loans or savings. For example, the payment service system may reduce a credit limit for a merchant if the merchant has taken significant loans from another entity. The payment service system may increase a credit limit for the merchant if the merchant has substantial savings that is likely available for repaying loans offered by the payment service” Santa Cruz Masoni Column 11 Lines 31-63).
It would have been obvious to one of ordinary skill of the art to have modified Wen’s teachings to incorporate Santa Cruz Masoni’s teachings, in order “to determine the merchant's eligibility for a credit offer as well as a proper credit limit for the merchant” Santa Cruz Masoni Column 1 Lines 62-64.
With respect to claims 3 and 13:
Wen does not teach but Savinon teaches:
wherein the action comprises a pay action, a lock action, a freeze action, and a repayment action, wherein responsive to receiving an instruction to take the pay action, the payment control circuit is to allow the first merchant to continue using its day-to-day revenue for regular operations, wherein responsive to receiving an instruction to take the lock action, the payment control circuit is to cause the financial institution to fix a customer account for the first merchant, wherein responsive to receiving an instruction to take freeze action, the payment control circuit is to restrict the first merchant's use of its day-to-day revenue, and wherein responsive to receiving an instruction to take repayment action, the payment control circuit is to cause the e-commerce platform to pay the financial institution using the first merchant’s sale revenue according to the repayment term (“Once the repayment ratio has been configured via the lender computing system (or the fintech computing system), and forwarded to the acquirer, the lender may receive periodic payments from the merchant POS terminal in an automated fashion. For example, during a daily settlement process, the acquirer may automatically push a percentage of the payment transactions captured by the POS terminal of the merchant that day to the lender's account. Each day, an additional push payment may be performed based on the percentage ratio set by the merchant. Thus, a secure repayment process can be created based on POS terminal transactions (swipes, readings, chip payments, e-commerce payments, etc.)” (Savinon Pgh. [0020]) and “According to various embodiments, a merchant may receive a loan (e.g., to operate their business, etc.) from a lender. Loans can be an integral part of getting a business started. However, lenders are notoriously hesitant to loan money to small businesses since they are perceived as a greater risk than a larger more established business. The example embodiments provide for an application programming interface (API) which facilitates communication among a merchant, a lender, and an acquirer (of the merchant). Through the API, a merchant may communicate/connect with a lender to set a percentage of every transaction made at a merchant terminal (e.g., merchant terminal 110 in FIG. 1) to be split such that a percentage of the transaction goes to the lender for repayment of a loan. The lender may connect to the acquirer via the API and forward the percentage to the of the merchant. During an overnight settlement process, the acquirer may split off a portion of the daily transactions corresponding to the percentage and route the percentage to the lender's account. Thus, a lender can receive a daily payment from the merchant based on POS receipts of the merchant which are processed by a merchant POS terminal and sent to the acquirer” Savinon Pgh. [0030]).
It would have been obvious to one of ordinary skill of the art to have modified Wen’s teachings to incorporate Savinon’s teachings, in order to “automatically route money from every transaction to the lender” Savinon Pgh. [0031]).
Claims 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over, Wen (U.S. Pub. No. 2023/0237569), in view of Savinon (U.S. Pub. No. 2021/0174324) and Santa Cruz Masoni (U.S. Pub. No. 11,144,990) and Hu (U.S. Pub. No. 2013/0226777).
With respect to claim 9:
Wen does not teach but Hu teaches:
wherein the one or more processing devices are further to: determine, based on the PD value and the LGD value calculated using the structural bond model, the target loan amount and the target loan interest rate; and generate a table of PD values and LGD values, and corresponding loan amounts and interest rates (“From the development data, the cut-off decision table of FIG. 8 can be constructed. The three columns are the accounts a, b . . . z; the ranking score of Equation (7); and the actual profit (Y). Region 802 represents cases where losses are expected and the credit line should be decreased; region 804 represents cases where the is no change in expected value and no action is taken; and region 806 represents cases where there are expected gains and the credit line is increased” Hu Pgh. [0110]).
It would have been obvious to one of ordinary skill of the art to have modified Wen’s teachings to incorporate Hu’s teachings, in order to “assign a ranking score to each account based on the expected value of the profitability” Hu Pgh. [0109].
Allowable Subject Matter
Claims 4-8, 10 and 14-19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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/M.H./Examiner, Art Unit 3694
/BENNETT M SIGMOND/Supervisory Patent Examiner, Art Unit 3694
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