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
This action is in reply to the communications filed on 05/11/2026.
Claims 10-20 have been withdrawn .
Claims 1-9 are currently pending and have been examined.
This action is made Non-FINAL.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/16/2025 was filed before the mailing date of a first Office Action on the merits. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Election/Restrictions
Applicant’s election without traverse of Invention I (claims 1-9), in the reply filed on May 11, 2026, is acknowledged.
Claims 10-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on May 11, 2026.
Examiner Request
The Applicant is requested to indicate where in the specification there is support for future claim amendments to avoid U.S.C 112(a) issues that can arise. The Examiner thanks the Applicant in advance.
Claim Objections
Claims 1 and 3 are objected to because of the following informalities:
Claim 1: line 5 recites the limitation “receiving, by a processor, a request to authorize a user.” “One or more processors” is initially and previously recited in Claim 1: line 2. Is the ‘processor’ recited in Claim 1: line 5 different than ‘one or more processors’ initially and previously recited in Claim 1: line 2? It appears there is a typographical mistake since the specification only points to a single instance of processor(s) for this interpretation. For compact examination purposes, Examiner interpreted the instances recited in Claim 1: line 5 as “receiving, by the processor, a request to authorize a user.” Appropriate correction is required.
Claim 3: line 1 recites the limitation “herein the machine learning algorithm.” “Herein” is written instead of “wherein”. It appears there is a typographical mistake. For compact examination purposes, Examiner interpreted the instance recited in Claim 3: line 1 as “wherein the machine learning algorithm.” Appropriate correction is required.
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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of processing user and application data to facilitate a lending agreement; without significantly more.
Claim 1 is directed to one or more non-transitory computer storage media, which is one of the statutory categories of invention. (Step 1: YES).
Claim 1 is directed to one or more non-transitory computer storage media storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform operations for a user application, the operations comprising: receiving, by a processor, a request to authorize a user, the request to authorize comprising a plurality of application data; retrieving, by the processor from a database, a plurality of metrics associated with the user, wherein the plurality of metrics are in a plurality of formats; converting, by the processor, each of the plurality of metrics from the plurality of formats into a first standard format; generating, by the processor, a risk metric score based on the plurality of metrics in the first standard format, the risk metric score being generated by a machine learning algorithm; selecting a first lender based on the plurality of metrics and a predetermined set of criteria; communicating through a secure communication protocol, by the processor, the risk metric score to the first lender; receiving through the secure communication protocol, by the processor, from the first lender an approval of the user based on the risk metric score; automatically generating terms and conditions for a lending agreement, wherein the terms and conditions are based on the plurality of metrics and a set of predefined rules of a lending agreement generating algorithm; and automatically causing a execution of the lending agreement by way of a smart contract. These series of steps describe the abstract idea of processing user and application data to facilitate a lending agreement (with the exception of the italicized and bolded terms above), which is mitigating risk associated with unauthorized access and impersonation while facilitating lending agreements between entities and individual lenders; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the processing of lending transactions between entities and individual lenders, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. The system limitations, e.g., one or more non-transitory computer storage media, computer-readable instructions, one or more processors, processor, user application, database, machine learning algorithm, secure communication protocol, lending agreement generating algorithm, and smart contract, do not necessarily restrict the claim from reciting an abstract idea. Thus, claim 1 recites an abstract idea (Step 2A-Prong 1: YES).
This judicial exception is not integrated into a practical application because the additional elements of one or more non-transitory computer storage media, computer-readable instructions, one or more processors, processor, user application, database, machine learning algorithm, secure communication protocol, lending agreement generating algorithm, and smart contract, are no more than simply applying the abstract idea using generic computer elements. The additional elements listed above are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Thus, claim 1 does not integrate the abstract idea into a practical application (Step 2A-Prong 2: NO).
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of one or more non-transitory computer storage media, computer-readable instructions, one or more processors, processor, user application, database, machine learning algorithm, secure communication protocol, lending agreement generating algorithm, and smart contract, are recited at a high level of generality in that it results in no more than simply applying the abstract idea using generic computer elements. The additional elements when considered separately and as an ordered combination do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment (Step 2B: NO). Thus, claim 1 is not patent eligible.
Dependent claims 2-9 are directed to one or more non-transitory computer storage media, which recites the steps that describe the abstract idea of processing user and application data to facilitate a lending agreement. Furthermore, dependent claims 3, 5, 8, and 9 are directed to one or more non-transitory computer storage media, which recite the steps: “the machine learning algorithm uses a neural network trained to analyze and interpret the plurality of metrics; wherein the secure communication protocol employed is based on transport layer security (TLS) or secure sockets layer (SSL) protocols, facilitating end-to-end encryption and data integrity checks; wherein the smart contract for executing the lending agreement is configured to automatically execute predetermined actions when specified conditions are met, facilitating a self-executing agreement; and notifying the user through a secure channel regarding a status of their application and details of the lending agreement upon receiving of the approval from the first lender.” These series of steps describe the abstract idea of processing user and application data to facilitate a lending agreement (with the exception of the italicized and bolded terms above), which is mitigating risk associated with unauthorized access and impersonation while facilitating lending agreements between entities and individual lenders; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the processing of lending transactions between entities and individual lenders, which is a commercial interaction. Thus, claims 2-9 are directed to an abstract idea. The additional elements of one or more non-transitory computer storage media, computer-readable instructions, one or more processors, processor, user application, database, machine learning algorithm, secure communication protocol, lending agreement generating algorithm, smart contract, neural network, transport layer security (TLS) or secure sockets layer (SSL) protocols, self-executing agreement, and secure channel are no more than simply applying the abstract idea using generic computer elements. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Furthermore, the additional elements: one or more non-transitory computer storage media, computer-readable instructions, one or more processors, processor, user application, database, machine learning algorithm, secure communication protocol, lending agreement generating algorithm, smart contract, neural network, transport layer security (TLS) or secure sockets layer (SSL) protocols, self-executing agreement, and secure channel, do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment.
Dependent claims 2-9 have further defined the abstract idea that is present in their respective independent claim: Claim 1; and thus correspond to Certain Methods of Organizing Human Activity and are abstract in nature for the reason presented above. The dependent claims 2-9 do not include any 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. Therefore, claims 2-9 are directed to an abstract idea without significantly more.
Thus, claims 1-9 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-9 are rejected under 35 U.S.C. 103 as being unpatentable over Plenet de Badts de Cugnac (U.S. Patent Application Publication No. US 2024/0029155 A1 hereinafter “Plenet de Badts de Cugnac”), in view of Selman (U.S. Patent Application Publication No. US 2023/0043318 A1; hereinafter “Selman”).
Regarding Claim 1:
Plenet de Badts de Cugnac teaches:
One or more non-transitory computer storage media storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform operations for a user application, the operations comprising: (Plenet de Badts de Cugnac, the invention may be performed by instructions that are stored upon a non-transitory computer readable medium. The non-transitory computer readable medium stores code including instructions that, if executed by one or more processors; the borrower submits an online loan application via the borrower dashboard 192. The loan application might request the identity of the borrower and borrowing entity, financial information and desired loan terms. (See, Abstract; Para. 40, 62, 118; Fig. 1, 2));
receiving, by a processor, a request to authorize a user, the request to authorize comprising a plurality of application data; (Plenet de Badts de Cugnac, front end system 190 includes dashboards, such as lender dashboard 191, borrower dashboard 192, and admin dashboard 193. These dashboards 191, 192, 193 enable users to interface with the system 100, such as enabling a borrower to submit financial information about a revenue stream... front end system 190 also allows the borrower to provide authorization to access the revenue stream by an integrating payment gateway 123, and accept terms of use (See, Abstract; Para. 22-25, Fig. 1));
retrieving, by the processor from a database, a plurality of metrics associated with the user, wherein the plurality of metrics are in a plurality of formats; (Plenet de Badts de Cugnac, The risk assessment system 141 uses data collected by a data collection module 142 and the inference and/or forecasts generated by a machine learning (ML) module 143 in order to compute one or more overall risk scores of the company… API of the data collection module 142 integrates with a user's financial, accounting, and management services to obtain historical information about the revenue stream, such as accounting, billing, customer relationship management (CRM) data, banking transactions, financial statements…. API of the system 142 can also take manual input, in the form of a file upload and/or answers to forms/questionnaires, and manual input from the risk management team… back end system 110 might store multiple trained ML models, and apply the appropriate model to a company generating the revenue stream. In accordance with some aspects and embodiments of the invention, the system 110 store only a single ML model in the ML module 143 that is applicable across several industries….ML module 143 may be trained on the historical accounting data, billing data, metrics and analytics, and CRM data associated with a revenue stream. The training determines the relative weights of the ML module 143. Accounting and billing data includes, without limitation, due diligence, corporate profits, financials, business performance and governance, and outlook. Tools for providing metrics and analytics are available from Google analytics, Userpilot, Heap, NapoleonCat, smart karrot, and ProfitWell (See, Abstract; Para. 34-42, Fig. 1, 2));
converting, by the processor, each of the plurality of metrics from the plurality of formats into a first standard format; (Plenet de Badts de Cugnac, system 110 store only a single ML model in the ML module 143 that is applicable across several industries…. ML module 143 may be trained on the historical accounting data, billing data, metrics and analytics, and CRM data associated with a revenue stream. The training determines the relative weights of the ML module 143. Accounting and billing data includes, without limitation, due diligence, corporate profits, financials, business performance and governance, and outlook. Tools for providing metrics and analytics are available from Google analytics, Userpilot, Heap, NapoleonCat, smart karrot, and ProfitWell (See, Abstract; Para. 34-42, Fig. 1, 2));
generating, by the processor, a risk metric score based on the plurality of metrics in the first standard format, the risk metric score being generated by a machine learning algorithm; (Plenet de Badts de Cugnac, the data system 140 includes the ML module 143 for analyzing the financial information. For example, the ML module 143 is trained to process the financial information to generate a score for the revenue stream or forecast the future values of the financial data. Depending on the training, the score may indicate different aspects, such as financial health relative to similar companies, and risk assessments and guidelines on investments to investors and borrowers. If there are top-tier venture capital firms that provide financial backing, there is a de-risk component. In accordance with some aspects and embodiments of the invention, the ML module 143 might be trained for a particular industry. Therefore, the back end system 110 might store multiple trained ML models, and apply the appropriate model to a company generating the revenue stream. In accordance with some aspects and embodiments of the invention, the system 110 store only a single ML model in the ML module 143 that is applicable across several industries. (See, Para. 4, 33-35, Fig. 1));
selecting a first lender based on the plurality of metrics and a predetermined set of criteria; (Plenet de Badts de Cugnac, matching module 153 matches the parameters of the company (such as: the name, the sector/industry, the company size, the market capitalization, and other data about the company; including data collected by the data collection system 142, the inferenced metrics and forecasts predicted by machine learning module 143, and the risk assessment provided by system 141) with the preferred parameters/investment thesis of the lenders, which they can provide via the lender dashboard (See, Para. 4, 33-35, 42-46; Fig. 1));
communicating through [a secure communication protocol], by the processor, the risk metric score to the first lender; (Plenet de Badts de Cugnac, ML module 143 is trained to process the financial information to generate a score for the revenue stream or forecast the future values of the financial data. Depending on the training, the score may indicate different aspects, such as financial health relative to similar companies, and risk assessments and guidelines on investments to investors and borrowers….. loan terms are determined and communicated to the borrower for any additional new loan….. a determination is made that the loan amount may be increased as supported by the higher revenue stream. If the increase is approved, the borrower is notified (See, Abstract; Para. 13, 33-35, 42-46, 87, 122; Fig. 1, 6A-B));
receiving through the [a secure communication protocol], by the processor, from the first lender an approval of the user based on the risk metric score; (Plenet de Badts de Cugnac, The risk assessment system 141 uses data collected by a data collection module 142 and the inference and/or forecasts generated by a machine learning (ML) module 143 in order to compute one or more overall risk scores of the company. After the scoring has been performed, the score is used to generate certain terms of a contract or certain constraints on the values of the terms of contract or certain recommendations that are displayed to the lenders…… due to a good score, a notice may be displayed to the lenders to recommend bidding in a certain range of lower interest rate range; the system 110 includes a smart contract factory or a marketplace system 150. The marketplace system 150 provides a method of matching one or multiple lenders to a single company. The marketplace system 150 includes a company-to-lender matching module 153 (See, Abstract; Para. 33-35, 40-43));
automatically generating terms and conditions for a lending agreement, wherein the terms and conditions are based on the plurality of metrics and a set of predefined rules of a lending agreement generating algorithm; and (Plenet de Badts de Cugnac, The risk assessment system 141 uses data collected by a data collection module 142 and the inference and/or forecasts generated by a machine learning (ML) module 143 in order to compute one or more overall risk scores of the company. After the scoring has been performed, the score is used to generate certain terms of a contract or certain constraints on the values of the terms of contract or certain recommendations that are displayed to the lenders. …..due to a good score, a notice may be displayed to the lenders to recommend bidding in a certain range of lower interest rate range. After the scoring has been performed, the score is used to generate certain terms of a contract. (See, Para. 33-35, Fig. 1));
automatically causing a execution of the lending agreement by way of a smart contract. (Plenet de Badts de Cugnac, responsive to the scoring by the ML model, for generating at least one smart contract on a distributed, decentralized network; The marketplace system 150 acts as a smart contract. The back end system 110 includes a trading system 160 that allows lenders to trade, swap, list, and buy the assetized revenue streams they own. (See, Abstract; Para. 4, 20, 25, 50-52)).
Plenet de Badts de Cugnac does not specifically teach a secure communication protocol.
However, Selman further teaches the following limitation:
a secure communication protocol (Selman, Creating requests can additionally include transforming instructions into an expected form or communication protocol, which may include applying a set encryption pattern to the instructions and the response to the instructions…… the banking component 104, the payroll component 106, and/or the payment component 108 can utilize such data in making determinations such as lending-related decisions…. the determination to offer the loan and/or the terms of the loan may be based at least in part on the payroll data 124 forwarded to the first-party service provider server(s) 102 via the mobile payment application 112. (See, Para. 29, 58, 59, 84, 136, 144-45, 234); use of an end-to-end encryption protocol (e.g., using Transport Layer Security (TLS) protocol; (See, Abstract; Para. 38; Fig. 1); protocol to use for providing the credential 120, a type of authentication to implement with the third-party service provider, such as multifactor authentication (MFA) (See, Abstract; Para. 74, 102)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to have modified Plenet de Badts de Cugnac with the features of Selman’s system because “techniques described herein relate to an alternative approach to accessing third-party service provider data, by enabling a user to provide a credential directly to a third-party service provider via a client (e.g., computing device) of the user. In one implementation, the techniques are client-driven and server-directed. That is, the credential is stored locally on the client and may not be provided to the first-party service provider. The third-party service provider can use the credential provided by the user via the client to access an associated user account and related data and can provide the related data to the client. In some implementations, the first-party service provider may craft specific instructions (e.g., including details of authentication, the type of data per client device, the type of data per transaction, the type of data per mobile application, or other such variables) based on which the client can execute instructions embedded in a request from the first-party service provider and fetch appropriate related and contextual data from the third-party service provider using the credential….. the third-party payroll service provider can control how long the session is authenticated, how long the session persists, and/or what kind of communication protocols exist between these entities. By controlling the parameters of the session and/or controlling access to certain types of data, security pertaining to sensitive data (e.g., mitigating risk of theft of credentials and/or sensitive data) is improved, and privacy of users is maintained.” (Selman, Para. 21).
Regarding Claim 2:
Plenet de Badts de Cugnac teaches:
wherein the plurality of metrics retrieved from the database include at least one of a user's credit score, transaction history, and payment history. (Plenet de Badts de Cugnac, The API of the data collection module 142 integrates with a user's financial, accounting, and management services to obtain historical information about the revenue stream, such as accounting, billing, customer relationship management (CRM) data, banking transactions, financial statements; At step 610, the ML model is continuously monitoring the borrower's revenue stream. At step 620, the ML model provides an updated scoring, risk and credit rating. For instance, the ML model generates a series of scores indicating that the borrower's financial conditions have improved over time, the revenue stream has increased, and the revenue stream can now support additional funding. (See, Abstract; Para. 34, 86, 104; Fig. 6A)).
Regarding Claim 3:
Plenet de Badts de Cugnac teaches:
herein the machine learning algorithm uses [a neural network] trained to analyze and interpret the plurality of metrics. (Plenet de Badts de Cugnac, The risk assessment system 141 uses data collected by a data collection module 142 and the inference and/or forecasts generated by a machine learning (ML) module 143 in order to compute one or more overall risk scores of the company. After the scoring has been performed, the score is used to generate certain terms of a contract or certain constraints on the values of the terms of contract or certain recommendations that are displayed to the lenders. …..due to a good score, a notice may be displayed to the lenders to recommend bidding in a certain range of lower interest rate range. After the scoring has been performed, the score is used to generate certain terms of a contract. (See, Para. 33-35, Fig. 1)).
Plenet de Badts de Cugnac does not specifically teach a neural network.
However, Selman further teaches the following limitation:
a neural network (Selman, Creating requests can additionally include transforming instructions into an expected form or communication protocol, which may include applying a set encryption pattern to the instructions and the response to the instructions…… the banking component 104, the payroll component 106, and/or the payment component 108 can utilize such data in making determinations such as lending-related decisions…. the determination to offer the loan and/or the terms of the loan may be based at least in part on the payroll data 124 forwarded to the first-party service provider server(s) 102 via the mobile payment application 112. (See, Para. 29, 58, 59, 84, 136, 144-45, 234); As part of the training process, weights may be set for machine learning. These weights may apply to a set of features included in the training data, as derived from historical data (e.g., previously collected user data). In some embodiments, the weights that are set during the training process may apply to parameters that are internal to the machine learning model(s) (e.g., weights for neurons in a hidden-layer of a neural network). (See, Abstract; Para. 104, 250)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to have modified Plenet de Badts de Cugnac with the features of Selman’s system because “the first-party service provider can dynamically determine whether to use a “local-credential”-based technique or a “distributed-credential”-based technique based on the type of credential, level of risk, and/or system preferences (e.g., how long a session needs to persist). For example, a first-party service provider server(s) may receive information about a credential associated with a user account of a user associated with a third-party service provider (i.e., without the first-party service provider server(s) actually receiving the credential), and the first-party service provider server(s) (e.g., a processor(s) thereof executing instructions) may determine a level of risk associated with the credential. If the level of risk is high (e.g., if the level of risk satisfies a threshold), the first-party service provider server(s) may dynamically determine to use a local-credential-based technique. As used herein, “dynamically determining” whether to use a local-credential- or a distributed-credential-based technique means using an automated process (e.g., a computer program, machine learning, etc.) to determine which technique to use, without user intervention, and in real-time, or near real-time. In the local-credential-based technique, the first-party service provider server(s) may send, to a mobile payment application executing on a client device of the user, an instruction for the mobile payment application to provision the credential to the third-party service provider directly (i.e., without providing the credential to the first-party service provider server(s)), which causes a session to be established between the mobile payment application and the third-party service provider server(s).” (Selman, Para. 24).
Regarding Claim 4:
Plenet de Badts de Cugnac teaches:
further comprising authenticating the user based on [a multi-factor authentication process] before receiving the request to authorize the user. (Plenet de Badts de Cugnac, the front end system 190 includes dashboards, such as lender dashboard 191, borrower dashboard 192, and admin dashboard 193. These dashboards 191, 192, 193 enable users to interface with the system 100, such as enabling a borrower to submit financial information about a revenue stream. The front end system 190 also allows the borrower to provide authorization to access the revenue stream by an integrating payment gateway 123, and accept terms of use (See, Abstract; Para. 22-25, Fig. 1, 2)).
Plenet de Badts de Cugnac does not specifically teach a multi-factor authentication process.
However, Selman further teaches the following limitation:
a multi-factor authentication process (Selman, Creating requests can additionally include transforming instructions into an expected form or communication protocol, which may include applying a set encryption pattern to the instructions and the response to the instructions…. determination to offer the loan and/or the terms of the loan may be based at least in part on the payroll data 124 forwarded to the first-party service provider server(s) 102 via the mobile payment application 112. (See, Para. 29, 58, 59, 84, 136, 144-45, 234); protocol to use for providing the credential 120, a type of authentication to implement with the third-party service provider, such as multifactor authentication (MFA) (See, Abstract; Para. 38, 74, 102, Fig. 1)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to have modified Plenet de Badts de Cugnac with the features of Selman’s system because “techniques described herein relate to an alternative approach to accessing third-party service provider data, by enabling a user to provide a credential directly to a third-party service provider via a client (e.g., computing device) of the user. In one implementation, the techniques are client-driven and server-directed. That is, the credential is stored locally on the client and may not be provided to the first-party service provider. The third-party service provider can use the credential provided by the user via the client to access an associated user account and related data and can provide the related data to the client. In some implementations, the first-party service provider may craft specific instructions (e.g., including details of authentication, the type of data per client device, the type of data per transaction, the type of data per mobile application, or other such variables) based on which the client can execute instructions embedded in a request from the first-party service provider and fetch appropriate related and contextual data from the third-party service provider using the credential….. the third-party payroll service provider can control how long the session is authenticated, how long the session persists, and/or what kind of communication protocols exist between these entities. By controlling the parameters of the session and/or controlling access to certain types of data, security pertaining to sensitive data (e.g., mitigating risk of theft of credentials and/or sensitive data) is improved, and privacy of users is maintained.” (Selman, Para. 21).
Regarding Claim 5:
Plenet de Badts de Cugnac does not specifically teach that the secure communication protocol employed is based on transport layer security (TLS) or secure sockets layer (SSL) protocols, facilitating end-to-end encryption and data integrity checks.
However, Selman further teaches the following limitation:
wherein the secure communication protocol employed is based on transport layer security (TLS) or secure sockets layer (SSL) protocols, facilitating end-to-end encryption and data integrity checks. (Selman, Creating requests can additionally include transforming instructions into an expected form or communication protocol, which may include applying a set encryption pattern to the instructions and the response to the instructions…… the banking component 104, the payroll component 106, and/or the payment component 108 can utilize such data in making determinations such as lending-related decisions…. the determination to offer the loan and/or the terms of the loan may be based at least in part on the payroll data 124 forwarded to the first-party service provider server(s) 102 via the mobile payment application 112. (See, Para. 29, 58, 59, 84, 136, 144-45, 234); use of an end-to-end encryption protocol (e.g., using Transport Layer Security (TLS) protocol; (See, Abstract; Para. 38; Fig. 1); protocol to use for providing the credential 120, a type of authentication to implement with the third-party service provider, such as multifactor authentication (MFA) (See, Abstract; Para. 74, 102)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to have modified Plenet de Badts de Cugnac with the features of Selman’s system because “techniques described herein relate to an alternative approach to accessing third-party service provider data, by enabling a user to provide a credential directly to a third-party service provider via a client (e.g., computing device) of the user. In one implementation, the techniques are client-driven and server-directed. That is, the credential is stored locally on the client and may not be provided to the first-party service provider. The third-party service provider can use the credential provided by the user via the client to access an associated user account and related data and can provide the related data to the client. In some implementations, the first-party service provider may craft specific instructions (e.g., including details of authentication, the type of data per client device, the type of data per transaction, the type of data per mobile application, or other such variables) based on which the client can execute instructions embedded in a request from the first-party service provider and fetch appropriate related and contextual data from the third-party service provider using the credential….. the third-party payroll service provider can control how long the session is authenticated, how long the session persists, and/or what kind of communication protocols exist between these entities. By controlling the parameters of the session and/or controlling access to certain types of data, security pertaining to sensitive data (e.g., mitigating risk of theft of credentials and/or sensitive data) is improved, and privacy of users is maintained.”(Selman, Para. 21).
Regarding Claim 6:
Plenet de Badts de Cugnac teaches:
wherein the predetermined set of criteria for selecting the first lender includes a first lender's preferences, financial thresholds, and lending history. (Plenet de Badts de Cugnac, The API of the data collection module 142 integrates with a user's financial, accounting, and management services to obtain historical information about the revenue stream, such as accounting, billing, customer relationship management (CRM) data, banking transactions, financial statements. (See, Para. 33-35) The matching module 153 matches the parameters of the company (such as: the name, the sector/industry, the company size, the market capitalization, and other data about the company; including data collected by the data collection system 142, the inferenced metrics and forecasts predicted by machine learning module 143, and the risk assessment provided by system 141) with the preferred parameters/investment thesis of the lenders (See, Abstract; Para. 42-45; Fig. 1) the system with the addition of a data system that provides a risk assessment of the company, (to aid the lenders' manual decision-making process regarding whether to lend money to the company and/or regarding the parameters of the loan (such as: interest rate, revenue percentage, maximum term, etc.)) and/or in order to automatically trigger the lending or an algorithmic computation of the loan parameters based on the lenders' configured lending preferences or criteria (See, Para. 62, 103)).
Regarding Claim 7:
Plenet de Badts de Cugnac teaches:
wherein one or more pre-defined rules of the lending agreement generating algorithm are adaptable based on regulatory requirements and changes in market conditions. (Plenet de Badts de Cugnac, the data system 140 performs its operation continuously, such that whenever a company's parameters change, the change is picked up by the system 142, it is reflected in the results of the ML module 143, and risk assessment provided by the system 141 is updated accordingly. Continuous processing by the data system 140 has certain allows real-time changes to the revenue stream can be detected, and the revenue stream can be rescored. If the new score indicates an improved outlook for the revenue stream, the assetized value of the revenue stream may be increased, and the smart contract may be automatically adjusted in accordance with the increased assetized value, through inputs received from software intermediaries of new additional data…. matching module 153 matches the parameters of the company (such as: the name, the sector/industry, the company size, the market capitalization, and other data about the company; including data collected by the data collection system 142, the inferenced metrics and forecasts predicted by machine learning module 143, and the risk assessment provided by system 141) with the preferred parameters/investment thesis of the lenders, which they can provide via the lender dashboard 191; decentralized trading module 170 includes a position smart contract 171 responsible for storing the ownership lenders have on their positions…. if the position smart contract 171 specifies a loan, the digital token provides proof of a lender's investment. The digital token may represent a lender's investment share and may store the legal loan agreement document in its metadata, as well as the key terms and parameters of the loan. (See, Para. 38-42, 50-52; Fig. 1)).
Regarding Claim 8:
Plenet de Badts de Cugnac teaches:
wherein the smart contract for executing the lending agreement is configured to automatically execute predetermined actions when specified conditions are met, facilitating a self-executing agreement. (Plenet de Badts de Cugnac, smart contract refers to a self-executing contract with the terms of an agreement between parties being directly written into lines of code; matching module 153 matches the parameters of the company (such as: the name, the sector/industry, the company size, the market capitalization, and other data about the company; including data collected by the data collection system 142, the inferenced metrics and forecasts predicted by machine learning module 143, and the risk assessment provided by system 141) with the preferred parameters/investment thesis of the lenders, which they can provide via the lender dashboard 191; decentralized trading module 170 includes a position smart contract 171 responsible for storing the ownership lenders have on their positions ….risk assessment system 141 uses data collected by a data collection module 142 and the inference and/or forecasts generated by a machine learning (ML) module 143 in order to compute one or more overall risk scores of the company. After the scoring has been performed, the score is used to generate certain terms of a contract or certain constraints on the values of the terms of contract or certain recommendations that are displayed to the lenders.….. After the scoring has been performed, the score is used to generate certain terms of a contract. (See, Para. 20, 33-35, 38-42; Fig. 1)).
Regarding Claim 9:
Plenet de Badts de Cugnac teaches:
further comprising notifying the user through [a secure channel] regarding a status of their application and details of the lending agreement upon receiving of the approval from the first lender. (Plenet de Badts de Cugnac, risk assessment system 141 uses data collected by a data collection module 142 and the inference and/or forecasts generated by a machine learning (ML) module 143 in order to compute one or more overall risk scores of the company. After the scoring has been performed, the score is used to generate certain terms of a contract or certain constraints on the values of the terms of contract or certain recommendations that are displayed to the lenders…due to a good score, a notice may be displayed to the lenders to recommend bidding in a certain range of lower interest rate range; the system 110 includes a smart contract factory or a marketplace system 150. The marketplace system 150 provides a method of matching one or multiple lenders to a single company. The marketplace system 150 includes a company-to-lender matching module (See, Para. 33-35, 40-43); After the scoring has been performed, the score is used to generate certain terms of a contract; At step 630 for a borrower whose scoring been changed, new loan terms are determined and communicated to the borrower for any additional new loan. For instance, a determination is made that the loan amount may be increased as supported by the higher revenue stream. If the increase is approved, the borrower is notified (See, Para. 87-88; Fig. 1, 6A-B)).
Plenet de Badts de Cugnac does not specifically teach a secure channel.
However, Selman further teaches the following limitation:
a secure channel; (Selman, Creating requests can additionally include transforming instructions into an expected form or communication protocol, which may include applying a set encryption pattern to the instructions and the response to the instructions…… the banking component 104, the payroll component 106, and/or the payment component 108 can utilize such data in making determinations such as lending-related decisions (See, Para. 29, 58, 59, 84, 136, 144-45, 234); use of an end-to-end encryption protocol (e.g., using Transport Layer Security (TLS) protocol; (See, Abstract; Para. 38, 74, 102; Fig. 1)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to have modified Plenet de Badts de Cugnac with the features of Selman’s system because “techniques described herein relate to an alternative approach to accessing third-party service provider data, by enabling a user to provide a credential directly to a third-party service provider via a client (e.g., computing device) of the user. In one implementation, the techniques are client-driven and server-directed. That is, the credential is stored locally on the client and may not be provided to the first-party service provider. The third-party service provider can use the credential provided by the user via the client to access an associated user account and related data and can provide the related data to the client. In some implementations, the first-party service provider may craft specific instructions (e.g., including details of authentication, the type of data per client device, the type of data per transaction, the type of data per mobile application, or other such variables) based on which the client can execute instructions embedded in a request from the first-party service provider and fetch appropriate related and contextual data from the third-party service provider using the credential….. the third-party payroll service provider can control how long the session is authenticated, how long the session persists, and/or what kind of communication protocols exist between these entities. By controlling the parameters of the session and/or controlling access to certain types of data, security pertaining to sensitive data (e.g., mitigating risk of theft of credentials and/or sensitive data) is improved, and privacy of users is maintained.”(Selman, Para. 21).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is the following:
Ghosh (U.S. Patent Pub. No. US-2023/0004976-A1) “System and method for observability, dashboard, alerting and pro-active action mechanism”
Mollman (U.S. Patent Pub. No. US-2023/0394568-A1) “Credit limit transfer”
Baciu (U.S. Patent Pub. No. US-2020/0090263-A1) “System for matching lenders and borrowers”
Morgan (U.S. Patent Pub. No. US-2014/0122321-A1) “Methods and systems related to lender matching”
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/MOHAMMED H MUSTAFA/Examiner, Art Unit 3693
/Mike Anderson/Supervisory Patent Examiner, Art Unit 3693
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