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
Claims 1-20 are pending in this instant application per original claims filed on 01/28/2025. Claims 1, 13 and 20 are independent claims reciting a lending system, a method and a personal loan-lending system claim respectively. Claims 2-12, 14-19 and none are respective dependent claims.
This Office Action is a non-final rejection on merits in response to the original claims filed by the Applicant on 28 JANUARY 2025 for its original application of the same date that is titled: “Person Loan-Lending System and Methods Thereof”.
Accordingly, pending Claims 1-20 are now being rejected herein.
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 directed to a judicial exception (abstract idea) without significantly more, wherein Claims 1, 13 and 20 are independent lending system, method and personal loan-lending system claims respectively.
Exemplary Analysis.
Claim 13 Ineligible.
The claim recites a series of steps. The claim is directed to a method reciting a series of steps, which is a statutory category of invention (Step 1: YES).
The claim is analyzed to determine whether it is directed to a judicial exception. The claim recites the limitations of a method for operating a loan server, wherein the method comprises: a personal loan-lending wherein one or more loans are generated by: collecting and preprocessing borrower data from multiple sources; conducting risk assessment and scoring to the collected data; generating one or more scores based on the conducted risk assessment; and approving a loan based on the one or more scores. In other words, the claim describes a system for personal loan-lending and methods thereof (per Field, para [0002] of Specification). These limitations, as drafted, are steps of a method that, under its broadest reasonable interpretation, covers performance of the limitations via steps of a method of organizing human activity such as fundamental economic principles or practices, and commercial or legal interactions, and managing personal behavior or relationships, but for the recitation of generic system and machine learning tools. These limitations fall under the “certain methods of organizing human activity” group (Step 2A1-YES).
Next, the claim is analyzed to determine if it is integrated into a practical application. The claim recites additional elements of: executing a loan-lending system, wherein the one or more scores are generated via one or more machine learning processes. These elements are considered extra-solution activities. The system and machine learning tools in these steps are recited at a high level of generality, i.e., as generic processors performing generic computer functions of processing data. These generic system and machine learning tools are no more than mere instructions to apply the exception using generic computer and/or computer components. 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. Thus, the claim is directed to the abstract idea (Step 2A2-NO).
Next, the claim is analyzed to determine if there are additional elements in this claim that individually, or as an ordered combination, to include claim amendments, ensure that the claim amounts to significantly more than the abstract ideas (whether claim provides inventive concept). As discussed with respect to Step 2A2 above, the additional elements in the claim amount to no more than mere instructions to apply the exception using generic computer and/or components. The same analysis applies here in Step 2B, i.e., mere instructions to apply an exception using a generic computer components over a network cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. When viewed either individually, or as an ordered combination, the additional elements do not amount to a claim as a whole that is significantly more than the abstract idea itself. Therefore, the claim does not amount to significantly more than the recited abstract idea (Step 2B: NO), and the claim is not patent eligible.
The analysis above applies to all statutory categories of the invention including system Claim 1 and system Claim 20. Furthermore, dependent method Claims 14-19, further narrow the independent method Claim 13 with additional steps and limitations (e.g., wherein executing the personal loan-lending system further comprises generating a borrower financial profile via one or more machine learning processes prior to conducting a risk assessment; wherein the one or more machine learning processes is a large language model; wherein the risk assessment utilizes the generated borrower financial profile; wherein executing the personal loan-lending system further comprises converting the risk assessment into an input compatible with a neural network; wherein the one or more machine learning processes utilize at least a neural network configured with at least the one or more scores as an input to the neural network; wherein the one or more machine learning processes utilize at least a neural network configured with at least the one or more scores, and converted risk assessments as inputs to the neural network; etc.); and do not resolve the issues raised in rejection of the independent method Claim 13. Similarly, dependent system Claims 2-12 also further narrow its independent Claim 1, which are rejected as ineligible for patenting under 35 U.S.C. 101 based upon the same analysis.
Therefore, Claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
This application currently names joint inventors. In considering patentability of the claims the Examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. The Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the Examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 1-20 are rejected under 35 USC 103 as unpatentable over a combination of references (Hu, Hockey, Walter, Nelsen and Tang for all independent and dependent claims, plus Walls, Berta, Hermiz, Nikankin, Gil & Dobson for some dependent claims) as described below for each claim/ limitation.
Independent Claim 1 is rejected under 35 USC 103 as unpatentable over Pub. No. US 2012/ 0143748 filed by Hu et al. (hereinafter “Hu”) in view of Pub. No. US 2019/ 0318122 filed by Hockey et al. (hereinafter “Hockey”), and further in view of Pub. No. US 2014/ 0058925 filed by Walter et al. (hereinafter “Walter”), and further in view of Pub. No. US 2016/ 0034900 filed by Nelsen et al. (hereinafter “Nelsen”), and further in view of Pub. No. US 2016/ 0189234 filed by Tang et al. (hereinafter “Tang”), and as described below for each claim/ limitation.
Examiner notes that all claims have been copied as recited by the Applicant to keep them readable and whole, even if the limitations within a claim that are not taught explicitly by the primary/previous reference (are noted in parentheses), but these limitations are noted explicitly as taught by a secondary/new reference whenever a secondary/new reference has been used.
Examiner notes that, for brevity in this rejection, the motivation statement has not been repeated herein every time a secondary reference has been used.
With respect to Claim 1, Hu teaches ---
1. A lending system, comprising:
a processor;
at least one network interface controller configured to (enable data exchange with external systems); and
(see at least: Hu Abstract and Summary in paras [0010]-[0012]; and Claim 21 for ‘computer processor’; and para [0008] about {“What is needed, therefore, is a loan processing system that provides an efficient interface between lender computers and broker computers that accesses data and resources already available on the broker computer system.”}; and para [0039] about {“...... Depending upon the interface between the lenders to the network and/or the processing and submission server, the loan information is input into the lender computer systems as seamlessly as possible. For example, if the interface between a lender and the network is web-based, such as the Freddie Mac Loan Prospector system, the information is populated directly into the lender web forms. …”}; and [0040] about {“As illustrated in system 100 of FIG. 1A, the interface system between the lender computer 104 and the broker computer 102, referred to as the "clickloan" interface, serves to eliminate the complex loan file export function and manual form-filing process currently required on lending and vendor web sites. …”}; and para [0070] about {“In one embodiment of the present invention, the loan origination system interface module for interfacing a lender computer to LOS programs on a broker computer can be implemented in conjunction with a server based desktop based loan application processing and submission server. …”}; and para [0078] about {“In the foregoing, a user interface system for processing and submitting loan applications through a network of loan originators and lender computer systems has been described. …”}; which together are the same as claimed limitations above to include ‘a lending system’, ‘a processor’, ‘one network’ and ‘interface system’ that together are the same as claimed ‘one network interface controller’ per BRI rules)
Hu teaches as disclosed above, but it may not explicitly teach about ‘enable data exchange with external systems’. However, Hockey teaches them explicitly.
(see at least: Hockey Abstract; and para [0427] about {“The network adapter device 1211 provides one or more wired or wireless interfaces for exchanging data and commands between the system and other devices, such as external user account systems (e.g., institutions 151, 152, and/or 153), external user-facing systems/ applications (e.g., applications 161 and/or 162), user devices (e.g., user devices 181 and/or 182), and/or the like. …”}; which together are the same as claimed limitations above to include ‘enable data exchange with external systems’)
It would have been obvious to an ordinary person of skill in the art before the effective filing date to modify the teachings of Hu with the teachings of Hockey. The motivation to combine these references would be to provide a loan processing system that provides an efficient interface between lender computers and broker computers that accesses data and resources already available on the broker computer system; and provide a web-based interface that transfers data from a lender computer network to a broker network without requiring the lender to install extensive programs or program interfaces (see paras [0008]-[0009] of Hu), and to create a new and useful system and method for secure permissioning of access to user accounts, including secure distribution of aggregated user account data (see para [0005] of Hockey).
Hu and Hockey teach ---
a memory communicatively coupled to the processor, wherein the (memory comprises a lending management logic) configured to:
(see at least: Hu ibidem and citations listed above to include ‘a lending system’, ‘a processor’, ‘one network’ and ‘interface system’ that together are the same as claimed ‘one network interface controller’)
(see at least: Hockey ibidem and citations listed above to include ‘enable data exchange with external systems’; and para [0423] about {“The bus 1202 interfaces with the processors 1201A-1201N, the main memory (e.g., a random access memory (RAM)) 1222, a read only memory (ROM) 1204, …”}; and para [0426] about {“The processors 1201A-1201N and the main memory 1222 form a processing unit 1299. …”}; and para [0574] about {“More specifically, the user device 2104 includes a processor 2106a, a memory 2104b, and a network interface 2104c. …”}; which together are the same as claimed limitations above to include ‘a memory communicatively coupled to the processor’)
Hu and Hockey teach as disclosed above, but they may not explicitly teach about ‘memory comprises a lending management logic’. However, Walter teaches them explicitly.
(see at least: Walter Abstract and Brief Summary in paras [0010]-[0029]; and para [0080] about {“The system memory 214 may also include communications programs, for example, a server 244 that causes the asset-based lending management system server computer 202 to serve electronic or digital documents or files via corporate intranets, extranets, or other networks as described below. …”}; and para [0090] about {“…… The system memory 269 may additionally include communications programs that permit the asset-based lending management computer system(s) 202 to extract, import from or retrieve asset-related electronic correspondence and/or electronic or digital documents or files from the borrower associated computer systems 206. …”}; which together are the same as claimed limitations above to include ‘memory comprises a lending management logic’)
It would have been obvious to an ordinary person of skill in the art before the effective filing date to modify the teachings of Hu and Hockey with the teachings of Walter. The motivation to combine these references would be to provide a loan processing system that provides an efficient interface between lender computers and broker computers that accesses data and resources already available on the broker computer system; and provide a web-based interface that transfers data from a lender computer network to a broker network without requiring the lender to install extensive programs or program interfaces (see paras [0008]-[0009] of Hu), and to create a new and useful system and method for secure permissioning of access to user accounts, including secure distribution of aggregated user account data (see para [0005] of Hockey), and to allow ERP systems typically track inventory held by or in possession of the borrower, and may track accounts receivable, and when asset valuation must be performed, assigning values to various assets (see paras [0008]-[0009] of Walter).
Hu, Hockey and Walter teach ---
execute a personal loan-lending system wherein one or more loans are generated by:
(see at least Hu ibidem and citations listed above to include ‘a lending system’, ‘a processor’, ‘one network’ and ‘interface system’ that together are the same as claimed ‘one network interface controller’; and para [0004] about {“The loan application process is often a complicated and confusing process for average borrowers. There are a great many different types of loans available depending upon the type of loan required, such as personal loans, home mortgages, business lines of credit, and so on.”}; and para [0039] about {“After the borrower enters the relevant personal and loan information required by the file template and initial loan application form, a processing client executes an automatic data flow process to populate the same information in all of the corresponding fields in all other documents and forms processed by the system that are related to the loan application.”}; and para [0048] about {“In one embodiment, the clickloan interface is hosted by a dedicated process within the processing and submission server. For this embodiment, the clickloan module 114 can be implemented as Microsoft Active X module that is downloaded one time to the lender web site hosting computer. There is a separate component for each LOS (Loan Origination Software) hosted on the loan broker computer 102.”}; and para [0063] about {“In one embodiment, the clickloan CAB files are hosted on the loan origination system server 102. All the vendors are provided with URLs for these files, and the codebase attributes should point to those URLs.”}; which together are the same as claimed limitations above to include ‘a personal loan-lending system’)
(see at least: Hockey ibidem and citations listed above to include ‘enable data exchange with external systems’ and ‘a memory communicatively coupled to the processor’)
(see at least: Walter ibidem and citations listed above to include ‘memory comprises a lending management logic’)
Hu, Hockey and Walter teach ---
collecting and preprocessing borrower data from multiple sources;
(see at least Hu ibidem and citations listed above to include ‘a lending system’, ‘a processor’, ‘one network’ and ‘interface system’ that together are the same as claimed ‘one network interface controller’; and ‘a personal loan-lending system’; and para [0028] about {“…… During the course of the loan application process, various items of information are transmitted among the parties, including borrower information and loan application data. This information is typically maintained in databases stored in the broker computer, or on the third party computers. …… For example, one company may be involved in the processing of a loan application, while another is involved with providing the loan itself, while yet another may be involved with the billing and collection of repayment from the borrower.”}; which together are the same as claimed limitations above per BRI rules to include ‘collecting and preprocessing borrower data from multiple sources’)
(see at least: Hockey ibidem and citations listed above to include ‘enable data exchange with external systems’ and ‘a memory communicatively coupled to the processor’)
(see at least: Walter ibidem and citations listed above to include ‘the memory comprises a lending management logic’)
Hu, Hockey and Walter teach ---
(conducting risk assessment and scoring to the collected data);
(see at least Hu ibidem and citations listed above to include ‘a lending system’, ‘a processor’, ‘one network’ and ‘interface system’ that together are the same as claimed ‘one network interface controller’; and ‘a personal loan-lending system’ plus ‘collecting and preprocessing borrower data from multiple sources’)
(see at least: Hockey ibidem and citations listed above to include ‘enable data exchange with external systems’ and ‘a memory communicatively coupled to the processor’)
(see at least: Walter ibidem and citations listed above to include ‘the memory comprises a lending management logic’)
Hu, Hockey and Walter teach as disclosed above, but they may not explicitly teach about ‘conducting risk assessment and scoring to the collected data’. However, Nelsen teaches them explicitly.
(see at least: Nelsen Abstract and Summary in paras [0005]-[0014]; and para [0124] about {“… … In some embodiments, translation module 310 may include information collection submodule 310A, format conversion submodule 310B, and message generation submodule 310C. Risk analysis module 320 may conduct a risk assessment based on information collected by server computing device 125.”}; and para [0144] about {“Risk analysis module 320 may comprise code to enable performing a risk assessment based on information in a received message. Risk analysis module 320 may receive information obtained by information collection submodule 310A, which may then be utilized for a risk analysis. …… In some cases, risk analysis module 320 may utilize information stored in risk information database 520 to conduct the risk assessment. For example, the risk information database 520 may include historical information surrounding the user and computing device conducting the transaction. …… Ultimately, the risk analysis module 320 may, in conjunction with data processors 125A, generate one or more risk analysis outputs (e.g., risk scores, risk assessment scores, etc.) associated with the first message 510. …”}; which together are the same as claimed limitations above to include ‘conducting risk assessment and scoring to the collected data’)
It would have been obvious to an ordinary person of skill in the art before the effective filing date to modify the teachings of Hu, Hockey and Walter with the teachings of Nelsen. The motivation to combine these references would be to provide a loan processing system that provides an efficient interface between lender computers and broker computers that accesses data and resources already available on the broker computer system; and provide a web-based interface that transfers data from a lender computer network to a broker network without requiring the lender to install extensive programs or program interfaces (see paras [0008]-[0009] of Hu), and to create a new and useful system and method for secure permissioning of access to user accounts, including secure distribution of aggregated user account data (see para [0005] of Hockey), and to allow ERP systems typically track inventory held by or in possession of the borrower, and may track accounts receivable, and when asset valuation must be performed, assigning values to various assets (see paras [0008]-[0009] of Walter), and by authenticating payers of online purchase transactions will reduce the levels of fraud, disputes, retrievals, and chargebacks, which subsequently will reduce the costs associated with each of these events (see para [0003] of Nelsen).
Hu, Hockey, Walter and Nelsen teach ---
generating one or more scores based on the conducted risk assessment; and
(see at least Hu ibidem and citations listed above to include ‘a lending system’, ‘a processor’, ‘one network’ and ‘interface system’ that together are the same as claimed ‘one network interface controller’; and ‘a personal loan-lending system’ plus ‘collecting and preprocessing borrower data from multiple sources’)
(see at least: Hockey ibidem and citations listed above to include ‘enable data exchange with external systems’ and ‘a memory communicatively coupled to the processor’)
(see at least: Walter ibidem and citations listed above to include ‘the memory comprises a lending management logic’)
(see at least: Nelsen ibidem and citations listed above to include ‘conducting risk assessment and scoring to the collected data’; and Abstract for ‘risk scores generated’; and para [0096] about {“The risk analysis module 320 may process this information (e.g., send queries to risk analysis services) and may generate its own risk analysis output value(s) (e.g., one or more risk scores). …”}; and para [0144] about {“…… Ultimately, the risk analysis module 320 may, in conjunction with data processors 125A, generate one or more risk analysis outputs (e.g., risk scores, risk assessment scores, etc.) associated with the first message 510. …”}; which together are the same as claimed limitations above to include ‘generating one or more scores’)
Hu, Hockey, Walter and Nelsen teach ---
approving a loan based on the one or more scores, wherein the (one or more scores are generated via one or more machine learning processes).
(see at least Hu ibidem and citations listed above to include ‘a lending system’, ‘a processor’, ‘one network’ and ‘interface system’ that together are the same as claimed ‘one network interface controller’; and ‘a personal loan-lending system’ plus ‘collecting and preprocessing borrower data from multiple sources’; and para [0046] for ‘submit the loan for approval’; and para [0073] about {“One or more of the loan underwriters 640 reviews the loan application and approves or denies the application. …”}; which together are the same as claimed limitations above to include ‘approving a loan’ per BRI rules)
(see at least: Hockey ibidem and citations listed above to include ‘enable data exchange with external systems’ and ‘a memory communicatively coupled to the processor’; and paras [0059] and [0064] for ‘loan approval’; and para [0270] for ‘a loan approval process’; which together are the same as claimed limitations above to include ‘approving a loan’)
(see at least: Walter ibidem and citations listed above to include ‘the memory comprises a lending management logic’)
(see at least: Nelsen ibidem and citations listed above to include ‘conducting risk assessment and scoring to the collected data’ and ‘generating one or more scores’)
Hu, Hockey, Walter and Nelsen teach as disclosed above, but they may not explicitly teach about ‘one or more scores are generated via one or more machine learning processes’. However, Tang teaches them explicitly.
(see at least: Tang Abstract and Summary in paras [0006]-[0009]; and para [0007] about {“...... In one example, the social networking system applies one or more machine learned models to features of a content item that generates a score based on similarity between features describing appearance of the content items and features describing appearances of content items previously identified as sponsored content items. …”}; and para [0013] about {“FIG. 4 is a flowchart of a method for one or more machine learned models model to generate a score providing a measure of similarity between an appearance of a content item and an appearance of a sponsored content item, in accordance with an embodiment.”}; and para [0039] about {“…… For example, the content selection module 235 identifies features of the content item and applies one or more machine learned models to the identified features to generate the score for the content item.…”}; and para [0046] about {“…… As described below in conjunction with FIG. 4, one or more machine learned models are applied to features of the content item that specify appearance of the content item to generate the score. …”}; and sub-section titled “Training One or More Machine Learned Models to Score Appearances of Content Items” in paras [0047]-[0054]; and para [0047] about {“FIG. 4 is a flowchart of one embodiment of a method for training one or more machine learned models model to generate a score providing a measure of similarity between an appearance of a content item and an appearance of a sponsored content item. …”}; and para [0057] about {“……In one embodiment, a machine learned model associates weights with various features of a content item and generates the score for the content item based on the weights associated with the features of the content item.”}; which together are the same as claimed limitations above to include ‘one or more scores generated via one or more machine learning processes’)
It would have been obvious to an ordinary person of skill in the art before the effective filing date to modify the teachings of Hu, Hockey, Walter and Nelsen with the teachings of Tang. The motivation to combine these references would be to provide a loan processing system that provides an efficient interface between lender computers and broker computers that accesses data and resources already available on the broker computer system; and provide a web-based interface that transfers data from a lender computer network to a broker network without requiring the lender to install extensive programs or program interfaces (see paras [0008]-[0009] of Hu), and to create a new and useful system and method for secure permissioning of access to user accounts, including secure distribution of aggregated user account data (see para [0005] of Hockey), and to allow ERP systems typically track inventory held by or in possession of the borrower, and may track accounts receivable, and when asset valuation must be performed, assigning values to various assets (see paras [0008]-[0009] of Walter), and by authenticating payers of online purchase transactions will reduce the levels of fraud, disputes, retrievals, and chargebacks, which subsequently will reduce the costs associated with each of these events (see para [0003] of Nelsen), and to permit the model may be trained via a training set of content items and user feedback regarding user perception of content items having various features as sponsored or as non-sponsored content items (see para [0007] of Tang).
Dependent Claims 2, 5 & 9 are rejected under 35 USC 103 as unpatentable over Hu in view of Hockey, Walter, Nelsen and Tang as applied to rejection of independent Claim 1 above, and as described below for each claim/ limitation.
With respect to Claim 2, Hu, Hockey, Walter, Nelsen and Tang teach ---
2. The lending system of claim 1, wherein the borrower data collected from one or more sources comprising:
customer data, environmental data, or compliance data.
(see at least Hu ibidem and citations listed above to include ‘a lending system’, ‘a processor’, ‘one network’ and ‘interface system’ that together are the same as claimed ‘one network interface controller’; and ‘a personal loan-lending system’ plus ‘collecting and preprocessing borrower data from multiple sources’ and ‘approving a loan’; and para [0070] about {“…… The data storage facility 632 stores various data related to the lenders and users within the system. …”}; which together are the same as claimed limitations above to include ‘customer data’ per BRI rules)
(see at least: Hockey ibidem and citations listed above to include ‘enable data exchange with external systems’ and ‘a memory communicatively coupled to the processor’; plus ‘approving a loan’; and Abstract and paras [0002], [005]-[0007], [0044] & [0048]; AND para [0430] about {“FIG. 13 illustrates an example network environment 1300 in which a permissions management system 1304 (i.e., a data management platform) may operate, according to an embodiment. …”}; which together are the same as claimed limitations above to include ‘customer data’ and ‘environmental data’ per BRI rules)
(see at least: Walter ibidem and citations listed above to include ‘the memory comprises a lending management logic’)
(see at least: Nelsen ibidem and citations listed above to include ‘conducting risk assessment and scoring to the collected data’ and ‘generating one or more scores’)
(see at least: Tang ibidem and citations listed above to include one or more scores are generated via one or more machine learning processes’)
With respect to Claim 5, Hu, Hockey, Walter, Nelsen and Tang teach ---
5. The lending system of claim 1, wherein the memory further comprises an external integration logic configured to retrieve additional borrower information from third-party data providers to enhance an accuracy of risk assessment.
(see at least Hu ibidem and citations listed above to include ‘a lending system’, ‘a processor’, ‘one network’ and ‘interface system’ that together are the same as claimed ‘one network interface controller’; and ‘a personal loan-lending system’ plus ‘collecting and preprocessing borrower data from multiple sources’, ‘approving a loan’ and ‘customer data’ ; and para [0028] about {“…… During the course of the loan application process, various items of information are transmitted among the parties, including borrower information and loan application data. This information is typically maintained in databases stored in the broker computer, or on the third party computers. …”}; which together are the same as claimed limitations above to include ‘retrieve additional borrower information from third-party data providers’)
(see at least: Hockey ibidem and citations listed above to include ‘enable data exchange with external systems’ and ‘a memory communicatively coupled to the processor’; plus ‘approving a loan’, ‘customer data’ and ‘environmental data’; and para [0545] about {“Continuing the example referenced above, the system 2000 can be used to securely disclose private financial data of a borrower to a lender. In this example, the borrower (the subject) operates the user device 2002. The lender (the third-party) operates and/or manages the third-party server 2004. …”}; and para [0554] about {“…… the requester interface 2016 can be operated and/or instantiated in preparation to receive a request from a third-party server, such as the third-party server 2004. As noted above, in this example, the third-party server 2004 is operated by a lender seeking specific financial data about the borrower. More specifically, the lender can operate the third-party server 2004 to submit a request to the data management platform 2008, via the requester interface 2016, for financial data of the borrower stored in the database 2010 or otherwise accessible …”}; and para [0615] about {“…… the following embodiments reference a transaction in which a lender (the third-party) requests private financial data from a borrower (the subject and the user of the electronic device). …”}; and para [0640] about {“…… In the illustrated embodiment, the third-party requests transaction history, account information, and income information from the borrower. It may be appreciated that although FIG. 22D lists “third-party,” in certain embodiments, an institution name can be provided to the borrower in the user interface 2204d.”}; which together are the same as claimed limitations above to include ‘retrieve additional borrower information from third-party data providers’)
(see at least: Walter ibidem and citations listed above to include ‘the memory comprises a lending management logic’)
(see at least: Nelsen ibidem and citations listed above to include ‘conducting risk assessment and scoring to the collected data’ and ‘generating one or more scores’)
(see at least: Tang ibidem and citations listed above to include ‘one or more machine learning processes’ and ‘training models … to improve an accuracy of the generated scores’)
With respect to Claim 9, Hu, Hockey, Walter, Nelsen and Tang teach ---
9. The lending system of claim 1, wherein the lending management logic is further configured to generate detailed risk assessment reports for each loan via the one or more machine learning processes, summarizing the collected data and scoring metrics.
(see at least Hu ibidem and citations listed above to include ‘a lending system’, ‘a processor’, ‘one network’ and ‘interface system’ that together are the same as claimed ‘one network interface controller’; and ‘a personal loan-lending system’ plus ‘collecting and preprocessing borrower data from multiple sources’, ‘approving a loan’ and ‘customer data’)
(see at least: Hockey ibidem and citations listed above to include ‘enable data exchange with external systems’ and ‘a memory communicatively coupled to the processor’; plus ‘approving a loan’, ‘customer data’ and ‘environmental data’)
(see at least: Walter ibidem and citations listed above to include ‘the memory comprises a lending management logic’)
(see at least: Nelsen ibidem and citations listed above to include ‘conducting risk assessment and scoring to the collected data’ and ‘generating one or more scores’)
(see at least: Tang ibidem and citations listed above to include ‘one or more machine learning processes’)
Dependent Claims 3 and 12 are rejected under 35 USC 103 as unpatentable over Hu in view of Hockey, Walter, Nelsen and Tang as applied to the rejection of Claims 1-2 above, and further in view of Pub. No. US 2008/ 0249937 filed by Walls et al. (hereinafter “Walls”), and as described below for each claim/ limitation.
With respect to Claim 3, Hu, Hockey, Walter, Nelsen and Tang teach ---
3. The lending system of claim 1, wherein the lending management logic is further
configured to validate the collected borrower against (at least Know Your Customer
(KYC) data and Anti-Money Laundering (AML) requirements).
(see at least Hu ibidem and citations listed above to include ‘a lending system’, ‘a processor’, ‘one network’ and ‘interface system’ that together are the same as claimed ‘one network interface controller’; and ‘a personal loan-lending system’ plus ‘collecting and preprocessing borrower data from multiple sources’, ‘approving a loan’ and ‘customer data’)
(see at least: Hockey ibidem and citations listed above to include ‘enable data exchange with external systems’ and ‘a memory communicatively coupled to the processor’; plus ‘approving a loan’, ‘customer data’ and ‘environmental data’)
(see at least: Walter ibidem and citations listed above to include ‘the memory comprises a lending management logic’)
(see at least: Nelsen ibidem and citations listed above to include ‘conducting risk assessment and scoring to the collected data’ and ‘generating one or more scores’)
(see at least: Tang ibidem and citations listed above to include ‘one or more machine learning processes’)
Hu, Hockey, Walter, Nelsen and Tang teach as disclosed above, but they may not explicitly teach about ‘at least Know Your Customer (KYC) data and Anti-Money Laundering (AML) requirements’. However, Walls teaches them explicitly.
(see at least: Walls Abstract; and para [0005] about {“…… These types of regulations are generally referred to as "anti-money laundering" (AML) provisions, and typically require that financial institutions and RSPs "know your customer" (KYC). Compliance with KYC and AML regulations may place significant cost and administrative burdens on formal international remittance channels. …”}; and paras [0024], [0032], [0036], [0042], [0077], [0096], [0104], [0106], [0116] & [0123] for KYC/AML information and/or requirements; which together are the same as claimed limitations above to include ‘at least Know Your Customer (KYC) data and Anti-Money Laundering (AML) requirements’)
It would have been obvious to an ordinary person of skill in the art before the effective filing date to modify the teachings of Hu, Hockey, Walter, Nelsen and Tang with the teachings of Walls. The motivation to combine these references would be to provide a loan processing system that provides an efficient interface between lender computers and broker computers that accesses data and resources already available on the broker computer system; and provide a web-based interface that transfers data from a lender computer network to a broker network without requiring the lender to install extensive programs or program interfaces (see paras [0008]-[0009] of Hu), and to create a new and useful system and method for secure permissioning of access to user accounts, including secure distribution of aggregated user account data (see para [0005] of Hockey), and to allow ERP systems typically track inventory held by or in possession of the borrower, and may track accounts receivable, and when asset valuation must be performed, assigning values to various assets (see paras [0008]-[0009] of Walter), and by authenticating payers of online purchase transactions will reduce the levels of fraud, disputes, retrievals, and chargebacks, which subsequently will reduce the costs associated with each of these events (see para [0003] of Nelsen), and to permit the model may be trained via a training set of content items and user feedback regarding user perception of content items having various features as sponsored or as non-sponsored content items (see para [0007] of Tang), and to provide compliance with KYC and AML regulations may place significant cost and administrative burdens on formal international remittance channels (see para [0005] of Walls).
With respect to Claim 12, Hu, Hockey, Walter, Nelsen, Tang and Walls teach ---
12. The lending system of claim 1, wherein the lending management logic further comprises utilizing compliance logic to generate at least one regulatory compliance report based on borrower data and the scoring process.
(see at least Hu ibidem and citations listed above to include ‘a lending system’, ‘a processor’, ‘one network’ and ‘interface system’ that together are the same as claimed ‘one network interface controller’; and ‘a personal loan-lending system’ plus ‘collecting and preprocessing borrower data from multiple sources’, ‘approving a loan’ and ‘customer data’)
(see at least: Hockey ibidem and citations listed above to include ‘enable data exchange with external systems’ and ‘a memory communicatively coupled to the processor’; plus ‘approving a loan’, ‘customer data’ and ‘environmental data’)
(see at least: Walter ibidem and citations listed above to include ‘the memory comprises a lending management logic’)
(see at least: Nelsen ibidem and citations listed above to include ‘conducting risk assessment and scoring to the collected data’ and ‘generating one or more scores’)
(see at least: Tang ibidem and citations listed above to include ‘one or more machine learning processes’)
(see at least: Walls ibidem and citations listed above to include ‘at least Know Your Customer (KYC) data and Anti-Money Laundering (AML) requirements’; and para [0028] about {“…… while also facilitating regulatory compliance by participating financial institutions (FIs) and RSPs.”}; and para [0032] about {“It may also be assumed that the FIs 104, 106, and the other FIs included in the remittance system 100 but not depicted in the drawings, are banks or other organizations that are subject to regulation to assure compliance with KYC and AML requirements. …”}; which together are the same as claimed limitations above to include ‘utilizing compliance logic to generate at least one regulatory compliance report’)
Dependent Claim 4 is rejected under 35 USC 103 as unpatentable over Hu in view of Hockey, Walter, Nelsen and Tang as applied to the rejection of Claims 1-2 above, and further in view of Pub. No. US 2017/ 0270603 filed by Berta et al. (herein-after “Berta”), and as described below for each claim/ limitation.
With respect to Claim 4, Hu, Hockey, Walter, Nelsen and Tang teach ---
4. The lending system of claim 1, wherein the one or more machine learning
processes include training models using historical data and (real-time borrower behavior) to improve an accuracy of the generated scores.
(see at least Hu ibidem and citations listed above to include ‘a lending system’, ‘a processor’, ‘one network’ and ‘interface system’ that together are the same as claimed ‘one network interface controller’; and ‘a personal loan-lending system’ plus ‘collecting and preprocessing borrower data from multiple sources’, ‘approving a loan’ and ‘customer data’ ; and para [0000] about {“…… }; which together are the same as claimed limitations above to include ‘training models using historical data’)
(see at least: Hockey ibidem and citations listed above to include ‘enable data exchange with external systems’ and ‘a memory communicatively coupled to the processor’; plus ‘approving a loan’, ‘customer data’ and ‘environmental data’)
(see at least: Walter ibidem and citations listed above to include ‘the memory comprises a lending management logic’)
(see at least: Nelsen ibidem and citations listed above to include ‘conducting risk assessment and scoring to the collected data’ and ‘generating one or more scores’)
(see at least: Tang ibidem and citations listed above to include ‘one or more machine learning processes’; and para [0051] about {“Based on the received 420 responses to the one or more questions, the social networking system 140 trains 425 one or more models to generate a score for a content item indicating a similarity between an appearance of a content item and an appearance of a sponsored content item. …… Weights associated with various features of content items presented from multiple training sets may be combined to improve the accuracy of the weights associated with various features in some embodiments. The trained model is stored 430 by the social networking system 140 for subsequent application …… In some embodiments, the social networking system 140 trains 425 and stores 430 different models in association with different users …… Application of the trained model to one or more content items selected for presentation to a user is further described below in conjunction with FIG. 5.”}; which together are the same as claimed limitations above to include ‘training models … to improve an accuracy of the generated scores’)
Hu, Hockey, Walter, Nelsen and Tang teach as disclosed above, but they may not explicitly teach about ‘real-time borrower behavior’. However, Berta teaches them explicitly.
(see at least: Berta Abstract and Summary in paras [0005]-[0007]; and para [0027] about {“… …for example, the length of the buyer/supplier relationship, behavioral data about the borrower available to the financial institution, …… The customer exposure appetite may also be generated on, for example, a big data platform using batch processing or real-time processing to provide updated values.”}; and para [0030] about {“… … The dynamically determined loan capacity represents the maximum exposure allowable to the borrower 102 for the requested transaction in real-time. …… dynamically calculated loan capacity (which may also be referred to as dynamic capacity) may be communicated to a borrower and may change based on the borrower's behavior in real-time. …”}; which together are the same as claimed limitations above to include ‘real-time borrower behavior’)
It would have been obvious to an ordinary person of skill in the art before the effective filing date to modify the teachings of Hu, Hockey, Walter, Nelsen and Tang with the teachings of Berta. The motivation to combine these references would be to provide a loan processing system that provides an efficient interface between lender computers and broker computers that accesses data and resources already available on the broker computer system; and provide a web-based interface that transfers data from a lender computer network to a broker network without requiring the lender to install extensive programs or program interfaces (see paras [0008]-[0009] of Hu), and to create a new and useful system and method for secure permissioning of access to user accounts, including secure distribution of aggregated user account data (see para [0005] of Hockey), and to allow ERP systems typically track inventory held by or in possession of the borrower, and may track accounts receivable, and when asset valuation must be performed, assigning values to various assets (see paras [0008]-[0009] of Walter), and by authenticating payers of online purchase transactions will reduce the levels of fraud, disputes, retrievals, and chargebacks, which subsequently will reduce the costs associated with each of these events (see para [0003] of Nelsen), and to permit the model may be trained via a training set of content items and user feedback regarding user perception of content items having various features as sponsored or as non-sponsored content items (see para [0007] of Tang), and to provide customer exposure appetite that may be a dynamically generated loan amount up to which the borrower 102 is approved (see para [0027] of Berta).
Dependent Claim 6 is rejected under 35 USC 103 as unpatentable over Hu in view of Hockey, Walter, Nelsen and Tang as applied to the rejection of Claims 1-2 above, and further in view of Pub. No. US 2014/ 0257832 filed by Hermiz et al. (herein-after “Hermiz”), and as described below for each claim/ limitation.
With respect to Claim 6, Hu, Hockey, Walter, Nelsen and Tang teach ---
6. The lending system of claim 1, wherein the (one or more scores include a credit score, a fraud likelihood score, and a financial health score), each generated using distinct algorithms tailored to specific data inputs.
(see at least Hu ibidem and citations listed above to include ‘a lending system’, ‘a processor’, ‘one network’ and ‘interface system’ that together are the same as claimed ‘one network interface controller’; and ‘a personal loan-lending system’ plus ‘collecting and preprocessing borrower data from multiple sources’, ‘approving a loan’ and ‘customer data’)
(see at least: Hockey ibidem and citations listed above to include ‘enable data exchange with external systems’ and ‘a memory communicatively coupled to the processor’; plus ‘approving a loan’, ‘customer data’ and ‘environmental data’)
(see at least: Walter ibidem and citations listed above to include ‘the memory comprises a lending management logic’)
(see at least: Nelsen ibidem and citations listed above to include ‘conducting risk assessment and scoring to the collected data’ and ‘generating one or more scores’)
(see at least: Tang ibidem and citations listed above to include ‘one or more machine learning processes’)
Hu, Hockey, Walter, Nelsen and Tang teach as disclosed above, but they may not explicitly teach about ‘one or more scores include a credit score, a fraud likelihood score, and a financial health score’. However, Hermiz teaches them explicitly.
(see at least: Hermiz Abstract and Summary of the Invention in paras [0006]-[0009]; and para [0045] about {“…… The rule generating processing component implements a rule-generation algorithm, described herein with respect to FIG. 3A, that generates a rule list model 75 tailored to the characteristics of the sparse data in the domain. …… Correspondingly, the structure of the rule list model 75 is an ordered list of rules where each rule is a conjunction of terms and each term specifies either the presence or the absence of some input binary variable.”}; and para [0055] about {“The algorithm for rule list model generation is tailored to the characteristics of the sparse data in this domain. …… The structure of the rule list model is an ordered list of rules where each rule is a conjunction of terms and each term specifies either the presence or the absence of some input binary variable. …”}; which together are the same as claimed limitations above to include ‘one or more scores include a credit score, a fraud likelihood score, and a financial health score’)
It would have been obvious to an ordinary person of skill in the art before the effective filing date to modify the teachings of Hu, Hockey, Walter, Nelsen and Tang with the teachings of Hermiz. The motivation to combine these references would be to provide a loan processing system that provides an efficient interface between lender computers and broker computers that accesses data and resources already available on the broker computer system; and provide a web-based interface that transfers data from a lender computer network to a broker network without requiring the lender to install extensive programs or program interfaces (see paras [0008]-[0009] of Hu), and to create a new and useful system and method for secure permissioning of access to user accounts, including secure distribution of aggregated user account data (see para [0005] of Hockey), and to allow ERP systems typically track inventory held by or in possession of the borrower, and may track accounts receivable, and when asset valuation must be performed, assigning values to various assets (see paras [0008]-[0009] of Walter), and by authenticating payers of online purchase transactions will reduce the levels of fraud, disputes, retrievals, and chargebacks, which subsequently will reduce the costs associated with each of these events (see para [0003] of Nelsen), and to permit the model may be trained via a training set of content items and user feedback regarding user perception of content items having various features as sponsored or as non-sponsored content items (see para [0007] of Tang), and to provide computer-aided audit systems that are often formulated in an ad hoc fashion, and may not adequately incorporate the relevant domain knowledge and data modeling expertise (see para [0004] of Hermiz).
Dependent Claim 7 is rejected under 35 USC 103 as unpatentable over Hu in view of Hockey, Walter, Nelsen and Tang as applied to the rejection of Claims 1-2 above, and further in view of Pub. No. US 2013/ 0138554 filed by Nikankin et al. (herein-after “Nikankin”), and as described below for each claim/ limitation.
With respect to Claim 7, Hu, Hockey, Walter, Nelsen and Tang teach ---
7. The lending system of claim 1, wherein the lending management logic is further configured to (dynamically adjust a loan approval criteria) based on at least one of:
environmental data, including geographic risk data or market condition data.
(see at least Hu ibidem and citations listed above to include ‘a lending system’, ‘a processor’, ‘one network’ and ‘interface system’ that together are the same as claimed ‘one network interface controller’; and ‘a personal loan-lending system’ plus ‘collecting and preprocessing borrower data from multiple sources’, ‘approving a loan’ and ‘customer data’; and para [0027] for ‘the broker obtains data from the borrower’ and ‘shops for loans from the available sources in the wholesale loan market’; which together are the same as claimed limitations above to include ‘market condition data’)
(see at least: Hockey ibidem and citations listed above to include ‘enable data exchange with external systems’ and ‘a memory communicatively coupled to the processor’; plus ‘approving a loan’, ‘customer data’ and ‘environmental data’; and para [0259] about {“……Another example of a data store is a hosted storage environment that includes a collection of physical data storage devices …”}; and para [0430] about {“FIG. 13 illustrates an example network environment 1300 in which a permissions management system 1304 (i.e., a data management platform) may operate, according to an embodiment. …”}; which together are the same as claimed limitations above to include ‘environment data’)
(see at least: Walter ibidem and citations listed above to include ‘the memory comprises a lending management logic’)
(see at least: Nelsen ibidem and citations listed above to include ‘conducting risk assessment and scoring to the collected data’ & ‘generating one or more scores’; and para [0096] about {“…
… the risk analysis module 320 may transform the collected information, including but not limited to translating an IP address of the cardholder device into a geolocation value (based upon a geolocation database) that identifies a geographic location of the cardholder device...”}; which together are the same as claimed limitations above to include ‘geographic risk data’)
(see at least: Tang ibidem and citations listed above to include ‘one or more machine learning processes’)
Hu, Hockey, Walter, Nelsen and Tang teach as disclosed above, but they may not explicitly teach about ‘dynamically adjust a loan approval criteria’. However, Nikankin teaches them explicitly.
(see at least: Nikankin Abstract and Summary in paras [0005]-[0009]; and Abstract about {“..... An approval component can determine whether the potential borrower is eligible for one or more loans as a function of the set of lending criteria corresponding to the determined classification, and a terms component can generate a set of terms for the one or more loans.”}; and para [0006] about {“…… and classify the potential borrower as a function of the risk, a standards generation component configured to determine a set of lending criteria for the determined classification, and an approval component configured to determine an eligibility of the potential borrower for one or more loans based on a comparison of a profile associated with the potential borrower and the set of lending criteria for.”}; and paras [0007]--[0009], [0029], [0039] & [0049] for “criteria” and “loan/s”; and para [0033] about {“The approval component 206 can determine whether the user 104 is eligible for one or more loans by comparing the profile 106 associated with the user 104 to the set of lending criterion. …… If the user 104 satisfies the first set of lending criterion, then credit standards component 105 can determine that the user 104 is eligible for one or more loans. If the user 104 does not satisfy the first set of lending criterion, then the credit standards component 105 can determine that the user 104 is ineligible for one or more loans. In addition, the approval component 206 can determine whether the user 104 is eligible for the one or more loans based at least in part on a set of additional criterion. For example, the set of additional criterion can include a prior loan history of the user 104, a promotional offer, a managerial override (e.g., authorized user input, etc.), and so forth.”}; and para [0041] about{“…… In addition, the approval component 206 can intelligently determine or infer the user's 104 eligibility for one or more loans. Additionally, the approval component 206 can intelligently determine or infer a set of terms for the one or more loans. …”}; and para [0055] about {“…… At 808, the set of lending criterion for respective classifications of potential borrowers can be updated as a function of the data regarding previously granted loans. For example, the set of lending criterion can be adjusted to increase the difficulty of obtaining a loan (e.g., raise a lending threshold) for a classification of potential borrowers for which the data regarding previously granted loans includes a large quantity of defaults. As an additional example, the set of lending criterion can be adjusted to reduce the difficulty of obtaining a loan (e.g., lower a lending threshold) for a set of potential borrowers previously classified as high risk, wherein the data regarding previously granted loans indicates that there have been a large quantity of loans that satisfy a desired return on investment. It is to be appreciated that such adjustments enable a lender to dynamically adjust lending requirements in a relatively short period of time in response to actual results obtained by the lender.”}; which together are the same as claimed limitations about ‘dynamically adjust a loan approval criteria’)
It would have been obvious to an ordinary person of skill in the art before the effective filing date to modify the teachings of Hu, Hockey, Walter, Nelsen and Tang with the teachings of Nikankin. The motivation to combine these references would be to provide a loan processing system that provides an efficient interface between lender computers and broker computers that accesses data and resources already available on the broker computer system; and provide a web-based interface that transfers data from a lender computer network to a broker network without requiring the lender to install extensive programs or program interfaces (see paras [0008]-[0009] of Hu), and to create a new and useful system and method for secure permissioning of access to user accounts, including secure distribution of aggregated user account data (see para [0005] of Hockey), and to allow ERP systems typically track inventory held by or in possession of the borrower, and may track accounts receivable, and when asset valuation must be performed, assigning values to various assets (see paras [0008]-[0009] of Walter), and by authenticating payers of online purchase transactions will reduce the levels of fraud, disputes, retrievals, and chargebacks, which subsequently will reduce the costs associated with each of these events (see para [0003] of Nelsen), and to permit the model may be trained via a training set of content items and user feedback regarding user perception of content items having various features as sponsored or as non-sponsored content items (see para [0007] of Tang), and to overcome an inability for credit lending organizations to quickly react to changing trends, or incorporate future events or trends that have not already occurred at the time of the reevaluation (see para [0003] of Nikankin).
Dependent Claims 8 & 10 are rejected under 35 USC 103 as unpatentable over Hu in view of Hockey, Walter, Nelsen and Tang as applied to the rejection of Claims 1-2 above, and further in view of Pub. No. US 2023/ 0410223 filed by Dobson et al. (herein-after “Dobson”), and as described below for each claim/ limitation.
With respect to Claim 8, Hu, Hockey, Walter, Nelsen and Tang teach ---
8. The lending system of claim 1, wherein the score generation is based on at least borrower demographics and financial profiles generated by (one or more large language models).
(see at least Hu ibidem and citations listed above to include ‘a lending system’, ‘a processor’, ‘one network’ and ‘interface system’ that together are the same as claimed ‘one network interface controller’; and ‘a personal loan-lending system’ plus ‘collecting and preprocessing borrower data from multiple sources’, ‘approving a loan’ and ‘customer data’)
(see at least: Hockey ibidem and citations listed above to include ‘enable data exchange with external systems’ and ‘a memory communicatively coupled to the processor’; plus ‘approving a loan’, ‘customer data’ and ‘environmental data’)
(see at least: Walter ibidem and citations listed above to include ‘the memory comprises a lending management logic’)
(see at least: Nelsen ibidem and citations listed above to include ‘conducting risk assessment and scoring to the collected data’ and ‘generating one or more scores’)
(see at least: Tang ibidem and citations listed above to include ‘one or more machine learning processes’; and para [0002] about {“…… Users may create profiles on the social networking system that are tied to their identities and include information about the users, such as interests and demographic information. …”}; and para [0022] about {“…… Examples of information stored in a user profile include biographic, demographic, and other types of descriptive information, such as work experience, educational history, gender, hobbies or preferences, location and the like. A user profile may also store other information provided by the user, for example, images or videos. …”}; and para [0030] about {“…… For example, a feature may represent the level of interest that a user has in a particular topic, the rate at which the user logs into the social networking system 140, or information describing demographic information about a user. …”}; which together are the same as claimed limitations above to include ‘borrower demographics and financial profiles’ per BRI rules)
Hu, Hockey, Walter, Nelsen and Tang teach as disclosed above, but they may not explicitly teach about ‘one or more large language models’. However, Dobson teaches them explicitly.
(see at least: Dobson Abstract and Summary in paras [0010]-[0026]; and para[0091] about {“... … Any inquiry may be searched and terms relevant of interest to a user to integrate predictive learnings, machine learning, and/or artificial driven analytics to generate a response in typo-graphical form such as text or in a designated depiction of graph, chart, mapping, decision tree, or otherwise. Generative AI with large language models (LLMs) are integrated, such as, for example and not limitation Open AI's ChatGPT. Such LLMs may be customized within a domain of data or to preserve proprietary data in a closed network. …”}; which together are the same as claimed limitations above to include ‘one or more large language models’)
It would have been obvious to an ordinary person of skill in the art before the effective filing date to modify the teachings of Hu, Hockey, Walter, Nelsen and Tang with the teachings of Dobson. The motivation to combine these references would be to provide a loan processing system that provides an efficient interface between lender computers and broker computers that accesses data and resources already available on the broker computer system; and provide a web-based interface that transfers data from a lender computer network to a broker network without requiring the lender to install extensive programs or program interfaces (see paras [0008]-[0009] of Hu), and to create a new and useful system and method for secure permissioning of access to user accounts, including secure distribution of aggregated user account data (see para [0005] of Hockey), and to allow ERP systems typically track inventory held by or in possession of the borrower, and may track accounts receivable, and when asset valuation must be performed, assigning values to various assets (see paras [0008]-[0009] of Walter), and by authenticating payers of online purchase transactions will reduce the levels of fraud, disputes, retrievals, and chargebacks, which subsequently will reduce the costs associated with each of these events (see para [0003] of Nelsen), and to permit the model may be trained via a training set of content items and user feedback regarding user perception of content items having various features as sponsored or as non-sponsored content items (see para [0007] of Tang), and to provide a platform that will beneficially be able to share data sets publicly across the network to create borrower/ user authorized interconnection with others (see para [0009] of Dobson).
With respect to Claim 10, Hu, Hockey, Walter, Nelsen, Tang and Dobson teach ---
10. The lending system of claim 1, wherein the one or more machine learning processes are configured to self-optimize by analyzing outcomes of previously approved loans to refine scoring algorithms.
(see at least Hu ibidem and citations listed above to include ‘a lending system’, ‘a processor’, ‘one network’ and ‘interface system’ that together are the same as claimed ‘one network interface controller’; and ‘a personal loan-lending system’ plus ‘collecting and preprocessing borrower data from multiple sources’, ‘approving a loan’ and ‘customer data’)
(see at least: Hockey ibidem and citations listed above to include ‘enable data exchange with external systems’ and ‘a memory communicatively coupled to the processor’; plus ‘approving a loan’, ‘customer data’ and ‘environmental data’; and para [0059] about {“…… The financial reports preferably characterize financial data as it was valid at the time of generating the financial reports. This may be important in many situations such as loan approval where an audit would want to review the state of information on which a loan decision was based. …”}; and para [0064] about {“….. As mentioned, the field of loan approval in particular may potentially benefit from such a system and method. …… A lender can integrate the data management platform into a digital loan application system. The digital loan application system can use a programmatic interface (e.g., API service) of the data management platform to allow a client device of the user to authenticate with one or more external user account systems.…”}; and para
[0270] about {“…… However, the method can be implemented in multiple instances across a data management platform for one or more users. Often in a loan approval process, a user would need to provide access to multiple sources of financial data. …”}; which together are the same as claimed limitations above to include ‘previously approved loans’ per BRI rules)
(see at least: Walter ibidem and citations listed above to include ‘the memory comprises a lending management logic’)
(see at least: Nelsen ibidem and citations listed above to include ‘conducting risk assessment and scoring to the collected data’ and ‘generating one or more scores’)
(see at least: Tang ibidem and citations listed above to include ‘one or more machine learning processes’ and ‘borrower demographics and financial profiles’)
(see at least: Dobson ibidem and citations listed above to include ‘one or more large language models’; and para [0026] about {“…… more refined graphical depictions or improved mathematical models, are encompassed in the heart of the disclosure herein.”}; and para [0070] about {“…… thus are disclosed and stored 610 in order for the system processor to optimize profile alignments, as based on a matching mathematical model that suggests profile connections to a user 612. The client-user and prospect user access information about each other 614 (as desired and as available)to make a selection or de-select an online profile connection 616. Usage data 617 is stored as to social support network(s) established per disease, etc. Rating data is stored 618 as programmed and selected via algorithm refined 620 to rate and refine connections of client users and prospective users. Machine learning algorithms are utilized and models defined to use data of client users to provide a score and rating 622, and align or proposes connection to client users and prospective users. …”}; which together are the same as claimed limitations above to include ‘self-optimize by analyzing outcomes’ and ‘refine scoring algorithms’)
Dependent Claim 11 is rejected under 35 USC 103 as unpatentable over Hu in view of Hockey, Walter, Nelsen and Tang as applied to the rejection of Claims 1-2 above, and further in view of Pub. No. US 2017/ 0255996 filed by Gil et al. (hereinafter “Gil”), and as described below for each claim/ limitation.
With respect to Claim 11, Hu, Hockey, Walter, Nelsen and Tang teach ---
11. The lending system of claim 1, wherein the lending management logic is further configured to (track and update borrower repayment history) in near real-time to continuously adjust risk and financial health scores.
(see at least Hu ibidem and citations listed above to include ‘a lending system’, ‘a processor’, ‘one network’ and ‘interface system’ that together are the same as claimed ‘one network interface controller’; and ‘a personal loan-lending system’ plus ‘collecting and preprocessing borrower data from multiple sources’, ‘approving a loan’ and ‘customer data’)
(see at least: Hockey ibidem and citations listed above to include ‘enable data exchange with external systems’ and ‘a memory communicatively coupled to the processor’; plus ‘approving a loan’, ‘customer data’ and ‘environmental data’)
(see at least: Walter ibidem and citations listed above to include ‘the memory comprises a lending management logic’)
(see at least: Nelsen ibidem and citations listed above to include ‘conducting risk assessment and scoring to the collected data’ and ‘generating one or more scores’)
(see at least: Tang ibidem and citations listed above to include ‘one or more machine learning processes’)
Hu, Hockey, Walter, Nelsen and Tang teach as disclosed above, but they may not explicitly teach about ‘track and update borrower repayment history’. However, Gil teaches them explicitly.
(see at least: Gil Abstract and Summary in paras [0004]-[0017]; and paras [0005], [0010], [0031] and [0046] about {“…… an updating of the first plurality of account histories …… combination of the updating of the first plurality of account histories and the plurality of account histories …”}; and para [0025] about {“…… although any necessary repayment of debt qualifies. Accounts may have zero or more “financial transactions” associated with them, financial transactions including but not limited to issuance of the associated debt, payments made and applied, credits applied, late charges issued, monthly interest compounded, etc. The “action history” or “loan account history” associated with an account is a history of financial transactions, including initial account amounts, payments made, dates associated with payments, payments missed, late charges charged, late charges paid, late charges waived, etc. …… An account contains one or more of the following (depending on the nature and particulars of the account): principal amount, interest rate, terms of repayment, date(s) of repayment made, dates of required payments, dates of missed payments, amount of required payments, date of service rendered, etc. …… Variables tracked include (if appropriate), but are not limited to, the origination/initiation date of the account, dates goods/services were provided or other disbursements, the original amount of the account balance, the remaining principle balance to be paid, the dates of the payments made, dates of payments due, the current interest rate, the terms of repayment, …”}; which together are the same as claimed limitations above to include ‘track and update borrower repayment history’ per BRI rules)
It would have been obvious to an ordinary person of skill in the art before the effective filing date to modify the teachings of Hu, Hockey, Walter, Nelsen and Tang with the teachings of Dobson. The motivation to combine these references would be to provide a loan processing system that provides an efficient interface between lender computers and broker computers that accesses data and resources already available on the broker computer system; and provide a web-based interface that transfers data from a lender computer network to a broker network without requiring the lender to install extensive programs or program interfaces (see paras [0008]-[0009] of Hu), and to create a new and useful system and method for secure permissioning of access to user accounts, including secure distribution of aggregated user account data (see para [0005] of Hockey), and to allow ERP systems typically track inventory held by or in possession of the borrower, and may track accounts receivable, and when asset valuation must be performed, assigning values to various assets (see paras [0008]-[0009] of Walter), and by authenticating payers of online purchase transactions will reduce the levels of fraud, disputes, retrievals, and chargebacks, which subsequently will reduce the costs associated with each of these events (see para [0003] of Nelsen), and to permit the model may be trained via a training set of content items and user feedback regarding user perception of content items having various features as sponsored or as non-sponsored content items (see para [0007] of Tang), and to fulfill a need that exists for a system, method, and apparatus determining and reducing future loan risk associated with a plurality of loan accounts (see para [0003] of Gil).
Conclusion
The prior art made of record and not relied upon, listed in Form 892, that is considered pertinent to the Applicant's disclosure and review for not traversing already issued patents and/or claimed inventions by the claims of the current invention of the Applicant. Examiner notes that Form 892 contains more references than those cited in the rejection above under 35 USC 103, and that all the references cited on said Form 892 are relevant to this application and form a part of the body of prior art.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Sanjeev Malhotra whose telephone number is (571) 272-7292. The Examiner can normally be reached during Monday-Friday between 8:30-17:00 hours on a Flexible schedule.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, the Applicant is encouraged to contact the Examiner directly.
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Electronic Communications
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/S.M./
Examiner, Art Unit 3691
sanjeev.malhotra@uspto.gov
/ABHISHEK VYAS/Supervisory Patent Examiner, Art Unit 3691