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 .
DETAILED ACTION
1. This Office Action is in response to the Amendment filed on February 10, 2026, which paper has been placed of record in the file.
2. Claims 1-5, 7-8, 10-15, 17-18, 20, and 23-28 are pending in this application.
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 1-5, 7-8, 10-15, 17-18, 20, and 23-28 are rejected under 35 U.S.C. 101 because the claim invention is directed to a judicial exception (i.e., law of nature, natural phenomenon, or abstract idea) without significantly more.
Regarding independent claim 1, which is analyzing as the following:
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites a method of fulfilling loan request. Thus, the claims are to a process, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
The claims recite a method of fulfilling and selecting for a loan request. The method matches potential borrowers to one or more lenders. The claim recites the steps: receiving a loan request for purchasing property…; receiving lender criteria from multiple lenders; recommending one or more of the multiple lenders for servicing the loan request based on the loan request and the lender criteria; performing a best fit match between the loan request and the multiple lenders…; reviewing, by the requestor of the loan request, the multiple lenders; and distributing, after the reviewing, the loan request to one of the multiple lenders, under its broadest reasonable interpretation when read in light of the Specification, falls within “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they cover performance of commercial or legal interactions including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, business relations.
Moreover, the claim recites receiving a loan request for purchasing property…; receiving lender criteria from multiple lenders; recommending one or more of the multiple lenders for servicing the loan request based on the loan request and the lender criteria; performing a best fit match between the loan request and the multiple lenders…; reviewing, by the requestor of the loan request, the multiple lenders; and distributing, after the reviewing, the loan request to one of the multiple lenders, as drafted, is a process that, under its broadest reasonable interpretation when read in light of the Specification, covers performance of the limitations in the mind, can be practically performed by human in their mind or with pen/paper, but for the recitation of generic computer components. That is, other than reciting “a computer/processor/automatically”, nothing in the claim elements preclude the steps from practically being performed in the mind. The mere nominal recitation of generic computing devices does not take the claim limitation out of the Mental Processes grouping of abstract ideas. Thus, if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, opinion). See MPEP 2106.04(a)(2), subsection III.
Therefore, the claim recites an abstract idea. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
The claim recites the additional elements of “receiving a loan request for purchasing property”, “receiving lender criteria from multiple lenders”, “recommending, using a machine learning recommendation algorithm, one or more of the multiple lenders, wherein the algorithm performs a best fit match between the loan request and the multiple lenders.” The claim also recites that the steps of “receiving a loan request for purchasing property…; receiving lender criteria from multiple lenders; recommending one or more of the multiple lenders for servicing the loan request based on the loan request and the lender criteria; performing a best fit match between the loan request and the multiple lenders…; reviewing, by the requestor of the loan request, the multiple lenders; and distributing, after the reviewing, the loan request to one of the multiple lenders” are performed by a processor.
The additional elements “receiving a loan request for purchasing property”, “receiving lender criteria from multiple lenders” are mere data gathering and transmitting recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and outputting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and transmitting. See MPEP 2106.05. Moreover, these additional elements do not provide any improvement to the technology, improvement to the functioning of the computer, improvement to the user interface, they are just merely used as general means for collecting and displaying data. It is similar to other concepts that have been identified by the courts Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48; Collecting information, analyzing it, and displaying certain results of the collection and analysis, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
The additional elements “recommending, using a machine learning recommendation algorithm, one or more of the multiple lenders, wherein the algorithm performs a best fit match between the loan request and the multiple lenders” provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
The additional elements “recommending, using a machine learning recommendation algorithm, one or more of the multiple lenders, wherein the algorithm performs a best fit match between the loan request and the multiple lenders” are used to generally apply the abstract idea without placing any limits on how the machine learning functions. Rather, this limitation only recites the outcome of “recommending the multiple lenders and performing a best fit match” and do not include any details about how the solution is accomplished. See MPEP 2106.05(f).
The additional elements “recommending, using a machine learning recommendation algorithm, one or more of the multiple lenders, wherein the algorithm performs a best fit match between the loan request and the multiple lenders” also merely indicate a field of use or technological environment in which the judicial exception is performed. Although the additional element “using a machine learning algorithm” limits the identified judicial exceptions “recommending the multiple lenders and performing a best fit match”, this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Further, the steps of “receiving a loan request for purchasing property, receiving lender criteria from multiple lenders, recommending one or more of the multiple lenders for servicing the loan request based on the loan request and the lender criteria, and distributing the loan request to one of the multiple lenders”, are recited as being performed by the processor. The processor is recited at a high level of generality and is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). The additional elements recite generic computer components the processor, a memory, and software programming instructions that are recited a high-level of generality that merely perform, conduct, carry out, implement, and/or narrow the abstract idea itself. Accordingly, the additional elements evaluated individually and in combination do not integrate the abstract idea into a practical application because they comprise or include limitations that are not indicative of integration into a practical application such as adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea -- See MPEP 2106.05(f).
Moreover, these additional elements do not provide any improvements to the technology, improvements to the functioning of the computer, the processor, improvement to the machine learning, or other technology. They just merely used as general means for performing the abstract idea. They do not recite a particular machine or manufacture that is integral to the claims, and do not transform or reduce a particular article to a different state or thing.
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception (Step 2A, Prong One: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole, amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As explained with respect to Step 2A, Prong Two, the additional elements of “recommending, using a machine learning recommendation algorithm, one or more of the multiple lenders, wherein the algorithm performs a best fit match between the loan request and the multiple lenders” are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f).
The additional elements “receiving a loan request for purchasing property”, “receiving lender criteria from multiple lenders” were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering and transmitting. However, a conclusion that an additional element is insignificant extra solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g).
As discussed in Step 2A, Prong Two above, the additional elements of “receiving a loan request for purchasing property”, “receiving lender criteria from multiple lenders” are recited at a high level of generality. These elements amount to gathering and transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely genetic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
As discussed in Step 2A, Prong Two above, the recitation of the processor to perform limitations “receiving a loan request for purchasing property…; receiving lender criteria from multiple lenders; recommending one or more of the multiple lenders for servicing the loan request based on the loan request and the lender criteria; performing a best fit match between the loan request and the multiple lenders…; reviewing, by the requestor of the loan request, the multiple lenders; and distributing, after the reviewing, the loan request to one of the multiple lenders”, amounts to no more than mere instructions to apply the exception using a generic computer component.
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. Therefore, the claim is not patent eligible. (Step 2B: NO).
Regarding independent claims 13 and 27-28, Alice Corp. establishes that the same analysis should be used for all categories of claims. Therefore, independent claim 13 directed to a method, independent claims 27-28 directed to a system, are also rejected as ineligible subject matter under 35 U.S.C. 101 for substantially the same reasons as independent method claim 1.
Regarding dependent claims 2-5, 7-8, 10-12, 17-18, 20, and 23-26, the dependent claims do not impart patent eligibility to the abstract idea of the independent claim. The dependent claims rather further narrow the abstract idea and the narrower scope does not change the outcome of the two-part Mayo test. Narrowing the scope of the claims is not enough to impart eligibility as it is still interpreted as an abstract idea, a narrower abstract idea.
Regarding dependent claims 2-3, and 15, the claims simply refine the abstract idea by further reciting wherein the recommending includes generating a weighted list of one or more of the multiple lenders…; collecting the responses of the multiple lenders to previous loan requests distributed thereto…, that fall under the category of Organizing Human activity and Mental process groupings of abstract ideas as described above in the independent claim 1. Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 4 and 17, the claims recite the additional element wherein the machine learning algorithm generates the weighted list by creating a matrix of data points associated with the loan request, which is used to generally apply the abstract idea without placing any limits on how the machine learning functions. Rather, this limitation only recites the outcome of “generates the weighted list by creating a matrix of data points” and does not include any details about how the solution is accomplished. See MPEP 2106.05(f). (see claim 1 above). Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 5 and 18, the claim simply refines the abstract idea by further reciting reviewing the one or more multiple lenders recommended for servicing and selecting at least some of the one or more multiple lenders based on the reviewing…, that fall under the category of Organizing Human activity and Mental process groupings of abstract ideas as described above in the independent claim 1. Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 7-8, 18, and 20, the claims simply refine the abstract idea by further reciting wherein at least one of the reviewing and the selecting is performed automatically by a computing device, and wherein the distributing is simultaneously and is performed automatically by a computing device, that fall under the category of Organizing Human activity and Mental process groupings of abstract ideas as described above in the independent claim 1. Moreover, the claims recite the additional element automatically by a computing device, which is recited at a high level of generality and is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. (see claim 1 above). Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 10-12 and 24-26, the claims simply refine the abstract idea by further reciting wherein the loan request includes one or more of loan type, real estate type…, wherein the lending criteria includes one or more of lender information…, and automatically calculating a brokering fee based on a percentage of debt corresponding to the loan request…, wherein the equity investor criteria includes one or more of high level investment information…, wherein the loan offer includes one or more of loan type, real estate type, deal type, financial details…, wherein when the loan offer relates to syndication, the loan offer further includes one or more of loan servicer information…, that fall under the category of Organizing Human activity and Mental process groupings of abstract ideas as described above in the independent claim 1. Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Therefore, none of the dependent claims alone or as an ordered combination add limitations that qualify as significantly more than the abstract idea.
Accordingly, claims 1-5, 7-8, 10-15, 17-18, 20, and 23-28 are not draw to eligible subject matter as they are directed to an abstract idea without significantly more and are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
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.
Claim Rejections - 35 USC § 102
5. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
6. Claims 1-5, 7-8, 10-12, and 27 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bjonerud et al. (hereinafter Bjonerud, US 2019/0102835).
Regarding to claim 1, Bjonerud discloses a method of fulfilling loan requests using machine learning, comprising:
receiving a loan request for purchasing property, wherein the property is commercial real estate (para [0047], The system and method utilizes a software-guided process to collect a set of data inputs related to commercial financings such as loans, letters of credits, lines of credit, term loans, bridge loans, revolvers, leases, and other credit facilities which may be $50 thousand USD through $500 million USD or above in size. The loans may be requested by the borrower virtually any type of financing including working capital, real estate financing, equipment finance/leasing, aircraft leasing, refinancing of existing debt, mergers and acquisitions financing, dividend financing, general corporate purposes, sale lease back transactions involving real estate or equipment, and other purposes);
receiving lender criteria from multiple lenders (para [0047], Collected data points will also include risk parameters of the lenders, existing credit facilities that are loaded onto the platform, market data collected from lenders, economic data sourced from the federal reserve, e.g., Federal Reserve economic data, and other macro-economic sources, lender bids on loan requests, failed lender bids, successful lender bids, bid response times, deal closing response times, failed borrower requests, successful borrower requests, financial information of borrowers, operational information of borrowers, the trends of all data collected, etc.);
recommending, using a machine learning recommendation algorithm, one or more of the multiple lenders for servicing the loan request based on the loan request and the lender lending criteria of each of the multiple lenders (para [0013], a method is disclosed that includes the extraction of borrower data that includes financial, industry operational and business data. The method also includes the extracting of lender data from prospective lenders that includes financial data and a target profile for each prospective lender. The method then generates, using computer based artificial intelligence, an autonomous ranked match of prospective lenders, wherein the artificial intelligence is used to identify relationships between the borrower data and the prospective lender data to generate the ranked match based on a preference of the borrower and a correlation between the identified relationships. Further, the artificial intelligence for identifying relationships evolves through machine learning derived from a collective intelligence of mass participation); wherein the algorithm performs a best fit match between the loan request and the multiple lenders using the lender criteria and responses of the multiple lenders to previous loan requests distributed thereto (para [0129], The matching is done entirely on the lender profile parameters as well as historical bidding behavior by each lender, e.g., if a lender failed to indicate that they could accommodate aircraft finance deals, but the platform had collected data indicating that the lender recently provided financing to aircraft deals, then they may be included as matching lenders in future aircraft finance deals; para [0138], The AI is basing debt capacity 1110 off the borrower's inputted financials as well as the risk parameters of network lenders, the historical deal data, and other data as the AI may determine; para [0156], The AI can also predict what the other lenders are likely to bid based on their previous bidding history and will include that prediction, under a certain weighting, which will be indicative as to the relevance to this particular deal and borrower, as the AI may determine, as part of the data set that the AI is using to indicate whether the typed in term is green, yellow, or red);
reviewing, by requestor of the loan request, the multiple lenders (para [0129], The matching lenders, if any, are listed for the borrower to select. However, instead of identifying the names of the lenders or lending institutions, they will be identified by rankings based on borrower preferences inputted as illustrated in FIG. 15. Once the borrower is informed of possible lenders, the borrower may then decide how many of the matching lenders they want to invite into the deal and will be able to select them; para [0187], allowing the prospective borrower to select certain or all lenders identified, though unnamed, as likely viable lenders for that specific borrower and the borrower's requested deal based on matching algorithms based on data previously inputted by borrower, data previously inputted by the lenders, and behavior of the lenders in prior bidding deals; para [0189], upon receiving at least one matching lender, the prospective borrower may be led through a template containing a series of questions, the responses of which could be typed text, which would automatically populate a formally formatted loan proposal for the prospective lenders to review);
distributing, after the reviewing, the loan request to at least some of the one or more of the multiple lenders according to the best fit match (para [0013], The method includes the creating and hosting of an internet based chat room where the borrower enters the internet based chat room and upon entering the identities of the prospective lenders are revealed. In addition, the borrower, within the internet based chat room, selects one or more prospective lenders to receive a finance request, and after selection by the borrower, the selected prospective lenders are first notified and informed of an existence of the finance request of the borrower).
Regarding to claim 2, Bjonerud discloses the method as recited in Claim 1, wherein the recommending includes generating a weighted list of one or more of the multiple lenders based on the loan request and the lender criteria of each of the multiple lenders (para [0096], Upon weighing all of these factors, the system will be able to both identify likely lenders and rank those lenders in an order based on the preferences of the borrower and the likelihood of the ability for the lender to fulfill the deal).
Regarding to claim 3, Bjonerud discloses the method as recited in Claim 2, further comprising collecting the responses of the multiple lenders to previous loan requests distributed thereto and adapting the weighted list based on the collected responses (para [0129], The matching is done entirely on the lender profile parameters as well as historical bidding behavior by each lender, e.g., if a lender failed to indicate that they could accommodate aircraft finance deals, but the platform had collected data indicating that the lender recently provided financing to aircraft deals, then they may be included as matching lenders in future aircraft finance deals).
Regarding to claim 4, Bjonerud discloses the method as recited in Claim 3, wherein the machine learning algorithm generates the weighted list by creating a matrix of data points associated with the loan request and the multiple lenders and weighting at least some of the data points on a sliding scale (para [0138], The AI is basing debt capacity 1110 off the borrower's inputted financials as well as the risk parameters of network lenders, the historical deal data, and other data as the AI may determine. Weightings are used to increase or dampen the impact of comparable borrowers and credit deals based on time, similarity, and market data. For example, the AI uses weightings to increase or decrease the impact of comparable data that relates to a borrower depending on its level of financial data and operational data similarities).
Regarding to claim 5, Bjonerud discloses the method as recited in Claim 1, further comprising reviewing the one or more multiple lenders recommended for servicing and selecting at least some of the one or more multiple lenders based on the reviewing, wherein the distributing is based on the selecting (para [0047], the artificial intelligence may review all the collected information, which may include more data points than are listed within this application, and will dynamically generate many outputs for the business benefit of both the borrowers and lenders).
Regarding to claim 7, Bjonerud discloses the method as recited in Claim 5, wherein at least one of the reviewing and the selecting is performed automatically by a computing device (para [0148], FIG. 15 is an illustration of a bank match screen 1500, according to an embodiment. Bank match screen 1500 includes a ranked set of lenders 1510 and classified as strong 1520, medium 1530 and low 1540. Lenders 1510 are shown anonymously. As such, the lenders are not yet aware that the borrower is looking for a loan. However, the AI has reviewed the borrower's financials, determined the borrower risk category, determined the requested credit facility's risk category, and has identified likely lenders based on their risk parameters and their recent deal history of loans that they have bid on and loans they have won/closed with other similar borrowers).
Regarding to claim 8, Bjonerud discloses the method as recited in Claim 1, wherein the distributing is simultaneously and is performed automatically by a computing device (para [0197], incorporating artificial intelligent analysis and decision making to provide instantaneous suggested competitive terms with a high, medium, and low probability of being selected based on system generated view of both competition, the lender's brand perception, and the borrower's selection criteria);
Regarding to claim 10, Bjonerud discloses the method as recited in Claim 1, wherein the loan request includes one or more of loan type, real estate type, deal type, financial details, market type, property details, closing date, or borrower information (para [0056], The system will collect and store criteria detailing the types of companies and types of loans that lenders are able to finance).
Regarding to claim 11, Bjonerud discloses the method as recited in Claim 1, wherein the lender criteria includes one or more of lender information, loan types of interest, investment strategies, market types, asset class, geographical preferences, high level loan details, or low level loan details (para [0094], Risk Parameters of Network Lenders: Lenders that have profiles on the platform are considered “Network Lenders.” Network Lenders need to input their risk parameters as part of their setup process. These parameters serve as guidelines for the types of borrowers, deals, and risk they are interested in bidding on. Sample parameters could include leverage levels, revenue size, EBITDA size, ownerships, geography, and an assortment of other ratios, financial and non-financial factors).
Regarding to claim 12, Bjonerud discloses the method as recited in Claim 1, further comprising automatically calculating a brokering fee based on a percentage of debt corresponding to the loan request (para [0078], Pricing: The borrowing index and the credit spread of the loan. Fees, commitment fees, unused fees, prepayment fees, and any other fees or costs related to the loan).
Regarding to claim 27, Bjonerud discloses a computer system for selecting lenders for a loan request comprising: one or more processors (para [0012], a processor executes instructions that cause the processor to dynamically extract and autonomously match one or more prospective lenders and a borrower) to perform operations at least including the steps found in claim 1 described above, therefore is rejected by the same rationale.
Claim Rejections - 35 USC § 103
7. 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 of this title, 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.
8. Claims 13-15, 17-18, 20, 23-26, and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Showalter et al. (hereinafter Showalter, US 11,416,926) in view of Bjonerud et al. (hereinafter Bjonerud, US 2019/0102835).
Regarding to claim 13, Showalter discloses a method of selling existing loans using machine learning, comprising:
receiving a loan offer associated with an existing property loan, wherein the property is commercial real estate (column 10, lines 58-63, the system may optionally allow for loans to be sold on a secondary market, or for insurance policies to be sold on the loans, at step 212. In particular embodiments, a sale of a loan on the second market, or sale of an insurance policy, may also be included on the ledger associated with the particular loan; column 12, lines 54-58, the process 600 begins at step 602, where the system receives loan data (e.g., a loan application). In various embodiments, the loan data may be in the format of a Form 1003, a Fannie 3.2 file, or another appropriate format);
obtaining lender criteria from multiple loan purchasers (column 12, lines 58-67, at step 604 the system may proceed to compare the loan data received at step 602 to a database of investor guidelines. As discussed above in association with FIG. 1, the investor guidelines may include publicly available rules or conditions that determine if a particular loan application is serviceable by a particular lender. For example, an investor (e.g., a bank) may require loan applicants to have FICO credit scores over 700 to qualify for a loan if his/her debt-to-income ratio is above a certain threshold);
recommending, using a machine learning recommendation algorithm, one or more of the multiple loan purchasers for assuming at least a portion of the loan based on the loan offer and loan purchaser criteria of each of the multiple loan purchasers (column 13, lines 1-7, Continuing with step 604, in certain embodiments, the system may compare the loan data to the investor guidelines and furthermore rank the investors based on the number of criteria satisfied by the loan data. According to various aspects of the present disclosure, the system may leverage the one or more Bayesian inference networks for predicting which investors are most likely to result in a successful transaction based on the loan data parameters; column 2, lines 4-6, the system may be supported by quantum ledger databases (“QLDB”) or blockchain technology, machine learning, Bayesian inference networks, and other technologies); wherein the algorithm performs a best fit match between the loan offer and the multiple loan purchasers using the loan purchaser criteria (column 6, lines 5-15, the one or more intelligent underwriting engines 102 allow for loan applications originated by a plurality of individuals or organizations to be optimally matched with a particular vendor or vendor “product”. For example, each vendor or vendor system 106 (e.g., a bank, financial institution, investor, interested party, lender, etc.) may include various options for satisfying loan applications. In one embodiment, the systems and methods discussed herein allow not only for loan applications to be matched with particular vendors, but also allow for the loan applications to be matched with the various products and options offered by those vendors); and
distributing the loan offer to at least some of the one or more of the multiple loan purchasers (column 13, lines 8-15, the system proceeds to step 606, where the particular loan application is processed according to the highest ranked investors from the comparison at step 604. In particular embodiments, the system records if the highest-ranked investor resulted in a closing (and subsequent selling) of the loan. In certain embodiments, the system may also record metrics such as how long the closing (and subsequent sale) took, and other appropriate metrics).
Showalter does not disclose, however, Bjonerud discloses:
wherein the algorithm performs a best fit match between the loan offer and the multiple loan purchasers using the loan purchaser criteria and responses of the multiple loan purchaser to previous loan offers distributed thereto (para [0129], The matching is done entirely on the lender profile parameters as well as historical bidding behavior by each lender, e.g., if a lender failed to indicate that they could accommodate aircraft finance deals, but the platform had collected data indicating that the lender recently provided financing to aircraft deals, then they may be included as matching lenders in future aircraft finance deals; para [0138], The AI is basing debt capacity 1110 off the borrower's inputted financials as well as the risk parameters of network lenders, the historical deal data, and other data as the AI may determine; para [0156], The AI can also predict what the other lenders are likely to bid based on their previous bidding history and will include that prediction, under a certain weighting, which will be indicative as to the relevance to this particular deal and borrower, as the AI may determine, as part of the data set that the AI is using to indicate whether the typed in term is green, yellow, or red).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to modify the Showalter’s to incorporate the features taught by Bjonerud above, for the purpose of providing more effectiveness in performing matching based on the previous loan offers. Since Showalter discloses the algorithm performs a best fit match between the loan offer and the multiple loan purchasers using the loan purchaser criteria, Bjonerud discloses the algorithm performs a best fit match between the loan offer and the multiple loan purchasers using the loan purchaser criteria and responses of the multiple loan purchaser to previous loan offers distributed thereto, as described above, therefore, one of ordinary skill in the art would have recognized that the combination of Showalter and Bjonerud would have yield predictable results in performing matching between the loan offer and the loan purchasers.
Regarding to claim 14, Showalter discloses the method as recited in Claim 13, wherein the loan purchasers are lenders, equity investors, or a combination of both (column 2, lines 6-30, Moreover, the immutable ledger may be accessed by the lender and/or by those investors who wish to buy the lender's loans as part of the origination of loans and their sale on the secondary market to investors).
Regarding to claim 15, Showalter does not disclose, however, Bjonerud discloses the method as recited in Claim 13, further comprising automatically collecting the responses of the multiple loan purchasers to previous loan offers distributed thereto, wherein the recommending includes generating a weighted list of one or more of the multiple loan purchasers based on the loan offer, the criteria of each of the multiple loan purchasers, ant the responses of the multiple loan purchasers to the previous loan offers distributed thereto (para [0129], The matching is done entirely on the lender profile parameters as well as historical bidding behavior by each lender, e.g., if a lender failed to indicate that they could accommodate aircraft finance deals, but the platform had collected data indicating that the lender recently provided financing to aircraft deals, then they may be included as matching lenders in future aircraft finance deals; para [0138], The AI is basing debt capacity 1110 off the borrower's inputted financials as well as the risk parameters of network lenders, the historical deal data, and other data as the AI may determine; para [0156], The AI can also predict what the other lenders are likely to bid based on their previous bidding history and will include that prediction, under a certain weighting, which will be indicative as to the relevance to this particular deal and borrower, as the AI may determine, as part of the data set that the AI is using to indicate whether the typed in term is green, yellow, or red).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to modify the Showalter’s to incorporate the features taught by Bjonerud above, for the purpose of providing more effectiveness in performing matching based on the previous loan offers. Since Showalter discloses the algorithm performs a best fit match between the loan offer and the multiple loan purchasers using the loan purchaser criteria, Bjonerud discloses the algorithm performs a best fit match between the loan offer and the multiple loan purchasers using the loan purchaser criteria and responses of the multiple loan purchaser to previous loan offers distributed thereto, as described above, therefore, one of ordinary skill in the art would have recognized that the combination of Showalter and Bjonerud would have yield predictable results in performing matching between the loan offer and the loan purchasers.
Regarding to claim 17, Showalter discloses the method as recited in Claim 15, wherein the machine learning algorithm generates the weighted list by creating a matrix of data points associated with the loan offer and the multiple loan purchasers and weighting at least some of the data points on a sliding scale (column 9, line 65-column 10, line 6, the system may also use a machine learning algorithm trained via deep learning methods for verifying the loan data. In certain embodiments, the deep learning algorithm may be trained via one or more model data samples including historical loan applications, and the deep learning algorithm may generate predicted outcomes based on the model training set. The deep learning algorithm estimates the relevant value, such as income or assets, and that estimate is used to verify the appropriate value in the application).
Regarding to claim 18, Showalter discloses the method as recited in Claim 13, further comprising reviewing the one or more multiple loan purchasers recommended for servicing and selecting at least one of the one or more recommended loan purchasers, wherein the distributing is based on the selecting and is performed automatically by a computing system (column 6, lines 5-15, According to various aspects of the present disclosure, the one or more intelligent underwriting engines 102 allow for loan applications originated by a plurality of individuals or organizations to be optimally matched with a particular vendor or vendor “product”. For example, each vendor or vendor system 106 (e.g., a bank, financial institution, investor, interested party, lender, etc.) may include various options for satisfying loan applications. In one embodiment, the systems and methods discussed herein allow not only for loan applications to be matched with particular vendors, but also allow for the loan applications to be matched with the various products and options offered by those vendors).
Regarding to claim 20, Showalter discloses the method as recited in Claim 18, wherein at least one of the reviewing and selecting are performed automatically by a computing system (column 6, lines 17-40, the one or more intelligent underwriting engines 102 may validate and verify each data element included in a loan application and furthermore compare, using a variety of analytical methodologies, those data elements to guidelines published by potential lenders in the vendor system, thereby allowing the one or more intelligent underwriting engines 102 to determine an optimal investor/vendor for a particular loan application. According to various aspects of the present disclosure, the system may automatically and periodically retrieve the investor guidelines (e.g., via APIs accessing the locations at which the guidelines are stored/published), or the system may provide investors with a portal (e.g., a web portal) for submitting updated guidelines).
Regarding to claim 23, Showalter discloses the method as recited in Claim 13, wherein the lender criteria includes one or more of lender information, loan types of interest, investment strategies, market types, asset class, geographical preferences, high level loan details, or low level loan details (column 10, lines 10-50, the system, via the intelligent underwriting engine 102, may determine the loan eligibility by comparing particular data elements from the loan application to a plurality of investor guidelines. These investor guidelines, in particular embodiments, may include publicly available rules or conditions that determine if a particular loan application is purchasable by a given loan buyer (i.e. investor). For example, an investor (e.g., a pension fund) may require loan applicants to have FICO credit scores over 700 and to have a debt-to-income ratios below a certain threshold in order to qualify (be eligible) to be purchased by this investor. Accordingly, each investor may publish guidelines for sets of loan products (e.g. conventional loans, 30-Year, FIXED v. conventional loan, 15-Year, FIXED etc.) and include hundreds of separate guidelines for each loan product set, creating a matrix of dozens (or more) of product sets, with hundreds (or more) of guidelines per set).
Regarding to claim 24, Showalter discloses the method as recited in Claim 13, wherein the equity investor criteria includes one or more of high level investment information, geographical preferences, high level investment details, granular investment details, or borrower details; typical deal structure description (column 10, lines 27-42, the underwriter may have at his/her disposal an entire portfolio of investor product set guidelines and be substantially challenged to determine which investor product set guidelines would be most appropriate for a given applicant and attempt to underwrite to that particular investor product set of guidelines. Investors and financial institutions generally update their product set guideline structure frequently as economic factors change (e.g., to adjust for lending rates, inflation, etc.), which creates a scenario where it is impossible for a loan underwriter to evaluate and compare each element of a loan application to each set of product guideline sets. It is estimated that any given lender may entertain a portfolio of over sixty investor product sets equating to over six-thousand individual guidelines (one-hundred, or more, per investor product set)).
Regarding to claim 25, Showalter discloses the method as recited in Claim 13, wherein the loan offer includes one or more of loan type, real estate type, deal type, financial details, market type, property details, closing date, or loan servicer information (column 7, lines 10-17, FIG. 2 is a flowchart depicting an exemplary system process 200. In a particular embodiment, the process 200 begins at step 202, where the intelligent underwriting engine 102 receives a loan application (from the loan origination system 104). In various embodiments, the loan application may be a loan application to purchase a home, or any other asset, which may include personal and financial data relating to the loan applicant).
Regarding to claim 26, Showalter discloses the method as recited in Claim 13, wherein when the loan offer relates to syndication, the loan offer further includes one or more of loan servicer information, syndicator information, credit score, requested participation value, current value, or interest rate (column 10, lines 15-20, These investor guidelines, in particular embodiments, may include publicly available rules or conditions that determine if a particular loan application is purchasable by a given loan buyer (i.e. investor). For example, an investor (e.g., a pension fund) may require loan applicants to have FICO credit scores over 700 and to have a debt-to-income ratios below a certain threshold in order to qualify (be eligible) to be purchased by this investor).
Regarding to claim 28, Showalter discloses a computer system for selecting lenders for a loan request comprising: one or more processors (column 5, lines 33-37, the system includes one or more servers and/or computer processors configured to automatically receive data from third-party resources, such as investor guidelines provided by those investors who buy loans on the secondary market) to perform operations at least including the steps found in claim 13 described above, therefore is rejected by the same rationale.
Response to Arguments/Amendment
9. Applicant's arguments with respect to claims 1-5, 7-8, 10-15, 17-18, 20, and 23-28 have been fully considered but are not persuasive.
I. Claim Rejections - 35 USC § 101
Claims 1-5, 7-8, 10-15, 17-18, 20, and 23-28 are rejected under 35 U.S.C. 101 because the claim invention is directed to a judicial exception (i.e., law of nature, natural phenomenon, or abstract idea) without significantly more. (See details above).
In response to the Applicant’s arguments that the new features added to the claims “recommending, using a machine learning recommendation algorithm, one or more of the multiple lenders for servicing the loan request based on the loan request and the lender criteria of each of the multiple lenders, wherein the algorithm performs a best fit match between the loan request and the multiple lenders using the lender criteria and responses of the multiple lenders to previous loan requests distributed thereto” overcome the rejection, the Examiner respectfully disagrees and submits that:
The additional elements “recommending, using a machine learning recommendation algorithm, one or more of the multiple lenders, wherein the algorithm performs a best fit match between the loan request and the multiple lenders” provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
The additional elements “recommending, using a machine learning recommendation algorithm, one or more of the multiple lenders, wherein the algorithm performs a best fit match between the loan request and the multiple lenders” are used to generally apply the abstract idea without placing any limits on how the machine learning functions. Rather, this limitation only recites the outcome of “recommending the multiple lenders and performing a best fit match” and do not include any details about how the solution is accomplished. See MPEP 2106.05(f).
The additional elements “recommending, using a machine learning recommendation algorithm, one or more of the multiple lenders, wherein the algorithm performs a best fit match between the loan request and the multiple lenders” also merely indicate a field of use or technological environment in which the judicial exception is performed. Although the additional element “using a machine learning algorithm” limits the identified judicial exceptions “recommending the multiple lenders and performing a best fit match”, this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Moreover, these additional elements do not provide any improvements to the technology, improvements to the functioning of the computer, the processor, improvement to the machine learning, or other technology. They just merely used as general means for performing the abstract idea. They do not recite a particular machine or manufacture that is integral to the claims, and do not transform or reduce a particular article to a different state or thing.
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application. Therefore, the claims are not patent eligible.
According, the 101 rejection is maintained.
II. Claim Rejections - 35 USC § 102
Applicant's arguments with respect to 1-5, 7-8, 10-12, and 27 have been fully considered but they are not persuasive.
In response to the Applicant’s arguments that Bjonerud does not disclose the new feature added to the claims “reviewing, by the requestor of the loan request, the multiple lenders” the Examiner respectfully disagrees and submits that Bjonerud described in para [0129], The matching lenders, if any, are listed for the borrower to select. However, instead of identifying the names of the lenders or lending institutions, they will be identified by rankings based on borrower preferences inputted as illustrated in FIG. 15. Once the borrower is informed of possible lenders, the borrower may then decide how many of the matching lenders they want to invite into the deal and will be able to select them; para [0187], allowing the prospective borrower to select certain or all lenders identified, though unnamed, as likely viable lenders for that specific borrower and the borrower's requested deal based on matching algorithms based on data previously inputted by borrower, data previously inputted by the lenders, and behavior of the lenders in prior bidding deals; para [0189], upon receiving at least one matching lender, the prospective borrower may be led through a template containing a series of questions, the responses of which could be typed text, which would automatically populate a formally formatted loan proposal for the prospective lenders to review. Thus, in Bjonerud’s, the method allows the borrower to review and select the matching lenders. Therefore, Bjonerud discloses “reviewing, by the requestor of the loan request, the multiple lenders” as claimed.
Applicant’s arguments with respect to claims 13-15, 17-18, 20, 23-26, and 28 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. New ground of 103 rejection described above.
Conclusion
10. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
11. Claims 1-5, 7-8, 10-15, 17-18, 20, and 23-28 are rejected.
12. The prior arts made of record and not relied upon are considered pertinent to applicant's disclosure:
Ginsberg et al. (US 2016/0225078) disclose the commercial loan platform includes a matching engine which matches the commercial loan request originated by the small commercial loan originator with the loan program and lender that best fits the parameters of the request.
Karageuzian et al. (US 2022/0138846) disclose the system provides a platform that automates matching and underwriting for a plurality of lenders. The matching algorithm of the system ensures that only those lenders and borrowers who can close a loan are matched.
Silva (US 2010/0250426) discloses systems and methods for processing electronic loan applications to match with one or more lending institutions.
13. Any inquiry concerning this communication or earlier communications from the examiner should be directed to examiner NGA B NGUYEN whose telephone number is (571) 272-6796. The examiner can normally be reached on Monday-Friday 7AM-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Beth Boswell can be reached on (571) 272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NGA B NGUYEN/Primary Examiner, Art Unit 3625 May 27, 2026
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