DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on July 7, 2025, has been entered.
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 . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Response to Amendment
Applicant’s amendment and remarks filed July 7, 2025, are responsive to the office action mailed March 5, 2025. Claims 1-30 were previously pending and claims 1, 5-6, 8, 12-13, 17-18, 20, 24-25, and 29-30, have been amended and claims 31-36 are new. Claims 1-36 are therefore currently pending and considered in this office action.
Pertaining to rejection under 35 USC § 103 in the previous office action
Claims 1-30 were rejected under 35 U.S.C. 103 as being unpatentable over Peters et al. (Paper No. 20241018; Pub. No. US 2023/0368290 A1) in view of TSO (lnt'I Pub. No. WO 2023/286019 A1). The amendment has necessitated a new ground of rejection in view of Rehder et al. (Patent No. US 11,315,179 B1).
Response to Arguments
Pertaining to rejection under 35 USC § 101 in the previous office action
Applicant's arguments filed July 7, 2025, have been fully considered but they are not persuasive. Claims 1-36 are rejected under 35 U.S.C. 101 because the claimed invention was directed to an abstract idea without significantly more.
Applicant argues that the amended claims are similar to those in Core Wireless Licensing S.A.R.L. v. LG Electronics, Inc., 880 F.3d 1356 (Fed. Cir. 2018) ("Core Wireless"). Remarks p.13. Applicant supports this argument only by asserting that a few snippets of rationale excerpted from Core Wireless could also apply to the present claims. Applicant makes no attempt to compare any presently claimed element to any of the operative facts discussed in the Core Wireless rationale. Exemplary claim 1 of Core Wireless recites
“A computing device comprising a display screen, the computing device being configured to display on the screen a menu listing one or more applications, and additionally being configured to display on the screen an application summary that can be reached directly from the menu, wherein the application summary displays a limited list of data offered within the one or more applications, each of the data in the list being selectable to launch the respective application and enable the selected data to be seen within the respective application, and wherein the application summary is displayed while the one or more applications are in an un-launched state. US Patent No. 8,713,476.”
These limitations and the combination wherein they appear are significant and lead directly to the outcome of the case. It is examiner’s position that there is no reasonable basis for comparison of the combination of limitations in Core Wireless with the present claims. Applicant then merely cites several additional case names where eligibility was maintained, without attempting any serious comparison or analysis of the present claims. The argument is not substantive and cannot be found persuasive.
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 1-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention (i.e., process, machine, manufacture, or composition of matter) (step 1). If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea) (step 2A), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception (step 2B). Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 189 L. Ed. 2d 296, 2014 U.S. LEXIS 4303, 110 U.S.P.Q.2D (BNA) 1976, 82 U.S.L.W. 4508, 24 Fla. L. Weekly Fed. S 870, 2014 WL 2765283 (U.S. 2014); MPEP 2106.
Step 1:
In the instant case claims 1-12 are directed to a process, claims 13-24 are directed to a machine, and claims 25-36 are directed to a manufacture. All claims are therefore within statutory categories. See MPEP 2106.03, Eligibility Step 1.
Step 2A, Prong 1:
These claims also recite, inter alia,
“receiving user information that is indicative of an income stream of a user and an asset associated with the user, wherein the user information continues to be received over time; receiving product qualification criteria data corresponding to a plurality of products, wherein different products of the plurality of products correspond to different product-specific qualification criteria of the product qualification criteria data, and wherein the product qualification criteria data changes over time; dynamically processing the user information and the product qualification criteria data using a trained machine learning (ML) model to generate a qualification decision and a ranking, wherein the trained ML model processes the user information and the product qualification criteria data in real-time as the user information and the product qualification criteria data continue to be received, wherein the qualification decision indicates a subset of the plurality of products that the user qualifies for at a specific time, wherein the subset includes an installment loan, and wherein the ranking indicates an order to arrange the subset based on relevance to the user, and wherein processing is based on numeric weights of the trained ML model;
removing a product from consideration for the user based on the product failing to meet a threshold associated with the user according to the qualification decision; outputting a limited list of recommendations for the subset within an interactive user interface, wherein the limited list of recommendations omits the product, and wherein the limited list of recommendations is arranged according to the order; receiving an input through the interactive user interface, the input corresponding to a selection of a product from the limited list of recommendations and a customization of the product; onboarding the user onto the selected customized product in response to the input, wherein onboarding includes running a function that registers the user in a database and allowing the user to use the product in a transaction; and dynamically updating the trained ML model in real-time, wherein updating includes using the subset and the selection as training data wherein updating includes adjusting at least one of the numeric weights, and wherein the trained ML model is updated to improve accuracy of the trained ML model for future qualification decisions.” Claim 1.
An analysis of the above limitations, each on its own and all together combined, results in the conclusion that each on its own recites an abstract idea and in combination they altogether simply recite a more detailed abstract idea. The recited abstract idea falls within the grouping of abstract ideas described as certain methods of organizing human activity, for example commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). See MPEP 2106.04(a); Eligibility Step 2A1. The claims must therefore be analyzed under the second prong of Eligibility Step 2 (Step 2A2; MPEP 2106.04(d)).
Step 2A, Prong 2:
In order to address prong 2 (MPEP 2106.04(d), Eligibility Step2A2) we must identify whether there are any additional elements beyond the abstract ideas and determine whether those additional elements (if there are any) integrate the abstract idea into a practical application. MPEP 2106.04(d), Eligibility Step 2A2. There are no additional elements in present claims 1-12. Claims 13-24 include at least one memory storing instructions and at least one processor, and claims 25-36 include a non-transitory computer readable storage medium having embodied thereon a program executable by a processor. These additional elements have been considered individually, in combination, and altogether as a whole together with the functions they perform, e.g., the memory and processor(s) of claims 13-36 are broadly and generally recited as performing all steps in terms describing intended results of functionally nonspecific activities. The “interactive user interface” is not considered an additional element because it is not recited as being implemented by a particular device or performing any operating functions in conjunction with a particular device, or even any device, though some sort of generic computer device may be implied. The limitations including the “interactive user interface” only identify user interactions by abstract recitations of their intended outcomes.
The additional elements do not integrate the judicial exception into a practical application because the claims lack any indication that any additional element practically applies any abstract element. The claims are almost entirely a recitation of abstract ideas. The substantive process is recited only by descriptions of abstract intended results of steps without indicating any particular functional acts performed by any device or structural element to obtain the intended results. The additional elements do not improve the functioning of any computer or other technology or technical field, they do not apply the judicial exception with or by use of a particular machine, they do not transform or reduce a particular article to a different state or thing, and they fail to apply or use the judicial exception beyond generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05.
If the disclosure describes any improvements to the functioning of a computer or to any other technology or technical field this improvement would need to be identifiable as the subject matter appearing in the claims. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies technical improvements realized by the claim over the prior art. The disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. MPEP 2106.05(a).
Claim limitations can integrate a judicial exception into a practical application by implementing the judicial exception with or using it in conjunction with a particular machine or manufacture that is integral to the claim. A general purpose computer that applies a judicial exception by use of generic computer functions does not qualify as a particular machine. Ultramercial, Inc. v. Hulu, LLC, (Fed. Cir. 2014); MPEP 2106.05(b),(f). There are no particular machines or manufactures identified in the present claims. Any claimed elements that are not abstract are identified broadly and generally as applying the method, and the method itself is described only by way of the intended functional results of unidentified activities, without reference to any particular functional acts or specific functions performed by any particularly identified machines, and without reference to its use in conjunction with any particular item of manufacture.
The claims do not affect the transformation or reduction of a particular article to a different state or thing. Changing to a different state or thing means more than simply using an article or changing the location of an article. A new or different function or use can be evidence that an article has been transformed. Purely mental processes in which data, thoughts, impressions, or human based actions are "changed" are not considered a transformation. MPEP 2106.05(c).
The claims do not apply or use the judicial exception in any other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. As a result the claim as a whole appears to be a drafting effort designed to monopolize the exception. MPEP 2106.05(e),(h).
The additional elements have not been found to integrate the abstract idea into a practical application.
Step 2B:
Although the additional elements have not been found to integrate the abstract idea into a practical application the claims could still be eligible if they recite additional elements that amount to an inventive concept (“significantly more” than the judicial exception). MPEP 2106.05, Eligibility Step 2B.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the sparse additional elements of the claim are mere props supporting instructions to implement an abstract idea or other exception by a processor. MPEP 2106.05(f). The claims invoke processor(s) or other machinery merely as tools to perform an abstract process. Simply adding a general purpose computer or computer components after the fact to an abstract idea does not provide significantly more. Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016); OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 2015 U.S. App. LEXIS 9721, 115 U.S.P.Q.2D (BNA) 1090 (Fed. Cir. 2015) (“relying on a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible.”); MPEP 2106.05(f)(2). The elements are recited at a high level of generality, merely implement abstract ideas using generic computers, and fail to present a technical solution to a technical problem created by the use of the surrounding technology. Limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself. See Ret. Capital Access Mgmt. Co. v. U.S. Bancorp, 611 Fed. Appx. 1007, 2015 U.S. App. LEXIS 14351 (Fed. Cir. 2015) (“It may be very clever; it may be very useful in a commercial context, but they are still abstract ideas,” said Circuit Judge Alan Lourie.). MPEP 2106.05(h).
No technical problem is indicated and the claims are not directed to a technical solution to such a problem. The method claimed is a nontechnical series of steps taken to practice an entrepreneurial activity. This conclusion is supported by applicant's disclosure, which elaborates upon the performance of the presently claimed method at length by describing the abstract ideas in detail while only incidentally or tangentially explaining the preexisting (prior art) computer equipment, without identifying any technical problem that arises within said equipment and without offering a technical solution to any such problem. It ultimately only describes the abstract idea while indicating the intention to “apply it.” The claimed subject matter merely takes advantage of an opportunity created by computers to use them as a tool for implementing a business plan, rather than solving a problem created by the computers. An equivalent business plan could be implemented without a computer (though it might be more cumbersome), and in any case merely confining the abstract idea to a particular field is insufficient to render it eligible subject matter. The claimed invention is patent ineligible because the innovative aspect (if there is one) is an entrepreneurial rather than a technological one. Bilski v. Kappos, 130 S. Ct. 3218, 3245; 177 L. Ed. 2d 792, 822; 2010 U.S. LEXIS 5521, 73; 95 U.S.P.Q.20 (BNA) 1001 (2010) (citing Merges, Property Rights for Business Concepts and Patent System Reform, 14 Berkeley Tech. L. J. 577, 585 (1999)); Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709 (Fed. Cir. Nov. 14, 2014) (“A rule holding that claims are impermissibly abstract if they are directed to an entrepreneurial objective, such as methods for increasing revenue, minimizing economic risk, or structuring commercial transactions, rather than a technological one, would comport with the guidance provided in both Alice and Bilski.” Mayer, J, concurring).
Finally, dependent claims 2-12, 14-24, and 26-36, do not add "significantly more" to establish eligibility because they merely recite additional abstract ideas that further describe the identification and manipulation of data used in implementing the abstract idea. A more detailed abstract idea is still abstract. PricePlay.com, Inc. v. AOL Adver., Inc., 627 Fed. Appx. 925, 2016 U.S. App. LEXIS 611, 2016 WL 80002 (Fed. Cir. Jan. 7, 2016) (in addressing a bundle of abstract ideas stacked together during oral argument, U.S. Circuit Judge Kimberly Moore said, "All of these ideas are abstract…. It’s like you want a patent because you combined two abstract ideas and say two is better than one."). Claims 3-4, 15-16, and 27-28, recite a graphical user interface for receiving user input. This implies a device upon which an interface could be implemented. Said device does not appear in the claims and as best understood the claims merely explain that the information received by the processor was originally input at a graphical user interface. The mere implication that another additional element might be present does nothing to advance the claims toward eligibility, particularly because it must be understood as the mere implication of the potential presence of another generic device merely receiving user input.
All of the above leads to the conclusion that additional claim elements do not provide meaningful limitations to transform the claimed subject matter into significantly more than an abstract idea. MPEP 2106.05; Eligibility Step 2B. As a result the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter because they recite an abstract idea without being directed to a practical application, and they do not amount to significantly more than the abstract idea. MPEP 2106.05, supra..
The preceding analysis applies to all statutory categories of invention. Accordingly, claims 1-36 are rejected as ineligible for patenting under 35 USC 101 based upon the same analysis.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-30 are rejected under 35 U.S.C. 103 as being unpatentable over Peters et al. (Paper No. 20241018; Pub. No. US 2023/0368290 A1) in view of TSO (Paper No. 20250225; Int’l Pub. No. WO 2023/286019 A1) and further in view of Rehder et al. (Patent No. US 11,315,179 B1).
Peters teaches a method for automated ranked multi-product qualification analysis that receives user information and determines user qualification for various product offers, and Peters also discloses, pertaining to
Claim 1. the method comprising: ● receiving user information that is indicative of an income stream of a user and an asset associated with the user, wherein the user information continues to be received over time (see at least figs. 1-2 (credit reporting includes both income and asset information), ¶0032 “instrument associated with an account that is secured by a deposit provided by an account holder,” ¶0037 “qualification request, the user 112 may provide their legal name, ... household income”. Please note: a person of ordinary skill would understand that any process in this environment would be a continuing process. The art would simply not exist if each process were only intended to be implemented once.); ● receiving product qualification criteria data corresponding to a plurality of products, wherein different products of the plurality of products correspond to different product-specific qualification criteria of the product qualification criteria data, and wherein the product qualification criteria data changes over time (see at least abstract, figs. 1-2, 4-5, 7, ¶0041 “vectors of similarity may include, but are not limited to, credit scores, changes in credit scores, spending habits, total amount of credit allocated, total amount of credit available, payment performance, demographic information, and the like. These similar accounts and/or account holders may correspond to different payment instruments issued to these account holders based on the one or more vectors of similarity,” ¶0099 “pre-qualification system 104 has indicated that this particular payment instrument has a ... required $500 security deposit”. Please note: see previous comment regarding the continuity of the process, the vectors of similarity address similarity of qualification criteria among different credit instruments.).
Peters teaches all of the above as noted and further discloses most of the limitation ● dynamically processing the user information and the product qualification criteria data using a trained machine learning (ML) model to generate a qualification decision and a ranking, wherein the trained ML model processes the user information and the product qualification criteria data in real-time as the user information and the product qualification criteria data continue to be received, wherein the qualification decision indicates a subset of the plurality of products that the user qualifies for at a specific time, wherein the subset includes an installment loan, wherein the ranking indicates an order to arrange the subset based on relevance to the user, and wherein processing is based on numeric weights of the trained ML model (see at least abstract “dynamic pre-qualification determinations associated with secured and unsecured payment instruments,” figs. 4-7, ¶0042 “algorithms are dynamically trained in real-time,” ¶0043 “the machine learning algorithm or artificial intelligence implemented by the prequalification system 104 may be dynamically trained, in real-time, to continuously, and automatically, process soft credit inquiries”).
Peters does not however explicitly disclose using a trained machine learning (ML) model to generate a qualification decision and a ranking, wherein the ranking indicates an order in which to recommend different products, and wherein processing is based on numeric weights.
Tso also teaches a) dynamically processing the user information and the product qualification criteria data using a trained machine learning (ML) model to generate a qualification decision, b) the trained ML model processes the user information and the product qualification criteria, and c) qualification decision indicates products that the user qualifies for, and further discloses using a trained machine learning (ML) model to generate a qualification decision and a ranking, wherein the ranking indicates an order in which to recommend different products, and wherein processing is based on numeric weights. Tso teaches
● dynamically processing the user information and the product qualification criteria data using a trained machine learning (ML) model to generate a qualification decision and a ranking, wherein the trained ML model processes the user information and the product qualification criteria data in real-time as the user information and the product qualification criteria data continue to be received, wherein the qualification decision indicates a subset of the plurality of products that the user qualifies for at a specific time, wherein the ranking indicates an order in which to recommend different products in the subset based on relevance to the user, and wherein processing is based on numeric weights of the trained ML model (see at least Tso abstract, figs. 1, p.4,LL 20-25, p.18,LL 20-30 “DISPLAY "Ranked" ASSET-LIST with "SCORES" ... Machine Learning Model (such as an "Artificial Neural Network" Model, a "Non-Linear Programming & Optimization" Model, etc.) which is continuously updated and which is configured to generate a score for each investable financial instrument by generating weightings across a plurality of objective parameters”);
● removing a product from consideration for the user based on the product failing to meet a threshold associated with the user according to the qualification decision (see at least Tso p.9,LL17-22 “An optimisation module 80 may be configured to exclude portfolios or assets which fall below a predetermined threshold (e.g. outside the Top-10, Top-20, Top-50 etc.) or do not qualify in satisfying the users' pre-defined criterion or preferences,” p.19,LL 6-7 “A predetermined threshold may be set to yield the Top-20/50/100 etc. scoring assets according to the objective data provided for the specified user”).
Peters in view of Tso teaches
● outputting a limited list of recommendations for the subset within an interactive user interface, wherein the limited list of recommendations omits the product, and wherein the limited list of recommendations is arranged according to the order (see at least Peters figs. 3, 6-7, ¶0003 “providing a set of offers corresponding to the set of payment instruments that the user is pre-qualified for” in view of Tso abstract “a first machine learning model to rank potentially investable financial instruments of the platform according to a first score,” figs. 1A-B, 2a-h, p.9,LL4-8 “asset quality may be ranked and ordered as is described in further detail herein,” LL15-22 “both types of information are analysed to prepare scores and ranking of financial instruments which collectively then comprise portfolios. An optimisation module 80 may be configured to exclude portfolios or assets which fall below a predetermined threshold (e.g. outside the Top-10, Top-20, Top-50 etc.) or do not qualify in satisfying the users' pre-defined criterion or preferences,” p.19,LL 6-7 “A predetermined threshold may be set to yield the Top-20/50/100 etc. scoring assets according to the objective data provided for the specified user”).
Peters in view of Tso teaches all of the above, and all of the below, as noted. It teaches, a) dynamically processing the user information and the product qualification criteria data using a trained machine learning (ML) model to generate a qualification decision, b) the trained ML model processes the user information and the product qualification criteria, and c) qualification decision indicates products that the user qualifies for, but does not explicitly disclose receiving an input through the interactive user interface, the input corresponding to a selection of a product from the limited list of recommendations and a customization of the product, and onboarding the user onto the selected customized product in response to the input, wherein onboarding includes running a function that registers the user in a database and allowing the user to use the product in a transaction.
Rehder also teaches a) dynamically processing the user information and the product qualification criteria data using a trained machine learning (ML) model to generate a qualification decision, b) the trained ML model processes the user information and the product qualification criteria, and c) qualification decision indicates products that the user qualifies for, and further discloses
● receiving an input through the interactive user interface, the input corresponding to a selection of a product from the limited list of recommendations and a customization of the product (see at least Peters fig. 3, ¶0003 “selection of an offer from the set of offers,” ¶0043 “user selections from a listing of recommended payment instruments,” in view of Rehder fig. 6A, c12:48-67 “credit card recommendation system can apply input data to models to generate the scores for credit cards specific to the user,” c20:29-43 “The user can select one or more credit card types on the user interface,” c19:47-58 “a user interface 500 for initiating customized recommendation of credit cards”);
● onboarding the user onto the selected customized product in response to the input, wherein onboarding includes running a function that registers the user in a database and allowing the user to use the product in a transaction (see at least Peters ¶0036 “processes applications ... issues secured dual-feature payment instruments, receives deposits used to secure the secured dual-feature payment instruments, processes transactions associated with secured dual-feature payment instruments, ... issues unsecured dual-feature payment instruments,” in view of Rehder figs. 1-2, 6A, 7B, c1:14-24 “consumer can apply for the credit card directly on the credit card company's website by selecting the credit card of choice, and providing his or her personal and financial information in an online form.” Please note: at least issuing the instrument is onboarding.).
Therefore it would have been obvious to one of ordinary skill in the art at the time of invention (for pre-AIA applications) or filing (for applications filed under the AIA ) to modify the method of Peters in view of Tso to include receiving an input through the interactive user interface, the input corresponding to a selection of a product from the limited list of recommendations and a customization of the product, as taught by Rehder since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictable and would result in an improvement. This is because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such features even from a variety of technical fields into methods and systems implemented using similar technological structures (i.e., generic computer and/or network hardware such as processors, servers, etc.). In this case the areas of technical endeavor are nonetheless similar and overlapping.
Applicant has not disclosed that the added feature solves any stated problem or is for any particular purpose beyond the performance of the functions they performed separately and since each element and its function are shown in the prior art the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. It would therefore have been an obvious matter of design choice to include the feature from Rehder in the method of Peters in view of Tso. Furthermore the combination solved no long felt need. Incorporating cumulative known features is additionally obvious to one of ordinary skill in the art because doing so increases commercial use of a method by attracting users that previously might have chosen between one of the previously known methods.
Peters in view of Tso and further in view of Rehder discloses
● dynamically updating the trained ML model in real-time, wherein updating includes using the subset and the selection as training data, wherein updating includes adjusting at least one of the numeric weights, and wherein the trained ML model is updated to improve accuracy of the trained ML model for future qualification decisions (see at least Peters figs. 3, 5, 8-9, ¶0003 “machine learning algorithm is updated using the set of offers, the selection of the offer from the set of offers, the historical data, and the issued payment instruments,” ¶0006 “updating the machine learning algorithm using the credit performances,” ¶0031 "systems may update the machine learning algorithm or artificial intelligence based on the user's selection of the particular payment instrument," ¶0041 "dataset of characteristics of accounts and users may, in an embodiment, include ... the payment instruments recommended to these users, and the users' feedback corresponding to these recommendations (e.g., user selections from a listing of recommended payment instruments, user rejections of recommended payment instruments, etc.)," ¶0043 "machine learning algorithm ... may be continuously updated, in real-time, as payment instrument recommendations are provided and as users respond to these payment instrument recommendations (e.g., user selections from a listing of recommended payment instruments, user rejections of recommended payment instruments, etc.)," ¶0118).
Claim 2. The method of claim 1, wherein the subset of the plurality of products includes at least a first product of a first type and a second product of a second type (see at least Peters abstract “secured and unsecured payment instruments”).Claim 3. The method of claim 1, wherein the user information is received through a graphical user interface (GUI) (see at least Peters fig. 1, ¶0019 “illustrative example of an environment in which secured and unsecured payment instrument offers are automatically generated in response to pre-qualification requests and to submission of applications,” ¶0045 “an interface, such as a graphical user interface (GUI). For instance, through the interface, the user 112 may ... determine whether to submit a complete application for a particular secured or unsecured dual-feature payment instrument”).Claim 4. The method of claim 1, wherein a first portion of the user information is received through a graphical user interface (GUI), and wherein a second portion of the user information is retrieved from a database based on a query using the first portion of the user information (see at least Peters figs. 1-4, ¶0005 “submitting a soft credit inquiry to obtain the credit evaluation. The soft credit inquiry includes the information corresponding to the user”).Claim 5. The method of claim 1, wherein the limited list of recommendations for the subset include a first recommendation for a first product of the subset and a second recommendation for a second product of the subset (see at least Peters figs. 3, 6, ¶0003 “providing a set of offers corresponding to the set of payment instruments that the user is pre-qualified for”. Please note: providing a set of recommendations must include at least a first product and a second product.).Claim 6. The method of claim 1, ● wherein onboarding the user onto the selected customized product includes automatically pre-filling an interactive field of an interactive form based on the user information (see at least Tso p.10, LL28-35 “pre-filled fields”).
Claim 7. The method of claim 1, wherein the subset includes the installment loan
and a credit card (see at least Peters figs. 6-7).
Claim 8. The method of claim 1, wherein updating the trained ML model includes using at least the subset, the ranking, and the selection as the training data (see at least Peters figs. 3, 5, 8-9, ¶¶0003, 0006, 0031, 0041, 0043, 0118 as detailed previously under claim 1, in view of Tso abstract, figs. 1, p.4,LL 20-25, p.18,LL 20-30 “DISPLAY "Ranked" ASSET-LIST with "SCORES" ... Machine Learning Model (such as an "Artificial Neural Network" Model, a "Non-Linear Programming & Optimization" Model, etc.) which is continuously updated and which is configured to generate a score for each investable financial instrument by generating weightings across a plurality of objective parameters”).Claim 9. The method of claim 1, wherein processing the user information and the product qualification criteria data using the trained ML model to generate the qualification decision and the ranking includes:
● processing the user information and the product qualification criteria data using the trained ML model to generate the qualification decision (see at least Peters abstract “dynamic pre-qualification determinations associated with secured and unsecured payment instruments,” figs. 4-7, ¶0042 “algorithms are dynamically trained in real-time,” ¶0043 “the machine learning algorithm or artificial intelligence implemented by the prequalification system 104 may be dynamically trained, in real-time, to continuously, and automatically, process soft credit inquiries”); and
● processing the user information and the product qualification criteria data using a second trained ML model to generate the ranking (see at least Peters ¶¶0031, 0046. Please note: descriptions of trained machine learning models utilized for at least two different purposes disclose at least first and second trained models.).Claim 10. The method of claim 1, further comprising: ● identifying a change in the user information (see at least Peters figs. 3-4, ¶0006 “updating the machine learning algorithm using the credit performances,” ¶¶0032, 0041 “changes in credit scores, spending habits, total amount of credit allocated, total amount of credit available, payment performance”); and ● identifying a change in the subset of the plurality of products that the user qualifies for based on the change in the user information (see at least Peters figs. 3-4, 8-9, ¶0006 “updating the machine learning algorithm using the credit performances,” ¶0041 “changes in credit scores, spending habits, total amount of credit allocated, total amount of credit available, payment performance,” ¶0043. Please note: this merely recites the ongoing nature of the process as previously discussed.).Claim 11. The method of claim 1, further comprising: ● identifying a change in the product qualification criteria data (see at least Peters figs. 7-8, ¶0003 “algorithm is updated using the set of offers, the selection of the offer from the set of offers, the historical data, and the issued payment instruments.” Please note: see previous comments concerning the continuous nature of the process implementation.); and ● identifying a change in the subset of the plurality of products that the user qualifies for based on the change in the product qualification criteria data (see at least Peters figs. 3-4, 8-9, ¶0006 “updating the machine learning algorithm using the credit performances,” ¶0041 “changes in credit scores, spending habits, total amount of credit allocated, total amount of credit available, payment performance,” ¶0043. Please note: this merely recites the ongoing nature of the process as previously discussed.).Claim 12. The method of claim 1, wherein the subset of the plurality of products includes the installment loan and a revolving line of credit (see at least Peters ¶0019 “a new line of credit,” in view of Rehder abstract. Please note: Peters refers to lines of credit and payment instruments throughout. A credit card is a line of credit, and it would be clear to a person of ordinary skill that the payment instruments refer to loans among other things. A credit card is a revolving line of credit.).
Claim 13. A system for automated ranked multi-product qualification analysis, the system comprising: ● at least one memory storing instructions (see at least Peters fig. 10, ¶0012); and ● at least one processor (see at least Peters fig. 10, ¶0012), wherein execution of the instructions by the at least one processor causes the at least one processor to: ● receive user information that is indicative of an income stream of a user and an asset associated with the user, wherein the user information continues to be received over time (see at least Peters figs. 1-2 (credit reporting includes both income and asset information), ¶0032 “instrument associated with an account that is secured by a deposit provided by an account holder,” ¶0037 “qualification request, the user 112 may provide their legal name, ... household income”. Please note: a person of ordinary skill would understand that any process in this environment would be a continuing process. The art would simply not exist if each process were only intended to be implemented once.); ● receive product qualification criteria data corresponding to a plurality of products, wherein different products of the plurality of products correspond to different product-specific qualification criteria of the product qualification criteria data, wherein the product qualification criteria data changes over time (see at least Peters abstract, figs. 1-2, 4-5, 7, ¶0041 “vectors of similarity may include, but are not limited to, credit scores, changes in credit scores, spending habits, total amount of credit allocated, total amount of credit available, payment performance, demographic information, and the like. These similar accounts and/or account holders may correspond to different payment instruments issued to these account holders based on the one or more vectors of similarity,” ¶0099 “pre-qualification system 104 has indicated that this particular payment instrument has a ... required $500 security deposit”. Please note: see previous comment regarding the continuity of the process, the vectors of similarity address similarity of qualification criteria among different credit instruments.);
● dynamically process the user information and the product qualification criteria data using a trained machine learning (ML) model to generate a qualification decision and a ranking, wherein the trained ML model processes the user information and the product qualification criteria data in real-time as the user information and the product qualification criteria data continue to be received, wherein the qualification decision indicates a subset of the plurality of products that the user qualifies for at a specific time, wherein the subset includes an installment loan, and wherein the ranking indicates an order to arrange the subset based on relevance to the user, and wherein processing is based on numeric weights of the trained ML model (see at least Peters abstract “dynamic pre-qualification determinations associated with secured and unsecured payment instruments,” figs. 4-7, ¶0042 “algorithms are dynamically trained in real-time,” ¶0043 “the machine learning algorithm or artificial intelligence implemented by the prequalification system 104 may be dynamically trained, in real-time, to continuously, and automatically, process soft credit inquiries,” in view of Tso abstract, figs. 1, p.4,LL 20-25, p.18,LL 20-30 “DISPLAY "Ranked" ASSET-LIST with "SCORES" ... Machine Learning Model (such as an "Artificial Neural Network" Model, a "Non-Linear Programming & Optimization" Model, etc.) which is continuously updated and which is configured to generate a score for each investable financial instrument by generating weightings across a plurality of objective parameters”);
● remove a product from consideration for the user based on the product failing to meet a threshold associated with the user according to the qualification decision (see at least Tso p.9,LL17-22 “An optimisation module 80 may be configured to exclude portfolios or assets which fall below a predetermined threshold (e.g. outside the Top-10, Top-20, Top-50 etc.) or do not qualify in satisfying the users' pre-defined criterion or preferences,” p.19,LL 6-7 “A predetermined threshold may be set to yield the Top-20/50/100 etc. scoring assets according to the objective data provided for the specified user”);
● output a limited list of recommendations for the subset within an interactive user interface, wherein the limited list of recommendations omits the product, and wherein the limited list of recommendations is arranged according to the order (see at least Peters figs. 3, 6-7, ¶0003 “providing a set of offers corresponding to the set of payment instruments that the user is pre-qualified for” in view of Tso abstract “a first machine learning model to rank potentially investable financial instruments of the platform according to a first score,” figs. 1A-B, 2a-h, p.9,LL4-8 “asset quality may be ranked and ordered as is described in further detail herein,” LL15-22 “both types of information are analysed to prepare scores and ranking of financial instruments which collectively then comprise portfolios. An optimisation module 80 may be configured to exclude portfolios or assets which fall below a predetermined threshold (e.g. outside the Top-10, Top-20, Top-50 etc.) or do not qualify in satisfying the users' pre-defined criterion or preferences,” p.19,LL 6-7 “A predetermined threshold may be set to yield the Top-20/50/100 etc. scoring assets according to the objective data provided for the specified user”);
● receive an input through the interactive user interface, the input corresponding to a selection of a product from the limited list of recommendations and a customization of the product (see at least Peters fig. 3, ¶0003 “selection of an offer from the set of offers,” ¶0043 “user selections from a listing of recommended payment instruments,” in view of Rehder fig. 6A, c12:48-67 “credit card recommendation system can apply input data to models to generate the scores for credit cards specific to the user,” c20:29-43 “The user can select one or more credit card types on the user interface,” c19:47-58 “a user interface 500 for initiating customized recommendation of credit cards”);
● onboarding the user onto the selected customized product in response to the input, wherein onboarding includes running a function that registers the user in a database and allowing the user to use the product in a transaction (see at least Peters ¶0036 “processes applications ... issues secured dual-feature payment instruments, receives deposits used to secure the secured dual-feature payment instruments, processes transactions associated with secured dual-feature payment instruments, ... issues unsecured dual-feature payment instruments,” in view of Rehder figs. 1-2, 6A, 7B, c1:14-24 “consumer can apply for the credit card directly on the credit card company's website by selecting the credit card of choice, and providing his or her personal and financial information in an online form.” Please note: at least issuing the instrument is onboarding.); and ● dynamically update the trained ML model in real-time, wherein updating includes using the subset and the selection as training data, wherein updating includes adjusting at least one of the numeric weights, and wherein the trained ML model is updated to improve accuracy of the trained ML model for future qualification decisions (see at least Peters figs. 3, 5, 8-9, ¶0003 “machine learning algorithm is updated using the set of offers, the selection of the offer from the set of offers, the historical data, and the issued payment instruments,” ¶0006 “updating the machine learning algorithm using the credit performances,” ¶0031 "systems may update the machine learning algorithm or artificial intelligence based on the user's selection of the particular payment instrument," ¶0041 "dataset of characteristics of accounts and users may, in an embodiment, include ... the payment instruments recommended to these users, and the users' feedback corresponding to these recommendations (e.g., user selections from a listing of recommended payment instruments, user rejections of recommended payment instruments, etc.)," ¶0043 "machine learning algorithm ... may be continuously updated, in real-time, as payment instrument recommendations are provided and as users respond to these payment instrument recommendations (e.g., user selections from a listing of recommended payment instruments, user rejections of recommended payment instruments, etc.)," ¶0118).
Claim 14. The system of claim 13, wherein the subset of the plurality of products includes at least a first product of a first type and a second product of a second type (see at least Peters abstract “secured and unsecured payment instruments”).Claim 15. The system of claim 13, wherein the user information is received through a graphical user interface (GUI) (see at least Peters fig. 1, ¶0019 “illustrative example of an environment in which secured and unsecured payment instrument offers are automatically generated in response to pre-qualification requests and to submission of applications,” ¶0045 “an interface, such as a graphical user interface (GUI). For instance, through the interface, the user 112 may ... determine whether to submit a complete application for a particular secured or unsecured dual-feature payment instrument”).Claim 16. The system of claim 13, wherein a first portion of the user information is received through a graphical user interface (GUI), and wherein a second portion of the user information is retrieved from a database based on a query using the first portion of the user information (see at least Peters figs. 1-4, ¶0005 “submitting a soft credit inquiry to obtain the credit evaluation. The soft credit inquiry includes the information corresponding to the user”).Claim 17. The system of claim 13, wherein the limited list of recommendations for the subset include a first recommendation for a first product of the subset and a second recommendation for a second product of the subset (see at least Peters figs. 3, 6, ¶0003 “providing a set of offers corresponding to the set of payment instruments that the user is pre-qualified for”. Please note: providing a set of recommendations must include at least a first product and a second product.).Claim 18. The system of claim 13, wherein onboarding the user onto the selected customized product includes automatically pre-filling an interactive field of an interactive form based on the user information (see at least Tso p.10, LL28-35 “pre-filled fields”).
Claim 19. The system of claim 13, wherein the subset includes the installment loan and a credit card (see at least Peters figs. 6-7).Claim 20. The system of claim 13, wherein updating the trained ML model includes using at least the subset, the ranking, and the selection as the training data (see at least Peters figs. 3, 5, 8-9, ¶¶0003, 0006, 0031, 0041, 0043, 0118 as detailed previously under claim 1, in view of Tso abstract, figs. 1, p.4,LL 20-25, p.18,LL 20-30 “DISPLAY "Ranked" ASSET-LIST with "SCORES" ... Machine Learning Model (such as an "Artificial Neural Network" Model, a "Non-Linear Programming & Optimization" Model, etc.) which is continuously updated and which is configured to generate a score for each investable financial instrument by generating weightings across a plurality of objective parameters”).
Claim 21. The system of claim 13, wherein processing the user information and the product qualification criteria data using the trained ML model to generate the qualification decision and the ranking includes:
● processing the user information and the product qualification criteria data using the trained ML model to generate the qualification decision (see at least Peters abstract “dynamic pre-qualification determinations associated with secured and unsecured payment instruments,” figs. 4-7, ¶0042 “algorithms are dynamically trained in real-time,” ¶0043 “the machine learning algorithm or artificial intelligence implemented by the prequalification system 104 may be dynamically trained, in real-time, to continuously, and automatically, process soft credit inquiries”); and
● processing the user information and the product qualification criteria data using a second trained ML model to generate the ranking (see at least Peters ¶¶0031, 0046. Please note: descriptions of trained machine learning models utilized for at least two different purposes disclose at least first and second trained models.).Claim 22. The system of claim 13, wherein the execution of the instructions by the at least one processor causes the at least one processor to: ● identify a change in the user information (see at least Peters figs. 3-4, ¶¶0032, 0041 “changes in credit scores, spending habits, total amount of credit allocated, total amount of credit available, payment performance”); and ● identify a change in the subset of the plurality of products that the user qualifies for based on the change in the user information (see at least Peters figs. 3-4, 8-9, ¶0006 “updating the machine learning algorithm using the credit performances,” ¶0041 “changes in credit scores, spending habits, total amount of credit allocated, total amount of credit available, payment performance,” ¶0043. Please note: this merely recites the ongoing nature of the process as previously discussed.).Claim 23. The system of claim 13, wherein the execution of the instructions by the at least one processor causes the at least one processor to: ● identify a change in the product qualification criteria data (see at least Peters figs. 7-8, ¶0003 “algorithm is updated using the set of offers, the selection of the offer from the set of offers, the historical data, and the issued payment instruments.” Please note: see previous comments concerning the continuous nature of the process implementation.); and ● identify a change in the subset of the plurality of products that the user qualifies for based on the change in the product qualification criteria data (see at least Peters figs. 3-4, 8-9, ¶0006 “updating the machine learning algorithm using the credit performances,” ¶0041 “changes in credit scores, spending habits, total amount of credit allocated, total amount of credit available, payment performance,” ¶0043. Please note: this merely recites the ongoing nature of the process as previously discussed.).Claim 24. The system of claim 13, wherein the subset of the plurality of products includes at least one of a credit card or a loan (see at least Peters ¶0019 “a new line of credit.” Please note: the prior art refers to lines of credit and payment instruments throughout. A credit card is a line of credit, and it would be clear to a person of ordinary skill that the payment instruments refer to loans among other things.).
Claim 25. A non-transitory computer readable storage medium having embodied thereon a program, wherein the program is executable by a processor to perform a method of automated ranked multi-product qualification analysis, the method comprising: ● receiving user information that is indicative of an income stream of a user and an asset associated with the user, wherein the user information continues to be received over time (see at least figs. 1-2 (credit reporting includes both income and asset information), ¶0032 “instrument associated with an account that is secured by a deposit provided by an account holder,” ¶0037 “qualification request, the user 112 may provide their legal name, ... household income”. Please note: a person of ordinary skill would understand that any process in this environment would be a continuing process. The art would simply not exist if each process were only intended to be implemented once.); ● receiving product qualification criteria data corresponding to a plurality of products, wherein different products of the plurality of products correspond to different product-specific qualification criteria of the product qualification criteria data, wherein the product qualification criteria data changes over time (see at least abstract, figs. 1-2, 4-5, 7, ¶0041 “vectors of similarity may include, but are not limited to, credit scores, changes in credit scores, spending habits, total amount of credit allocated, total amount of credit available, payment performance, demographic information, and the like. These similar accounts and/or account holders may correspond to different payment instruments issued to these account holders based on the one or more vectors of similarity,” ¶0099 “pre-qualification system 104 has indicated that this particular payment instrument has a ... required $500 security deposit”. Please note: see previous comment regarding the continuity of the process, the vectors of similarity address similarity of qualification criteria among different credit instruments.);
● dynamically processing the user information and the product qualification criteria data using a trained machine learning (ML) model to generate a qualification decision and a ranking, wherein the trained ML model processes the user information and the product qualification criteria data in real-time as the user information and the product qualification criteria data continue to be received, wherein the qualification decision indicates a subset of the plurality of products that the user qualifies for at a specific time, wherein the subset includes an installment loan, and wherein the ranking indicates an order to arrange the subset based on relevance to the user, and wherein processing is based on numeric weights of the trained ML model (see at least Peters abstract “dynamic pre-qualification determinations associated with secured and unsecured payment instruments,” figs. 4-7, ¶0042 “algorithms are dynamically trained in real-time,” ¶0043 “the machine learning algorithm or artificial intelligence implemented by the prequalification system 104 may be dynamically trained, in real-time, to continuously, and automatically, process soft credit inquiries,” in view of Tso abstract, figs. 1, p.4,LL 20-25, p.18,LL 20-30 “DISPLAY "Ranked" ASSET-LIST with "SCORES" ... Machine Learning Model (such as an "Artificial Neural Network" Model, a "Non-Linear Programming & Optimization" Model, etc.) which is continuously updated and which is configured to generate a score for each investable financial instrument by generating weightings across a plurality of objective parameters”);
● removing a product from consideration for the user based on the product failing to meet a threshold associated with the user according to the qualification decision (see at least Tso p.9,LL17-22 “An optimisation module 80 may be configured to exclude portfolios or assets which fall below a predetermined threshold (e.g. outside the Top-10, Top-20, Top-50 etc.) or do not qualify in satisfying the users' pre-defined criterion or preferences,” p.19,LL 6-7 “A predetermined threshold may be set to yield the Top-20/50/100 etc. scoring assets according to the objective data provided for the specified user”);
● outputting a limited list of recommendations for the subset within an interactive user interface, wherein the limited list of recommendations omits the product, and wherein the limited list of recommendations is arranged according to the order (see at least Peters figs. 3, 6-7, ¶0003 “providing a set of offers corresponding to the set of payment instruments that the user is pre-qualified for” in view of Tso abstract “a first machine learning model to rank potentially investable financial instruments of the platform according to a first score,” figs. 1A-B, 2a-h, p.9,LL4-8 “asset quality may be ranked and ordered as is described in further detail herein,” LL15-22 “both types of information are analysed to prepare scores and ranking of financial instruments which collectively then comprise portfolios. An optimisation module 80 may be configured to exclude portfolios or assets which fall below a predetermined threshold (e.g. outside the Top-10, Top-20, Top-50 etc.) or do not qualify in satisfying the users' pre-defined criterion or preferences,” p.19,LL 6-7 “A predetermined threshold may be set to yield the Top-20/50/100 etc. scoring assets according to the objective data provided for the specified user”);
● receiving an input through the interactive user interface, the input corresponding to a selection of a product from the limited list of recommendations and a customization of the product (see at least Peters fig. 3, ¶0003 “selection of an offer from the set of offers,” ¶0043 “user selections from a listing of recommended payment instruments,” in view of Rehder fig. 6A, c12:48-67 “credit card recommendation system can apply input data to models to generate the scores for credit cards specific to the user,” c20:29-43 “The user can select one or more credit card types on the user interface,” c19:47-58 “a user interface 500 for initiating customized recommendation of credit cards”);
● onboarding the user onto the selected customized product in response to the input, wherein onboarding includes running a function that registers the user in a database and allowing the user to use the product in a transaction (see at least Peters ¶0036 “processes applications ... issues secured dual-feature payment instruments, receives deposits used to secure the secured dual-feature payment instruments, processes transactions associated with secured dual-feature payment instruments, ... issues unsecured dual-feature payment instruments,” in view of Rehder figs. 1-2, 6A, 7B, c1:14-24 “consumer can apply for the credit card directly on the credit card company's website by selecting the credit card of choice, and providing his or her personal and financial information in an online form.” Please note: at least issuing the instrument is onboarding.); and ● dynamically updating the trained ML model in real-time, wherein updating includes using the subset and the selection as training data, wherein updating includes adjusting at least one of the numeric weights, and wherein the trained ML model is updated to improve accuracy of the trained ML model for future qualification decisions (see at least Peters figs. 3, 5, 8-9, ¶0003 “machine learning algorithm is updated using the set of offers, the selection of the offer from the set of offers, the historical data, and the issued payment instruments,” ¶0006 “updating the machine learning algorithm using the credit performances,” ¶0031 "systems may update the machine learning algorithm or artificial intelligence based on the user's selection of the particular payment instrument," ¶0041 "dataset of characteristics of accounts and users may, in an embodiment, include ... the payment instruments recommended to these users, and the users' feedback corresponding to these recommendations (e.g., user selections from a listing of recommended payment instruments, user rejections of recommended payment instruments, etc.)," ¶0043 "machine learning algorithm ... may be continuously updated, in real-time, as payment instrument recommendations are provided and as users respond to these payment instrument recommendations (e.g., user selections from a listing of recommended payment instruments, user rejections of recommended payment instruments, etc.)," ¶0118).
Claim 26. The non-transitory computer readable storage medium of claim 25, wherein the subset of the plurality of products includes at least a first product of a first type and a second product of a second type (see at least Peters abstract “secured and unsecured payment instruments”).Claim 27. The non-transitory computer readable storage medium of claim 25, wherein the user information is received through a graphical user interface (GUI) (see at least Peters fig. 1, ¶0019 “illustrative example of an environment in which secured and unsecured payment instrument offers are automatically generated in response to pre-qualification requests and to submission of applications,” ¶0045 “an interface, such as a graphical user interface (GUI). For instance, through the interface, the user 112 may ... determine whether to submit a complete application for a particular secured or unsecured dual-feature payment instrument”).Claim 28. The non-transitory computer readable storage medium of claim 25, wherein a first portion of the user information is received through a graphical user interface (GUI), and wherein a second portion of the user information is retrieved from a database based on a query using the first portion of the user information (see at least Peters figs. 1-4, ¶0005 “submitting a soft credit inquiry to obtain the credit evaluation. The soft credit inquiry includes the information corresponding to the user”).Claim 29. The non-transitory computer readable storage medium of claim 25, wherein the limited list of recommendations for the subset include a first recommendation for a first product of the subset and a second recommendation for a second product of the subset (see at least Peters figs. 3, 6, ¶0003 “providing a set of offers corresponding to the set of payment instruments that the user is pre-qualified for”. Please note: providing a set of recommendations must include at least a first product and a second product.).Claim 30. The non-transitory computer readable storage medium of claim 25, wherein onboarding the user onto the selected customized product includes automatically pre-filling an interactive field of an interactive form based on the user information (see at least Tso p.10, LL28-35 “pre-filled fields”).
Claim 31. The non-transitory computer readable storage medium of claim 25, wherein the subset includes the installment loan and a credit card (see at least Peters figs. 6-7).
Claim 32. The non-transitory computer readable storage medium of claim 25, wherein updating the trained ML model includes using at least the subset, the ranking, and the selection as the training data (see at least Peters figs. 3, 5, 8-9, ¶¶0003, 0006, 0031, 0041, 0043, 0118 as detailed previously under claim 1, in view of Tso abstract, figs. 1, p.4,LL 20-25, p.18,LL 20-30 “DISPLAY "Ranked" ASSET-LIST with "SCORES" ... Machine Learning Model (such as an "Artificial Neural Network" Model, a "Non-Linear Programming & Optimization" Model, etc.) which is continuously updated and which is configured to generate a score for each investable financial instrument by generating weightings across a plurality of objective parameters”).
Claim 33. The non-transitory computer readable storage medium of claim 25, wherein processing the user information and the product qualification criteria data using the trained ML model to generate the qualification decision and the ranking includes:
● processing the user information and the product qualification criteria data using the trained ML model to generate the qualification decision (see at least Peters abstract “dynamic pre-qualification determinations associated with secured and unsecured payment instruments,” figs. 4-7, ¶0042 “algorithms are dynamically trained in real-time,” ¶0043 “the machine learning algorithm or artificial intelligence implemented by the prequalification system 104 may be dynamically trained, in real-time, to continuously, and automatically, process soft credit inquiries”); and
● processing the user information and the product qualification criteria data using a second trained ML model to generate the ranking (see at least Peters ¶¶0031, 0046. Please note: descriptions of trained machine learning models utilized for at least two different purposes disclose at least first and second trained models.).
Claim 34. The non-transitory computer readable storage medium of claim 25, further comprising: ● identifying a change in the user information (see at least Peters figs. 3-4, ¶0006 “updating the machine learning algorithm using the credit performances,” ¶¶0032, 0041 “changes in credit scores, spending habits, total amount of credit allocated, total amount of credit available, payment performance”); and ● identifying a change in the subset of the plurality of products that the user qualifies for based on the change in the user information (see at least Peters figs. 3-4, 8-9, ¶0006 “updating the machine learning algorithm using the credit performances,” ¶0041 “changes in credit scores, spending habits, total amount of credit allocated, total amount of credit available, payment performance,” ¶0043. Please note: this merely recites the ongoing nature of the process as previously discussed.).
Claim 35. The non-transitory computer readable storage medium of claim 25, further comprising: ● identifying a change in the product qualification criteria data (see at least Peters figs. 7-8, ¶0003 “algorithm is updated using the set of offers, the selection of the offer from the set of offers, the historical data, and the issued payment instruments.” Please note: see previous comments concerning the continuous nature of the process implementation.); and ● identifying a change in the subset of the plurality of products that the user qualifies for based on the change in the product qualification criteria data (see at least Peters figs. 3-4, 8-9, ¶0006 “updating the machine learning algorithm using the credit performances,” ¶0041 “changes in credit scores, spending habits, total amount of credit allocated, total amount of credit available, payment performance,” ¶0043. Please note: this merely recites the ongoing nature of the process as previously discussed.).
Claim 36. The non-transitory computer readable storage medium of claim 25, wherein the subset of the plurality of products includes the installment loan and a revolving line of credit (see at least Peters ¶0019 “a new line of credit,” in view of Rehder abstract. Please note: Peters refers to lines of credit and payment instruments throughout. A credit card is a line of credit, and it would be clear to a person of ordinary skill that the payment instruments refer to loans among other things. A credit card is a revolving line of credit.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
● Ports, III et al., Pub. No. US 2023/0252115 A1: teaches determining whether user qualifies for digital account onboarding.
● KESIBOYANA et al., Pub. No. US 2020/0372575 A1: teaches users seeking financing from multiple potential lenders, lenders are pre-screened by one or more rule sets to ultimately submit applicant information to a subset of the multiple potential lenders which are found to be suitable for lending to the applicant based on the applicant information. Lender microservices are then run in a jailed, firewalled, and self-contained, autonomous environment, and the results of said lender microservices are reported to the user.
● Cella, Patent No. US 11,494,836 B2: teaches varying the terms of a loan (customization), loan management, and related activities.
● Schwartz et al., Pub. No. US 2021/0201404 A1: teaches customization of an offer upon determination that the user qualifies for the offer, selects the offer, and continues to be qualified after selection.
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/ADAM L LEVINE/Primary Examiner, Art Unit 3689 September 24, 2025
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