DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 3/31/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Status
The claims filed 4/2/2026 have been entered.
Claims 1-3, 6-9, 12-15, 18-23, and 27-29 are pending.
Claims 1, 9, and 15 are independent.
Claims 1, 9, and 15 are currently amended.
Claims 21-23 and 27-29 are previously presented.
Claims 2-3, 6-8, 12-14, and 18-20 are original.
Response to Arguments
Applicant's arguments filed 4/2/2026 have been considered but they are not fully persuasive.
35 U.S.C. 101
Applicant’s arguments with regards to the rejection of claims 1-3, 6-9, 12-15, 18-23, and 27-29 under 35 U.S.C. 101 as being directed to an abstract idea without significantly more have been considered but are not persuasive.
With regards to independent claims 1, 9, and 15, Applicant argues that the identified abstract idea does not recite a judicial exception because the focus of the claims is on specific technical implementations that improve computer functionality, particularly improvements to machine learning model training and user interface technology (see Remarks, pg. 10)
The argument is not persuasive. Here, there is no clear improvement to computer functionality, machine learning model training, or user interface technology. Thus, streamlined eligibility analysis is not employed because the claims do not have self-evident eligibility. Instead, the full two step analysis is applied. Per MPEP 2106.06(b), if the claims are a “close call” such that it is unclear whether the claims improve technology or computer functionality, a full eligibility analysis should be performed to determine eligibility. See BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1349, 119 USPQ2d 1236, 1241 (Fed Cir. 2016). Only when the claims clearly improve technology or computer functionality, or otherwise have self-evident eligibility, should the streamlined analysis be used. For example, because the claims in BASCOM described the concept of filtering content, which is a method of organizing human behavior previously found to be abstract, the Federal Circuit considered them to present a “close call” in the first step of the Alice/Mayo test (Step 2A), and thus proceeded to the second step of the Alice/Mayo test (Step 2B) to determine their eligibility. Id.
Applicant further argues that the claims should be found eligible in view of Desjardins, which emphasized that claims must be evaluated “as a whole” without oversimplification; and that improvements to machine learning may provide a technological improvement (see Remarks, pp. 11-14).
The argument is not persuasive. The claims in Desjardins were found eligible by the Appeals Review Panel (ARP) because they recited a specific machine-learning technique that improved the functioning of the computer system itself by training and using a predictive model in a particular manner to provide improvements with regards to learning new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. The Appeals Review Panel (ARP) also credited benefits including reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks as technological improvements that were disclosed in the specification. In contrast, the present claims and specification are not drawn to similar technological benefits and do not recite a particular improvement to machine learning. For example, there is no particular improvement to decision-tree training. Instead, the use of machine learning is merely a tool to implement the abstract idea encompassing optimizing loan margins and selecting a lender. This does not provide a practical application under Step 2A Prong 2, nor does it provide an inventive concept under Step 2B.
Applicant argues that the claims provide a specific improvement to computer functionality in machine learning training. More specifically, Applicant argues that the specification at para. 0009 describes a different training methodology, which describes building models sequentially to reduce errors, as well as reducing amount of time by a dealership in a lender selection process from 30-45 minutes to less than one minute by improving the speed of extraction and use of features in lender selection (see Remarks, pp. 14-15).
The argument is not persuasive. As discussed in the rejection, the limitations drawn to iterative refinement to reduce errors encompass generic boosting, such as gradient boosting, which does not go beyond “apply it” with regards to machine learning. The specification only discusses these features at a high level of generality in a manner which does not convey a specific technical improvement in machine learning to one of ordinary skill in the art (see para. 0029). Neither the claims nor specification offer a specific improvement to model training. As such, the claims broadly encompass generic machine learning invoked as a tool to perform the abstract idea. Here, use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. With regards to the argument that the process reduces amount of time spent by a dealership, the argument is not persuasive because the argued benefits are a result of such use of machine learning in its ordinary capacity. “Claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). This does not provide a practical application under Step 2A Prong 2, nor does it provide an inventive concept under Step 2B.
Applicant further argues claims 1, 11, and 17, provide improvements to user interface technology. More specifically, Applicant points to the limitation of filtering one or more lending entities and submitting the loan application. Applicant also points to the specification para. 0029 which describes the filtering. Applicant likens these features to eligible claims in USPTO Example 23 and Example 40, and argues that the claims are distinguished from those found ineligible in Trading Technologies (see Remarks, pg. 16).
The argument is not persuasive. Applicant’s argued improvements fall under improvements to the abstract idea itself and are not improvements to user interface technology. Unlike the examples cited by Applicant, the claims do not recite a specific technological improvement to a user interface or computer functionality. The claims do not recite any particular graphical arrangement, display mechanism, or other change to how a user interacts with the interface. Claims 1, 9, and 15 result in the selection of a lending entity and do not recite displaying anything on a user interface at all. Here, the argued features drawn to filtering and use of the filtered data is not a user interface improvement, but mere use of the computer as tool to perform the abstract idea encompassing making lending decisions. This does not provide a practical application under Step 2A Prong 2, nor does it provide an inventive concept under Step 2B.
Applicant further argues that the claims recite a technological solution to a technological problem inherent in computer-based automated loan application submission. Applicant points to the Desjardins memo, citing DDR Holdings and Thales Visionix as examples (see Remarks, pg. 17).
The argument is not persuasive. As discussed above, the argued improvements fall under the abstract idea itself rather than improvements to machine learning, graphical user interfaces, or any other computer technology. As also discussed above, the claims merely employ generic machine-learning techniques as tools to perform the abstract idea. This does not provide a practical application under Step 2A Prong 2, nor does it provide an inventive concept under Step 2B.
Applicant further argues that the claims are drawn to a specific technical implementation which is not generic machine learning. More specifically, Applicant argues that the claims or specification provide technical specificity including a gradient boosting algorithm, creating multiple iterations of learning trees based on errors from a current set of trees, specific feature engineering, and specific UI integration (see Remarks, pg. 17).
The argument is not persuasive. The claims and specification broadly describe creating additional sets of binary trees based on error and repeating the process until an error threshold is satisfied, but do not specify any particular boosting methodology beyond what one of ordinary skill in the art would recognize as generic gradient boosting. Here, the specification does not provide any new type of gradient boosting algorithm but merely describes the feature at a high level of generality (see para. 0029). Likewise, the claims and specification do not provide any specific improvement to feature extraction but merely uses machine learning as a tool to perform the abstract idea. Furthermore, as discussed above, the claims do not recite any improvements to graphical user interfaces. This does not provide a practical application under Step 2A Prong 2, nor does it provide an inventive concept under Step 2B.
Applicant further argues that the claims recite a non-conventional computer implementation. More specifically, Applicant argues that repeating creating set of trees based on error from a current set of trees is not conventional (see Remarks, pp. 18-19).
The argument is not persuasive. Applicant makes the argument under Step 2A Prong 2. However, Step 2A Prong 2 specifically excludes consideration of whether the additional elements represent well-understood, routine, conventional activity. As discussed above, the limitations drawn to iterative refinement to reduce errors encompass generic boosting, such as gradient boosting, which does not go beyond “apply it” with regards to machine learning. The specification only discusses these features at a high level of generality in a manner which does not convey a specific technical improvement in machine learning to one of ordinary skill in the art (see para. 0029). Neither the claims nor specification recite any specific improvement to model training. As such, the claims broadly encompass machine learning invoked as a tool to perform the abstract idea. This does not provide a practical application under Step 2A Prong 2, nor does it provide an inventive concept under Step 2B.
Applicant further argues that the independent claims recite an ordered combination of elements that, taken together, provide an inventive concept under Step 2B of the Alice/Mayo framework. Applicant argues that the claims recite a specific, unconventional implementation that improvements loan application approval prediction technology, including an unconventional combination of multiple instance learning and UI integration (see Remarks, pp. 19-20).
The argument is not persuasive. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer/machine learning implementation.
For the above reasons, the rejections are maintained herein.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3, 6-9, 12-15, 18-23, and 27-29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-3, 6-9, 12-15, 18-23, and 27-29 are directed to a method (process), system (machine), or non-transitory computer readable medium (manufacture), and thus fall within the statutory categories of invention. (Step 1: YES).
Step 2A - Prong 1
The Examiner has identified independent method claim 1 as the claim that represents the claimed invention for analysis and is similar to independent system claim 9 and product claim 15. Claim 1 recites the limitations of:
1. A method comprising:
receiving, using a device comprising at least one processor, a loan application acceptance package from each of a plurality of lending entities for a loan application;
extracting, using a first machine learning module of the device, loan variables comprising a buy lending rate and a margin from the loan application acceptance package, the margin comprising one or more of: a margin rate at the buy lending rate, a flat at the buy lending rate, and an incremental margin rate and the flat corresponding to each incremental margin rate from the buy lending rate;
predicting, by a second machine learning module of the device, a target margin based on the buy lending rate and the margin for each of the plurality of lending entities and a customer profile of a customer associated with the loan application, wherein predicting the target margin comprises predicting the target margin to maximize both a probability of acceptance by the customer and a profit for a user at the target margin wherein predicting, using the second machine learning module of the device, the target margin comprises:
creating an initial set of binary trees for respective inputs from the buy lending rate and the margin for each of the plurality of lending entities and the customer profile, wherein each of the initial set of binary trees provides an output for a corresponding input,
comparing outputs from the initial set of binary trees with a historical data,
determining, based on the comparison, an error between the outputs from the initial set of binary trees and the historical data,
creating a next set of binary trees sequentially in response to determining that the error between the outputs from the initial set of binary trees and the historical data is greater than a predetermined value, wherein the next set of binary trees are created in sequence based on the error in the initial set of trees, and wherein each of the next set of binary trees provides a next output for the corresponding input, and
repeating creating the next set of binary trees until the error between the outputs from a previous set of binary trees and the historical data is less than the predetermined value; and
selecting, by the device, a lending entity from the plurality of lending entities based on the target margin, wherein selecting the lending entity from the plurality of lending entities comprises selecting the lending entity from the plurality of lending entities further based on a funding time associated with each of the plurality of lending entities, and wherein selecting the lending entity from the plurality of lending entities further based on the funding time associated with each of the plurality of lending entities comprising:
assigning a weight to each of the probability of acceptance by the customer, the profit for the user, and the funding time; and
selecting the lending entity from the plurality of lending entities based on aggregate weight for each of the plurality of lending entities.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as “Certain Methods of Organizing Human Activity”. The claim limitations delineated in bold above recite a fundamental economic practice, as they set forth or describe selecting a lending entity based on a probability of loan acceptance, profit, and funding time. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a fundamental economic practice, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.
These limitations, under their broadest reasonable interpretation, also cover performance of the limitation as “Mathematical Concepts” as they set forth or describe mathematical calculations. The limitations drawn to “creating…, comparing…, determining …, creating…, repeating…”, given their plain meaning, set forth or describe an optimization algorithm which computes parameters to build a model (see USPTO “July 2024 Subject Matter Eligibility Examples”, Example 47 Claim 2).
Accordingly, the claim recites an abstract idea. The limitations are considered together as a single abstract idea for Step 2A Prong 2 and Step 2B (see MPEP 2106.04(II)(B)). The device, including the processor and machine learning modules, is just applying generic computer components to the recited abstract limitations. The recitation of generic computer components in a claim does not necessarily preclude that claim from reciting an abstract idea. Claims 9 and 15 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims recite an abstract idea)
Step 2A - Prong 2
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of:
Claim 1: device (including processor and first/second machine learning modules)
Claim 9: memory storage; processing unit; first and second machine learning modules
Claim 15: non-transitory computer-readable medium, device (including processor and first/second machine learning modules)
The computer hardware/software is/are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. For example, the specification states “In addition, embodiments of the disclosure may be practiced within a general purpose computer or in any other circuits or systems” (see para. 0039). Furthermore, the specification also only discusses machine learning algorithms at a high level of generality in a manner which does not convey a specific technical improvement to one of ordinary skill in the art (see para. 0029).
The limitations drawn to “creating…, comparing…, determining …, creating…, repeating…”, provide nothing more than mere instructions to implement the abstract idea on a generic computer. Even if considered as an additional element, the limitations encompass generic gradient boosting algorithms which do not go beyond “apply it” with regards to machine learning. The specification only discusses these features at a high level of generality in a manner which does not convey a specific technical improvement in machine learning to one of ordinary skill in the art (see para. 0029).
The recitation of “using a first machine leaning module” and “using a second machine learning module” also merely indicates a field of use or technological environment in which the judicial exception is performed. This type of limitation merely confines the use of the abstract idea to a particular technological environment and thus fails to integrate the claims into a practical application. See MPEP 2106.05(h).
Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality.
Therefore, claims 1, 9, and 15 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See Applicant’s specification about implementation using general purpose or special purpose computing devices and MPEP 2106.05(f) where applying a computer as a tool is not indicative of significantly more. For example, the specification states “In addition, embodiments of the disclosure may be practiced within a general purpose computer or in any other circuits or systems” (see para. 0039). The specification also only discusses the machine learning algorithms at a high level of generality in a manner which does not convey a specific technical improvement to one of ordinary skill in the art (see para. 0029).
Furthermore, the additional elements do not go beyond what is well-understood, routine, and conventional activity. For example, gradient boosting is well-understood, routine, conventional activity:
Dzugan (US 2024/0095611 A1) - [0055] … For example, the machine learning model may be a gradient-boosted random forest of decision trees. As will be appreciated by one having ordinary skill in the art, gradient-boosting is a machine learning technique used, for example, in regression and classification tasks that may be applied to conventional random forest models. Gradient boosting generates a predictive model in the form of an ensemble of weak predictive models, which may be decision trees. When a decision tree is the weak learner, the resulting algorithm may be called a gradient-boosted tree. The gradient boosted decision tree may be appreciated as often outperforming a conventional random forest model. While the gradient-boosted trees model is built in a stage-wise fashion as in other boosting methods, it generalizes the other boosting methods by allowing optimization of an arbitrary differentiable loss function.
Zhu (US 2021/0286995 A1) - [0062] In a preferred embodiment of the present disclosure, the fault diagnosis model unit 4 inputs the extracted fault feature index (vector) of the bearing into a trained bearing fault diagnosis model based on a gradient boosting tree (which is an existing algorithm in the conventional technology and thus is not described in detail herein), to perform pattern recognition, to diagnose the fault state of the bearing
Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination.
Thus, claims 1, 9, and 15 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent Claims
Dependent claims 2-3, 6-8, 12-14, 18-23, and 27-29 further define the abstract idea that is present in their respective independent claims 1, 9, and 15 and thus correspond to “Certain Methods of Organizing Human Activity” and “Mathematical Concepts” and hence are abstract for the reasons presented above.
Dependent claims 7, 8, 13, 14, 19, and 20 recite the additional element of training machine learning modules. However, the specification only discusses the machine learning algorithms at a high level of generality which does not convey a specific technical improvement to one of ordinary skill in the art (see para. 0029). Here, the computer hardware/software is/are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims are directed to an abstract idea without significantly more.
Thus, claims 1-3, 6-9, 12-15, 18-23, and 27-29 are not patent-eligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Thakkar (“Credit Risk Assessment With Gradient Boosting Machines”) presents a case study on the application of GBMs for credit risk assessment using a real-world dataset. The study details the data characteristics, model architecture, training process, and evaluation metrics.
THIS ACTION IS MADE FINAL. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC T WONG whose telephone number is (571)270-3405. The examiner can normally be reached 9am-5pm M-F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael W Anderson can be reached at 571-270-0508. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ERIC T WONG/Primary Examiner, Art Unit 3693
ERIC WONG
Primary Examiner
Art Unit 3693
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