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 .
Application Status
This Office Action is in response to amendments and remarks received 1/23/2026.
Claims 1, 11, and 16 have been amended.
Claims 1-20 are pending and have been examined.
This action is final, necessitated by Applicant amendment.
Withdrawn Rejections
The 35 U.S.C. § 103 rejections of claims 1-20 are withdrawn in view of the amendments received.
Claim Rejections
Claims 1-201 remain rejected under 35 U.S.C. §101 for being directed to an abstract idea without significantly more.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of earlier application filing dates as follows:
The later-filed applications must be an application for a patent for an invention which is also disclosed in the prior application(s) (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994). The disclosure of the prior-filed applications 63/148,771 and 17/221,939, fail to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for the claims of this application. For example, limitations of independent claim 1, including those identified as “Wadler fails to disclose…” in the 35 USC §103 rejections below, with special attention paid to the limitations “training a multi-output machine learning model …”, “… simulating a plurality of mixes of financial assets … using the trained multi-output machine learning model;”, and “selecting a mix of financial assets … based on the … output predictors to form the portfolio of financial assets;” do not appear in the specifications of either provisional application 63/148,771, or parent application 17/221,9392. In addition, Figure 8 of the instant application drawn to the above subject matter is not in the specifications corresponding to 63/148,771 and 17/221,939. The instant application (18/512,380) appears to provide the earliest adequate support to the currently filed claims. Accordingly, the effective filing date of instant claims 1-20 is determined to be 11/17/2023.
Acknowledgement of Issues Raised by Applicant
Applicant’s arguments with respect to the 35 U.S.C. § 101 rejections of claims 1-20 have been fully considered but are not persuasive.
Response to Arguments
35 U.S.C. § 101
With respect to the 35 U.S.C. § 101 rejections, examiner notes Applicant asserts the claims are patent eligible under 35 U.S.C. §101 and Alice/Mayo analysis per the claims providing additional elements that go beyond the judicial exception and integrate the judicial exception into a practical application, as the claims provide a technological solution to a technological problem3. The Examiner respectfully disagrees and respectfully maintains the claims are not patent eligible under 35 U.S.C. §101 (analysis continues below).
Examiner’s Response to Step 2A Prong II Arguments
Examiner respectfully disagrees with Applicant arguments4 that the claims are directed to an improvement to the functioning of a computer, or another technology or technical field5 and integrate the judicial exception into a practical application for the following reasons:
When viewed as a whole, the claims amount to merely invoking computers as tools to perform an abstract business process of financial asset management and merely limit the use of the judicial exception to a particular technological environment (e.g., a device indistinguishable from that of a general-purpose computer6, with a non-descript multi-output machine learning model limited only in terms of its inputs and outputs) – see MPEP §§ 2106.05 (f), (h).
Applicant’s specification and claims fail to provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement to the functioning of a computer or to any other technology or technical field (MPEP §§2106.04(d)(1) & 2106.05(a)). This is further evidenced by the following:
The Applicant’s Specification does not appear to contend to have invented multi-output machine learning technology, nor have they, as evidenced in the prior art of record and Applicant specification. At best, the Applicant specification seemingly contends Applicants have invented a particular implementation of the abstract financial asset management using a multi-output machine learning model on a general-purpose computer.
Pages 28-29 of Applicant specification merely provides a high-level description of various known types of machine-learning compatible with multi-output techniques at a high degree of generality. I.e., the specification does not at all provide any technical details outlining any particular architecture for the exemplary forms of models, and simply states various forms of machine learning generally known in the technical field of machine learning as being applicable embodiments for Applicant’s invention. It is for this reason that Applicant’s arguments asserting “the claimed invention represents a fundamental advancement in machine learning architecture that overcomes significant technical limitations inherent in conventional ML approaches…” are not persuasive, as the claims fail to provide any particular technological details distinguishable from that of a generic multi-output machine learning model – see MPEP § 2106.04(d)(1): “if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification”. In other words, Examiner respectfully submits Applicant’s arguments are not commensurate with claim scope and do not reflect any of the purported technological improvements – see MPEP § 2106.04(d)(1): “if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification.”
The instant claims, like the specification, do not include any specific technical details as to how the claimed multi-output machine learning model is specifically used to generate the plurality of output predictors, beyond merely stating “… using the plurality of input features included in the training data set…” (emphasis added), and only requires the model to generally “capture non-linear correlations”. This amounts to the machine learning model simply being applied – see MPEP §2106.05 (f)(1): “…claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words ‘apply it’”, and merely limiting the use of the abstract idea to a particular technological environment (e.g., general-purpose computer with a multi-output machine learning model).
Applicant’s remarks drawn to the Desjardins decision and corresponding USPTO 12/05/2025 memorandum is not persuasive, as neither the claims nor the Alice/Mayo analyses of Desjardins are analogous to the instant claims for at least the following reasons:
The eligibility rationale relied upon in Desjardins included a determination that the claims provided a technological solution to a technological problem at step 2A Prong II7, where the technological problem of “catastrophic forgetting” in continual learning systems was directly addressed8; the claims of Desjardins reflected a particular training strategy that allows the model to preserve performance on earlier tasks learned, even as it learns new ones9. Conversely, the instant claims do not indicate an improvement to any technology or technical field, as previously maintained and explained further below in the 101 rejections – the examiner respectfully maintains the instant claims provide a technical solution to an abstract business problem – see MPEP § 2106.05(a): “[I]t is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology”. Unlike Desjardins, Examiner respectfully fails to see as to how the instant claim limitations provide any particular technological solution to a technological problem, as the instant claims do not reflect any technological solution addressing technological problems concerned with the functioning of a computer or to any other technology or technical field – what is addressed are financial problems / considerations, as evidenced by the Background section of Applicant specification.
Accordingly, for the reasons provided above, as well as in the 101 rejections further below, the Examiner respectfully maintains that the claims do not integrate the judicial exception into a practical application (Step 2A Prong II of Alice/Mayo Test: NO, the additional elements do not integrate the judicial exception into a practical application), as the focus of the claims is not an improvement in computers as tools, but rather on an abstract idea of financial asset management that uses computers as tools. Considered both separately and as an ordered combination, the additional elements of the claims do no more than represent computers performing functions that correspond to (,i.e., implement,) the acts of the abstract financial asset management within a particular technological environment, and do not provide details such that one of ordinary skill in the art would recognize the claims as reflecting an improvement to the functioning of a computer or any other technology or technical field.
Accordingly, in view of the analysis performed with respect to steps 2A Prong I and 2A Prong II, the examiner respectfully maintains the claims are directed to an abstract idea under step 2A (Step 2A: The claims are directed to an abstract idea of financial asset management).
In order to further support the aforementioned determinations that the aforementioned additional elements are merely applied and do not provide improvements to the functioning of a computer or to any other technology or technical field under steps 2A Prong II and 2B, Examiner notes the following evidentiary support10:
Examiner notes the following prior art indicates or otherwise suggests multi-output machine learning as well-understood routine and conventional activity in the technical field of machine learning when described at a high degree of generality, as the case here (i.e., not an improvement in the technical field of machine learning)11:
Non-Patent Literature “A survey on multi-output regression” (Borchani), disclosing in abstract and introduction (emphasis added): “[Abstract] In recent years, a plethora of approaches have been proposed to deal with the … task of multi-output regression… [Introduction] Multi-output regression, also known in the literature as multi-target, multi-variate, or multi-response…”. In other words, Borchani makes clear the concept of multi-output machine learning in the form of regression is a known technique in the field of machine learning, and goes by multiple different names.
United States Patent Application Publication No. US 20220276737 A1 (Cunha), disclosing (emphasis added): “The system and method may utilize a machine learning technique to help determine the relationship. For example, multi-output regression is a standard machine learning/statistics technique for which many algorithms exist, including Linear Regression, Decision Trees, Neural Networks, Random Forests, etc. ….”
Non-Patent Literature “Deep Learning Models for Multi-Output Regression” (Brownlee), disclosing (emphasis added): “Deep learning neural networks are an example of an algorithm that natively supports multi-output regression problems. Neural network models for multi-output regression tasks can be easily defined and evaluated using the Keras deep learning library. [I.e., an open-source software library.]”
Non-Patent Literature “Machine Learning for Multi-Output Regression: When should a holistic multivariate approach be preferred over separate univariate ones?” (Schmid), disclosing in Introduction (emphasis added): “Multivariate data occur in a variety of disciplines, for example in … econometrics. Data are said to be multivariate if the response not only consists of one variable, but of d≥2 output variables … we are often interested in finding a functional relationship between the output Y and some feature variables … i.e., we want to perform a multivariate (also called multi-output regression analysis…multivariate regression wants to specify the relationship of several outcome variables with … [feature variables] … simultaneously.”.
Non-Patent Literature “Survey on Multi-Output Learning” (Xu), disclosing in abstract (emphasis added): “The aim of multi-output learning is to simultaneously predict multiple outputs given an input. … In recent times, an increasing number of research studies have focused on ways to predict multiple outputs at once. … Classic cases of multi-output machine learning include multi-label learning, multi-dimensional learning, multi-target regression, and others”.
Non-Patent Literature “The Elements of Statistical Learning …” (Hastie), disclosing on page 392, pertaining to §11.3 (emphasis added): “Neural Networks”: “…For regression, typically K = 1 and there is only one output unit Y1 … . However, these networks can handle multiple quantitative responses in a seamless fashion, so we will deal with the general case. …”
In light of the above evidentiary support, the aforementioned additional elements drawn to multi-output machine learning are not sufficient to show an improvement to any technology or other technical field at Steps 2A Prong Two and 2B, as the additional elements, viewed both individually and as an ordered combination, amount to gathering and analyzing information using techniques conventional to both computer technology and the technical field of machine learning – See MPEP §§2106.05(a) & 2106.05(a) II citing TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48: “Examples that the courts have indicated may not be sufficient to show an improvement to technology include: … Gathering and analyzing information using conventional techniques and displaying the result … ”.
As evidenced by the above, the additional elements generally corresponding to multi-output machine learning are not indicative of improvements to the functioning of a computer or to any other technology or technical field under either step 2A Prong II or step 2B, and instead are indicative of the system and machine learning being merely applied (MPEP 2106.05(f)). Even though claims 1, 11, and 16 and utilize computer components and multi-output machine learning, nothing in the claims indicate specific steps undertaken by the computers / systems that are beyond conventional functionality of generic computers / computer components being used at a high degree of generality, excepting the abstract idea they are merely used as a tool for. Accordingly, the claimed computer implementation itself is wholly generic, when considered in light of the technological environment of computers and the technical field of machine learning.
Accordingly, the Examiner respectfully disagrees with Applicant’s arguments and respectfully maintains the claims are not patent eligible under 35 U.S.C. § 101, as the claims do not integrate the judicial exception into a practical application or amount to significantly more.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
As an initial matter, the relevant test is the Alice/Mayo test12. The following analysis provided in this section results from the instant application’s claims being examined within the scope of the Alice/Mayo test framework.
When analyzed under step 1 of the Alice/Mayo test13, claims 1-10 are directed to a “…method...” and claims 11-20 are directed to a “… database processing system …”. Therefore, each of the claims are directed to one of the four statutory categories of invention (Step 1 of Alice/Mayo Test: YES).
In light of step 2A Alice/Mayo analysis performed on the instant claims14, claims 1-20 have been determined to be directed to an abstract idea of financial asset management. The rationales for the aforementioned determination are explained further below.
When analyzed under prong I of revised step 2A, claims 1-20 recite a method of organizing human activity15 because independent claims 1 and 11 recite:
“1. A method of managing distribution of income from a portfolio of financial assets, the method executed …, the method comprising:
acquiring a training data set including a plurality of input features that influence performance of the portfolio of financial assets and a plurality of output predictors of the performance of the portfolio of financial assets, the plurality of input features including at least: (i) characteristics of loans comprising the portfolio, (ii) actuarial fund performance data, and (iii) threshold rate data for achieving the target actuarial values, and the plurality of output predictors including at least: (a) likelihood of full redemption at maturity, (b) likelihood of need for principal deflection prior to maturity, and (c) likelihood of early redemption;
… [updating] a … model using the plurality of input features included in the training data set to … generate the plurality of output predictors, …;
simulating a plurality of mixes of financial assets and generating the plurality of output predictors of the performance of each of the plurality of mixes of financial assets using the … [updated] … model;
selecting a mix of financial assets of the plurality of mixes of financial assets based on the generated plurality of output predictors to form the portfolio of financial assets by evaluating the likelihood of full redemption at maturity, likelihood of need for principal deflection, and likelihood of early redemption for each mix;
receiving a debt service payment from the portfolio of financial assets;
determining whether a fund benefitted by the portfolio of financial assets meets a target actuarial value;
in a case where the fund benefitted by the portfolio of financial assets meets the target actuarial value, distributing the debt service payment to one or more investors in the portfolio of financial assets;
and in a case where the fund benefitted by the portfolio of financial assets does not meet the target actuarial value:
calculating a deferrable principal payment based on a difference between the target actuarial value of the fund and a present actuarial value of the fund;
distributing the deferrable principal payment to the fund benefitted by the portfolio of financial assets;
subtracting the deferrable principal amount from the debt service payment to determine a remainder debt service payment;
and distributing the remainder debt service payment to the one or more investors in the portfolio of financial assets.”
“11. …
acquire a training data set including a plurality of input features that influence performance of the portfolio of financial assets and a plurality of output predictors of the performance of the portfolio of financial assets, the plurality of input features including at least: (i) characteristics of loans comprising the portfolio, (ii) actuarial fund performance data, and (iii) threshold rate data for achieving the target actuarial values, and the plurality of output predictors including at least: (a) likelihood of full redemption at maturity, (b) likelihood of need for principal deflection prior to maturity, and (c) likelihood of early redemption;
… [update] a … model using the plurality of input features included in the training data set to … generate the plurality of output predictors, …;
simulate a plurality of mixes of financial assets and generating the plurality of output predictors of the performance of each of the plurality of mixes of financial assets using the … [updated] … model;
select a mix of financial assets of the plurality of mixes of financial assets based on the generated plurality of output predictors to form the portfolio of financial assets by evaluating the likelihood of full redemption at maturity, likelihood of need for principal deflection, and likelihood of early redemption for each mix;
receive a debt service payment from the portfolio of financial assets;
determine whether a fund benefitted by the portfolio of financial assets meets a target actuarial value;
in a case where the fund benefitted by the portfolio of financial assets meets the target actuarial value, distributing the debt service payment to one or more investors in the portfolio of financial assets;
and in a case where the fund benefitted by the portfolio of financial assets does not meet the target actuarial value:
calculate a deferrable principal payment based on a difference between the target actuarial value of the fund and a present actuarial value of the fund;
distribute the deferrable principal payment to the fund benefitted by the portfolio of financial assets;
subtract the deferrable principal amount from the debt service payment to determine a remainder debt service payment;
and distribute the remainder debt service payment to the one or more investors in the portfolio of financial assets.”
Under broadest reasonable interpretation consistent with the specification16, the above claim limitations of claims 1 and 11 both recite fundamental economic practices of financial asset management, the recited financial asset management including steps of (A) selecting a mix of financial assets to form a portfolio of financial assets, (B) receiving a debt service payment from the portfolio of financial assets, and (C) distributing funds from the debt service payment to one or more investors in the portfolio of financial asset pursuant to a determination of whether or not a fund benefitted by the portfolio of financial assets meets a target actuarial value. (Step 2A Prong I of Alice/Mayo Test: Yes, the claims recite an abstract idea).
The aforementioned determination made under step 2A Prong I of Alice/Mayo analysis is supported by the following17:
Applicant Specification’s “Technical Field” section, disclosing: “The present invention is generally related to the field of … algorithms for managing financial assets, and more particularly to … models for managing financial assets secured by municipal leveraged loans”.
Additionally, when analyzed under prong I of revised step 2A, claims 1-20 recite mathematical concepts18 because independent claims 1 and 11 recite:
“1. A method … comprising:
acquiring a training data set including a plurality of input features that influence performance of the portfolio of financial assets and a plurality of output predictors of the performance of the portfolio of financial assets …;
… [updating] a … model using the plurality of input features included in the training data set to … generate the plurality of output predictors, the … model capturing non-linear correlations among the plurality of output predictors;
simulating a plurality of mixes … and generating the plurality output predictors of the performance of each of the plurality of mixes … using the … [updated] … model;
selecting a mix … of the plurality of mixes … based on the generated one or more output predictors …;
…
determining whether a fund … meets a target actuarial value;
in a case where the fund … meets the target actuarial value, distributing …;
and in a case where the … does not meet the target actuarial value:
calculating a deferrable principal payment based on a difference between the target actuarial value of the fund and a present actuarial value of the fund;
distributing the deferrable principal payment to the fund benefitted …;
subtracting the deferrable principal amount from the debt service payment to determine a remainder debt service payment;
and distributing the remainder debt service payment ….”
“11. …
acquire a training data set including a plurality of input features that influence performance of the portfolio of financial assets and a plurality of output predictors of the performance of the portfolio of financial assets …;
… [update] a … model using the plurality of input features included in the training data set to … generate the plurality of output predictors, the … model capturing non-linear correlations among the plurality of output predictors;
simulate a plurality of mixes … and generating the plurality output predictors of the performance of each of the plurality of mixes … using the … [updated] … model;
selecta mix … of the plurality of mixes … based on the generated one or more output predictors …;
…
determine whether a fund … meets a target actuarial value;
in a case where the fund … meets the target actuarial value, distributing …;
and in a case where the … does not meet the target actuarial value:
calculate a deferrable principal payment based on a difference between the target actuarial value of the fund and a present actuarial value of the fund;
distribute the deferrable principal payment to the fund benefitted …;
subtract the deferrable principal amount from the debt service payment to determine a remainder debt service payment;
and distribute the remainder debt service payment ….”
Under broadest reasonable interpretation consistent with the specification, the above claim limitations of claims 1 and 11 both recite mathematical calculations including (A) updating / simulating via a mathematical model and (B) calculations corresponding to deferrable principal payments – mathematical calculations of which are used in furtherance of the abstract financial asset management concept. Hence, claims 1 and 11 recite an additional abstract idea when analyzed under Step 2A Prong I of the Alice/Mayo test. Adding one abstract idea (e.g., the aforementioned mathematical calculations) to another (e.g., the recited financial asset management) does not render a claim non-abstract – see MPEP § 2106.04 II A 2 citing RecogniCorp, LLC v. Nintendo Co. 855 F.3d 1322.
Prior to proceeding to step 2A Prong II of Alice/Mayo analysis, Examiner notes the following:
Consistent with guidance set forth in MPEP §2106.04 II B19, Examiner, under step 2A Prong II and step 2B analysis, treats the identified abstract ideas of the claims as a single abstract idea of ‘financial asset management’ (including mathematical calculations), as the identified mathematical concepts are in furtherance of the abstract financial asset management recited.
This judicial exception recited is not integrated into a practical application because, when analyzed under prong II of revised step 2A of the Alice/Mayo test20:
The additional elements of “…executed by a programmed data processing device system…”, “…simultaneously…”, “…the multi-output machine learning capturing non-linear correlations among plurality of output predictors …” “…train[ing] a multi-output machine learning model…”, “… using the trained multi-output machine learning model …”, “ …the data processing device system configured at least by the program to…”, and “…A database processing system comprising … an input-output device system communicatively connected to a display device system; a memory device system storing a program; and a data processing device system communicatively connected to the input-output device system and the memory device system, the data processing device system configured at least by the program to:”, when evaluated both individually and as an ordered combination, amount to no more than mere instructions to implement the abstract idea and merely limit the use of the abstract idea to a particular technological environment (MPEP §§ 2106.05 (f), (h)). Stating an abstract idea while adding the words "apply it" (or an equivalent) is insufficient to impart patent eligibility under Alice. See Alice Corp. v. CLS Bank International, 573 U.S. 208, 223-24 (2014): "… Stating an abstract idea "while adding the words ‘apply it’ " is not enough for patent eligibility. … Nor is limiting the use of an abstract idea " ‘to a particular technological environment.’ … Stating an abstract idea while adding the words "apply it with a computer" simply combines those two steps, with the same deficient result”.
The claims merely invoke computers as tools to perform an abstract business process (e.g., the recited financial asset management – see MPEP §2106.05(f)(2)). This stance is supported by Page 1 of Applicant Specification describing the invention as “generally related to the field of … algorithms for managing financial assets, and more particularly … models for managing financial assets secured by municipal leveraged loans” and Page 9 of Applicant specification describing embodiments of the claimed “data processing device system” as generally including “one or more data processing devices … [,where the phrase] ‘data processing device’ … is intended to include any data processing device, such as a central processing unit (‘CPU’), … and any other device configured to process data” (emphasis added). Furthermore, Pages 9-10 and Fig. 1 of Applicants’ Specification describes the corresponding computers components at a high degree of generality, such that the claimed devices/systems are indistinguishable from general purpose computers and associated elements thereof (e.g., memory, processor, I/O interfaces, displays, etc.). For example, with respect to both the claimed “database processing system” of claim 11 and the “data processing system” of claim 1, Examiner notes that nearly every general-purpose computer commercially available off the shelf includes a processor / memory capable of performing “acquiring …”, “selecting …”, “receiving…”, “determining…”, “calculating…”, “subtracting…” functionality, are typically communicatively coupled to some form of a “…display device system…” (e.g., monitor) and some form of “input-output device system” (e.g., keyboard / mouse, etc.), and are capable of being configurable via computer programs, such as, for example, programs with machine learning functions/routines. Furthermore, the “multi-output machine learning model” is claimed at a high degree of generality and merely used to process data drawn to the abstract idea21 at a high degree of generality,22 so as to achieve desired results rooted in the abstract idea recited (i.e., “simulating a plurality of mixes of financial assets and generating … output predictors of the performance of each of the plurality of mixes of financial assets … [to select] … a mix of financial assets based on the generated one or more output predictors to form the portfolio of financial assets”(emphasis added)). Accordingly, even when considered as an ordered combination, the claims’ additional elements are indistinguishable from mere addition of general-purpose computers added to the abstract idea ‘after the fact’ / ‘post-hoc’, which is insufficient to indicate improvements to computer functionality23.
The Applicant’s claims fail to provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement to the functioning of a computer or to any other technology or technical field (MPEP §§ 2106.04(d)(1) & 2106.05(a)). For example, the instant claims do not include any specific details as to how the claimed multi-output machine learning model performs “simulating a plurality of mixes of financial assets and generating the one or more output predictors of the performance of each of the plurality of mixes of financial assets…” beyond merely stating “… using the trained multi-output machine learning model;” (emphasis added).
In light of the above rationales provided for step 2A Prong II analysis, the Examiner respectfully submits the focus of the claims is not on an improvement in computers as tools, but rather on an abstract idea that uses computers as tools. Considered both separately and as an ordered combination, the additional elements of the independent claims do not integrate the abstract idea into a practical application, as they do no more than represent computers performing functions that correspond to (i.e., implement,) the acts of the abstract financial asset management and do not provide details such that one of ordinary skill in the art would recognize the claimed additional elements includes components or steps, either alone or in combination, that provides an improvement to the functioning of a computer or any other technology or technical field. (Step 2A Prong II of Alice/Mayo Test: NO, the additional claimed elements are not integrated into a practical application). Accordingly, claims 1 and 11 are determined to be directed to an abstract idea (Step 2A of Alice/Mayo Test: The claims are directed to an abstract idea).
When analyzed under step 2B24, claims 1 and 11 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As previously explained in step 2A Prong II analysis, each of the claims’ additional elements, when viewed as a whole, amount to no more than mere instructions to implement the abstract financial asset management concept within a particular technological environment, which is insufficient for patent eligibility under Alice – see MPEP §§ 2106.05 (f), (h) and Alice Corp. v. CLS Bank International, 573 U.S. 208, 223-24 (2014). Accordingly, when considered both separately and as an ordered combination, none of the elements of the claims add significantly more to the abstract idea itself (i.e., an inventive concept), as merely employing computers as tools to automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself – see BSG Tech LLC vs. BuySeasons, Inc., 899 F.3d 1281, 1290 (Fed. Cir. 2018): “It has been clear since Alice that a Claimed invention’s use of the ineligible concept to which it is directed cannot supply the inventive concept that renders the invention ‘significantly more’ than that ineligible concept” (Step 2B: No. The claims do not amount to significantly more).
Hence, independent claims 1 and 11 are not patent eligible.
With respect to the dependent claims, they have each been given the full Alice/Mayo analysis, including analyzing the additional elements both individually and as an ordered combination (if any). The dependent claims are also held patent ineligible under 35 U.S.C. § 101 because of the same reasoning as above, and because the claim limitations of the dependent claims fail to establish that the claims are integrated into a practical application or amount to significantly more. The rationales for the aforementioned determinations are explained further below.
With respect to dependent claims 2-10 and 12-20 (i.e., all the dependent claims), their limitations each fail to provide any further additional elements outside the abstract idea. Furthermore, their limitations do not indicate that the previously mentioned additional elements of their respective parent claims successfully integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself, either individually or as an ordered combination. Accordingly, claims 2-10 and 12-20 do not integrate the judicial exception into a practical application or amount to significantly more. Therefore, dependent claims 2-10 and 12-20 (i.e., all the dependent claims,) are also not patent eligible.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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 MARK A MALKOWSKI whose telephone number is (313)446-6624. The examiner can normally be reached Monday - Friday, 9:00AM - 5:00PM ET.
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, Matthew Gart can be reached on (571) 272-3955. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/M.A.M./Examiner, Art Unit 3696
/MATTHEW S GART/Supervisory Patent Examiner, Art Unit 3696
1 I.e., the subject matter claimed.
2 The same analogous rationale applies to independent claim 11, mutatis mutandis.
3 See pages 8-14 of Applicant Remarks received 1/23/2026.
4 Pages 8-14 of Applicant Remarks received 1/23/2026.
5 I.e., a technological solution to a technological problem.
6 See the following of Applicant’s specification: “The data processing device system 110 includes one or more data processing devices that implement …control programs associated with some of the various embodiments. Each of the phrases "data processing device", … is intended to include any data processing device, such as a central processing unit ("CPU"), a desktop computer, a laptop computer, …, and any other device configured to process data, manage data, or handle data, …”.
7 See MPEP § 2106.05(a).
8 See pages 7 and 9 of the Desjardins decision.
9 Again, see pages 7 and 9 of the Desjardins decision.
10 MPEP §2106.07, underline emphasis added: “When evaluating a claimed invention for compliance with the substantive law on eligibility, examiners should review the record as a whole (e.g., the specification, claims, the prosecution history, and any relevant case law precedent or prior art) before reaching a conclusion with regard to whether the claimed invention sets forth patent eligible subject matter.”
11 See MPEP §2106.05(f), emphasis added: “For example, because this [Mere Instructions To Apply An Exception] consideration often overlaps with the improvement consideration (see MPEP § 2106.05(a)), the particular machine and particular transformation considerations (see MPEP § 2106.05(b) and (c), respectively), and the well-understood, routine, conventional consideration (see MPEP § 2106.05(d)), evaluation of those other considerations may assist examiners in making a determination of whether an element (or combination of elements) is more than mere instructions to apply an exception”.
12 See MPEP § 2106 I.
13 See MPEP §§ 2106.03 I, II.
14 See MPEP §§ 2106.04 I, II, (d) I.
15 See MPEP § 2106.04(a)(2) II
16 See MPEP § 2111.
17 MPEP §2106.07, underline emphasis added: “When evaluating a claimed invention for compliance with the substantive law on eligibility, examiners should review the record as a whole (e.g., the specification, claims, the prosecution history, and any relevant case law precedent or prior art) before reaching a conclusion with regard to whether the claimed invention sets forth patent eligible subject matter.”
18 See MPEP § 2106.04(a)(2) I.
19 MPEP §2106.04 II B, underline emphasis added: “For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A Prong One to make the analysis clear on the record. However, if possible, the examiner should consider the limitations together as a single abstract idea for Step 2A Prong Two and Step 2B ... rather than as a plurality of separate abstract ideas to be analyzed individually.”
21 E.g., “…training data set including a plurality of input features that influence performance of the portfolio of financial assets and or out more output predictors of the performance of the portfolio of assets;”.
22 E.g., “training … [the] machine learning …using the plurality of input features included in the training data set … using the trained multi-output machine learning model”.
23 See MPEP §§ 2106.05(f)(2) & 2106.05(a) I.
24 See MPEP § 2106.05.