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 . This office action is in response to applicant's communication of January 26, 2026. The rejections are stated below. Claims 1-19 are pending and have been examined.
Response to Arguments
This response addresses the applicant's arguments concerning the rejection of all claims under 35 U.S.C. § 101 for lack of patent-eligible subject matter. The rejection is maintained. The following explains why the applicant’s arguments are not persuasive.
The rejection is grounded in the two-step framework established by the Supreme Court in Alice Corp. Pty. Ltd. v. CLS Bank International and Mayo Collaborative Services v. Prometheus Laboratories, Inc.. This is the governing legal standard for evaluating patent eligibility under § 101. The instant claims are ineligible at Step 1. They are directed to the abstract idea of financial portfolio optimization and risk management using mathematical models, a fundamental economic practice and a method of organizing human activity.
The characterization of what a claim is "directed to" under Alice Step 1 is a legal inquiry. The claim recites a process of simulating market scenarios, calculating expected returns using trained models (conditional Beta, default probability), and optimizing portfolio weights. At its core, this is the abstract idea of managing financial risk and optimizing a portfolio. The recitation of specific data processing techniques to perform calculations central to this abstract idea does not alter this conclusion. The analysis at Step 1 considers the claim's focus, which here remains on the abstract financial concept of portfolio construction, not on a specific improvement to computing technology. This aligns with recent USPTO guidance, which reminds examiners not to evaluate claims at such a high level of generality that meaningful technical limitations are dismissed without adequate explanation. All limitations have been considered, but they are directed to implementing the abstract idea.
Applicant's arguments citing Berkheimer v. HP Inc., Applicant argues the Office must provide a factual determination that claim elements are well understood, routine, and conventional.
Applicant conflates the distinct inquiries of Alice Step 1 and Step 2. The principle from Berkheimer pertains to the Step 2 search for an "inventive concept." The instant rejection is based on Step 1. The claim is deemed ineligible because it is directed to an abstract idea. An evaluation of whether the specific steps are routine is not required for this Step 1 determination. Applicant's repeated requests for evidence on this point are therefore misdirected in the context of this rejection. The Berkheimer decision does not preclude a finding of ineligibility at Step 1.
Applicant's Argument: The claims are analogous to those found eligible in Enfish, LLC v. Microsoft Corp. (software improvements) and DDR Holdings, LLC v. Hotels.com, L.P. (modified protocols).
The cited cases are distinguished. In Enfish, the claims were found not abstract at Step 1 because they were specifically directed to an innovative self-referential database table, which was an improvement in computer functionality itself.
In contrast, the instant claims are directed to a financial method. The use of a "machine learning portfolio generating apparatus" and "decision tree ensembles" is for the purpose of performing financial calculations and optimizations. The claim is not focused on improving the functioning of the machine learning models, database structures, or computer systems themselves. As stated in Alice, "merely requiring generic computer implementation fails to transform an abstract idea into a patent-eligible invention."
Applicant's Argument’s citing ex parte Desjardins decision and related guidance clarify that machine learning innovations and processes that cannot practically be performed in the human mind are not abstract mental processes.
The Desjardins decision involved claims to a specific method of training machine learning models to mitigate "catastrophic forgetting," a technical problem in computer science. The Appeals Review Panel found the claims eligible at Step 2 because they provided an improvement to how the machine learning model itself operates.
The instant claim is materially different. It applies existing machine learning models to financial forecasting which is a field outside computing. The specification describes applying these models to "estimate conditional Beta" and "conditional default probability." The claimed improvement is directed to the output (an optimized portfolio), not to the internal operation or efficiency of the machine learning technology. Therefore, it remains an application of computational techniques to an abstract financial idea.
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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 is directed to the abstract idea of “portfolio optimization” which is grouped under “organizing human activity… fundamental economic practice” [mitigating risk) in prong one of step 2A (See 2019 Revised Patent Subject Matter Eligibility Guidance).
Claim 1 recites “a …, comprising:
at least one …;
a … stored in the …;
any of at least one … disposed in communication with the … the any of at least one … from the …, …of the … with … comprising:
obtain, a …, the … to including a set of optimization parameters including a universe of securities, a time period length, a conditional value at risk portion, a conditional value at risk threshold, a portfolio value amount;
determine, a set of stimulated market scenarios associated with the time period length, the set of simulated market scenarios generated using a set of …, each simulated market scenario in the set of simulated market scenarios structured including a set of stimulated market factor values in which the set of simulated market scenarios are processed based on a machine learning processing request and discerns a number of scenario simulations per time period;
retrieve, a set of expected returns for securities in the universe of securities for the set of simulated market scenarios, each expected return in the set of expected returns structured as calculated for a security during a simulated market scenario using:
the respective security’s conditional Beta during the respective simulated market scenario, determined using a set of decision tree ensembles, … estimating conditional Beta of the respective security, based on a first subset of the set of simulated market factor values, and the respective security’s conditional default probability during the respective simulated market scenario, determined using a set of decision tree ensembles, … estimating conditional default probability of the respective security, based on a second subset of the set of simulated market factor values;
optimize, a … with portfolio weights of securities in the universe of securities in accordance with the conditional value at risk portion, the conditional value at risk threshold, and the portfolio value amount, using the set of expected returns, to generating a set of tradeable transactions that maximize expected portfolio return of an optimized portfolio; and
generate, an updated … based on the set of tradeable transactions generating the optimized portfolio;
execute, the set of tradeable transactions to base on the updated …”.
These limitations describe an abstract idea of portfolio optimization and corresponds to Certain Methods of Organizing Human Activity (fundamental economic practice including mitigating risk and hedging). Accordingly, claim 1 recites an abstract idea (Step 2A: Prong 1: YES).
The claim also recites as additional elements such as “machine learning portfolio generating apparatus, comprising at least one memory; a component collection stored in the at least one memory”, “at least one processor disposed in communication with the at least one memory, the any of at least one processor executing processor-executable instructions from the component collection, storage of the component collection structed with processor-executable instructions”, “portfolio construction request datastructure structured”, “multi-variate mixture datastructures”, “trained”, and “portfolio structure datastructure” which do no more than implement the abstract idea and/or provide a particular technological environment. Therefore, claim 1 recites an abstract idea without a practical application (Step 2A - Prong 2: NO).
Further, as the additional elements of claim 1 do no more than serve as a tool to implement the abstract idea and/or provide a particular technological environment, they do not improve computer functionality or improve another technology or technical field. Thus, claim 1 is not patent eligible (Step 2B: NO).
Claims 17-19 also recite the abstract idea of idea of portfolio optimization and corresponds to Certain Methods of Organizing Human Activity (fundamental economic practice including mitigating risk and hedging) step one of step 2A (MPEP 2106.04). Claim 17 includes the additional elements of “machine learning portfolio generating processor-readable, non-transient medium, the medium storage a component collection, storage of the component collection structured with processor-executable instructions”, “portfolio construction request datastructure structured”, “multi-variate mixture datastructures”, “trained”, and “portfolio structure datastructure”. Claim 18 includes the additional elements of “machine learning portfolio generating processor-implemented system”, “means to store a component collection”, “means to process processor-executable instructions from the component collection, storage of the component collection structured with processor-executable instructions”, “portfolio construction request datastructure structured”, “multi-variate mixture datastructures”, “trained”, and “portfolio structure datastructure”. Claim 19 includes the additional elements of “machine learning portfolio generating processor-implemented system including processing processor-executable instructions via any of at least one processor from a component collection stored in at least one memory, storage of the component collection structured with processor-executable instructions”, “portfolio construction request datastructure structured”, “multi-variate mixture datastructures”, “trained”, and “portfolio structure datastructure”. The additional elements do no more than serve as a tool to implement the abstract idea and/or link the abstract idea a particular technological environment. Therefore, as they do no more than serve as a tool to implement the abstract idea and/or provide a particular technological environment, they do not improve computer functionality or improve another technology or technical field.
Claim 2 recites “determine, a set of historical market scenarios and a set of time period buckets;
determine, for each time period bucket from the set of time period buckets, a subset of historical market scenarios, from the set of historical market scenarios, associated with the respective time period bucket;
…, for each time period bucket from the set of time period buckets, a …, from the set of …, using the subset of historical market scenarios associated with the respective time period bucket;
determine, for each time period bucket from the set of time period buckets, a number of simulated market scenarios generating using the … associated with the respective time period bucket; and
generate, for each time period bucket from the set of time period buckets, the determined number of simulated market scenarios for the respective time period bucket, using the … associated with the respective time period bucket” which further define the abstract idea. The claim includes “train”, “multi-variate mixture datastructure” , and ”trained multi-variate mixture datastructure” as additional elements. The additional elements do no more than serve as a tool to implement the abstract idea and/or link the abstract idea a particular technological environment. And, as they do no more than serve as a tool to implement the abstract idea and/or provide a particular technological environment, they do not improve computer functionality or improve another technology or technical field.
Claim 3 recites “determine a historical data set and a set of market factors; determine a set of rolling window periods for the historical data set; and calculate for each market factor from the set of market factors, for each rolling window period from the set of rolling window periods, a change to the respective market factor during the respective rolling window period, each historical market scenario from the set of historical market scenarios structured including calculated changes to the set of market factors during a rolling window period” which further define the abstract idea.
Claim 4 recites “determine, the delta between values of the market factor at two time point of the rolling window period” which further define the abstract idea.
Claim 5 recites “determine, that historical data for the market factor during the rolling window period is unavailable for a time point; and impute, the unavailable historical data for the time point using a k-Nearest Neighbors method” which further define the abstract idea.
Claim 6 recites “further, comprising: the length of a rolling window period is structured to as equal to the time period length” which further define the abstract idea. Claim 7 recites “the set of time period buckets 1s structured to have a fixed length for each time period bucket” which further define the abstract idea.
Claim 8 recites “the set of time period buckets 1s structured to have a fixed length for each time period bucket” which further define the abstract idea.
Claim 9 recites “… a … for a time period bucket using the associated subset of historical market scenarios are structured including;
determine, for each market factor from the set of market factors, a distribution using the respective market factor for the time period bucket;
fit, for each market factor from the set of market factors, the distribution using the respective market factor for the time period bucket using the associated subset of historical market scenarios;
determine, a copula for the set of market factors for the time period bucket; and
…, the … for the time period bucket using the fitted distributions and the copula for the set of market factors” which further defines the abstract idea. The claim includes “train a multi-variate mixture datastructure” as an additional element. The additional element does no more than serve as a tool to implement the abstract idea and/or link the abstract idea a particular technological environment. And, as it does no more than serve as a tool to implement the abstract idea and/or provide a particular technological environment, they do not improve computer functionality or improve another technology or technical field.
Claim 10 recites “fit the distribution to using feta market factor for the time period bucket using the associated subset of historical market scenarios are structured to including: calculate the mean of the market factor’s values in the associated subset of historical market scenarios” which further defines the abstract idea.
Claim 11 recites “generate simulated market scenarios for a time period bucket, using the trained multi- variate mixture data structure associated with the time period bucket, are structured to including: generate a simulated market scenario, from the stimulated market scenarios for the time period bucket, by sampling the trained multi-variate mixture data structure associated with the time period bucket” which further defines the abstract idea.
Claim 12 recites “filter, the set of simulated market scenarios associated with the time period length based on specified ranges of allowable values for specified customized market factors” which further defines the abstract idea.
Claim 13 recites “filter, the set of simulated market scenarios associated with the time period length based on specified business cycle settings” which further defines the abstract idea.
Claim 14 recites “initialize, starting portfolio weights of securities 1n the universe of securities benchmarking portfolio weights of a benchmark portfolio” which further defines the abstract idea.
Claim 15 recites “further, comprising: the portfolio weights of securities in the universe of securities are structured including: find a mixed integer linear programming portfolio solution” which further defines the abstract idea.
Claim 16 recites “further, comprising: the set of simulated market scenarios is further generated using a …” which further defines the abstract idea. The claim includes “set of deep learning neural networks” as additional elements. The additional elements do no more than serve as a tool to implement the abstract idea and/or link the abstract idea a particular technological environment. And, as they do no more than serve as a tool to implement the abstract idea and/or provide a particular technological environment, they do not improve computer functionality or improve another technology or technical field.
Conclusion
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 KEVIN T POE whose telephone number is (571)272-9789. The examiner can normally be reached on Monday-Friday 9:30am through 6pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ryan Donlon can be reached on 571-270-3602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/K.T.P/Examiner, Art Unit 3692 /KEVIN T POE/
/RYAN D DONLON/Supervisory Patent Examiner, Art Unit 3692 March 13, 2026