DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to claims filed 08/27/2025 and Applicant’s communication for application 17/383323 filed 08/27/2025.
Claims 1-20 have been examined with this office 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 has been entered.
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 of portfolio return computation without significantly more.
Subject Matter Eligibility Standard
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. 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), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include fundamental economic practices; certain methods of organizing human activities; an idea itself; and mathematical relationships/formulas. Alice Corporation Pty. Ltd. v.CLS Bank International, et al., 573 U.S. _ (2014) as provided by the interim guidelines FR 12/16/2014 Vol. 79 No. 241.
Analysis
Step 1, the claimed invention must be to one of the four statutory categories. 35 U.S.C. 101 defines the four categories of invention that Congress deemed to be the appropriate subject matter of a patent: processes, machines, manufactures and compositions of matter. In this case independent claims 1 and all claims which depend from it are directed toward an apparatus, independent claim 19 and all claims which depend from it are directed toward a system, and independent claim 18 and 20 and all claims which depend from it are directed toward a computer readable medium storing instruction to perform functions/steps. As such, all claims fall within one of the four categories of invention deemed to be the appropriate subject matter.
Step 2A Prong 1, Under Step 2 A, Prong 1 of the 2019 Revised § 101 Guidance, it is determined whether the claims are directed to a judicial exception such as a law of nature, a natural phenomenon, or an abstract idea (See Alice, 134 S. Ct. at 2355) by identify the specific limitation(s) in the claim that recites abstract idea(s); and then determine whether the identified limitation(s) falls within at least one of the groupings of abstract ideas enumerated in the 2019 PEG.
Specifically, claim 1 comprises inter alia the functions or steps of “A machine learning portfolio return computation engine apparatus, comprising:at least one memory; a component collection stored in the at least one memory; any of at least one a-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 structured with processor-executable instructions comprising: obtain a portfolio return computation request datastructure, the portfolio return computation request datastructure structured specifying a set of simulated market scenarios and a set of filters, the set of simulated market scenarios generated with a set of multi-variate mixture datastructures, each simulated market scenario in the set of simulated market scenarios structured comprising a set of simulated market factor values corresponding to a set of market factors; where a number of simulated market scenarios are structured with a set of learning neural networks and are generated with the trained multi-variate mixture datastructure associated with a respective time quantum period; determine a set of constituent portfolio securities of a portfolio datastructure, each constituent portfolio security in the set of constituent portfolio securities associated with a portfolio security weight of the respective constituent portfolio security in the portfolio datastructure; filter the set of simulated market scenarios based on the set of filters determining a set of filtered simulated market scenarios having simulated market factor values that fall within the range of allowable values for each filter in the set of filters; retrieve a set of expected returns for the set of constituent portfolio securities of the portfolio datastructure for the set of filtered simulated market scenarios; calculate for each constituent portfolio security in the set of constituent portfolio securities, an expected constituent portfolio security return for the set of filtered simulated market scenarios as an average of expected returns in the set of expected returns of the respective constituent portfolio security; and6 calculate an expected portfolio return for the set of filtered simulated market scenarios as a weighted average of the calculated expected constituent portfolio security returns for the set of filtered simulated market scenarios, an expected constituent portfolio security return weighted in accordance with the portfolio security weight of the associated portfolio security”.
Claim 18 comprises inter alia the functions or steps of “A machine learning portfolio return computation engine processor-readable, non-transient medium, the medium storing a component collection, storage of the component collection structured with processor-executable instructions structured comprising:
obtain a portfolio return computation request datastructure, the portfolio return computation request datastructure structured specifying a set of simulated market scenarios and a set of filters, the set of simulated market scenarios generated with a set of multi-variate mixture datastructures, each simulated market scenario in the set of simulated market scenarios structured comprising a set of simulated market factor values corresponding to a set of market factors; where a number of simulated market scenarios are structured with a set of learning neural networks and are generated with the trained multi-variate mixture datastructure associated with a respective time quantum period; determine a set of constituent portfolio securities of a portfolio datastructure, each constituent portfolio security in the set of constituent portfolio securities associated with a portfolio security weight of the respective constituent portfolio security in the portfolio datastructure; filter the set of simulated market scenarios based on the set of filters determining a set of filtered simulated market scenarios having simulated market factor values that fall within the range of allowable values for each filter in the set of filters; retrieve a set of expected returns for the set of constituent portfolio securities of the portfolio datastructure for the set of filtered simulated market scenarios; calculate, via at least one processor, for each constituent portfolio security in the set of constituent portfolio securities, an expected constituent portfolio security return for the set of filtered simulated market scenarios as an average of expected returns in the set of expected returns of the respective constituent portfolio security; and calculate an expected portfolio return for the set of filtered simulated market scenarios as a weighted average of the calculated expected constituent portfolio security returns for the set of filtered simulated market scenarios, an expected constituent portfolio security return weighted in accordance with the portfolio security weight of the associated portfolio security“.
Claim 19 comprises inter alia the functions or steps of “A machine learning portfolio return computation engine processor- implemented system, comprising: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 comprising: obtain a portfolio return computation request datastructure, the portfolio return computation request datastructure structured specifying a set of simulated market scenarios and a set of filters, the set of simulated market scenarios generated with a set of multi-variate mixture datastructures, each simulated market scenario in the set of simulated market scenarios structured comprising a set of simulated market factor values corresponding to a set of market factors; where a number of simulated market scenarios are structured with a set of learning neural networks and are generated with the trained multi-variate mixture datastructure associated with a respective time quantum period; determine a set of constituent portfolio securities of a portfolio datastructure, each constituent portfolio security in the set of constituent portfolio securities associated with a portfolio security weight of the respective constituent portfolio security in the portfolio datastructure; filter the set of simulated market scenarios based on the set of filters determining a set of filtered simulated market scenarios having simulated market factor values that fall within the range of allowable values for each filter in the set of filters; retrieve a set of expected returns for the set of constituent portfolio securities of the portfolio datastructure for the set of filtered simulated market scenarios; calculate for each constituent portfolio security in the set of constituent portfolio securities, an expected constituent portfolio security return for the set of filtered simulated market scenarios as an average of expected returns in the set of expected returns of the respective constituent portfolio security; and calculate an expected portfolio return for the set of filtered simulated market scenarios as a weighted average of the calculated expected constituent portfolio security returns for the set of filtered simulated market scenarios, an expected constituent portfolio security return weighted in accordance with the portfolio security weight of the associated portfolio security”.
Claim 20 comprises inter alia the functions or steps of “A machine learning portfolio return computation engine processor- implemented process, 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 executing processor- executable instructions comprising: obtain a portfolio return computation request datastructure, the portfolio return computation request datastructure structured specifying a set of simulated market scenarios and a set of filters, the set of simulated market scenarios generated with a set of multi-variate mixture datastructures, each simulated market scenario in the set of simulated market scenarios structured comprising a set of simulated market factor values corresponding to a set of market factors; where a number of simulated market scenarios are structured with a set of learning neural networks and are generated with the trained multi-variate mixture datastructure associated with a respective time quantum period; determine a set of constituent portfolio securities of a portfolio datastructure, each constituent portfolio security in the set of constituent portfolio securities associated with a portfolio security weight of the respective constituent portfolio security in the portfolio datastructure; filter the set of simulated market scenarios based on the set of filters determining a set of filtered simulated market scenarios having simulated market factor values that fall within the range of allowable values for each filter in the set of filters; retrieve a set of expected returns for the set of constituent portfolio securities of the portfolio datastructure for the set of filtered simulated market scenarios; calculate for each constituent portfolio security in the set of constituent portfolio securities, an expected constituent portfolio security return for the set of filtered simulated market scenarios as an average of expected returns in the set of expected returns of the respective constituent portfolio security; and calculate an expected portfolio return for the set of filtered simulated market scenarios as a weighted average of the calculated expected constituent portfolio security returns for the set of filtered simulated market scenarios, an expected constituent portfolio security return weighted in accordance with the portfolio security weight of the associated portfolio security”.
Those claim limits in bold are identified as claim limitations which recite the abstract idea, while those that are un-bolded are identified as additional elements.
The cited limitations as drafted are systems and methods that, under their broadest reasonable interpretation, covers performance of a method of organizing human activity, but for the recitation of the generic computer components. Further, none of the limitations recite technological implementations details for any of the steps but, instead, only recite broad functional language being performed by the generic use of at least one processor. Portfolio return computation is a fundamental economic practice long prevalent in commerce systems. If a claim limitation, under its broadest reasonable interpretation, covers a fundamental economic principle or practice but for the general linking to a technological environment, then it falls within the organizing human activity grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2, Next, it is determined whether the claim is directed to the abstract concept itself or whether it is instead directed to some technological implementation or application of, or improvement to, this concept, i.e., integrated into a practical application. See, e.g., Alice, 573 U.S. at 223, discussing Diamond v. Diehr, 450 U.S. 175 (1981). The mere introduction of a computer or generic computer technology into the claims need not alter the analysis. See Alice, 573 U.S. at 223—24. “[T]he relevant question is whether the claims here do more than simply instruct the practitioner to implement the abstract idea on a generic computer.” Alice, 573 U.S. at 225.
In the present case, the judicial exception is not integrated into a practical application. The claim limitations are not indicative of integration into a practical application by claiming an improvement to the functioning of the computer or to any other technology or technical field. Further, the claim limitations are not indicative of integration into a practical application by applying or using the judicial exception in some other meaningful way. In particular the claim limits of receiving, storing (datastructures), transmitting data, machine learning, and deep learning neural networks, training, are claimed and described at a high level of generality and are functions any general purpose computer performs such that it amount no more than mere instruction to apply the exception to a particular technological environment. Further, none of the limitations recite technological implementations details for any of the steps but, instead, only recite broad functional language being performed by the generic use of at least one processor. The claim limits also recite the use of a processor, memory, and a network. However, the use of these additional elements described at a high level of generality and perform generic computer functions such that it amount no more than mere instruction to apply the exception to a particular technological environment. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaning limits on practicing the abstract idea. Thus, the claim is directed toward an abstract idea.
Step 2B, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more that the abstract idea(s). As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the abstract idea(s) amounts to no more than mere instructions to apply the exaction using a generic computer component. Mere instruction to apply an exertion using a generic computer component cannot provide an inventive concept. These generic computer components are claimed at a high level of generality to perform their basic functions which amount to no more than generally linking the use of the judicial exception to the particular technological environment of field of use (Specification [2585-2587 2592 2603 2616 2638-2640 2650]) and further see insignificant extra-solution activity MPEP § 2106.05 I. A. iii, 2106.05(b), 2106.05(b) III, 2106.05(g). Thus, the claims are not patent eligible.
As for dependent claims 2-17, these claims recite limitations that further define the same abstract idea noted from the respective independent claims from which they depend. Additionally the claim limits of receiving, storing (including databases), transmitting data, and machine learning are and training are claimed and described at a high level of generality and are functions any general purpose computer performs such that it amount no more than mere instruction to apply the exception to a particular technological environment. Further, none of the limitations recite technological implementations details for any of the steps but, instead, only recite broad functional language being performed by the generic use of at least one processor. The claim limits also recite the use of a processor, memory, and a network. However, the use of these additional elements described at a high level of generality and perform generic computer functions such that it amount no more than mere instruction to apply the exception to a particular technological environment. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaning limits on practicing the abstract idea. Thus, the claim is directed toward an abstract idea. Therefore, the cited dependent claims are considered patent ineligible for the reasons given above.
Prior Art
The claims overcome the prior art of record such that none of the cited prior art reference’s disclosures can be applied to form the basis of a 35 USC § 102 rejection nor can they be combined to fairly suggest in combination, the basis of a 35 USC § 103 rejection when the limitations “obtain a portfolio return computation request, the portfolio return computation request structured specifying a set of simulated market scenarios and a set of filters, the set of simulated market scenarios generated with a set of multi-variate mixture, each simulated market scenario in the set of simulated market scenarios structured comprising a set of simulated market factor values corresponding to a set of market factors; where a number of simulated market scenarios are structured with a set of learning neural networks and are generated with the trained multi-variate mixture associated with a respective time quantum period; determine a set of constituent portfolio securities of a portfolio, each constituent portfolio security in the set of constituent portfolio securities associated with a portfolio security weight of the respective constituent portfolio security in the portfolio; filter the set of simulated market scenarios based on the set of filters determining a set of filtered simulated market scenarios having simulated market factor values that fall within the range of allowable values for each filter in the set of filters; retrieve a set of expected returns for the set of constituent portfolio securities of the portfolio for the set of filtered simulated market scenarios; calculate for each constituent portfolio security in the set of constituent portfolio securities, an expected constituent portfolio security return for the set of filtered simulated market scenarios as an average of expected returns in the set of expected returns of the respective constituent portfolio security; and calculate an expected portfolio return for the set of filtered simulated market scenarios as a weighted average of the calculated expected constituent portfolio security returns for the set of filtered simulated market scenarios, an expected constituent portfolio security return weighted in accordance with the portfolio security weight of the associated portfolio security” are read in the particular environment of the claims. Therefore, the claims may be allowable if amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action.
Response to Arguments
Applicant's arguments with regards to claims have been fully considered but they are not persuasive.
EXAMINER’S RESPONSE TO APPLICANT REMARKS CONCERNING Claim Rejections - 35 USC § 101: Applicant's arguments with regards to 35 USC § 101 have been fully considered but is not persuasive. The examiner has given proper weight to and has followed the 2019 PEG which is the current analysis required for patent eligibility and the basis of the factual determination. Nothing in the claims has been “ignore” as applicant contends. The machine learning, deep learning neural network and trainer are all claimed at a high level of generality and are merely applied to the abstract idea of the claims. The machine learning is claimed at a high level of generality and are merely applied to the abstract idea of the claims. The phrase “multi-variate mixture” as used in the claims and described in the specification appears to be merely the application of an off-the-self product (Specification [0206] “org.apache.spark.ml.clustering.GaussianMixture multi-variate mixture datastructure”) which contain data directed toward the abstract idea. There are no claimed implementation details of an improved database or data structure claimed or described in the specification. The machine learning and deep learning neural network is claimed at a high level of generality and are merely applied to the abstract idea of the claims. There is no improvement to a computer or technology (including to data structures) and does not apply the abstract idea to a particular machine. The examiner notes that a general purpose computer is flexible—it can do anything it is programmed to do. Therefore, the disclosure of a general purpose computer or a microprocessor as corresponding structure for a software function does nothing to limit the scope of the claim and “avoid pure functional claiming.” Further, the “prohibition against patenting abstract ideas ‘cannot be circumvented by attempting to limit the use of the formula to a particular technological environment’ or adding ‘insignificant postsolution activity.’” Bilski v. Kappos, 561 U.S. 593, 610–11 (2010) (quoting Diamond v. Diehr, 450 U.S. 175, 191–92 (1981)). Further, the claims do no apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment. Filtering data is part of the abstract idea and not a technical solution to a problem.
With regard to applicant's argument directed toward case law such as DDR Holdings, AMDOCS …, the cited applications made an improvement to an underlying technology, whereas, the present application used a generic technology and computer to implement an abstract idea. Thus, the cited case law is readily distinguishable from the present claims. As such, the examiner maintains the rejection.
Conclusion
For prior art made of record and not relied upon is considered pertinent to applicant's disclosure see Notice of References Cited items A-E submitted 06/03/2022 used as prior art and in the conclusion section in the office action submitted 06/03/2022.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Gregory A Pollock whose telephone number is (571) 270-1465. The examiner can normally be reached M-F 8 AM - 4 PM.
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/Gregory A Pollock/Primary Examiner, Art Unit 3691
10/06/2025