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 04/10/2026 and Applicant’s communication regarding application 18/210442 filed 04/10/2026.
Claims 9, 11, and 15-18 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 15 and any claims which depend therefrom are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. Applicant introduces new matter to claims 15. Applicant amended claims 15 recite the limit (or an equivalent) "wherein the server computing device transmits the 2D matrix images and corresponding generated labels to a remote computing device and receives, from the remote computing device, one or more changed labels for corresponding ones of the 2D matrix images”. Paragraph [0049] is the only mention of 2D matrix images. However, the examiner can find no support for the cited claim limitation within the paragraph. As such, the amended claims as cited adds new matter as claimed and is rejected along with any claims which depend therefrom fail to comply with the written description requirement.
Claims 17 and any claims which depend therefrom are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. Applicant introduces new matter to claims 17. Applicant amended claims 17 recite the limit (or an equivalent) "transmitting, by the server computing device, the 2D matrix images and corresponding generated labels to a remote computing device; and receiving, by the server computing device, one or more changed labels for corresponding ones of the 2D matrix images from the remote computing device”. Paragraph [0049] is the only mention of 2D matrix images. However, the examiner can find no support for the cited claim limitation within the paragraph. As such, the amended claims as cited adds new matter as claimed and is rejected along with any claims which depend therefrom fail to comply with the written description requirement.
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 9, 11, and 15-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of prediction of investment philosophy alignment for one or more portfolio managers 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 claim 9 and all claims which depend from it are directed toward a system, and independent claim 11 and all claims which depend from it are directed toward a method. 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 9 comprises inter alia the functions or steps of “A system for generating a discriminative investment classification model using noisy ground truth data, the system comprising a server computing device having a memory for storing computer-executable instructions and a processor that executes the computer-executable instructions to:generate noisy labels for a first corpus of unlabeled investment data by applying a plurality of labeling functions to the unlabeled investment data, each labeling function comprising programmatic code corresponding to one or more rules or heuristics that express weak supervision;aggregate the noisy labels into a label matrix; learn a deep generative model using the first corpus of unlabeled investment data and the label matrix, including digesting the labeling functions according to labeling propensity, accuracy, and pairwise correlation, and learning one or more parameters of the deep generative model without access to ground-truth labels by minimizing negative log marginal likelihood; execute the deep generative model to generate probabilistic training labels for the unlabeled investment data; generate a probabilistic training dataset using the first corpus of unlabeled training data and the probabilistic training labels; train a discriminative investment classification model using the probabilistic training dataset as input; sample a re-training dataset from a second corpus of unlabeled training investment data using classification uncertainty sampling; execute the discriminative investment classification model on the re-training dataset to generate labels for the re-training dataset; transmit the re-training dataset and the generated labels to a remote computing device for review and receive one or more changed labels from the remote computing device; and re-train the discriminative investment classification model using the changed labels”.
Claim 11 comprises inter alia the functions or steps of “A computerized method of generating a discriminative investment classification model using noisy ground truth data, the method comprising: generating, by a server computing device, noisy labels for a first corpus of unlabeled investment data by to the unlabeled investment data, each labeling function comprising programmatic code corresponding to one or more rules or heuristics that express weak supervision; aggregating, by the server computing device, the noisy labels into a label matrix; learning, by the server computing device, a deep generative model using the first corpus of unlabeled investment data and the label matrix, including digesting the labeling functions according to labeling propensity, accuracy, and pairwise correlation, and learning one or more parameters of the deep generative model without access to ground-truth labels by minimizing negative log marginal likelihood; executing, by the server computing device, the deep generative model to generate probabilistic training labels for the first corpus of unlabeled investment data; generating, by the server computing device, a probabilistic training dataset using the unlabeled training data and the probabilistic training labels; training, by the server computing device, a discriminative investment classification model using the probabilistic training dataset as input; and sampling, by the server computing device, a re-training dataset from a second corpus of unlabeled investment data using classification uncertainty sampling; executing, by the server computing device, the discriminative investment classification model on the re-training dataset to generate labels for the re-training dataset: transmitting, by the server computing device, the re-training dataset and the generated labels to a remote computing device for review and receiving one or more changed labels from the remote computing device; and re-training, by the server computing device, the discriminative investment classification model using the changed labels”.
Those claim limitations 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. Prediction of investment philosophy alignment for one or more portfolio managers 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 claims contain the following additional elements: a system; a discriminative investment classification model; a server computing device; a memory; computer-executable instructions; a processor; automatically; a deep generative model; train a discriminative investment classification model; train a classification model; a remote computing device. However, the specification description of the additional elements a system ([Figures 1 and 2, element 100] [0044]); a discriminative investment classification model ([0127] “…Example structures for the discriminative investment classification model 1209 can include, but are not limited to, logistic regression, support vector machine, neural networks, Random Forest, among others …”); a server computing device ([Figures 1 and 2, element 200] [0044]); a memory ([Figures 1 and 2, element 204] [0044]); computer-executable instructions ([0073-0074]); a processor ([Figures 1 and 2, element 202] [0044]); automatically (interpreted as a programmed computer); a deep generative model ([0122] [0126]); train a discriminative investment classification model ([0127]); train a classification model ([0041] [0067]); a remote computing device ([0018-0019] [0080] ); are at a high level of generality using exemplary language or as part of a generic technological environment 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. 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 as cited above for additional elements) 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 15-18, these claims recite limitations that further define the same abstract idea using previously identified additional elements noted from the respective independent claims from which they depend. In addition, the cited dependent claims recite the additional elements:
a plurality of 2D matrix images generated by converting time-series investment data using a Gramian Angular Difference Field (GADF) algorithm ([claims 15 and 17]);
the server computing device transmits the 2D matrix images and corresponding generated labels to a remote computing device and receives, from the remote computing device, one or more changed labels for corresponding ones of the 2D matrix images ([claims 15 and 17])
the discriminative investment classification model comprises a convolutional neural network trained using the 2D matrix images as image inputs, each time series of a metric forming a channel to the convolutional neural network ([claims 16 and 18]).
However, the specification description of the additional elements a plurality of 2D matrix images generated by converting time-series investment data using a Gramian Angular Difference Field (GADF) algorithm ([0049]);
the server computing device transmits the 2D matrix images and corresponding generated labels to a remote computing device and receives, from the remote computing device, one or more changed labels for corresponding ones of the 2D matrix images (the claim is not supported but 2D matrix images are mentioned in [0049]);
the discriminative investment classification model comprises a convolutional neural network trained using the 2D matrix images as image inputs, each time series of a metric forming a channel to the convolutional neural network ([0049]). are at a high level of generality using exemplary language or as part of a generic technological environment 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. Even in combination, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. Therefore, the cited dependent claims are ineligible.
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 directed toward the abstract idea of prediction of investment philosophy alignment for one or more portfolio managers 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 are not persuasive. Regarding applicant’s arguments that the characterization of prediction of investment philosophy alignment for one or more portfolio managers no longer applies and that as amended, claim 9 is directed to a specific machine-learning training and re-training architecture that materially alters how a computer system generates, supervises, and refines classification models in the presence of noisy and unlabeled data”, the examiner disagrees. Paragraphs [0003-0004] describe the problem where portfolio managers often work with teams of analysts and researchers to develop and apply a successful investment philosophy. The exact phrase “investment philosophy alignment for one or more portfolio managers” appears in Figure 13 and paragraphs [0018 0019 0022 0023 0127] and the data (labels) within the claims are understood from the specification to be abstract ideas involving investment philosophy alignment for one or more portfolio managers. The claims still merely apply the deep generative model and classification models to the abstract idea of the claims. The examiner can find nothing describing an improvement to any of the models and the applicant has not specifically indicated the described improvement to any of the models within the specification or claims.
With regard to applicant's argument directed toward Enfish, the application in Enfish made an improvement to an underlying technology. The examiner maintains that the present application used a generic technology (machine learning models) and computer to implement an abstract idea. There is no claimed improvement to creating, training, and re-training machine learning classification models or to the computer. Thus, Enfish is readily distinguishable from the present claims. Machine learning models typically are re-training, which is implicit in a machine “learning” models. There is no claimed improvement to how a machine learning model is re-trained within the 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 submitted 08/25/2025 used as prior art and in the conclusion section in the office action submitted 08/25/2025.
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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abhishek Vyas can be reached on 571 270-1836. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Gregory A Pollock/Primary Examiner, Art Unit 3691 05/28/2026
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