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 .
DETAILED ACTION
Status of the Claims
This action is in response to the application filed on 12/19/2024.
Claim 1 is pending and examined below.
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.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 1 is directed to a method, which is a process, machine, manufacturer or composition of matter and thus statutory category of invention (Step 1: YES).
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea without significantly more. The claim recites “…acquiring a training data set including a plurality of training samples, each training sample of the plurality of training samples defining a portfolio of financial assets and including a plurality of input features and an output predictor of the performance of the portfolio of financial assets, the plurality of input features including one or more qualitative features and one or more first quantitative features; converting the one or more qualitative features into one or more second quantitative features; training a machine learning model using the one or more first quantitative features, the one or more second quantitative features, and the output predictor for each training sample; receiving a new portfolio of financial assets whose rating indicator is to be generated; generating a new plurality of input features for the new portfolio of financial assets, the new plurality of input features including one or more new qualitative features and one or more new first quantitative features; converting the one or more new qualitative features into one or more new second quantitative features; inputting the one or more new first quantitative features and the one or more new second quantitative features into the trained machine learning model to generate a new output predictor for the new portfolio of financial assets; and generating the rating indicator for the new portfolio of financial assets based at least on the generated new output predictor”. These recited limitations, as drafted, recite a process that, under its broadest reasonable interpretation, covers performance of fundamental economic principles or practices (including mitigating risk, i.e. mitigating and generating the risk for the new portfolio of financial assets) but for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers concepts of fundamental economic principles or practices but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The limitations (besides those that recite the abstract idea) that are all recited at a high level of generality to amount no more than mere instructions to apply the exception using generic computer component. Accordingly, the limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claim recites the limitations that are all recited at a high level of generality to amount to nothing more than an instruction to “apply it”. When viewing the limitations either individually or as an ordered combination, the claim as a whole does not amount to significantly more than the judicial exception because the claim does not include improvements to another technology or technical field, improvements to the function of the computer itself, and does not provide meaningful limitations beyond general linking the use of an abstract idea to a particular technological environment. In effect, the limitations add the words “apply it” (or an equivalent) to the judicial exception, or mere instructions to implement an abstract idea on a computer. Mere instructions to apply an exception using the generic computer component cannot provide an inventive concept. Thus, the claim is not patent eligible.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claim 1 of the instant application is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 12,205,164 (co-pending U.S. Application No. 18/733,186). Both claim 1 of the instant application and claim 1 of U.S. Patent No. 12,205,164 (co-pending U.S. Application No. 18/733,186) are directed to a method of generating a rating indicator. Thus, claim 1 of U.S. Patent No. 12,205,164 teaches or suggests all of the limitations of claim 1 of the instant application. However, claim 1 of U.S. Patent No. 12,205,164 also contains additional limitations not found in claim 1 of the instant application, such as the limitations “…via the programmed data processing device system; wherein the converting includes performing text mining on the one or more qualitative features to segment unstructured text data in the one or more qualitative features into textual snippets, extract a set of reference concepts from the textual snippets, and generate the one or more second quantitative features corresponding to the set of reference concepts; combining via the programmed data processing device system, the one or more first quantitative features and the one or more second quantitative features into a plurality of input feature vectors, each input feature vector of the plurality of input feature vectors being associated with one financial asset of the portfolio of financial assets, for training a neural network model that generates an output predictor for the portfolio of financial assets; training, via the programmed data processing device system, the neural network model using the plurality of input feature vectors including the one or more first quantitative features, the one or more second quantitative features, and the output predictor, wherein the neural network model is a two-stage neural network model including a plurality of first stage neural network models corresponding to the plurality of input feature vectors and one second stage neural network model, wherein each first stage neural network model of the plurality of first stage neural network models is trained using a subset of the input feature vectors of the plurality of input feature vectors as input to generate one or more outputs, and wherein the second stage neural network model is trained using, as input, the one or more outputs from each of the first stage models and generates, as output, the output predictor; wherein the converting includes performing text mining on the one or more new qualitative features to segment unstructured text data in the one or more new qualitative features into textual snippets, extract a new set of reference concepts from the textual snippets, and generate the one or more second quantitative features corresponding to the new set of reference concepts; combining via the programmed data processing device system, the one or more new first quantitative features and the one or more new second quantitative features into a new plurality of input feature vectors, each new input feature vector of the new plurality of input feature vectors being associated with one financial asset of the new portfolio of financial assets; inputting, via the programmed data processing device system, the new plurality of input feature vectors including the one or more new first quantitative features and the one or more new second quantitative features into the trained neural network model to generate a new output predictor for the new portfolio of financial assets”. Accordingly, claim 1 of U.S. Patent No. 12,205,164 is directed to a species of claim 1 of the instant application (see MPEP § 804(II)(B)(2)). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the additional limitations of claim 1 of U.S. Patent No. 12,205,164 so that the rating indicator would be generated more efficiently.
Notes: Gebara et al. (2019/0325524) teaches a method and a system related to techniques for accurate evaluation of a financial portfolio. Techniques described herein may provide computing technology for fast processing of financial performance information being stored across a computer-networked environment. Rather than waiting for the end of trading, for example, the systems process fund prices in real-time and predict certain portions of the financial performance information to within a level of accuracy. The systems further recite the Machine learning which is a branch of artificial intelligence that relates to mathematical models that can learn from, categorize, and make predictions about data. Such mathematical models, which can be referred to as machine-learning models, can classify input data among two or more classes; cluster input data among two or more groups; predict a result based on input data; identify patterns or trends in input data; identify a distribution of input data in a space. Grigg et al. (2018/0040064) teaches a method and a system related to accessing, by an action history system via a network connection, data stored on a database, the data comprising transactions exchanged between a set of entities; generating, by the action history system, a set of performance values for each entity based on the data; for each entity of the set of entities, generating an action score based on at least one performance value of the set of performance values, the action score being an indication of performance of the entity with respect to the set of performance values; receiving a request, from a user, for one or more action probabilities regarding an event associated with a first entity, each action probability being a probability that a second entity responds to the event associated with the first entity within a predetermined time frame; automatically determining the related entities associated with the event; automatically determining the action probability for each entity of the related entities, the action probability being determined based on the action score of the related entity; and automatically causing presentation of the action probabilities within a graphical user interface (GUI) of a display device. Towriss (2017/0161758) teaches a method and a system related to offering, managing, and administering annuities. Annuity companies have for many years implemented computer systems (which are sometimes networked with internal and external systems) that manage and administer annuities which were issued to its customers (the policyholders). These systems can include complex software components that are implemented on computer hardware (and networks) to provide the management and administration of annuities. The machine learning techniques described herein provide the advantage of generating a large set of potential permutations of product features that are tailor-made to a specific customer. The techniques provide a robust process of probing the needs of a customer to fully understand their objectives, risk tolerances, fee constraints, and other factors, independent of any product recommendation. The techniques then leverage a product/price optimization phase that considers every possible permutation of product attribute to arrive at a few recommendations that are both feasible (i.e., the insurance company can issue on those terms) and which maximize compatibility with a user needs profile. It should be understood that the techniques advantageously enable pricing to be performed non-linearly ‘on the fly’ (e.g., more complex than simply adding up a few rider fees).
However, the combination of prior arts of record would be hind-sight reasoning to combine the individual elements disclosed in the prior art in order to achieve Applicant's claimed invention.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tien C. Nguyen whose telephone number is 571-270-5108. The examiner can normally be reached on Monday-Thursday (6am-2pm EST).
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor,
Bennett Sigmond can be reached on 303-297-4411. The fax phone number for the organization where this application or proceeding is assigned is 571-270-6108.
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/TIEN C NGUYEN/Primary Examiner, Art Unit 3694
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