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
This Non-Final office action is in response to the application filed on May 28, 2024, the amendments to the claims filed on April 20, 2026, and the Request for Continued Examination filed on August 7, 2026.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 August 7, 2026 has been entered.
Drawings
The drawings were received on April 20, 2026. These drawings are accepted.
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 1-8 and 11-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-8 and 11-19 are directed to a system, method, or product which are/is one of the statutory categories of invention. (Step 1: YES).
The Examiner has identified independent method Claim 1 as the claim that represents the claimed invention for analysis and is similar to independent product Claim 14. Claim 1 recites the limitations of receiving an actual dataset comprising a time-ordered sequence of actual historical price data of an actual security during a first timeframe, wherein the actual security is listed on a financial exchange, and wherein the time-ordered sequence of actual historical price data comprises a market price of the actual security on the financial exchange at each time point of a plurality of time points during the timeframe; based on the actual dataset, generating a plurality of synthetic datasets for a plurality of fictional securities during the first timeframe, wherein each fictional security of the plurality of fictional securities is not listed on any financial exchange, wherein an individual synthetic dataset for an individual fictional security comprises a synthetic time-ordered sequence of historical price data for the fictional security, and wherein the time-ordered sequence of synthetic historical price data comprises a synthetic price of the fictional security at each time point of the plurality of time points during the timeframe; and training a machine learning model with data comprising the actual dataset and the plurality of synthetic datasets, wherein after training the machine learning model, the machine learning model is configured to forecast future price data of the actual security; after training the machine learning model with data comprising the accrual dataset and the plurality of synthetic datasets, predicting a future price for the actual security; after training the machine learning model, receiving a subsequent actual dataset comprising a new time-ordered sequence of actual historical price data for the actual security during a subsequent timeframe, wherein the subsequent actual dataset includes market price data of the actual security on the financial exchange at each time point of a plurality of time points during the subsequent timeframe; based on the subsequent actual dataset, generating a subsequent synthetic dataset for each fictional security during the subsequent timeframe, wherein an individual subsequent synthetic dataset for an individual fictional security comprises a time-ordered sequence of synthetic price data for the fictional security comprising a synthetic price of the fictional security at each time point of the plurality of time points during the subsequent timeframe; generating an updated actual dataset by appending the subsequent actual dataset with at least a portion of the previously-received actual dataset; generating a plurality of updated synthetic datasets by generating an updated synthetic dataset for each fictional security, wherein generating an updated synthetic dataset for an individual fictional security comprises appending the subsequent synthetic dataset for the fictional security with at least a portion of the previously-generated synthetic dataset for the fictional security; and re-training the machine learning model on data comprising the updated actual dataset and the plurality of updated synthetic datasets, wherein after re-training, the machine learning model is configured to forecast future price data of the actual security
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity/mathematical concepts. Training a model to forecast a future price of a security recites a fundamental economic practice/mathematical calculations. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a fundamental economic practice/mathematical calculations, then it falls within the “Certain Methods of Organizing Human Activity/Mathematical Concepts” grouping(s) of abstract ideas. Accordingly, the claim recites an abstract idea. There are no computer components generic or otherwise recited in Claim 1. The processors of the computing system in Claim 14 are only recited within the preamble. Performing the judicial exception steps using ML merely confines the use of the abstract idea to a particular technological environment and thus fails to add an inventive concept to the claims. See MPEP 2105(h). The generating a plurality of synthetic datasets in Claims 1 and 14 appears to be just software. Claim 14 is also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
This judicial exception is not integrated into a practical application. In particular, the claims only recite processors of the computing system in the preamble of Claim 14 and generating a plurality of synthetic datasets software in Claims 1 and 14. The computer hardware is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The training of a machine learning model with received data and the plurality of synthetic datasets to configure the model to forecast a future price of the actual security are described at a high level of generality and are insignificant extra-solution activity. (see MPEP 2105.06(g)) Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claims 1 and 14 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. The training of the machine learning model is extra-solution activity. The term “extra-solution activity” can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent. (MPEP 2105.06(g)) As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See Applicant’s specification para. [0139-0145] about implementation using general purpose or special purpose computing devices and MPEP 2106.05(f) where applying a computer as a tool is not indicative of significantly more. In addition, performing the judicial exception steps using ML merely confines the use of the abstract idea to a particular technological environment and thus fails to add an inventive concept to the claims. See MPEP 2105(h). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus claims 1 and 14 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 2-8, 11-13, and 15-19 further define the abstract idea that is present in their respective independent claims 1 and 14 and thus correspond to Certain Methods of Organizing Human Activity/Mathematical Concepts and hence are abstract for the reasons presented above. Claims 2-6, 11, 12, 15, and 16 further set forth details for generating the synthetic data set relating to the mathematical formulas/calculations without including details beyond insignificant extra-solution activity. Claims 7, 8, 17, and 18 further include details about the types of features of the datasets without adding significantly more to the judicial exception. Claims 13 and 19 further defines the actual security dataset without adding significantly more to the judicial exception. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the claims 2-8, 11-13, and 15-19 are directed to an abstract idea. Thus, the claims 1-8 and 11-19 are not patent-eligible.
Response to Arguments
Applicant's arguments filed August 7, 2026 have been fully considered but they are not persuasive.
Applicant’s arguments regarding the 35 USC 101 rejection of record (Remarks, pages 1-2) are acknowledged, however they are not persuasive. Specifically, applicant’s argue that, “the independent claims are amended to recite the subject matter of Figures 9A and 9B” (Remarks, pages 1-2). However, claim 1 fails to recite any computer components generic or otherwise and the processors of the computing system in Claim 14 are only recited within the preamble and not within the body of the claim as performing any of the functional elements of the claim. The absence of structure within the claims means that there are no additional elements and therefore nothing to produce a practical application. Here, the claims are clearly focused on the combination of those abstract-idea processes. The advance they purport to make is a process of gathering and analyzing information of a specified content, and not any particular assertedly inventive technology for performing those functions. They are therefore directed to an abstract idea.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LINDSAY M MAGUIRE whose telephone number is (571)272-6039. The examiner can normally be reached Monday to Friday 8:30 to 5:00.
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Lindsay Maguire
9/4/26
/LINDSAY M MAGUIRE/Primary Examiner, Art Unit 3619
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