Prosecution Insights
Last updated: July 17, 2026
Application No. 18/675,228

Generating Synthetic Data For Machine Learning Training

Final Rejection §101
Filed
May 28, 2024
Priority
Aug 03, 2023 — IN 202311052251
Examiner
MAGUIRE, LINDSAY M
Art Unit
3619
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
Data-Core Systems Inc.
OA Round
2 (Final)
51%
Grant Probability
Moderate
3-4
OA Rounds
1y 4m
Est. Remaining
83%
With Interview

Examiner Intelligence

Grants 51% of resolved cases
51%
Career Allowance Rate
318 granted / 621 resolved
-0.8% vs TC avg
Strong +32% interview lift
Without
With
+31.9%
Interview Lift
resolved cases with interview
Typical timeline
3y 6m
Avg Prosecution
37 currently pending
Career history
655
Total Applications
across all art units

Statute-Specific Performance

§101
33.6%
-6.4% vs TC avg
§103
45.1%
+5.1% vs TC avg
§102
12.1%
-27.9% vs TC avg
§112
3.9%
-36.1% vs TC avg
Black line = Tech Center average estimate • Based on career data from 621 resolved cases

Office Action

§101
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 Final office action is in response to the application filed on May 28, 2024 and the amendments to the claims filed on April 20, 2026. 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-20 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 with a forecasting accuracy that is at least equivalent to a forecasting accuracy achieved by training the machine learning model with a larger quantity of actual historical price data spanning a second timeframe that is longer than the first timeframe. 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. 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 IRL 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 2-13 and 15-20 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, 9-12, 15, 16, and 20 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 1-13 and 15-20 are directed to an abstract idea. Thus, the claims 1-20 are not patent-eligible. Response to Arguments Applicant's arguments filed April 20, 2026 have been fully considered but they are not persuasive. Applicant’s arguments regarding the 35 USC 101 rejection of record (Remarks, pages 1-4) are acknowledged, however they are not persuasive. Specifically, applicant’s argue that, “the claim elements as a whole integrate any alleged abstract idea into a practical application” (Remarks, pages 2-3). 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. Applicant’s arguments that the claims are analogous to those of Desjardins and, “recite a specific technical solution to the operation of a machine learning system” (Remarks, pages 3-4), are acknowledged, however they are not persuasive. MPEP 2106.04(d)(1) sets forth that, “the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement in the functioning of a computer, or an improvement to other technology or a technical field. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement only in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine that the claim improves technology or a technical field. Second, if the specification sets forth an improvement in technology or a technical field, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement”. In Ex Parte Desjardins, “the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. Indeed, enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation. However, the current claims are different, as they fail to set forth any particular technology to perform the actions of the claims. The current claims do not purport to have a technological solution to a technological problem but rather the focus of the claims is not on such an improvement in computers as tools, but on certain independently abstract ideas that use computers as tools. 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 LINDSAY M MAGUIRE whose telephone number is (571)272-6039. The examiner can normally be reached Monday to Friday 8:30 to 5:00. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anita Coupe can be reached at (571) 270-3614. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. Lindsay Maguire 4/20/26 /LINDSAY M MAGUIRE/Primary Examiner, Art Unit 3619
Read full office action

Prosecution Timeline

May 28, 2024
Application Filed
Feb 13, 2026
Non-Final Rejection mailed — §101
Apr 20, 2026
Response Filed
May 07, 2026
Final Rejection mailed — §101 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

3-4
Expected OA Rounds
51%
Grant Probability
83%
With Interview (+31.9%)
3y 6m (~1y 4m remaining)
Median Time to Grant
Moderate
PTA Risk
Based on 621 resolved cases by this examiner. Grant probability derived from career allowance rate.

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