DETAILED ACTION
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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 04/13/2026 has been entered.
Response to Arguments
Applicant's arguments filed 03/25/2026 have been fully considered, but they are not fully persuasive. The updated 35 USC § 101 rejection of 1, 4-10, and 13-19 are applied in light of Applicant's amendments.
The Applicant argues "Applicant submits that amended claim 1 is directed to a practical application of technology and overcomes the outstanding rejections under 35 USC 101.” (Remarks 03/25/2026)
In response, the Examiner respectfully disagrees. The Examiner has thoroughly reviewed and analyzed the claims, arguments, and specification. The Examiner attempted to find eligible subject matter in the claims and/or specification, but was unsuccessful; and thus, is unable to provide any suggestions/Examiner’s amendment to overcome the 101 rejection.
The claimed subject matter, is directed to an abstract idea by reciting mathematical relationships, mathematical formulas or equations, mathematical calculations which falls into the “Mathematical concepts” group within the enumerated groupings of abstract ideas set.
A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.
The mere nominal recitation of a generic computer does not take the claim limitation out of methods of the mathematical concepts grouping.
The claimed subject matter is merely claims a method for calculating and analyzing information regarding datasets. Although it may be intended to be performed in a digital environment, the claimed subject matter (as currently claimed in the independent claim) speaks to the calculating and analyzing (modeling and projecting) data. Such steps are not tied to the technological realm, but rather utilizing technology to perform the abstract idea. The steps of calculating data, training/updating models, and generating a model can be performed by a human (mental process/pen and paper). The practice of calculating information and constructing models with set parameters and timelines can be performed without computers, and thus are not tied to technology nor improving technology.
The solution mentioned in the amended limitation is not implemented/integrated into technology and thus not an improvement to the technical field. Further, there is no integration into a practical application as the claims can be interpreted as humans per se, as the claims fail to tie the steps to technology; insignificant extra solution activities (which are merely calculating and/or analyzing data).
The use of Artificial Intelligence (AI), machine learning (ML) models, and/or artificial neural networks (ANN) fall within the realm of abstract ideas. They are, at their core, mathematical algorithms implemented on a computer. As highlighted in Examples 47-49 of the 2024 Patent Subject Matter Eligibility Guidance, the USPTO has consistently viewed claims directed to such models as being drawn to abstract ideas. These examples illustrate claims that, while couched in the language of specific applications, ultimately boil down to mathematical relationships and calculations.
The steps relied upon by the Applicant as recited does not improve upon another technology, the functioning of the computer itself, or allow the computer to perform a function not previously performable by a computer. The claims do not mention to any use of a specialized computer and/or processor. The Applicant is using generic computing components (processors) to perform in a generic/expected way (obtaining and analyzing data).The abstract idea is not particular to a technological environment, but is merely being applied to a computer realm. The process of calculating and analyzing data specifically for service project(s), and performing additional analysis can be done without a computer, and thus the claims are not “necessarily rooted", but rather they are utilizing computer technology to perform the abstract idea. The Examiner does not recognize any elements of the Applicant's claims and/or specification that would improve or allow the computer to perform a function(s) not previously performable by the computer or improve the functioning of the computer itself. It is insufficient to indicate that the claims are novel and non-obvious and thus contain “something more.” Just because the components may perform a specialized function does not mean that that the computer components are specialized. As such the application of the abstract idea of collecting and analyzing data regarding a service system, and performing correlation analysis is insufficient to demonstrate an improvement to the technology.
The use of Artificial Intelligence (AI), machine learning (ML) models, and/or artificial neural networks (ANN) fall within the realm of abstract ideas. They are, at their core, mathematical algorithms implemented on a computer. As highlighted in Examples 47-49 of the 2024 Patent Subject Matter Eligibility Guidance, the USPTO has consistently viewed claims directed to such models as being drawn to abstract ideas. These examples illustrate claims that, while couched in the language of specific applications, ultimately boil down to mathematical relationships and calculations. While this claim appears to have a practical application, a closer examination reveals that the core of the invention is the underlying mathematical model and its training process. Even if the claim recites specific steps related to data collection, preprocessing, or post-processing, these steps often represent well-understood, conventional activities. As demonstrated in Examples 47-49, adding such conventional elements to a claim directed to an abstract idea does not necessarily transform it into a patent-eligible application. These examples illustrate situations where the additional steps were deemed insufficient to provide an "inventive concept" that meaningfully narrowed the scope of the abstract idea. In the context of machine learning, simply collecting and preparing data for input into a model, or applying the model's output to a particular problem, falls into this category of conventional activity. Furthermore, “training a downstream machine learning model” recites training at a high level, with no particular technique for how the model is trained or how its operation is improved, and thus amounts to mere instructions to apply the abstract idea using a generic machine learning model (MPEP 2106.05(f)). See Recentive Analytics, Inc v. Fox Corp (Fed. Cir. 2025) (“Iterative training using selected training material and dynamic adjustments based on real time changes are incident to the very nature or machine learning.”).
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, 4-10, and 13-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 1, 4-10, and 13-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is 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.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the method (claims 1-9), apparatus (claims 10-18), and CRM (claims 19-20) are directed to potentially eligible categories of subject matter (i.e., process, machine, and article of manufacture respectively). Thus, Step 1 is satisfied.
With respect to Step 2, and in particular Step 2A Prong One, it is next noted that the claims recite an abstract idea by reciting mathematical relationships, mathematical formulas or equations, mathematical calculations which falls into the “Mathematical concepts” group within the enumerated groupings of abstract ideas set.
A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.
The limitations reciting the abstract idea(s) (Mathematical concepts), as set forth in exemplary claim 1, are: receiving… a first dataset that includes a plurality of original data points, each respective original data point including a first coordinate that relates to sensitive demographic features, a second coordinate that relates to decision- making features, and a third coordinate that relates to a decision outcome…; determining…a demographic parity constraint to be applied to the first dataset; generating…a second dataset that includes a plurality of synthetic data points, each respective synthetic data point including the first coordinate that relates to the sensitive demographic features, the second coordinate that relates to the decision-making features, and the third coordinate that relates to the decision outcome …; computing…a set of respective sample-level weights that correspond to each synthetic data point included in the plurality of synthetic data points; and generating…a third dataset by applying the set of respective sample-level weights to the second dataset, wherein the computing of the set of respective sample weights dataset, wherein the third dataset reduces dimensionality of the first dataset such that a number of datapoints included in the third dataset is smaller than a number of datapoints included in the first dataset by a factor of at least ten while maintaining the demographic parity of the first dataset, wherein the determining of the demographic parity constraint comprises selecting a maximum fairness violation threshold value that relates to a distance between a conditional distribution of the third dataset with respect to the third coordinate and a target distribution of the first dataset with respect to the third coordinate, wherein the computing of the set of respective sample weights comprises minimizing…a Wasserstein distance between the first dataset and a weighted version of the second dataset while satisfying the demographic parity constraint, wherein the generating of the third dataset includes iteratively computing a centroid for each subset in a partition of the second dataset and subsequently re-partitioning an input based on closeness to such centroids, wherein the third dataset is…wherein the first dataset includes a first predetermined number of data points that is equal to N, each of the second dataset and the third dataset includes a second predetermined number of data points that is equal to M, and N is greater than M by at least a factor of ten… Independent claims 10 and 19 recite the apparatus and CRM for performing the method of independent claim 1 without adding significantly more. Thus, the same rationale/analysis is applied.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. The additional elements are directed to by the at least one processor… that is generated by a machine learning model… wherein the third dataset is stored in a memory and utilized in training a downstream machine learning model…wherein the downstream machine learning model is to be trained with the third dataset and not with the first dataset that is greater in size by at least a factor often over the third dataset, and wherein the training of the downstream machine learning model with the third dataset generates an output representative of the first dataset in accordance with the demographic parity constraint; A computing apparatus for generating synthetic data that corresponds to an original dataset while maintaining demographic parity, the computing apparatus comprising: a processor; a memory; and a communication interface coupled to each of the processor and the memory, wherein the processor is configured to…; A non-transitory computer readable storage medium storing instructions for generating synthetic data that corresponds to an original dataset while maintaining demographic parity, the storage medium comprising executable code which, when executed by a processor, causes the processor to…; (as recited in claims 1,10, and 19). However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
The additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h).
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitation(s) is/are directed to: by the at least one processor… that is generated by a machine learning model… wherein the third dataset is stored in a memory and utilized in training a downstream machine learning model…;; A computing apparatus for generating synthetic data that corresponds to an original dataset while maintaining demographic parity, the computing apparatus comprising: a processor; a memory; and a communication interface coupled to each of the processor and the memory, wherein the processor is configured to…; A non-transitory computer readable storage medium storing instructions for generating synthetic data that corresponds to an original dataset while maintaining demographic parity, the storage medium comprising executable code which, when executed by a processor, causes the processor to…; (as recited in claims 1,10, and 19) for implementing the claim steps/functions. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim.
In addition, Applicant’s Specification (paragraph [0037]) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. See, e.g., Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself. Further, the courts have found the presentation of data to be a well-understood, routine, conventional activity, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 (see MPEP 2106.05(d)).
The dependent claims (3-9, 12-18, 19) are directed to the same abstract idea as recited in the independent claims, and merely incorporate additional details that narrow the abstract idea via additional details of the abstract idea. For example claims 4-9 “wherein the first dataset includes a first predetermined number of data points that is equal to N, each of the second dataset and the third dataset includes a second predetermined number of data points that is equal to M, and N is greater than M by at least a factor often; wherein the determining of the demographic parity constraint comprises selecting a maximum fairness violation threshold value that relates to a distance between a conditional distribution of the third dataset with respect to the third coordinate and a target distribution of the first dataset with respect to the third coordinate; further comprising reformulating the minimizing of the Wasserstein distance as a linear program (LP); further comprising performing the minimizing of the Wasserstein distance by applying a predetermined majority minimization algorithm to the LP; herein the machine learning model is configured to use an artificial intelligence technique for making a decision based on input data that relates to a person, and wherein the decision relates to at least one from among a consumer finance question, a health insurance question, and a hiring question; wherein the sensitive demographic features include at least one from among race, gender, national origin, and disability; wherein the decision-making features include at least one from among a level of education, a grade point average (GPA), and a level of income; wherein the first dataset includes one from among an Adult dataset, a German Credit dataset, a Communities and Crime dataset, and a Drug dataset”, without additional elements that integrate the abstract idea into a practical application and without additional elements that amount to significantly more to the claims. The remaining dependent claims (11-18 and 20) recite the apparatus and CRM for performing the method of claims 4-9. Thus, the same rationale/analysis is applied. Thus, all dependent claims have been fully considered, however, these claims are similarly directed to the abstract idea itself, without integrating it into a practical application and with, at most, a general-purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea itself.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Beddo; Michael Ervin. SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR FORECASTING PRODUCT SALES, .U.S. PGPub 20140108094 The present invention relate to systems, methods, and computer program products for determining forecasting data relating to a product using a neural network and accessing that forecasting data. In some embodiments, a system is provided that includes (a) forecasting apparatus, which stores product information and a neural network; and (b) a computing system that access the forecasting apparatus via a web portal and transmits some or all of the product information to the forecasting apparatus. In some embodiments, the forecasting apparatus is configured to determine an initial sales forecast using at least a portion of the product information and the neural network, modify the initial sales forecast to generate a final sales forecast, and present the final sales forecast to the computing system via the web portal.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Arif Ullah, whose telephone number is (571) 270-0161. The examiner can normally be reached from Monday to Friday between 9 AM and 5:30 PM.
If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Beth Boswell, can be reached at (571) 272-6737. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
/Arif Ullah/Primary Examiner, Art Unit 3625
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