DETAILED 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 3/04/2025 has been entered.
Claim Objections
Claims 15 and 16 are objected to because of the following informalities:
Claims 15 and 16 should be deleted as their limitations are put into the independent claim
Appropriate correction is required.
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, 7 - 15, and 18 - 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As to claims 1, 10, and 12,
Step 2A, Prong One
The claim recites in part:
discriminate whether the original data embedding vector and the fake data embedding vector are fake data.
Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas. For example, a human can evaluate the original data embedding vector and the fake data embedding vector and determine if they are fake data if their characteristics are not realistic.
The claim further recites:
wherein the likelihood is derived from a distribution from a distribution of an input latent vector having a standard Gaussian distribution N (0,1) using variable conversion theory, including obtaining Tr(af/az(t)), the regulation term having a regulation parameter as a scale factor such that, when the regulation parameter is increased in a positive direction, similarity to original data is increased and a degree of privacy is decreased, and when the regulation parameter is increased in a negative direction, the similarity to the original data is decreased and the degree of privacy is increased,
Under the broadest reasonable interpretation, these limitations are process steps that cover a mathematical relationship, mathematical formula, or algorithm, which is identified as an abstract idea. Specifically, the recited likelihood is just a understanding and processing a mathematical concept.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
receive and/or generate an original data embedding vector
receive the original data embedding vector and the fake data embedding vector
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The claim further recites:
generate a fake data embedding vector by using an invertible neural network, wherein the invertible neural network is configured based on a neural ordinary differential equation (ODE) defined by a differential equation
wherein the generated fake data embedding vector is used to synthesize a privacy-preserving of original data for downstream machine learning on a computer system
these elements are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
The claim further recites:
train the invertible neural network based on a loss function including a regulation term including a likelihood that is a probability distribution of the original data embedding vector for generating an estimated data embedding vector from the original data embedding vector
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The claim further recites a hardware processor, generating unit, discriminating unit, computer-readable storage medium, and computer system which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
In addition, the recitation of generating synthetic data, generative adversarial network, embedding vector, invertible neural network, and downstream machine learning amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
receive and/or generate an original data embedding vector
receive the original data embedding vector and the fake data embedding vector
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The limitations:
generate a fake data embedding vector by using an invertible neural network, wherein the invertible neural network is configured based on a neural ordinary differential equation (ODE) defined by a differential equation
wherein the generated fake data embedding vector is used to synthesize a privacy-preserving of original data for downstream machine learning on a computer system
are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
The claim further recites:
train the invertible neural network based on a loss function including a regulation term including a likelihood that is a probability distribution of the original data embedding vector for generating an estimated data embedding vector from the original data embedding vector
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The hardware processor, generating unit, discriminating unit, computer-readable storage medium, and computer system are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The recitation of generating synthetic data, generative adversarial network, embedding vector, invertible neural network, and downstream machine learning amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 2 and 13, the limitations
the invertible neural network comprises:
a first artificial neural network configured to generate an original data latent vector from the original data embedding vector; and
a second artificial neural network configured to generate an estimated data embedding vector from the original data latent vector,
wherein the first artificial neural network and the second artificial neural network are in an inverse function
are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
As to claims 3 and 14, the limitations “wherein the second artificial neural network is configured to receive an input latent vector having a normal distribution and to generate the fake data embedding vector” amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
As to claims 4 and 15, the limitations “wherein the second artificial neural network is configured to receive an input latent vector having a normal distribution and to generate the fake data embedding vector” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
As to claims 7, 11, and 18, the limitations “wherein the processor is further configured to: receive original data and generate the original data embedding vector by converting the original data into data in a lower dimension; and reconstruct data in the same dimension as the original data from the original data embedding vector” are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
As to claims 8 and 19, the limitations “wherein the processor is further configured to use a third artificial neural network trained to reconstruct data similar to the original data from the original data embedding vector” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
As to claims 9 and 20, the limitations “wherein the processor is further configured to receive the fake data embedding vector and generate fake data by using the third artificial neural network” are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
Response to Arguments
Applicant's arguments filed 9/08/2025 have been fully considered but they are not persuasive.
Claim Rejections - 35 USC § 103
The newly added limitations overcome the 103 Rejection and the 103 Rejection has been withdrawn.
Claim Rejections - 35 USC § 101
The 101 Rejection still has not been overcome. The claims are abstract and the steps in the claims can be completed with a mental process and/or generic computer components. Additionally, the steps in the claims do not describe an improvement of technology in any way.
The applicant argues:
While the claims involve calculations as part of training a model, the pending claims do not merely recite a mathematical relationship in the abstract. As amended, the claims require a specific technical implementation of the invertible neural network, namely that the invertible neural network is configured based on a neural ordinary differential equation (ODE) defined by a differential equation. This is not a recitation of an idea that can be performed mentally; rather, itis a particular machine-implemented model formulation that necessarily requires computer execution and constrains the claim scope to a defined computational architecture. The specification explains that configuring the INN as a neural ODE reduces model complexity and avoids selection of a particular number of layers.
The examiner disagrees. The claims still recite a mathematical concept, specifically calculations and relationships associated with train a module using a neural ordinary differential equation (ODE). Differential equations constitute mathematical formulars and computations, which fall within the abstract idea category of mathematical concepts under the USPTO eligibility guidance. The recitation of invertible neural network doesn’t not remove the mathematical nature of the limitation, as it merely describes the framework in which the mathematical calculations are performed.
As per MPEP 2106.04(a)(2)(III)(C)), a claim that requires a computer may still recite a mental process. In evaluating whether a claim that requires a computer recites a mental process, examiners should carefully consider the broadest reasonable interpretation of the claim in light of the specification. For instance, examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process
The applicant argues:
The amended claims further require that the "likelihood" in the regulation term is derived from a standard Gaussian latent distribution N(0,I) using variable conversion theory, including obtaining a specific trace term Tr(2f/dz(t)).
These limitations tie the alleged mathematical operations to a particular, concrete implementation of a likelihood-computation mechanism used in training a neural-ODE-based invertible model. In other words, the claims are not merely directed to "optimizing" or "balancing" privacy and utility as an end result; instead, they recite how the computing device must implement the model and compute likelihood during training. Accordingly, the claims are integrated into a practical application, namely, a specific synthetic-data generation pipeline implemented through a neural-ODE invertible network with a defined likelihood derivation mechanism, rather than an abstract idea "as such."
The examiner disagrees. Although the claims recite deriving likelihood from a Gaussian laten distribution using variable conversion theory and computing a trace term, these limitations still described mathematical operations used in a training model. The recited neural-ODE based invertible model merely provides the environment in which the mathematical calculations are performed.
The claims do not apply the mathematical concept in a particular technological improvement or impractical application but instead use the calculations to train or generate data using a machine-learning model, which as claimed is a form of data manipulation and analysis. The additional limitations therefore do not meaningfully limit the judicial exception.
The claim further recites “train the invertible neural network based on a loss function including a regulation term including a likelihood that is a probability distribution of the original data embedding vector for generating an estimated data embedding vector from the original data embedding vector”. No detail is given as to how the training is performed or the task that it is trained to perform. Consequently, this limitation merely appears to be a generic training process performed on the general purpose computer to apply the abstract idea and is not sufficient to integrate the abstract idea into a practical application or amount to significantly more (MPEP 2106.05(f)).
The applicant argues:
The amended claims further require that the "likelihood" in the regulation term is derived from a standard Gaussian latent distribution N(0,I) using variable conversion theory, including obtaining a specific trace term Tr(2f/dz(t)).
These limitations tie the alleged mathematical operations to a particular, concrete implementation of a likelihood-computation mechanism used in training a neural-ODE-based invertible model. In other words, the claims are not merely directed to "optimizing" or "balancing" privacy and utility as an end result; instead, they recite how the computing device must implement the model and compute likelihood during training. Accordingly, the claims are integrated into a practical application, namely, a specific synthetic-data generation pipeline implemented through a neural-ODE invertible network with a defined likelihood derivation mechanism, rather than an abstract idea "as such."
The examiner disagrees. The newly added limitations amount to mathematical calculations used in training a machine learning model. Implementing these calculations within an invertible neural network configured as an neural ODE merely applies the abstract mathematical concepts using a generic computer0based machine learning model. The claimed limitations do not amount to an inventice concept because they represent the use of known mathematical techniques and conventional model training operations. As claimed, the elements amount to no more than instructions to apply mathematical concepts using a computer, which is well-understood and conventional. The claims do not amount to significantly more than the judicial exception.
The additional elements of “train the invertible neural network based on a loss function including a regulation term including a likelihood that is a probability distribution of the original data embedding vector for generating an estimated data embedding vector from the original data embedding vector” do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. MPEP 2106.5(h)(vi):
It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018))
It is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology (MPEP 2106.05(a)(II).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON S COLE whose telephone number is (571)270-5075. The examiner can normally be reached Mon - Fri 7:30pm - 5pm EST (Alternate Friday's Off).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez can be reached at 571-272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRANDON S COLE/ Primary Examiner, Art Unit 2128