DETAILED ACTION
This action is in response to the filing on 02/09/2026. Claims 1, 3, 6, 8-9, 11, 14, and 16-18 are pending and have been considered below.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 02/09/2026 has been entered.
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, 3, 6, 8-9, 11, 14, and 16-18 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent Claims 1, 8, and 9
Step 1:
Claims 1, 8, and 9 recite a method, manufacture, and system respectively; therefore, they are directed to one of the four categories of statutory subject matter (process/method, machine/product/apparatus, manufacture, or composition of matter).
Step 2A Prong 1:
Claim 1 recites a method comprising:
A method of classifying data, the method comprising: — Under its broadest reasonable interpretation, this limitation encompasses the abstract idea of a mental process, or a concept that can be performed in the human mind with the use of a physical aid (e.g. pen and paper), including observation, evaluation, judgement or opinion (see MPEP § 2106.04(a)(2)(III)). Or a mathematical concept (see MPEP § 2106.04(a)(2)(I)), specifically organizing information and manipulating information through mathematical correlations.
training a classification model for classifying input data into at least one class, such that a first output value is generated — Under its broadest reasonable interpretation, this limitation encompasses the abstract idea of a mental process, or a concept that can be performed in the human mind with the use of a physical aid (e.g. pen and paper), including observation, evaluation, judgement or opinion (see MPEP § 2106.04(a)(2)(III)).
training a classification model for classifying input data into at least one class, such that a first output value is generated, wherein the training the classification model comprises training the classification model using only a distribution of samples x from source data and a conditional distribution of the samples x given labels y — Under its broadest reasonable interpretation, this limitation encompasses the abstract idea of a mathematical concept (see MPEP § 2106.04(a)(2)(I)), specifically mathematical formulas or equations (see MPEP § 2106.04(a)(2)(I)(B)).
generating a second output value by applying, to the first output value, information indicating a label distribution of target data — Under its broadest reasonable interpretation, this limitation encompasses the abstract idea of a mental process, or a concept that can be performed in the human mind with the use of a physical aid (e.g. pen and paper), including observation, evaluation, judgement or opinion (see MPEP § 2106.04(a)(2)(III)). Or a mathematical concept (see MPEP § 2106.04(a)(2)(I)), specifically organizing information and manipulating information through mathematical correlations.
classifying facial images into the at least one class by using the second output value — Under its broadest reasonable interpretation, this limitation encompasses the abstract idea of a mental process, or a concept that can be performed in the human mind with the use of a physical aid (e.g. pen and paper), including observation, evaluation, judgement or opinion (see MPEP § 2106.04(a)(2)(III)). Or a mathematical concept (see MPEP § 2106.04(a)(2)(I)), specifically organizing information and manipulating information through mathematical correlations.
Claim 8 recites a system comprising:
executing the method of claim 1 — Under its broadest reasonable interpretation, this limitation encompasses the abstract idea of a mental process, or a concept that can be performed in the human mind with the use of a physical aid (e.g. pen and paper), including observation, evaluation, judgement or opinion (see MPEP § 2106.04(a)(2)(III)). Or a mathematical concept (see MPEP § 2106.04(a)(2)(I)), specifically organizing information and manipulating information through mathematical correlations.
Claim 9 recites a system comprising:
training a classification model for classifying input data into at least one class, such that a first output value is generated — Under its broadest reasonable interpretation, this limitation encompasses the abstract idea of a mental process, or a concept that can be performed in the human mind with the use of a physical aid (e.g. pen and paper), including observation, evaluation, judgement or opinion (see MPEP § 2106.04(a)(2)(III)).
training a classification model for classifying input data into at least one class, such that a first output value is generated, wherein the training the classification model comprises training the classification model using only a distribution of samples x from source data and a conditional distribution of the samples x given labels y — Under its broadest reasonable interpretation, this limitation encompasses the abstract idea of a mathematical concept (see MPEP § 2106.04(a)(2)(I)), specifically mathematical formulas or equations (see MPEP § 2106.04(a)(2)(I)(B)).
generating a second output value by applying, to the first output value, information indicating a label distribution of target data — Under its broadest reasonable interpretation, this limitation encompasses the abstract idea of a mental process, or a concept that can be performed in the human mind with the use of a physical aid (e.g. pen and paper), including observation, evaluation, judgement or opinion (see MPEP § 2106.04(a)(2)(III)). Or a mathematical concept (see MPEP § 2106.04(a)(2)(I)), specifically organizing information and manipulating information through mathematical correlations.
classifying facial images into the at least one class by using the second output value — Under its broadest reasonable interpretation, this limitation encompasses the abstract idea of a mental process, or a concept that can be performed in the human mind with the use of a physical aid (e.g. pen and paper), including observation, evaluation, judgement or opinion (see MPEP § 2106.04(a)(2)(III)). Or a mathematical concept (see MPEP § 2106.04(a)(2)(I)), specifically organizing information and manipulating information through mathematical correlations.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application.
Claim 8 recites the additional element of:
A non-transitory, computer-readable recording medium having recorded thereon a program for executing the method of claim 1 on a computer — This element amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). This element merely limits the use of the abstract idea to a generic computer component.
Claim 9 recites the additional elements of:
A device for classifying data, the device comprising: a memory storing at least one program; and a processor configured to execute the at least one program to — This element amounts to no more than a generic device comprising generic computer components.
Step 2B:
The claims do not contain significantly more than the judicial exception.
Claim 8 recites the additional element of:
A non-transitory, computer-readable recording medium having recorded thereon a program for executing the method of claim 1 on a computer — This element amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). This element merely limits the use of the abstract idea to a generic computer component.
Claim 9 recites the additional elements of:
A device for classifying data, the device comprising: a memory storing at least one program; and a processor configured to execute the at least one program to — This element amounts to no more than a generic device comprising generic computer components.
As such claims 1, 8, and 9 are not patent eligible.
Dependent Claims 3, 6, 11, 14, and 16-18
Step 1:
Claims 3, 6, 11, 14, and 16-18; therefore, they are directed to one of the four categories of statutory subject matter (process/method, machine/product/apparatus, manufacture, or composition of matter).
Step 2A Prong 1:
Claims 3, 6, 11, 14, and 16-18 merely narrow the previously cited abstract idea limitations. For the reasons described above with respect to independent claim1 and 9 this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claim(s) disclose similar limitations described for the independent claim(s) above and do not provide anything more than the abstract idea.
Claim 3 recites a method comprising:
wherein, in the generating of the second output value, the information indicating the label distribution of the target data is applied to the first output value by performing a multiplication operation — Under its broadest reasonable interpretation, this limitation encompasses the abstract idea of a mental process, or a concept that can be performed in the human mind with the use of a physical aid (e.g. pen and paper), including observation, evaluation, judgement or opinion (see MPEP § 2106.04(a)(2)(III)). Or a mathematical concept (see MPEP § 2106.04(a)(2)(I)), specifically organizing information and manipulating information through mathematical correlations.
Claim 11 recites a system comprising:
the processor is further configured to execute the at least one program to apply, to the first output value, the information indicating the label distribution of the target data by performing a multiplication operation — Under its broadest reasonable interpretation, this limitation encompasses the abstract idea of a mental process, or a concept that can be performed in the human mind with the use of a physical aid (e.g. pen and paper), including observation, evaluation, judgement or opinion (see MPEP § 2106.04(a)(2)(III)). Or a mathematical concept (see MPEP § 2106.04(a)(2)(I)), specifically organizing information and manipulating information through mathematical correlations.
Claim 16 recites a method comprising:
wherein the classification model is trained by using a regularized Donsker-Varadhan (DV) representation represented by the following formula:
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where P and Q denote arbitrary distributions that satisfy supp(P) supp(Q) and for every function T: Ω ➔ R some domain Ω, the function T that minimizes the regularized DV representation is the log-likelihood ratio of P and Q — Under its broadest reasonable interpretation, this limitation encompasses the abstract idea of a mathematical concept (see MPEP § 2106.04(a)(2)(I)), specifically organizing information and manipulating information through mathematical correlations.
Claim 17 recites a method comprising:
wherein the classification model is trained by plugging
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— Under its broadest reasonable interpretation, this limitation encompasses the abstract idea of a mathematical concept (see MPEP § 2106.04(a)(2)(I)), specifically organizing information and manipulating information through mathematical correlations.
Claim 18 recites a method comprising:
wherein the classification model is trained by using information indicating the label distribution of the source data
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, where LLADER is a loss, λ, a1, ..., ac denotes hyperparameters, and C denotes a total number of classes — Under its broadest reasonable interpretation, this limitation encompasses the abstract idea of a mathematical concept (see MPEP § 2106.04(a)(2)(I)), specifically organizing information and manipulating information through mathematical correlations.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application.
Claim 6 recites the additional element of:
wherein, in the training of the classification model, the classification model is trained by using information indicating regularization with respect to the label distribution of the source data — This element amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). This element merely limits the use of the abstract idea to training a classification model with particular information.
Claim 14 recites the additional element of:
the processor is further configured to execute the at least one program to train the classification model by using information indicating regularization with respect to the label distribution of the source data — This element amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). This element merely limits the use of the abstract idea to training a classification model with particular information.
Step 2B:
The claims do not contain significantly more than the judicial exception.
Claim 6 recites the additional element of:
wherein, in the training of the classification model, the classification model is trained by using information indicating regularization with respect to the label distribution of the source data — This element amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). This element merely limits the use of the abstract idea to training a classification model with particular information.
Claim 14 recites the additional element of:
the processor is further configured to execute the at least one program to train the classification model by using information indicating regularization with respect to the label distribution of the source data — This element amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)). This element merely limits the use of the abstract idea to training a classification model with particular information.
As such claims 3, 6, 11, 14, and 16-18 are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 8, 9, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Southern et al. (US 2022/0095974 A1, first cited in office action mailed 10/08/2025), hereinafter Southern, in view of Langseth, H., Nielsen, T.D. (“Classification using Hierarchical Naïve Bayes models”. Mach Learn 63, 135–159 (2006). doi:10.1007/s10994-006-6136-2), hereinafter Langseth.
Regarding claim 1, Southern teaches a method of classifying data, the method comprising (The present invention relates to the determination or classification of the mental state of a user and associated confidence values for the determined mental state. [see Southern, Abstract]):
training a classification model for classifying input data into at least one class, such that a first output value is generated (Southern discloses that their classification model is trained prior to deployment [see Southern, para. 93] to classify input data into one or more classes [see Southern, para. 101, and 103], in party by calculating a posterior probability according to Bayes' rule in which a label distribution component is used [see Southern, para. 77 and Equation 1]);
generating a second output value by applying, to the first output value, information indicating a label distribution of target data (Next a confidence threshold parameter α is used to tune predictions to a specified level of model uncertainty. For example, when α=0.95, at least 95% of the output distribution must lie in a given class zone in order for the input sample to be classified as belonging to that class (see FIG. 5). [see Southern, para. 87]);
classifying facial images into the at least one class by using the second output value (Southern discloses using facial images as input data [see Southern, para. 46] for classifying data into one or more classes [see Southern, para. 93, 101, and 103] according to the second output value [see Southern, para. 87]).
However, Southern fails to teach wherein the training the classification model comprises training the classification model using only a distribution of samples x from source data and a conditional distribution of the samples x given labels y.
In the same field of endeavor, Langseth teaches:
wherein the training the classification model comprises training the classification model using only a distribution of samples x from source data and a conditional distribution of the samples x given labels y (Langseth discloses that Bayesian classifiers learn from a set of labeled training samples denoted by Dn, such that P(C=c|A=a, Dn) is the a posteriori conditional probability that C=c given A=a after observing Dn [see Langseth, Section 2, pg. 137-138 para. 1]).
It would have been obvious to one of ordinary skill, in the art at the time before the effective filing date of the invention to incorporate wherein the training the classification model comprises training the classification model using only a distribution of samples x from source data and a conditional distribution of the samples x given labels y as suggested in Langseth into Southern because both methods use Bayesian classifiers [see Southern, para. 22; see Langseth, Abstract]. Incorporating the teaching of Langseth into Southern would aid in extending the utility of training the classification model to classify input data into at least one class [see Southern, para. 89-93 and 101].
Regarding claim 8, claim 8 contains substantially similar limitations to those found in claim 1. Therefore it is rejected for the same reason as claim 1 above. Additionally, it would have been obvious to one of ordinary skill in the art to implement the method of Southern on a computer readable medium to execute on a computer to further teach a non-transitory, computer-readable recording medium having recorded thereon a program for executing the method of claim 1 on a computer.
Regarding claim 9, claim 9 contains substantially similar limitations to those found in claim 1. Therefore it is rejected for the same reason as claim 1 above. Additionally, it would have been obvious to one of ordinary skill in the art to implement the method of Southern on a general purpose computer with a processor and memory storing a program to implement the method to teach A device for classifying data, the device comprising: a memory storing at least one program; and a processor configured to execute the at least one program to.
Regarding claim 17, the combination of Southern and Langseth as applied in claim 1 above teaches all the limitations of claim 1 and further teaches:
wherein the classification model is trained by plugging
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(Southern discloses that the machine learning model uses a Monte Carlo dropout method during training to approximate posterior distributions [see Southern, para. 78]).
Regarding claim 18, the combination of Southern and Langseth as applied in claim 1 above teaches all the limitations of claim 1 and further teaches:
wherein the classification model is trained by using information indicating the label distribution of the source data
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, where LLADER is a loss, λ, a1, ..., ac denotes hyperparameters, and C denotes a total number of classes (Southern discloses a posterior probability according to Bayes' rule in which a label distribution component is used [see Southern, para. 77-78 and Equation 1]).
Claims 3 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Southern et al. (US 2022/0095974 A1, first cited in office action mailed 10/08/2025), hereinafter Southern, in view of Langseth, H., Nielsen, T.D. (“Classification using Hierarchical Naïve Bayes models”. Mach Learn 63, 135–159 (2006). doi:10.1007/s10994-006-6136-2), hereinafter Langseth, as applied in claim 1 above, and further in view of Richard et al. (Neural Network Classifiers Estimate Bayesian a posteriori Probabilities, first cited in office action filed 04/03/2025), hereinafter Richard.
Regarding claim 3, the combination of Southern and Langseth as applied in claim 1 above teaches all the limitations of claim 1.
However, the combination of Southern and Langseth fails to teach wherein, in the generating of the second output value, the information indicating the label distribution of the target data is applied to the first output value by performing a multiplication operation.
In the same field of endeavor, Richard teaches:
wherein, in the generating of the second output value, the information indicating the label distribution of the target data is applied to the first output value by performing a multiplication operation (However, the output y.sub.i(X) is implicitly the corresponding a priori class probability p(C.sub.i) times the class likelihood p(X | C.sub.i) divided by the unconditional input probability p(X). It is possible to vary a priori class probabilities during classification without retraining, since these probabilities occur only as multiplicative terms in producing the network outputs. As a result, class probabilities can be adjusted during use of a classifier to compensate for training data with class probabilities that are not representative of actual use or test conditions. Correct class probabilities can be used during classification by first dividing network outputs by training-data class probabilities and then multiplying by the correct class probabilities. Training-data class probabilities can be estimated as the frequency of occurrence of patterns from different classes in the training data. Correct class probabilities required for testing can be obtained from an independent set of training data that needs to contain only class labels and not input patterns. Such data are often readily available. [see Richard, Subsection 4.1 Compensating for Varying a priori Class Probabilities, para. 1, lines 3-18]).
It would have been obvious to one of ordinary skill, in the art at the time before the effective filing date of the invention to incorporate wherein, in the generating of the second output value, the information indicating the label distribution of the target data is applied to the first output value by performing a multiplication operation as suggested in Richard into the combination of Southern and Langseth because both methods classify data with classification models (see Southern, Abstract; see Richard, Subsection 2.1 Pattern Classification and Bayesian Probabilities, para. 1, lines 6-23).. Incorporating the teaching of Richard into the combination of Southern and Langseth would make it possible to compensate for differences in pattern class probabilities between test and training data, to combine outputs of multiple classifiers for higher level decision making, to use alternative risk functions different from minimum-error risk, to implement conventional optimal rules for pattern rejection, and to compute alternative measures of network performance (see Richard, pg. 462, lines 4-9).
Regarding claim 11, claim 11 contains substantially similar limitations to those found in claim 3 above. Consequently, claim 11 is rejected for the same reasons.
Claims 6, 14, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Southern et al. (US 2022/0095974 A1, first cited in office action mailed 10/08/2025), hereinafter Southern, in view of Langseth, H., Nielsen, T.D. (“Classification using Hierarchical Naïve Bayes models”. Mach Learn 63, 135–159 (2006). doi:10.1007/s10994-006-6136-2), hereinafter Langseth, as applied in claim 1 above, and further in view of Mroueh et al. (US 11,630,989 B2, first cited in office action filed 04/03/2025), hereinafter Mroueh.
Regarding claim 6, the combination of Southern and Langseth as applied in claim 1 above teaches all the limitations of claim 1.
However, the combination of Southern and Langseth fails to teach wherein, in the training of the classification model, the classification model is trained by using information indicating regularization with respect to the label distribution of the source data.
In the same field of endeavor, Mroueh teaches:
wherein, in the training of the classification model, the classification model is trained by using information indicating regularization with respect to the label distribution of the source data (What is provided herein is a new estimator of MI that can be used in direct MI maximization or as a regularizer, thanks to its unbiased gradients. Our starting point is the DV lower bound of the KL divergence that is represented equivalently via a joint optimization that is referred to herein as η-DV on a witness function ƒ and an auxiliary variable η. [see Mroueh, Col. 3, lines 62-67]; The η-DV bound restricted to an RKHS, amounts to the following regularized convex minimization: P=(min.sub.w,ηL(w,η)+Ω(w)) [see Mroueh, Col. 7, lines 54-56]; Mutual Information (MI) is information indicating similar label distribution between the source data and the target data. Thus, using regularized DV representation to approximate the MI, would be using a regularized DV representation with respect to information indicating label distribution of training data).
It would have been obvious to one of ordinary skill, in the art at the time before the effective filing date of the invention to incorporate wherein, in the training of the classification model, the classification model is trained by using information indicating regularization with respect to the label distribution of the source data as suggested in Mroueh into the combination of Southern and Langseth because both systems are computing devices that perform machine learning (see Southern, Abstract; see Mroueh, Col. 4, lines 16-36). Incorporating the teaching of Mroueh into the combination of Southern and Langseth would improve computational efficiency and accuracy in computing devices that perform machine learning and artificial intelligence (see Col. 3, lines 45-47).
Regarding claim 14, claim 14 contains substantially similar limitations to those found in claim 6 above. Consequently, claim 14 is rejected for the same reasons.
Regarding claim 16, the combination of Southern and Langseth as applied in claim 1 above teaches all the limitations of claim 1.
However, the combination of Southern and Langseth fails to teach wherein the classification model is trained by using a regularized Donsker-Varadhan (DV) representation represented by the following formula:
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where P and Q denote arbitrary distributions that satisfy supp(P) supp(Q) and for every function T: Ω ➔ R some domain Ω, the function T that minimizes the regularized DV representation is the log-likelihood ratio of P and Q.
In the same field of endeavor, Mroueh teaches:
wherein the classification model is trained by using a regularized Donsker-Varadhan (DV) representation represented by the following formula:
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where P and Q denote arbitrary distributions that satisfy supp(P) supp(Q) and for every function T: Ω ➔ R some domain Ω, the function T that minimizes the regularized DV representation is the log-likelihood ratio of P and Q (Mroueh discloses that the regularized neural network using DV-MINE converges after 140k steps [see Mroueh, Col. 12, lines 54-59], thus, it was trained using a regularized DV representation).
It would have been obvious to one of ordinary skill, in the art at the time before the effective filing date of the invention to incorporate wherein the classification model is trained by using a regularized Donsker-Varadhan (DV) representation represented by the following formula:
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where P and Q denote arbitrary distributions that satisfy supp(P) supp(Q) and for every function T: Ω ➔ R some domain Ω, the function T that minimizes the regularized DV representation is the log-likelihood ratio of P and Q as suggested in Mroueh into the combination of Southern and Langseth because both systems are computing devices that perform machine learning (see Southern, Abstract; see Mroueh, Col. 4, lines 16-36). Incorporating the teaching of Mroueh into the combination of Southern and Langseth would improve computational efficiency and accuracy in computing devices that perform machine learning and artificial intelligence (see Col. 3, lines 45-47).
Response to Arguments
Applicant’s arguments, filed 02/09/2026, traversing the rejection of claims 1, 3, 6, 8-9, 11, 14, and 16-18 under 35 U.S.C. 101 have been fully considered and are not persuasive. Applicant cites to the November designation of the Appeals Review Panel Decision in Ex parte Desjardins as precedential, and in view thereof amended claims 1 and 9 are directed to eligible subject matter consistent with the precedential opinion in Ex parte Desjardins, however, Applicant does not identify how the claims are consistent with the precedential opinion, or how the claims are now directed to eligible subject matter. Applicant also cites para. 43 of the specification, however, Applicant does not identify why para. 43 has been cited. As Applicant has not provided sufficient reasoning for why the claims as amended are directed to eligible subject matter, and has only asserted that the claims are directed to eligible subject matter, Examiner respectfully disagrees. The rejections of claims 1, 3, 6, 8-9, 11, 14, and 16-18 under 35 U.S.C. 101 are respectfully maintained.
Applicant’s arguments, filed 02/09/2026, traversing the rejection of claims 1, 3, 6, 8-9, 11, 14, and 16-18 under 35 U.S.C. 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
S. Molavipour, G. Bassi and M. Skoglund, ("Conditional Mutual Information Neural Estimator," ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 2020, pp. 5025-5029, doi: 10.1109/ICASSP40776.2020.9053422.) teaches estimating conditional mutual information with neural network classifiers based on the Donsker-Varadhan representation of KL-divergence.
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/J.T.B./Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143