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 .
Status of Claims
Claims 1-13 are pending and examined herein.
Claims 1-13 are rejected under 35 U.S.C. 101.
Claims 1-13 are rejected under 35 U.S.C. 102.
Response to Arguments
Applicant’s arguments, see page 6, filed 12/31/2025, with respect to the objection of claim 5 have been fully considered and are persuasive. The objection of claim 5 has been withdrawn.
Applicant’s arguments, see page 6, filed 12/31/2025, with respect to the 35 U.S.C. 112(a) and 35 U.S.C. 112(b) rejections of claims 7-12 have been fully considered and are persuasive. The 35 U.S.C. 112(a) and 35 U.S.C. 112(b) rejections of claims 7-12 has been withdrawn.
Applicant's arguments filed 12/31/2025 have been fully considered but they are not persuasive.
Applicant argues that "Section 2107.07(a) of the MPEP, titled "Formulating a Rejection For Lack of Subject Matter Eligibility," states that "[w]hen making the rejection, the Office action must provide an explanation as to why each claim is unpatentable, which must be sufficiently clear and specific to provide applicant sufficient notice of the reasons for ineligibility and enable the applicant to effectively respond." The Patent Office has failed to meet this standard, since its conclusory statement quoted above is not "clear and specific" because it does not analyze the particular wording of the claim element asserted to have recited the mathematical concept judicial exception. That is, the attachment of the this conclusory label to the claim element does not enable Applicant to understand why it recites a mathematical concept."
Examiner respectfully disagrees. The limitation was given a specific interpretation and linked to the specific mathematical concept of mathematical calculations. The explanation provided Applicant sufficient notice of the reasons for ineligibility and allowed the applicant to effectively respond.
Applicant argues "Determining whether a claim element satisfies one of the above three definitions is necessary to distinguish "whether the claim recites a mathematical concept or merely limitations that are based on or involve a mathematical concept." MPEP at 2106.04(a)(2). Since the Patent Office has not provided this level of analysis in its treatment of Prong One for the limitation "wherein the training of the autoencoder takes place based on a probability distribution and a loss function, and wherein the loss function has a reconstruction term and a regularization term," Applicant submits that the Patent Office has failed to establish that this limitation recites a mathematical concept."
Examiner respectfully disagrees. The analysis stated why the limitation recited a mathematical concept, as "The broadest reasonable interpretation of this limitation is performing an algorithm". The MPEP provides examples of mathematical calculations, including "v. using an algorithm for determining the optimal number of visits by a business representative to a client, In re Maucorps, 609 F.2d 481, 482, 203 USPQ 812, 813 (CCPA 1979);". As the limitation performs the algorithm of training using an explicitly cited loss function, the limitation falls under the definition of mathematical concept.
Applicant argues "Here, the claimed invention improves the technology of training autoencoders based on the limitation "training the autoencoder bases on the training data, wherein the training of the autoencoder takes place based on a probability distribution and a loss function, and wherein the loss function has a reconstruction term and a regularization term."
MPEP 2106.05(a)(II) states "However, 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."
As in the reply to the previous argument, the limitation cited by Applicant, "training the autoencoder base[d] on the training data, wherein the training of the autoencoder takes place based on a probability distribution and a loss function, and wherein the loss function has a reconstruction term and a regularization term," is interpreted as an abstract idea of mathematical calculation. As stated by the MPEP, the improvement cannot come from the abstract idea itself, rather additional elements or the combination of an abstract idea and additional elements. Thus, the claims do not recite an improvement to technology.
Applicant further argues "Thus, if the Patent Office decides that the claim limitation at issue does not integrate the judicial exception into a practical application under Prong Two, it would be obligated to analyze this element under Step 2B by determining whether it is well-understood, routine, or conventional on the basis of evidence conforming to one or more of categories (A)-(D)."
Examiner respectfully asserts that the limitation at issue is an abstract idea, as explained in response to the above arguments, not an additional element, and does not require an analysis under Eligibility Step 2B.
Applicant's arguments filed 12/31/2025 regarding the 35 U.S.C. 103 rejection of claim 1-13 have been fully considered but they are not persuasive.
Applicant argues "Ghosh does not anticipate the limitation "wherein the autoencoder is configured to compress sample data representing objects..."
Examiner respectfully disagrees. The analysis explained that the "As the deterministic autoencoder is derived from this, it also has a low-dimensional latent space." Page 2 of Ghosh states "We propose to adopt a simpler, deterministic version of VAEs that scales better, is simpler to optimize, and, most importantly, still produces a meaningful latent space and equivalently good or better samples than VAEs or stronger alternatives, e.g., Wasserstein Autoencoders (WAEs) (Tolstikhin et al., 2017)." Though the analysis did not specifically quote this, the analysis was based on this statement. As it was explained that the deterministic autoencoder derived from the VAE, the analysis is sufficient to establish that the deterministic autoencoder has a low-dimensional latent space.
Applicant further argues "Moreover, even if the deterministic autoencoder of Ghosh functioned on the basis of lowdimensional patent space Z, the Patent Office, without any reasoned analysis, simply equates low-dimensional latent space with compression. Apparently, although unclear, the Patent Office assumes that low-dimensional latent space means less data, and less data always signifies compression."
Examiner respectfully disagrees. The section cited in the explanation has the context of, as stated by Ghosh "For a general discussion, we consider a collection of high-dimensional i.i.d. samples
X
=
x
i
i
=
1
N
drawn from the true data distribution
p
data
over a random variable
X
taking values in the input space. The aim of generative modeling is to learn from
X
a mechanism to draw new samples
x
new
~
p
data
." As the low-dimensional latent space is learned from the high-dimensional samples, inferred by the autoencoder, the input samples are compressed. Note that, as stated on page 2 of Ghosh, "the input space is mapped to the latent space via a stochastic encoder". When starting with high-dimensional data, learning a low-dimensional latent space is compression. For further evidence, the Wikipedia article, “Latent Space” states "In most cases, the dimensionality of the latent space is chosen to be lower than the dimensionality of the feature space from which the data points are drawn, making the construction of a latent space an example of dimensionality reduction, which can also be viewed as a form of data compression.”
Applicant further argues "Second, Applicant disagrees that Ghosh discloses "wherein the training of the autoencoder takes place based on a probability distribution...”.
Examiner respectfully disagrees. As stated in the previous office action, the fitting of the density estimation is interpreted as the probability distribution used in training. Firstly, Wikipedia, “Density Estimation” defines probability density estimation as "In statistics, probability density estimation or simply density estimation is the construction of an estimate, based on observed data, of an unobservable underlying probability density function." Therefore, the fitting of the density estimation is fitting to a probability distribution, meaning that the fitting of the density estimation is the probability distribution. Secondly, although the fitting of the density estimation is ex-post, the density estimation is part of the autoencoder. Therefore, fitting the density estimation, in other words training to fit the probability distribution, is part of the training of the autoencoder in the broadest reasonable interpretation.
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 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
MPEP § 2109(III) sets out steps for evaluating whether a claim is drawn to patent-eligible subject
matter. The analysis of claims 1-13, in accordance with these steps, follows.
Step 1 Analysis:
Step 1 is to determine whether the claim is directed to a statutory category (process, machine,
manufacture, or composition of matter. Claims 1-6 are directed to processes, and claims 7-13 are directed to manufactures. All claims are directed to statutory categories and analysis proceeds.
Step 2A Prong One, Step 2A Prong Two, and Step 2B Analysis:
Step 2A Prong One asks if the claim recites a judicial exception (abstract idea, law of nature, or natural phenomenon). If the claim recites a judicial exception, analysis proceeds to Step 2A Prong Two, which asks if the claim recites additional elements that integrate the abstract idea into a practical application. If the claim does not integrate the judicial exception, analysis proceeds to Step 2B, which asks if the claim amounts to significantly more than the judicial exception. If the claim does not amount to significantly more than the judicial exception, the claim is not eligible subject matter under 35 U.S.C. 101.
None of the claims represent an improvement to technology.
Regarding claim 1, the following are abstract ideas
compress sample data representing objects and subsequently to reconstruct the sample data again, and (One could compress data representing objects (i.e. map the data into a smaller number) and reconstruct the data (i.e. map the data again) practically in the human mind. This is a mental process.)
generate data representing additional objects, the method comprising the following steps: (One could generate data representing additional objects practically in the human mind. This is a mental process.)
wherein the training of the autoencoder takes place based on a probability distribution and a loss function, and wherein the loss function has a reconstruction term and a regularization term. (The broadest reasonable interpretation of this limitation is performing an algorithm, which is mathematical calculations, which are mathematical concepts.)
The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
A method for training a deterministic autoencoder, wherein the autoencoder is configured to (This recites a generic machine learning component and generic training of a machine learning system. This is mere instructions to apply an exception. See MPEP § 2106.05(f).)
wherein the autoencoder is further configured to (This recites a generic machine learning component and generic training of a machine learning system. This is mere instructions to apply an exception.)
providing training data representing objects; (This is the generic computer process of transmitting data, which amounts to mere instructions to apply an exception. See MPEP § 2106.05(f)(2).)
training the autoencoder bases on the training data, (This limitation recites generic machine learning training; this is mere instructions to apply an exception.)
Regarding claim 2, the rejection of claim 1 is incorporated herein. The following are abstract ideas:
weighting the reconstruction term and the regularization term; (Weighting terms is performing an algorithm to find a mathematical equation, which are mathematical concepts.)
wherein the training of the autoencoder takes place on based on the probability distribution, the loss function, and weightings of the reconstruction term and of the regularization term. (The broadest reasonable interpretation of this limitation is performing an algorithm, which is mathematical calculations, which are mathematical concepts.)
Regarding claim 3, the rejection of claim 1 is incorporated herein. The following is an abstract idea:
wherein the probability distribution is a Gaussian mixture model. (This limitation recites a probabilistic model, which is a mathematical formula. This is a mathematical concept.)
Regarding claim 4, the rejection of claim 1 is incorporated herein. The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
wherein the training data are sensor data. (This merely specifies a type of data, which is an insignificant extra-solution activity. See MPEP § 2106.05(g) ‘Selecting a particular data source or type of data to be manipulated’.)
Regarding claim 5, the following are abstract ideas:
A method for generating data representing further objects using a deterministic autoencoder, the method comprising the following steps: (One could generate data representing additional objects practically in the human mind. This is a mental process.)
The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
providing a trained deterministic autoencoder, the autoencoder being configured to … (This is the generic computer process of transmitting data, which amounts to mere instructions to apply an exception. See MPEP § 2106.05(f)(2).)
The remainder of claim 5 recites substantially similar subject matter to claim 1 and is rejected with the same rationale, mutatis mutandis.
Regarding claim 6, the rejection of claim 5 is incorporated herein. The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
optimizing the generated data such that the objects represented in the generated data match in at least one property. (This is an explanation of an objective function, which is a generic machine learning process.)
Regarding claim 7, the following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
A controller configured to … the controller including a hardware processor configured to: (A controller, in the broadest reasonable interpretation, is a generic computer. A hardware processor is a generic computer component. This is mere instructions to apply an exception.)
The remainder of claim 7 recites substantially similar subject matter to claim 1 and is rejected with the same rationale, mutatis mutandis.
Claims 8-10 recite substantially similar subject matter to claims 2-4 and are rejected with the same rationale, mutatis mutandis.
Regarding claim 11, the following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
A controller configured to … the controller comprising: (A controller, in the broadest reasonable interpretation, is a generic computer. This is mere instructions to apply an exception.)
receive a deterministic autoencoder … (This recites a generic computer function (receiving data) implemented on a generic computer, which amounts to mere instructions to apply an exception.)
receive training data … (This recites a generic computer function (receiving data) implemented on a generic computer, which amounts to mere instructions to apply an exception.)
The remainder of claim 11 recites substantially similar subject matter to claim 5 and is rejected with the same rationale, mutatis mutandis.
Claim 12 recites substantially similar subject matter to claim 6 and is rejected with the same rationale, mutatis mutandis.
Regarding claim 13, the following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
A non-transitory computer-readable data carrier on which is stored having program code of a computer program for (This limitation recites generic computer components and processes. This is mere instructions to apply an exception.)
The remainder of claim 13 recites substantially similar subject matter to claim 1 and is rejected with the same rationale, mutatis mutandis.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-13 is/are rejected under 35 U.S.C. 102(a) as being anticipated by Ghosh (“From Variational to Deterministic Autoencoders”, 2020).
Note that the reference was made available by the applicant through the IDS.
Regarding claim 1, Ghosh teaches
A method for training a deterministic autoencoder, (Page 4 states "We propose to substitute noise injection with an explicit regularization scheme for the decoder. This entails the substitution of the variational framework in VAEs, which enforces regularization on the encoder posterior through, with a deterministic framework that applies other flavors of LK decoder regularization. By removing noise injection from a CV-VAE, we are effectively left with a deterministic autoencoder (AE). Coupled with explicit regularization for the decoder, we obtain a Regularized Autoencoder (RAE)." Training details for the model are located in Appendix C, page 14.
wherein the autoencoder is configured to compress sample data representing objects and subsequently to reconstruct the sample data again, and wherein the autoencoder is further configured to generate data representing additional objects, the method comprising the following steps: (Page 2 states "The aim of generative modeling is to learn from
X
a mechanism to draw new samples
x
n
e
w
∼
p
d
a
t
a
. Variational Autoencoders provide a powerful latent variable framework to infer such a mechanism. The generative process of the VAE is defined as
z
n
e
w
∼
p
Z
,
x
n
e
w
∼
p
θ
(
X
|
Z
=
z
n
e
w
)
where
p
Z
is a fixed prior distribution over a low-dimensional latent space
Z
." As the deterministic autoencoder is derived from this, it also has a low-dimensional latent space. This low-dimensional latent space is interpreted as the compressed sample data. Page 14 states "All models are trained for a maximum of 100 epochs on MNIST and CIFAR and 70 epochs on CelebA." As one of ordinary skill in the art would recognize, MNIST is an image dataset of numbers, which are objects, CIFAR is an image dataset of different objects, and CelebA is an image dataset of people, which can be interpreted as objects. Therefore, the sample data represents objects. Page 7 states "We evaluate each model by REC.: test sample reconstruction;
N
: random samples generated according to the prior distribution
p
(
z
)
(isotropic Gaussian for VAE/WAE, another VAE for 2SVAE) or by fitting a Gaussian to
q
δ
(
z
)
(for the remaining models); GMM: random samples generated by fitting a mixture of 10 Gaussians in the latent space; Interp: mid-point interpolation between random pairs of test reconstructions." The test sample reconstruction is interpreted as the reconstruction of the sample data, and generating random samples is interpreted as the generated data representing objects. Figure 1 shows that the RAE, the deterministic autoencoder presented in the paper, reconstructs sample data representing objects, and generates data representing additional objects, in the random samples.
providing training data representing objects; (Page 14 states "All models are trained for a maximum of 100 epochs on MNIST and CIFAR and 70 epochs on CelebA." As one of ordinary skill in the art would recognize, MNIST is an image dataset of numbers, which are objects, CIFAR is an image dataset of different objects, and CelebA is an image dataset of people, which can be interpreted as objects. Therefore, the sample data represents objects. As the training data is used, the training data was provided.)
training the autoencoder bases on the training data, wherein the training of the autoencoder takes place based on a probability distribution and a loss function, and (Page 14 states "All models are trained for a maximum of 100 epochs on MNIST and CIFAR and 70 epochs on CelebA." As one of ordinary skill in the art would recognize, MNIST is an image dataset of numbers, which are objects, CIFAR is an image dataset of different objects, and CelebA is an image dataset of people, which can be interpreted as objects. Therefore, the sample data represents objects. Page 4 states “Training a RAE thus involves minimizing the simplified loss
L
RAE
=
L
REC
+
β
L
z
RAE
+
λ
L
REG
(11) where
L
REG
represents the explicit regularizer for
D
θ
”. Page 5 states "To overcome both i) and ii), we instead propose to employ ex-post density estimation over
Z
.” This is interpreted as the probability distribution. We fit a density estimator”. Although the fitting of the estimator occurs after the initial training, as it is trained as a part of the autoencoder, it is involved in the training of the autoencoder.)
wherein the loss function has a reconstruction term and a regularization term. (Page 4 states “Training a RAE thus involves minimizing the simplified loss
L
RAE
=
L
REC
+
β
L
z
RAE
+
λ
L
REG
(11) where
L
REG
represents the explicit regularizer for
D
θ
”. Page 3 states that
L
REC
is the reconstruction loss.)
Regarding claim 2, the rejection of claim 1 is incorporated herein. Ghosh teaches
weighting the reconstruction term and the regularization term; (Page 4 states “Training a RAE thus involves minimizing the simplified loss
L
RAE
=
L
REC
+
β
L
z
RAE
+
λ
L
REG
(11)” Page 4 further states "Finally,
β
and
λ
are two hyper parameters that balance the different loss terms." These hyperparameters are interpreted as weights. Though the reconstruction term is not explicitly weighted, as the other terms are weighted, the weighting of the other terms will weigh the reconstruction in terms of the other terms.)
wherein the training of the autoencoder takes place on based on the probability distribution, the loss function, and weightings of the reconstruction term and of the regularization term. (Page 4 states “Training a RAE thus involves minimizing the simplified loss
L
RAE
=
L
REC
+
β
L
z
RAE
+
λ
L
REG
(11)” As the weightings of the terms are in the loss function, they are involved in the training of the autoencoder. Page 5 states "To overcome both i) and ii), we instead propose to employ ex-post density estimation over
Z
.” This is interpreted as the probability distribution. We fit a density estimator”. Although the fitting of the estimator occurs after the initial training, as it is trained as a part of the autoencoder, it is involved in the training of the autoencoder.)
Regarding claim 3, the rejection of claim 1 is incorporated herein. Ghosh teaches
wherein the probability distribution is a Gaussian mixture model. (Page 6 states "Striving for simplicity, we employ and compare a full covariance multivariate Gaussian with a 10-component Gaussian mixture model (GMM) in our experiments.")
Regarding claim 4, the rejection of claim 1 is incorporated herein. Ghosh teaches
wherein the training data are sensor data. (Page 14 states "All models are trained for a maximum of 100 epochs on MNIST and CIFAR and 70 epochs on CelebA." As one of ordinary skill in the art would recognize, MNIST is an image dataset of numbers, which are objects, CIFAR is an image dataset of different objects, and CelebA is an image dataset of people, which are objects. Images are taken by a camera, which is a sensor. Therefore, the training data are sensor data.)
Regarding claim 5, Ghosh teaches
A method for generating data representing further objects using a deterministic autoencoder, the method comprising the following steps: (Page 14 states "All models are trained for a maximum of 100 epochs on MNIST and CIFAR and 70 epochs on CelebA." As one of ordinary skill in the art would recognize, MNIST is an image dataset of numbers, which are objects, CIFAR is an image dataset of different objects, and CelebA is an image dataset of people, which are objects. Page 7 states "We evaluate each model by REC.: test sample reconstruction;
N
: random samples generated according to the prior distribution
p
(
z
)
(isotropic Gaussian for VAE/WAE, another VAE for 2SVAE) or by fitting a Gaussian to
q
δ
(
z
)
(for the remaining models); GMM: random samples generated by fitting a mixture of 10 Gaussians in the latent space; Interp: mid-point interpolation between random pairs of test reconstructions." The test sample reconstruction is interpreted as the reconstruction of the sample data, and generating random samples is interpreted as the generating of further objects. Page 4 states "We propose to substitute noise injection with an explicit regularization scheme for the decoder. This entails the substitution of the variational framework in VAEs, which enforces regularization on the encoder posterior through, with a deterministic framework that applies other flavors of LK decoder regularization. By removing noise injection from a CV-VAE, we are effectively left with a deterministic autoencoder (AE). Coupled with explicit regularization for the decoder, we obtain a Regularized Autoencoder (RAE).")
providing a trained deterministic autoencoder, the autoencoder being configured to … (As the trained deterministic autoencoder is used, the trained deterministic autoencoder must have been provided.)
The remainder of claim 5 recites substantially similar subject matter to claim 1 and is rejected with the same rationale, mutatis mutandis.
Regarding claim 6, the rejection of claim 5 is incorporated herein. Ghosh teaches
optimizing the generated data such that the objects represented in the generated data match in at least one property. (Page 3 states "With the 1-sample approximation,
L
REC
can be computed as the mean squared error between input samples and their mean reconstructions
μ
θ
by a decoder that is deterministic in practice:
L
REC
=
-
x
-
μ
θ
E
ϕ
x
2
2
(8)". As
L
REC
is part of the loss function, it is optimized. And as the loss includes the difference between the input samples and their reconstructions, it optimizes the generated data such that they match in at least one property.)
Regarding claim 7, Ghosh teaches
A controller configured to train a deterministic autoencoder, (The autoencoder is a neural network, and as one of ordinary skill in the art would understand, is implemented on a computer, which is a controller. Therefore, it is a controller that performs the training of the autoencoder, which as explained above in relation to claim 1, is deterministic.)
the controller including a hardware processor configured to: (The autoencoder is a neural network, and as one of ordinary skill in the art would understand, is implemented on a computer, which is a controller. The computer necessarily includes a hardware processor on that is configured to execute instructions for the described method.)
The remainder of claim 7 recites substantially similar subject matter to claim 1 and is rejected with the same rationale, mutatis mutandis.
Claims 8-10 recite substantially similar subject matter to claims 2-4 and are rejected with the same rationale, mutatis mutandis.
Regarding claim 11, Ghosh teaches
A controller configured to generate data representing further objects using a deterministic autoencoder, the controller including a hardware processor configured to: (The autoencoder is a neural network, and as one of ordinary skill in the art would understand, is implemented on a computer, which is a controller. Therefore, it is a controller that performs the generation of data using the autoencoder, which as explained above in relation to claim 1, is deterministic. The computer necessarily includes a hardware processor on that is configured to execute instructions for the described method.)
receive a deterministic autoencoder … (As the method is performed on a computer, the processor must receive the autoencoder from memory.)
receiving training data … (As the method is performed on a computer, the computer must receive the training data from memory.)
The remainder of claim 11 recites substantially similar subject matter to claim 5 and is rejected with the same rationale, mutatis mutandis.
Claim 12 recites substantially similar subject matter to claim 6 and is rejected with the same rationale, mutatis mutandis.
Regarding claim 13, Ghosh teaches
A non-transitory computer-readable data carrier on which is stored having program code of a computer program for (The autoencoder is a neural network, and as one of ordinary skill in the art would understand, is implemented on a computer, which uses a processor to control the computer to perform instructions stored on a non-transitory computer readable storage medium. In order for the processor to perform the instructions, the program code of the method must be stored on a non-transitory computer-readable data carrier.)
The remainder of claim 13 recites substantially similar subject matter to claim 1 and is rejected with the same rationale, mutatis mutandis.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 JESSICA THUY PHAM whose telephone number is (571)272-2605. The examiner can normally be reached Monday - Friday, 9 A.M. - 5:00 P.M..
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li Zhen can be reached at (571) 272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.T.P./ Examiner, Art Unit 2121
/Li B. Zhen/ Supervisory Patent Examiner, Art Unit 2121
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