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 .
Response to Amendment
This Office action has been issued in response to amendment filed on 06/03/2026, Claims (1-12, 14-16), 12 and 14 are pending. Applicants' arguments have been carefully and respectfully considered and addressed. Accordingly, this action has been made FINAL necessitated by amendment.
Claims (1-12, 14-16), 12 and 14 are presented for examination.
Claim 13 is cancelled.
Response to Arguments
Applicants' arguments have been carefully and respectfully considered and addressed. The arguments presented are moot based on amendment.
With regards to 101 rejections, the rejection is withdrawn but the amendments triggered the 112(f) interpretation.
With regards to arguments that pertain to 103 rejection, Applicant arguments and amendment were fully considered and are moot in view of the new ground rejection wherein Li et al. US Patent Application Publication US 20220084204 A1 (hereinafter Li) in view of Liu Ming-Yu et al. Foreign Patent Application Publication CN 109196526 A (hereinafter Liu) and further in view of Lee et al. US Patent Application Publication US 20180285771 A1 (hereinafter Lee) for teaching the amended claims.
Claim Interpretation - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Use of the word “means” (or “step for”) in a claim with functional language creates a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is invoked is rebutted when the function is recited with sufficient structure, material, or acts within the claim itself to entirely perform the recited function.
Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim elements in this application that use the word “means” (or “step for”) are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word “means” (or “step for”) are presumed not to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
Claim 12 is presumed to be viewed under the §112(f) analysis when the generic placeholder (“Unit”, “Configured to”) is used followed by functional language. This presumption can be rebutted when there is no corresponding structure to that functional language. A review of the specification shows that no corresponding structure is described within the specification. For there to be sufficient structure, either in the specification or the claims, there needs to be material describing the physical hardware that is performing the functions described. Examiner suggests removing these terms/phrases from the claims or adding the corresponding structure within the claims, if there is support in the specification.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may amend the claim so that they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9,2011).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
.
Claim 12 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
The claim limitations “network structure and a second neural network structure, wherein the computer system is configured to…” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification states that the claimed functions are performed by a system comprising an artificial neural network which is not adequate structure for performing the claimed functions and it does not describe a particular structure. As would be recognized by those of ordinary skill in the art, the term “neural network” could be interpreted as hardware, software or a combination of the two. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which module structure or structures perform(s) the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If the applicant intends to amend the claims limitations that include ‘units” so that they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, the claim limitations that include “modules” would be subject of rejection under 35 U.S.C. § 101 due to software per se. Therefore, the amendment should include structure that performs the claimed functions processed by the units and modules.
Claim Rejections - 35 USC § 103
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 of this title, 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 1-12 and 14 are rejected under AIA 35 U.S.C. 103(a) as being unpatentable over Li et al. US Patent Application Publication US 20220084204 A1 (hereinafter Li) in view of Liu Ming-Yu et al. Foreign Patent Application Publication CN 109196526 A (hereinafter Liu) and further in view of Lee et al. US Patent Application Publication US 20180285771 A1 (hereinafter Lee).
Regarding claim 1, Li teaches A process for training a first artificial neural network structure (1), the process comprising ([0120] wherein Li describes improving a neural network and determines the accuracy of generator network of GAN) classifying data samples are classified in different classes (4) by the first artificial neural network structure (1), whereby at least some of the classes (4) are unsupervised classes (6), which are generated and/or filled by unsupervised learning (FIGS. 3A-3B, 4, 6, [0067], [0069], [0089], [0092] wherein Li describes a neural network for classifying data samples in unsupervised manner by generating labels and notations for images, wherein labels indicates classification to each pixel within the image) wherein the generated artificial candidates (7) are labelled and/or annotated in a supervised learning for labelling and/or annotating the said unsupervised class (7) ([0078], [0103], [0489] wherein Li incorporates artificial network to generate labels and annotation classes for images). ([0076] wherein Li incorporates GAN that is a class of artificial intelligence system that uses two types of artificial neural networks contesting with each other. A GAN includes a first type of artificial neural networks, referred to as a generator network, that generates candidates and a second type of artificial neural networks, referred to as a discriminator network, that evaluates generated candidates. A generator network learns to map from a latent space to a particular data distribution of interest (a data distribution of changes to input images that are indistinguishable from photographs to human eyes), while a discriminator network discriminates between instances from a training dataset and candidates produced by generator network. In at least one embodiment, a GAN can have a generator network and two discriminator networks. A first discriminator network evaluates synthetic images generated by generator network, and a second discriminator network evaluates synthetic images and corresponding labels generated by generator network).
Li does not teach and training a second artificial neural network structure (2) to generate artificial candidates (7) belonging to at least one of the unsupervised classes (6).
However in analogous art of training artificial neural network structure, Liu teaches training a second artificial neural network structure (2) to generate artificial candidates (7) belonging to at least one of the unsupervised classes (6) (Claim 1 text, Abstract, page. 2, paragraph 6, page. 3 paragraph 3, page. 10, paragraphs 4-6 wherein Liu teaches training a first and second neural network for generating multi-mode digital images in supervised manner).
It would have been obvious to a person in the ordinary skill in the art before the effective filing date of the claimed invention to combine Li with Liu by incorporating the method of training a second artificial neural network structure (2) to generate artificial candidates (7) belonging to at least one of the unsupervised classes (6) of Liu into the method of classifying data samples are classified in different classes (4) by the first artificial neural network structure (1), whereby at least some of the classes (4) are unsupervised classes (6), which are generated and/or filled by unsupervised learning of Li for the purpose of generating advanced features of the digital images, and other layers in the first neural network and the second neural network. Wherein the advanced features can be the type and configuration description of the object in the image, and the low-level characteristic can be based on the type of the object and the configuration determined object edge (Liu: page. 3, paragraph 1).
Li does not teach wherein the unsupervised classes (6) have no pre-existing labels assigned to the data samples in that class, and wherein the unsupervised classes (6); training a second artificial neural network structure (2) based on classification results produced by the first artificial neural network structure (1) to generate artificial candidates (7) belonging to at least one of the unsupervised classes ( 6) identified by the first artificial neural network structure (I); and wherein the labels assigned to the generated artificial candidates (7) are used as input to the first artificial neural network structure (1).
However in analogous art of training artificial neural network structure, Lee teaches wherein the unsupervised classes (6) have no pre-existing labels assigned to the data samples in that class, and wherein the unsupervised classes (6); training a second artificial neural network structure (2) based on classification results produced by the first artificial neural network structure (1) to generate artificial candidates (7) belonging to at least one of the unsupervised classes ( 6) identified by the first artificial neural network structure (I); and wherein the labels assigned to the generated artificial candidates (7) are used as input to the first artificial neural network structure (1) (FIGS. 1-4. [0006-0007], [0024-0048] wherein Lee processes unlabeled data through a semi-supervised learning and training the supervised learning based on the result of the updated classifier and augmented labeled data produced by the semi-supervised and transmitted to supervised learning. Wherein the assigned labeled are used as input to the first learning as illustrated in FIG. 3, wherein the first learning neural network is marked as 100 receiving augmented labeled data 112).
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It would have been obvious to a person in the ordinary skill in the art before the effective filing date of the claimed invention to combine Li with Lee by incorporating the method of wherein the unsupervised classes (6) have no pre-existing labels assigned to the data samples in that class, and wherein the unsupervised classes (6); training a second artificial neural network structure (2) based on classification results produced by the first artificial neural network structure (1) to generate artificial candidates (7) belonging to at least one of the unsupervised classes ( 6) identified by the first artificial neural network structure (I); and wherein the labels assigned to the generated artificial candidates (7) are used as input to the first artificial neural network structure (1) of Lee into the method of classifying data samples are classified in different classes (4) by the first artificial neural network structure (1), whereby at least some of the classes (4) are unsupervised classes (6), which are generated and/or filled by unsupervised learning of Li for the purpose of identifying high confidence data from the unlabeled data whose label can be inferred based upon the labeled data and the classifier (Lee: [0024]).
Regarding claim 2, Li as modified by Liu and Lee teaches wherein the process is a process for image classification whereby the data samples are images especially taken by at least one surveillance camera ([0143-0148], [0168] wherein Li’s data samples are images captured by a camera).
Regarding claim 3, Li as modified by Liu and Lee teaches wherein the first artificial network structure (1) is trained with the labelled and/or annotated artificial candidates (7) in order to label and/or annotate the said supervised class (6) (FIGS. 3A-3B, 4, 6, [0056], [0067], [0070], [0078], [0103], [0107] wherein Li describes training neural network with labelled updated images).
Regarding claim 4, Li as modified by Liu and Lee teaches wherein the first artificial neural network structure (1) is a convolutional artificial neural network and/or that the data samples are images ([0006-0008], [0067], [0072-0074] wherein Li’s data samples are images).
Regarding claim 5, Li as modified by Liu and Lee teaches wherein a part of the classes (4) are supervised classes (5), which are generated and/or filled by supervised learning (FIGS. 3A-3B, 4, 6, [0056], [0067], [0070], [0078], [0103], [0107] wherein Li describes generating labels by supervised learning).
Regarding claim 6, Li as modified by Liu and Lee teaches wherein the second artificial neural network structure (2) is trained by improving a loss-function of the probability density function of the respective unsupervised class (6) (page. 11, paragraphs 5-9, wherein Liu describes measuring the loss function between images and maximizing probability density).
Regarding claim 7, Li as modified by Liu and Lee teaches wherein the second artificial neural network structure (2) comprises a generative artificial neural network ([0006], [0067], [0072-0073], [0078] wherein Li describes generating training dataset using a generative adversarial network (GAN) that generates synthetic images and an associated trained neural network that generates labels for synthetic images generated by the GAN).
Regarding claim 8, Li as modified by Liu and Lee teaches wherein the second artificial neural network structure (2) comprises a discriminative artificial neural network, whereby the generative and the discriminative artificial neural network form a generative adversarial network (GAN) ([0076] wherein Li incorporates GAN that is a class of artificial intelligence system that uses two types of artificial neural networks contesting with each other. A GAN includes a first type of artificial neural networks, referred to as a generator network, that generates candidates and a second type of artificial neural networks, referred to as a discriminator network, that evaluates generated candidates. A generator network learns to map from a latent space to a particular data distribution of interest (a data distribution of changes to input images that are indistinguishable from photographs to human eyes), while a discriminator network discriminates between instances from a training dataset and candidates produced by generator network. In at least one embodiment, a GAN can have a generator network and two discriminator networks. A first discriminator network evaluates synthetic images generated by generator network, and a second discriminator network evaluates synthetic images and corresponding labels generated by generator network).
Regarding claim 9, Li as modified by Liu and Lee teaches wherein the first artificial neural network structure (1) is realized as a discriminative artificial neural network, whereby the generative and the discriminative artificial neural network form a generative adversarial network (GAN) ([0076] wherein Li incorporates GAN that is a class of artificial intelligence system that uses two types of artificial neural networks contesting with each other. A GAN includes a first type of artificial neural networks, referred to as a generator network, that generates candidates and a second type of artificial neural networks, referred to as a discriminator network, that evaluates generated candidates. A generator network learns to map from a latent space to a particular data distribution of interest (a data distribution of changes to input images that are indistinguishable from photographs to human eyes), while a discriminator network discriminates between instances from a training dataset and candidates produced by generator network. In at least one embodiment, a GAN can have a generator network and two discriminator networks. A first discriminator network evaluates synthetic images generated by generator network, and a second discriminator network evaluates synthetic images and corresponding labels generated by generator network).
Regarding claim 10, Li as modified by Liu and Lee teaches wherein the generative artificial neural network is a variational autoencoder (VAEs) ([0073] wherein Li teaches a generative model other than a GAN is used to generate a synthetic version of an input image and to generate one or more labels of objects in synthetic version. In at least one embodiment, a generative network that is used is a normalizing flow. In at least one embodiment, a generative model that is used is a latent dirichlet allocation, a naive Bayes network, a Gaussian mixture model, a restricted Boltzmann machine, or a variational autoencoder. In at least one embodiment, a generative network that is used is a Style Generative Adversarial Network (StyleGAN). StyleGAN is an extension to a GAN architecture to give control over disentangled style properties of generated images).
Regarding claim 11, Li as modified by Liu and Lee teaches wherein, characterized that in the unsupervised class (6) only the artificial candidates (7) are labelled and/or annotated ([0078], [0103], [0489] wherein Li incorporates artificial network to generate labels and annotation classes for images). ([0076] wherein Li incorporates GAN that is a class of artificial intelligence system that uses two types of artificial neural networks contesting with each other. A GAN includes a first type of artificial neural networks, referred to as a generator network, that generates candidates and a second type of artificial neural networks, referred to as a discriminator network, that evaluates generated candidates. A generator network learns to map from a latent space to a particular data distribution of interest (a data distribution of changes to input images that are indistinguishable from photographs to human eyes), while a discriminator network discriminates between instances from a training dataset and candidates produced by generator network. In at least one embodiment, a GAN can have a generator network and two discriminator networks. A first discriminator network evaluates synthetic images generated by generator network, and a second discriminator network evaluates synthetic images and corresponding labels generated by generator network).
Regarding claim 12, Li teaches a computer system (Abstract). Claim 12 is similar in scope to claim 1, therefore the claim is rejected under similar rationale.
Regarding claim 14, Li teaches A non-transitory, computer-readable medium having stored thereon instructions that when executed by a computer cause the computer ([0570]). Claim 14 is similar in scope to claim 1, therefore the claim is rejected under similar rationale.
Regarding claim 15, Li as modified by Liu and Lee teaches wherein the second artificial neural network structure (2) is configured to generate artificial candidates (7) belonging to the unsupervised class (6), such that the artificial candidates (7) are classified into the unsupervised class (6) by the first artificial neural network structure (1), and wherein the unsupervised class (6) comprises unlabeled original data samples and labeled and/or annotated artificial candidates (7), whereby the artificial candidates (7) at least partially label and/or annotate the unsupervised class (6) (FIGS. 1-4. [0006-0007], [0024-0048] wherein Lee describes generating augmented labeled data using the semi-supervised learning using a classifier and generating data of potential new classes).
Regarding claim 16, Li as modified by Liu and Lee teaches wherein when the first artificial neural network (1) incorrectly classifies the artificial candidates (7) into a class (4) other than the unsupervised class (6), the incorrectly classified artificial candidates (7) are returned to the second artificial neural network structure (2) in order to train the network structure of the second artificial neural network structure (2) (FIGS. 1-4. [0006-0007], [0024-0048] wherein Lee identifies high confidence data from the unlabeled data whose label can be inferred based upon the labeled data and the classifier. Wherein the updated classifier and the unlabeled data are processed by the active learning module executed by a computer program of the machine learning tool, which identifies an uncertain or unusual subset of the unlabeled data as well as samples of the high confidence data for validation and requests labels from a labeler in the form of a data label request. The labeler determines the labels for the unlabeled data in the data label request and returns the labels for requested data including labels for the samples of the high confidence data. The active learning module combines the labels for requested data with the uncertain unlabeled data and sampled high confidence data identified in the data label request to produce the augmented labeled data).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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 HASSAN MRABI whose telephone number is (571)272-8875. The examiner can normally be reached on Monday-Friday, 7:30am-5pm. Alt, Friday, EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Viker Lamardo can be reached on 571-270-5871. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HASSAN MRABI/Examiner, Art Unit 2144
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