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 .
This action is responsive to the application filed 2/17/2023.
Claims 1-9 are presented for examination.
Priority
Applicant’s claim for the benefit of a prior filed application PCT/JP2020/037257, filed 9/30/2020, is acknowledged.
Information Disclosure Statement
The information disclosure statements (IDS) submitted 2/17/2023 and 3/8/2023 have been considered by the examiner.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
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.
Claims 1-9 are 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding independent claim 1, lines 2-4 in the claim limitations, reciting in part “a conversion circuitry configured to convert first data into a first frequency component and convert second data generated by a generator that configures an adversarial learning model into a second frequency component”, it is unclear what the scope of the limitations is and whether these limitations should be read as a conversion circuitry is configured to convert second data into a second frequency component, or a generator that configures an adversarial model into a second frequency component separate from a conversion circuitry configured to…generally convert second data, or something else. Thus, claim 1 is indefinite. For the purposes of examination, the said limitations are interpreted as “a conversion circuitry configured to convert first data into a first frequency component and convert second data, generated by a generator that configures an adversarial learning model, into a second frequency component”.
Dependent claims 2-4 variously depend from rejected base claim 1 and do not cure the deficiencies of claim 1. Thus, claims 2-4 are also rejected under 35 U.S.C. 112(b) for at least being dependent on rejected base claim 1.
Regarding independent claim 5, lines 3-5 in the claim limitations, reciting in part “converting first data into a first frequency component and converting second data generated by a generator that configures an adversarial learning model into a second frequency component”, it is unclear what the scope of the limitations is and whether these limitations should be read as converting second data into a second frequency component, or a generator that configures an adversarial model into a second frequency component separate from generally converting second data, or something else. Thus, claim 1 is indefinite. For the purposes of examination, the said limitations are interpreted as “converting first data into a first frequency component and converting second data, generated by a generator that configures an adversarial learning model, into a second frequency component”.
Dependent claims 6-9 variously depend from rejected base claim 5 and do not cure the deficiencies of claim 5. Thus, claims 6-9 are also rejected under 35 U.S.C. 112(b) for at least being dependent on rejected base claim 5.
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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
Claim 1
Step 1: The claim recites “A learning device, comprising:”; therefore, it is directed to the statutory category of machines.
Step 2A Prong 1: The claim recites, inter alia:
convert first data into a first frequency component and convert second data into a second frequency component: These limitations recite mathematical relationships of converting first data into a first frequency component and converting second data into a second frequency component similar to a conversion between binary coded decimal and pure binary per MPEP 2106.04(a)(2)(A)(ii).
calculate a loss function that simultaneously optimizes the generator: These limitations recite mathematical calculations.
and discriminates between the first data and the second data: These limitations recite a mentally performable process of using judgement to discriminate between first data and second data.
and discriminates between the first frequency component and the second frequency component: These limitations recite a mentally performable process of using judgement to discriminate between the first frequency component and the second frequency component.
update parameters of the generator, the first discriminator, and the second discriminator so that the loss function calculated by the calculation circuitry is optimized: These limitations recite a mathematical relationship of organizing and manipulating update parameters of the generator, the first discriminator, and the second discriminator through mathematical correlation of optimizing the loss function calculated by the calculation circuitry similar to organizing information and manipulating information through mathematical correlations per MPEP 2106.04(a)(2)(A)(iv).
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
A learning device, comprising: conversion circuitry configured to…calculation circuitry configured to…and update circuitry configured to: These additional elements are recited at a high level of generality and merely invokes a generic computer machinery as a tool to perform the underlying abstract ideas and thus fails to integrate the abstract idea into a practical application. See MPEP 2106.05(f).
generated by a generator that configures an adversarial learning model: These additional elements are recited at a high level of generality and amount to generally linking the use of a judicial exception, e.g. converting data into a frequency component, to a particular technological environment or field of use, e.g. wherein the data is generated by a generator that configures an adversarial learning model. Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. See MPEP 2106.05(h).
a first discriminator that configures the adversarial learning model: These additional elements are recited at a high level of generality and amount to generally linking the use of a judicial exception, e.g. discriminating between the first data and the second data, to a particular technological environment or field of use, e.g. a first discriminator that configures the adversarial learning model. Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. See MPEP 2106.05(h).
and a second discriminator that configures the adversarial learning model: These additional elements are recited at a high level of generality and amount to generally linking the use of a judicial exception, e.g. discriminating between the first frequency component and the second frequency component, to a particular technological environment or field of use, e.g. a second discriminator that configures the adversarial learning model. Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. See MPEP 2106.05(h).
Step 2B: The additional elements from Step 2A Prong 2 include invoking computers or other machinery to apply the underlying judicial exception and generally linking the use of the judicial exception to indicate a field of use or technological environment. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 2
Step 1: a machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
calculates a loss function having a first term that decreases as discrimination accuracy of the first discriminator increases, and a second term that decreases as discrimination accuracy of the second discriminator increases: These limitations recite mathematical calculations/mathematical relationships.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein: the calculation circuitry further: These additional elements are recited at a high level of generality and merely invokes a generic computer machinery as a tool to perform the underlying abstract ideas and thus fails to integrate the abstract idea into a practical application. See MPEP 2106.05(f).
Step 2B: The additional elements from Step 2A Prong 2 include invoking computers or other machinery to apply the underlying judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 3
Step 1: a machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
calculates a loss function by multiplying the first term by a first coefficient larger than 0 and smaller than 1, and multiplying the second term by a second coefficient obtained by subtracting the first coefficient from 1: These limitations recite mathematical calculations/mathematical relationships.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein: the calculation circuitry: These additional elements are recited at a high level of generality and merely invokes a generic computer machinery as a tool to perform the underlying abstract ideas and thus fails to integrate the abstract idea into a practical application. See MPEP 2106.05(f).
Step 2B: The additional elements from Step 2A Prong 2 include invoking computers or other machinery to apply the underlying judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 4
Step 1: a machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
calculates a loss function that decreases as a difference between discrimination accuracy of the first discriminator and discrimination accuracy of the second discriminator decreases: These limitations recite mathematical calculations/mathematical relationships.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein: the calculation circuitry further: These additional elements are recited at a high level of generality and merely invokes a generic computer machinery as a tool to perform the underlying abstract ideas and thus fails to integrate the abstract idea into a practical application. See MPEP 2106.05(f).
Step 2B: The additional elements from Step 2A Prong 2 include invoking computers or other machinery to apply the underlying judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claims 5 and 7-9
Step 1: These claims are directed to “A learning method, comprising:”; therefore, it is directed to the statutory category of a process.
Step 2A Prong 1: Claims 5 and 7-9 recite the same abstract ideas as Claims 1-4, respectively.
Step 2A Prong 2: The judicial exception recited in these claims are not integrated into a
practical application. The analysis at this step for Claims 5 and 7-9 mirrors that of Claims 1-4, respectively.
Step 2B: The additional elements from Step 2A Prong 2 do not contain significantly more
than the judicial exception for these claims. The analysis at this step for Claims 5 and 7-9 mirrors that of Claims 1-4, respectively.
Claim 6
Step 1: The claim recites “A non-transitory computer readable medium storing a learning program for causing a computer to perform the method of Claim 5”; therefore, it is directed to the statutory category of an article of manufacture.
Step 2A Prong 1: The claim recites the same abstract ideas as Claim 5.
Step 2A Prong 2: The judicial exception recited in this claim is not integrated into a
practical application. The only difference between Claim 6 and Claim 5, is that Claim 6 is directed to “A non-transitory computer readable medium storing a learning program for causing a computer to perform the method of”. However, mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, i.e. a non-transitory computer readable medium storing a learning program for causing a computer to perform the method of, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f). With that exception, the analysis at this step for Claim 6 mirrors that of Claim 5.
Step 2B: The additional elements from Step 2A Prong 2 do not contain significantly more
than the judicial exception for these claims. The only difference between Claim 6 and Claim 5, is that Claim 6 is directed to “A non-transitory computer readable medium storing a learning program for causing a computer to perform the method of”. However, mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, i.e. a non-transitory computer readable medium storing a learning program for causing a computer to perform the method of, cannot amount to significantly more than the judicial exception. See MPEP 2106.05(f). With that exception, the analysis at this step for Claim 6 mirrors that of Claim 5.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Durall et al., “Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributions” (June 2020) (ABSTRACT Generative convolutional deep neural networks, e.g. popular GAN architectures, are relying on convolution based up-sampling methods to produce non-scalar outputs like images or video sequences. In this paper, we show that common up-sampling methods, i.e. known as up-convolution or transposed convolution, are causing the inability of such models to reproduce spectral distributions of natural training data correctly. This effect is independent of the underlying architecture and we show that it can be used to easily detect generated data like deepfakes with up to 100% accuracy on public benchmarks. To overcome this drawback of current generative models, we propose to add a novel spectral regularization term to the training optimization objective. We show that this approach not only allows to train spectral consistent GANs that are avoiding high frequency errors. Also, we show that a correct approximation of the frequency spectrum has positive effects on the training stability and output quality of generative networks).
Su et. al, “HiFi-GAN: High-Fidelity Denoising and Dereverberation Based on Speech Deep Features in Adversarial Networks” (September 21, 2020) (ABSTRACT Real-world audio recordings are often degraded by factors such as noise, reverberation, and equalization distortion. This paper introduces HiFi-GAN, a deep learning method to transform recorded speech to sound as though it had been recorded in a studio. We use an end-to-end feed-forward WaveNet architecture, trained with multi-scale adversarial discriminators in both the time domain and the time-frequency domain. It relies on the deep feature matching losses of the discriminators to improve the perceptual quality of enhanced speech. The proposed model generalizes well to new speakers, new speech content, and new environments. It significantly outperforms state-of-the-art baseline methods in both objective and subjective experiments. Index Terms: speech enhancement, denoising, dereverberation, generative adversarial networks, deep features).
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/KC CHEN/Primary Patent Examiner, Art Unit 2143
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