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 .
Applicant’s Arguments/Remarks filed on 03/11/2026 have been received and made of record.
Claims 1-20 remained pending.
The outstanding USC 101 rejection to claims 1-11, and 13-19 have been maintained. And, the outstanding USC 101 rejection to claims 12 and 20 have been withdrawn based on the arguments/remarks filed on 03/11/2026. Please refer to the action below.
Examiner Notes
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. However, the claimed subject matter, not the specification, is the measure of the invention.
Response to Remarks/Arguments
Applicants’ arguments of 03/11/2026, corresponding to the outstanding 101 rejection of pages 7-9 corresponding to step 2A, Prong 1 citing “These rejections are respectively traversed. However, in Ex parte Desjardins, Appeal No. 2024-000567 (ARP Sept. 26, 2025) (precedential), the Director of the USPTO John A. Squires ("Director") stated: Under a charitable view, the overbroad reasoning of the original panel below is perhaps understandable given the confusing nature of existing § 101 jurisprudence, but troubling, because this case highlights what is at stake. Categorically excluding Al innovations from patent protection in the United States jeopardizes America's leadership in this critical emerging technology. Yet, under the panel's reasoning, many Al innovations are potentially unpatentable-even if they are adequately described and nonobvious-because the panel essentially equated any machine learning with an unpatentable "algorithm" and the remaining additional elements as "generic computer components," without adequate explanation. Dec. 24. Examiners and panels should not evaluate claims at such a
high level of generality. At the same time, the claims at issue stand rejected under § 103. This case demonstrates that H@ 102, 103 and 112 are the traditional and appropriate tools to limit patent protection to its proper scope. These statutory provisions should be the focus of examination. Accordingly, Applicant respectfully submits that the 35 U.S.C. § 101 claim rejections are invalid and should be withdrawn. Moreover: I. Step 2A and the 2019 Revised Patent Subject Matter Eligibility Guidance Under the 2019 Revised Patent Subject Matter Eligibility Guidance issued by the USPTO on January 7, 2019 (hereinafter "January 2019 Guidance"), "[o]nly when a claim recites a judicial exception and fails to integrate the exception into a practical application, is the claim 'directed to' a judicial exception, thereby triggering the need for further analysis pursuant to the second step of the Alice/Mayo test (USPTO Step 2B)." January 2019 Guidance at 5. Accordingly, the analysis of whether a claim is directed to an abstract idea consists of two prongs, wherein a claim is directed to an abstract idea only if(1) the claim recites an abstract idea, and (2) the claim fails to integrate the abstract idea into a practical application. See id. Applicant respectfully submits the present claims (1) do not recite an abstract idea, and (2) the claims integrate their respective features into a practical application. A. Prong 1: The present claims do not recite an abstract idea A claim recites an abstract idea only if it recites a mathematical concept, a certain method of organizing human activity, or a mental process. See January 2019 Guidance at 9-11. The January 2019 Guidance defines these three categories of abstract ideas as follows: Mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations Certain methods of organizing human activity - fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) Mental processes - concepts performed in the human mind (including an observation, evaluation, judgment, opinion) Id. at 9-11. The Office Action asserts claimed features "recite(s) mental processes and software processes." Office Action at 3. i. The present claims do not recite mental processes abstract ideas The January 2019 Guidance and the USPTO's October 2019 Update: Subject Matter Eligibility ("October 2019 Guidance") make clear that the present claims do not recite "mental processes." For instance, the October 2019 Guidance states "Claims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations." At 7. Applicant respectfully submits that, e.g., "generate a transformed frequency image by transforming an input image into a frequency domain; obtain a frequency filter corresponding to the input image by inputting the generated transformed frequency image to a filter generation neural network; and generate an output image in which a noise distribution of the input image is normalized by applying the obtained frequency filter to the generated transformed frequency image," as recited in claim 1, cannot practically be performed in the human mind, as the human mind is not equipped to perform such complex operations. Accordingly, Applicant respectfully submits the present claims do not recite any mental processes deemed to be abstract ideas.”.
A. The Examiner respectfully disagrees with the above assertions. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The independent claims, 1, and 13, recite subject matter that falls within the following groups of abstract ideas: mathematical concepts and mental processes. 101 Analysis – Step 1
Claim 1 is directed to a device (i.e., a machine) and claim 13 is directed to a method (i.e., a process). Therefore, claims 1 and 13 are within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 Revised Patent Subject Matter Eligibility Guidance (PEG), the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c). certain methods of organizing human activity.
Independent claim 13 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 35 U.S.C. 101 rejection. Claim 13 recites:
Claim 13 recites:
A processor-implemented method comprising:
generating a transformed frequency image by transforming an input image into a frequency domain; obtaining a frequency filter corresponding to the input image by inputting the generated transformed frequency image to a filter generation neural network; and generating an output image in which a noise distribution of the input image is normalized by applying the obtained frequency filter to the generated transformed frequency image.
The examiner submits that the foregoing bolded limitation(s) constitute a “mathematical calculation” or a “mental process”. Specifically, the “generating” and “obtaining” steps encompass the mathematical steps found in transforming and quantizing image data. Furthermore, the “generating …output image” step, encompasses a person, observing or organizing mental activities. Accordingly, claims 1 and 13 recites an abstract idea.
B. Applicants’ arguments of page 9 corresponding to Prong 2 further argues that “Prong 2: The claimed features are integrated into a practical application Even if the Office were to maintain that the claims recite an abstract idea, Applicant respectfully submits that the claims integrate their features into a practical application. "A claim that recites a judicial exception is not directed to that judicial exception, if the claim as a whole 'integrates the recited judicial exception into a practical application of that exception."' October 2019 Guidance at 10, if "the claim improves technology, the claim imposes meaningful limits on any recited judicial exception," then "the claim would be eligible under the 2019 PEG at least at Step 2A Prong Two," id. at 11, and "the analysis should take into consideration all the claim limitations and how those limitations interact and impact each other when evaluating whether the exception is integrated into a practical application." Id. at 12. As shown, if the claim as a whole integrates the recited judicial exception into a practical application of that exception, e.g., if the claim improves technology, the claim imposes meaningful limits on any recited judicial exception, then the claim is patent eligible, irrespective of any recitation of additional elements. i. The present claims use the claimed features in a manner that imposes a meaningful limit on the claimed features "A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception." January 2019 Guidance at 12, 13, 18 (emphasis added). Applicant respectfully submits that the present claims impose a meaningful limit on the claimed features cited by the Office, such that the claims are more than a drafting effort designed to monopolize the claimed features cited by the Office”.
The Examiner further disagrees with the above assertions. Regarding the analysis of 101 Analysis – Step 2A, Prong II.
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A processor-implemented method comprising:
generating a transformed frequency image by transforming an input image into a frequency domain; obtaining a frequency filter corresponding to the input image by inputting the generated transformed frequency image to a filter generation neural network; and generating an output image in which a noise distribution of the input image is normalized by applying the obtained frequency filter to the generated transformed frequency image.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “A processor-implemented method …” and “obtaining a frequency filter corresponding to the input image by inputting the generated transformed frequency image to a filter generation neural network; and generating an output image in which a noise distribution of the input image is normalized by applying the obtained frequency filter to the generated transformed frequency image…” the examiner submits that these limitations are an attempt to generally link additional elements to a technological environment. In particular, the “processor-implemented method …” and “obtaining a frequency filter corresponding to the input image by inputting the generated transformed frequency image to a filter generation neural network; and generating an output image in which a noise distribution of the input image is normalized by applying the obtained frequency filter to the generated transformed frequency image” limitations are recited at a high level of generality and merely automates the concurrently performed steps of “obtaining”, and “generating” steps, respectively, therefore acting as a generic computer to perform the abstract idea. The processor-implemented method is claimed generically and do not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. These additional limitation(s) is/are no more than mere instructions to apply the exception using a computer (the data provider and the central node). Furthermore, regarding the additional limitation of “processor-implemented method …” and “obtaining a frequency filter corresponding to the input image by inputting the generated transformed frequency image to a filter generation neural network; and generating an output image in which a noise distribution of the input image is normalized by applying the obtained frequency filter to the generated transformed frequency image”, the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer to perform the process. Specifically, they are insignificant extra-solution activities, which are mere mental processes and/or simple mathematical calculations of data gathering, image preprocessing, and outputting a cleaned or restored image indicative of the generated transformed frequency image.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an order combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
C. Applicants’ arguments of pages 9-11 corresponding to Prong 2 further argues that “The present claims recite features that reflect an improvement in the functioning of a computer, or an improvement to another technology or technical field Moreover, the Guidance provides a non-exhaustive list of examples wherein claimed features integrate an alleged abstract idea into a practical application, including wherein an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field. See January 2019 Guidance at 19, 20. Further, if "the claim improves technology, the claim imposes meaningful limits on any recited judicial exception," and "the claim would be eligible under the 2019 PEG at least at Step 2A Prong Two." October 2019 Guidance at 11. "Consideration of improvements is relevant to the integration analysis regardless of the technology of the claimed invention. That is, the consideration applies equally whether it is a computer-implemented invention, an invention in the life sciences, or any other technology." October 2019 Guidance at 13. In evaluating whether the claimed as a whole integrates an alleged abstract idea into a practical application, the Office must "give weight to all additional elements, whether or not they are conventional," as "revised Step 2A specifically excludes consideration of whether the additional elements represent well-understood, routine, conventional activity." January 2019 Guidance at 19. Moreover, the present claims improve the technical fields of frequency filtering, image reconstruction, and artificial intelligence, as non-limiting examples. Accordingly, each independent claim sets forth respective subject matter that improves the technological functioning of devices on which it may be implemented, and thus are directed to technological improvements. Accordingly, the present claims recite features that reflect an improvement in the functioning of a computer, or an improvement to another technology or technical field, and therefore the claimed features are integrated into a practical application, and therefore the claims are not "directed to" an abstract idea, and therefore the claims recite patent-eligible subject matter. Accordingly, Applicant respectfully submits the rejection of the claims under 35 U.S.C. § 101 is deficient and Applicant respectfully requests the rejection be withdrawn”.
The Examiner further disagrees with the above assertions. Regarding 101 Analysis – Step 2B
Regarding Step 2B of the PEG, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of the concurrently performed steps of “generating”, “obtaining”, and “generating” steps limitations amount to nothing more than mere instructions to apply the exception using a generic computer component (the processor implemented). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The additional limitations of “obtaining a frequency filter corresponding to the input image by inputting the generated transformed frequency image to a filter generation neural network; and generating an output image in which a noise distribution of the input image is normalized by applying the obtained frequency filter to the generated transformed frequency image”, are well-understood in the art of performing denoising on an image having a different noise distribution from that of the training images and to effectively denoise an image utilizing the claimed filter network. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. The additional limitation of “generating an output image in which a noise distribution of the input image is normalized by applying the obtained frequency filter to the generated transformed frequency image” is a well-understood, routine, and conventional activity because the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere outputting of data is a well understood, routine, and conventional function. Hence, claims 1 and 13 are not subject matter eligible.
D. Applicant’s arguments of pages 11-16 regarding the prior arts of Kapoor citing “Kapoor Fig. 2a, As shown, while Kapoor applies a filter to generate filtered image data in operation 120, Kapoor fails to disclose that the filter used in Fig. la is obtained by inputting the image data transformed into the frequency domain in operation 115 to a filter generation neural network. To the contrary, Kapoor discloses in Fig. 2a that the filter is generated based on a set of images that are different than the image processed in Fig. la. Accordingly, Kapoor fails to disclose, teach, or suggest "obtain a frequency filter corresponding to the input image by inputting the generated transformed frequency image to a filter generation neural network," as recited in independent claim 1. Moreover, Kapoor merely discloses that the filter is configured to suppress adversarial perturbations within the image, and Kapoor fails to disclose that the filtered image is an image in which a noise distribution of the image is normalized. Accordingly, Kapoor fails to disclose, teach, or suggest "generate an output image in which a noise distribution of the input image is normalized by applying the obtained frequency filter to the generated transformed frequency image," as recited in independent claim 1”, have been considered. The above arguments/remarks regarding independent claims 12 and 20 are moot, as the rejection of the said claims is withdrawn. Additionally, the arguments in regards to independent claims 1 and 13 are not persuasive.
The Examiner would like to respectfully note that Kapoor teaches in at least Figs. 1a, 2a, and 3a-3b a system 10 further comprising processing/electronic devices 14 configured for transforming obtained image data into the frequency domain and applying a filter on the image data in the frequency domain to generate filtered image data. And, steps 115, 220 of at least Figs. 1a, 2a, 3a-3b further teaches said generated transformed frequency image by transforming an input image into a frequency domain, in addition, Figs. 1a and 2a and 3b teaches a filter generation neural network adapted for generating and obtaining a lowpass frequency filter corresponding to the input image by inputting said generated transformed frequency image to said filter generation neural network. And lastly,
processing output filtered image of S130 of Figs. 1a and 2a by further transforming the image of further Fig. 3b to generate said output image in which a noise distribution of the input image is normalized by applying the obtained frequency filter to the generated transformed frequency image.
E. Applicant’s arguments of pages 16-18 regarding the prior arts of Yang citing “Yang merely discloses: [0148] Image Fourier transform converts the image from the spatial domain to the frequency domain. The frequency of the image is represented by the gradient between adjacent pixels. The larger the gradient, the greater the frequency, that is, the greater the change in the light intensity of adjacent pixels. Using Fourier transform to convert the image from the spatial domain to the frequency domain can well locate the required image edge position and other information. To the contrary, Yang uses the already-obtained Fourier filter to generate the image converted to the frequency domain. Accordingly, Yang fails to disclose, teach, or suggest "obtain a frequency filter corresponding to the input image by inputting the generated transformed frequency image to a filter generation neural network," as recited in independent claim 1. Moreover, Yang merely discloses that the Fourier filter converts the image from the spatial domain to the frequency domain, and Yang fails to disclose that the image converted to the frequency domain is an image in which a noise distribution of the image is normalized”.
The Examiner respectfully disagrees with the above assertions of Yang. The prior art of Yang clearly teaches transforming an input image into a frequency domain of at least Abstract and para. 0148 further supported by para. 0010 so as to generate a transformed frequency image by utilizing at least a frequency Fourier and/or bandpass filter algorithm of at least para. 0010, and 0149 corresponding to the input image by inputting the generated transformed frequency image to a filter generation algorithm neural network. And wherein using methods and system of further para. 0010 and 0149 further adapted for generating an output image in which a noise distribution of the input image is normalized by applying the obtained frequency filter to the generated transformed frequency image.
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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Independent claims 1, and 13 are rejected under 35 U.S.C. 102 (a)(1) as being unpatentable by Kapoor et al. (WO 2022043010, A1).
Regarding claim 1, Kapoor teaches an electronic device (the disclosure cites in at least Figs. 1a, 2a, and 3a-3b a system 10 further comprising processing/electronic devices 14 configured for transforming obtained image data into the frequency domain and applying a filter on the image data in the frequency domain to generate filtered image data), comprising:
one or more processors (the one or more processors of at least device 14 of the disclosure where as cited “the one or more processing devices 14 may be implemented using one or more processing units”), configured to:
generate a transformed frequency image by transforming an input image into a frequency domain (steps 115, 220 of at least Figs. 1a, 2a, 3a-3b further teaches said generated transformed frequency image by transforming an input image into a frequency domain);
obtain a frequency filter corresponding to the input image by inputting the generated transformed frequency image to a filter generation neural network (Figs. 1a and 2a and 3b teaches a filter generation neural network adapted for generating and obtaining a lowpass frequency filter corresponding to the input image by inputting said generated transformed frequency image to said filter generation neural network);
and generate an output image in which a noise distribution of the input image is normalized by applying the obtained frequency filter to the generated transformed frequency image (processing output filtered image of S130 of Figs. 1a and 2a by further transforming the image of further Fig. 3b to generate said output image in which a noise distribution of the input image is normalized by applying the obtained frequency filter to the generated transformed frequency image).
Regarding claim 13, Kapoor teaches in at least Fig. 2b an processor-implemented method comprising:
generating a transformed frequency image by transforming an input image into a frequency domain (steps 115, 220 of at least Figs. 1a, 2a, 3a-3b further teaches said generated transformed frequency image by transforming an input image into a frequency domain);
obtaining a frequency filter corresponding to the input image by inputting the generated transformed frequency image to a filter generation neural network (Figs. 1a and 2a and 3b teaches a filter generation neural network adapted for generating and obtaining a lowpass frequency filter corresponding to the input image by inputting said generated transformed frequency image to said filter generation neural network);
and generating an output image in which a noise distribution of the input image is normalized by applying the obtained frequency filter to the generated transformed frequency image (processing output filtered image of S130 of Figs. 1a and 2a by further transforming the image of further Fig. 3b to generate said output image in which a noise distribution of the input image is normalized by applying the obtained frequency filter to the generated transformed frequency image).
Claims 1-9, and 13-19 are further rejected under 35 U.S.C. 102 (a)(1) as being unpatentable by Yang et al. (CN 110738605, cited in IDS).
Regarding claim 1, Yang teaches in at least the Abstract an electronic device comprising: one or more processors (processing of the Abstract by one or more implied processors of the Abstract);
configured to: generate a transformed frequency image by transforming an input image into a frequency domain (transforming an input image into a frequency domain of at least Abstract and para. 0148 further supported by para. 0010 so as to generate a transformed frequency image);
obtain a frequency filter corresponding to the input image by inputting the generated transformed frequency image to a filter generation neural network (utilizing at least a frequency Fourier and/or bandpass filter algorithm of at least para. 0010, and 0149 corresponding to the input image by inputting the generated transformed frequency image to a filter generation algorithm neural network);
and generate an output image in which a noise distribution of the input image is normalized by applying the obtained frequency filter to the generated transformed frequency image (methods and system of further para. 0010 and 0149 further adapted for generating an output image in which a noise distribution of the input image is normalized by applying the obtained frequency filter to the generated transformed frequency image).
Regarding claim 2 (according to claim 1), Yang further implies wherein further comprising: an image sensor comprising a plurality of photodiodes, wherein the one or more processors are configured to generate the input image using the plurality of photodiodes of the image sensor (input image of at least the Abstract may obviously comprise in the art obtained noisy images by image sensors such as obviously an image sensor comprising obviously a plurality of photodiodes which may in case generate noisy images).
Regarding claim 3 (according to claim 1), Yang further teaches wherein the one or more processors are configured to generate a denoised image in which noise is removed from the input image by inputting the generated output image to a denoising neural network (denoised images of at least the Abstract and para. 0149 in which noise is removed from the input image by inputting the generated output image to a denoising neural network).
Regarding claim 4 (according to claim 3), Yang further teaches wherein the one or more processors are configured to train the filter generation neural network and the denoising neural network together based on a loss calculated using the generated denoised image and a true denoised image mapped to the input image (training the noise distribution network further embodying the Fourier filter algorithm as said filter generation neural network and the denoising neural network together based on a loss of further para. 0137-0138 calculated using the generated denoised image and a true denoised image mapped to the input image).
Regarding claim 5 (according to claim 4), Yang further teaches wherein the one or more processors are configured to train the filter generation neural network based on a loss calculated using the generated denoised image and a true denoised image mapped to the input image (training the noise distribution network further embodying the Fourier filter algorithm as said filter generation neural network and the denoising neural network together based on a loss of further para. 0137-0138 calculated using the generated denoised image and a true denoised image mapped to the input image).
Regarding claim 6 (according to claim 1), Yang further teaches wherein the one or more processors are configured to train the filter generation neural network based on a loss calculated using the generated output image and a true output image mapped to the input image (training the noise distribution network further embodying the Fourier filter algorithm as said filter generation neural network and the denoising neural network together based on a loss of further para. 0137-0138 calculated using the generated output image and a true output image mapped to the input image).
Regarding claim 7 (according to claim 1), Yang further teaches wherein for the generating of the output image, the one or more processors are configured to: generate an intermediate image in the frequency domain by applying the obtained frequency filter to the generated transformed frequency image (para. 0001 and 0010 further teaches said generating of the output image, including a generated intermediate image in the frequency domain by applying the obtained frequency filter to the generated transformed frequency image); and generate the output image by transforming the generated intermediate image into a spatial domain (at least para. 0010 further teaches transforming the image back to a spatial domain).
Regarding claim 8 (according to claim 1), Yang further teaches wherein the filter generation neural network comprises one or more convolutional neural networks (CNNs) and a fully connected layer connected to the one or more CNNs (the at least CNN of Fig. 1 and para. 0050-0052 and 0113-0114).
Regarding claim 9 (according to claim 8), Yang further teaches wherein first output data output from the fully connected layer indicates a frequency range of the frequency domain in the frequency filter, and second output data output from the fully connected layer indicates a weighted value of a component corresponding to the frequency range indicated by the first output data (output data of further para. 0113-0114 comprise in at least para. 0147-0149 output from the fully connected layer indicates a frequency range of the frequency domain in the frequency filter and output from the fully connected layer indicates a weighted value of a component corresponding to the frequency range indicated by the first output data).
Regarding claim 11 (according to claim 1), Yang further teaches wherein the filter generation neural network is configured to output different frequency filters according to a noise distribution of an image input to the filter generation neural network (filter algorithm network of further para. 0010 and 0149 further comprises filter generation neural network configured to obviously output different frequency filters in low and high ranges according to a noise distribution of an image input to the filter generation neural network).
Regarding claim 13, Yang teaches at least in the Abstract a processor-implemented method comprising: generating a transformed frequency image by transforming an input image into a frequency domain (transforming an input image into a frequency domain of at least Abstract and para. 0148 further supported by para. 0010 so as to generate a transformed frequency image);
obtaining a frequency filter corresponding to the input image by inputting the generated transformed frequency image to a filter generation neural network (utilizing at least a frequency Fourier and/or bandpass filter algorithm of at least para. 0010, and 0149 corresponding to the input image by inputting the generated transformed frequency image to a filter generation algorithm neural network);
and generating an output image in which a noise distribution of the input image is normalized by applying the obtained frequency filter to the generated transformed frequency image (methods and system of further para. 0010 and 0149 further adapted for generating an output image in which a noise distribution of the input image is normalized by applying the obtained frequency filter to the generated transformed frequency image).
Regarding claim 14 (according to claim 13), Yang further teaches wherein further comprising generating a denoised image in which noise is removed from the input image by inputting the generated output image to a denoising neural network (denoised images of at least the Abstract and para. 0149 in which noise is removed from the input image by inputting the generated output image to a denoising neural network).
Regarding claim 15 (according to claim 13), Yang further teaches wherein the generating of the output image comprises: generating an intermediate image in the frequency domain by applying the obtained frequency filter to the generated transformed frequency image (generated images of at least para. 0010 and the Abstract further comprises an intermediate image in the frequency domain after applying the obtained Fourier frequency filter to the generated transformed frequency image);
and generating the output image by transforming the generated intermediate image into a spatial domain (the at least Abstract and para. 0010 further teaches a case of transforming the image back to the spatial domain further indicative of said generating output image by transforming the generated intermediate image into the spatial domain).
Regarding claim 16 (according to claim 13), Yang further teaches wherein the filter generation neural network comprises one or more convolutional neural networks (CNNs) and a fully connected layer connected to the one or more CNNs (the at least CNN of Fig. 1 and para. 0050-0052 and 0113-0114).
Regarding claim 17 (according to claim 16), Yang further teaches wherein first output data output from the fully connected layer indicates a frequency range of the frequency domain in the frequency filter, and second output data output from the fully connected layer indicates a weighted value of a component corresponding to the frequency range indicated by the first output data (output data of further para. 0113-0114 comprise in at least para. 0147-0149 output from the fully connected layer indicates a frequency range of the frequency domain in the frequency filter and output from the fully connected layer indicates a weighted value of a component corresponding to the frequency range indicated by the first output data).
Regarding claim 18 (according to claim 13), Yang further teaches wherein the filter generation neural network is configured to output different frequency filters according to a noise distribution of an image input to the filter generation neural network (filter algorithm network of further para. 0010 and 0149 further comprises filter generation neural network configured to obviously output different frequency filters in low and high ranges according to a noise distribution of an image input to the filter generation neural network).
Regarding claim 19, Yang further teaches a non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, configure the one or more processors to perform the method of claim 13 (the Abstract further teaches at least a medium known to comprise at least said non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, configure the one or more processors to perform the method of claim 13).
Claims Standings
Claims 12 and 20 allowed over the prior arts of record. Claim 10 remained objected over the prior arts of record as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims, and if all outstanding rejections are overcome.
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 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 MARCELLUS AUGUSTIN whose telephone number is (571)270-3384. The examiner can normally be reached 9 AM- 5 PM.
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/MARCELLUS J AUGUSTIN/Primary Examiner, Art Unit 2682 06/08/2026
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