CTNF 18/548,135 CTNF 94099 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 in response to the Application filed on 08/28/2023. Claims 1-20 are pending in the case. All claims are examined and rejected accordingly. Information Disclosure Statement 3. As required by MPEP 609 (c), the Applicants’ submission of the Information Disclosure Statement(s) filed on 08/28/2023 and 09/25/2023 are acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. Priority 4. The present application claims priority under 35 U.S.C. §119 to FEDERAL REPUBLIC OF GERMANY patent Application DE10 2021 206 106.0, filed on 06/15/2021. Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119 and/or 35 U.S.C. 120 is acknowledged. Claim Objections 07-29-01 AIA 5. Claim 1 is objected to because of the following informalities: Line 13, please delete close bracket “)” Appropriate correction is required. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 6. 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. 7. Claims 14-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards an abstract idea, without significantly more. Step 1 According to the first part of the analysis, in the instant case, claim is directed to a computer implemented method, which is a process and falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter). Regarding Claim 14, 19, 23 and 24, At step 2A, prong 1, Does the claim recite a judicial exception? Claim 1 further recites the steps of : providing a first input signal and a first value to a first part of the machine learning system, wherein the first input signal characterizes a noisy signal and the first value characterizes a randomly drawn value ( This step relies on receiving data for mathematical processing which falls into the “Mathematical concept” grouping of abstract ideas.) , determining, by the first part, a first output signal for the first input signal and the first value (This step relies on mathematical calculations and mathematical modeling , which falls into the “Mathematical concepts” grouping of abstract ideas.) , determining, by a second part of the machine learning system, a second value based on the first output signal, wherein the second value characterizes a probability of the first output signal to characterize a noisy signal (This step relies on mathematical calculations and mathematical evaluation, which falls into the “Mathematical concepts grouping of abstract ideas.) , determining, by the second part, a third value based on a supplied second input signal, wherein the second input signal characterizes a non-noisy signal and wherein the third value characterizes a probability of the second input signal characterize a non- noisy signal (This step relies on mathematical calculations and mathematical modeling , which falls into the “Mathematical process” grouping of abstract ideas.) ,; and adapting a plurality of parameters of the first part according to a gradient of the second value with respect to a plurality of parameters of the first part (This step relies on mathematical model which falls into the “Mathematical Concepts” and “mathematical Processes” grouping of abstract ideas.) , adapting a plurality of parameters of the second part according to a gradient of a sum of the second value and the third value with respect to the plurality of parameters of the second part (This step relies on mathematical model which falls into the “Mathematical Concepts” and “mathematical Processes” grouping of abstract ideas.) , The claim recites mathematical modeling ( ML training), gradient optimization and neural network inference which collectively fall under the Mathematical Concepts and Mental Process ( evaluation/comparison steps). Accordingly, the claims recite an abstract idea. Step 2A prong 2 : Does the claim recite additional elements? Do those additional elements, individually and in combination, integrate the judicial exception into a practical application? Further, the claim does not recite any additional element which could integrate this abstract idea into a practical application, because the additional elements recited of consist of: “… The computer-implemented method for training a machine learning system to denoise a provided input signal, …” (claim 14), “A training system configured to train a machine learning system to denoise a provided input signal …”, (claim 23) ( Generic computer components on which to implement the math abstract idea (see MPEP 2106.05(f)); “A non-transitory machine-readable storage medium on which is stored a computer program for training a machine learning system to denoise a provided input signal, the computer program, when executed by a processor, causing the processor to perform the following steps” ( data gathering, insignificant extra solution); The additional elements are recited at a high level of generality and do not amount to significantly more than the abstract idea (MPEP 2106.05(f)). The claim use a computer to perform a math and does not improve the function of the computer or other technology. Accordingly the claim does not integrate the abstract idea into practical application. Thus the claim is directed towards the abstract idea. Step 2B: Do the additional elements, considered individually and in combination, amount to significantly more than the judicial exception? No, As shown above with respect to integration of the abstract idea into a practical application, “… The computer-implemented method for training a machine learning system to denoise a provided input signal, …” (claim 14), “A training system configured to train a machine learning system to denoise a provided input signal …”, (claim 23) ( Generic computer components on which to implement the math abstract idea (see MPEP 2106.05(f)); “A non-transitory machine-readable storage medium on which is stored a computer program for training a machine learning system to denoise a provided input signal, the computer program, when executed by a processor, causing the processor to perform the following steps” ( data gathering, insignificant extra solution); The additional elements, alone and in combination, fail to integrate the abstract idea into a practical application or add “significantly more.” Thus, the claims are not patent eligible. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Neither can insignificant extra-solution activity. All of these additional elements as generically claimed are thus considered well-understood, routine, and conventional. Therefore, these limitations, taken alone or in combination, do not integrate the abstract idea into a practical application or recite significantly more that the abstract idea. Thus, these independent claims are not patent eligible. The dependent claims respectively recite a judicial exception in limitations of: “wherein the method further comprises the following steps: providing a third input signal and a fourth value to the first part, wherein the third input signal characterizes a non-noisy signal; determining, by the first part, a second output signal for the third input signal and the fourth value; adapting a plurality of the parameters of the first part according to a deviation of the second output signal to the third input signal.”( claims 15 ), “wherein the method further comprises the following steps:determining, by the first part and based on the first input signal and the first value, a fifth value characterizing a classification of the type of noise characterized by the first input signal; adapting a plurality of the parameters of the first part according to a deviation of a class characterized by the fifth value and a class of noise type corresponding to the first input signal.”( claims 16) , “wherein the method further comprises the following steps:determining, by the first part and based on the third input signal and the fourth value, a fifth value characterizing a classification of the type of noise characterized by the third input signal; adapting a plurality of the parameters of the first part according to a deviation of a class characterized by the fifth value and a class characterizing an absence of noise.”( claims 17 ), “he method according to claim 15, wherein the deviation of the second output signal to the third input signal is characterized by the following formula.”( claim 18 ), “wherein the denoised signal is used as input of a control system, wherein the control system is configured to determine a control signal of an actuator based on the denoised signal.”( claim 20 ), “wherein the denoised signal is used as input to a virtual sensor for determining a property of the input signal that is not measured by the input signal itself.”( claim 21 ), “wherein first input signal and/or second input signal and/or third input signal and/or the input signal are sensor signals. ”(Claim 22), These additional limitations ( in claims 14-18 and 20-22 ) also constitute concepts performed Mathematical concept or mathematical operation groupings of abstract ideas. This judicial exception is not integrated into a practical application. Additional elements “computer readable medium comprising: computer program code ( in claims 14-18 and 20-22 ) , all amount to no more than adding insignificant extra-solution activity/specifications related to data gathering, data input, or data transmittal. These additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of non-transitory computer readable medium comprising: computer program code are again insignificant extra-solution activity steps that cannot provide an inventive concept. All of these additional elements as generically claimed are considered well-understood, routine, and conventional. Therefore, these limitations, taken alone or in combination, do not integrate the abstract idea into a practical application or recite significantly more that the abstract idea. Thus, all of the dependent claims are also not patent eligible. Examiner Comments 07-06 AIA 15-10-15 8. 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. Claim Rejections - 35 USC § 103 07-20-aia AIA 9. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-21-aia AIA 10. Claim s 14-24 are rejected under 35 U.S.C. 103 as being unpatentable over Vogels (Pub. No.: US 20200184605 B1, Pub. Date 2020-06-11) in view of Fang (US 20190251612 A1, 2019-08-15) Regarding independent claim 14, Vogels teaches t he computer-implemented method for training a machine learning system to denoise a provided input signal (see Vogels : Fig.1, abstract “Supervised machine learning using neural networks is applied to denoising images rendered by MC path tracing.”), the training of the machine learning system comprising the following steps: providing a first input signal and a first value to a first part of the machine learning system (see Vogels : Fig.13, [0184], “A noisy input image rendered by a renderer 1310 may be input Pseudinto a generator 1320 and a discriminator 1330. The input may also include a set of auxiliary buffers (also referred herein as “feature buffers”) that encode scene information, as well as their corresponding variances. The auxiliary buffers may include information about surface normal, albedo, depth, and the like.”), wherein the first input signal characterizes a noisy signal and the first value characterizes a randomly drawn value (see Vogels : Fig.13, [0206], “the input to the generator 1320 may also include some pseudo-random noise to be superimposed on the noisy input image, as illustrated in FIG. 13. With the addition of the pseudo-random noise the input image,”) determining, by the first part, a first output signal for the first input signal and the first value (see Vogels : Fig.13, [0206], “With the addition of the pseudo-random noise the input image, the generator 1320 may produce multiple different denoised images for the same noisy input image, and therefore may capture a full conditional distribution of the denoised images for a given noisy input image. This may be useful for adaptive sampling. For instance, a user may tell the renderer 1310 to render the regions where the distribution is broad with more rays.”) determining, by a second part of the machine learning system, a second value based on the first output signal, wherein the second value characterizes a probability of the first output signal to characterize a noisy signal (see Vogels : Fig.13, [0185], “The denoised image output from the generator 1320 is input to the discriminator 1330. The discriminator 1330 also receives a corresponding reference image (i.e., the ground truth) as input. The reference image may be a high-quality image that has been rendered with many rays. It may be important that the ground truth image looks exactly like a desired output image. The generator 1320 may be trained to produce a mapping that resembles the mapping of the ground truth image.”); determining, by the second part, a third value based on a supplied second input signal, wherein the second input signal characterizes a non-noisy signal and wherein the third value characterizes a probability of the second input signal characterize a non- noisy signal (see Vogels : Fig.14A, [0190], “As the training converges, the generator 1320 may have learned to produce a high quality denoised image that looks very “real” and can fool the discriminator 1330 enough that the discriminator 1330 may have an equal probability of identifying it as belonging to the class of reference images and as belonging to the class of denoised images, such that a mean value of the quality metric approaches 2.”; and training the machine learning system (see Vogels : Fig.13, [0184], “a GAN-based system for denoising images rendered by MC path tracing.”), wherein training includes: adapting a plurality of parameters of the first part according to a gradient of the second value with respect to a plurality of parameters of the first part (see Vogels : Fig.14A, [0191], “training may alternate between the generator 1320 and the discriminator 1330 with varying update steps for each of the generator 1320 and the discriminator to balance the convergence speed.”) Vogels doe not teach the system wherein: training the machine learning include adapting a plurality of parameters of the second part according to a gradient of a sum of the second value and the third value with respect to the plurality of parameters of the second part . However, Fang teach the system wherein: training the machine learning include adapting a plurality of parameters of the second part according to a gradient of a sum of the second value and the third value with respect to the plurality of parameters of the second part (see Fang : Fig.4A, [0084], “trained personalized preference network 301 to score or rank fashion items for a user in accordance with one or more embodiments. As shown, the trained personalized preference network 301 includes a trained neural network 317, trained latent user features 321, and the preference predictor 330. In one or more embodiments, the trained neural network 317 employs the shared weights and parameters from the Siamese convolutional neural network 314. In other words, once trained, the Siamese convolutional neural network 314 need only employ one of the two convolutional neural networks (e.g., the positive neural network 316 or the negative neural network 318), since both networks have the same weights and parameters that were optimized through the joint training described above.”) Because both Vogels and Fang are in the same/similar field of endeavor of image generative adversarial (neural) network accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the system, of Vogels to include the system that training the machine learning include adapting a plurality of parameters of the second part according to a gradient of a sum of the second value and the third value with respect to the plurality of parameters of the second part as taught by Fang . After modification of Vogels , the GAN based de-noising system can be modified by the GAN training technique of Fang using backpropagation based on real and generated image classifications as taught by Fang . One would have been motivated to make such a combination in order to provide effective and accurate image analysis and denoising to produce accurate image in an efficient and timely manner. Regarding Claim 15, Vogels and Fang teaches all the limitations of Claim 14. Vogels further teaches the method further comprising: providing a third input signal and a fourth value to the first part, wherein the third input signal characterizes a non-noisy signal (see Vogels : Fig.8, [0119], “At 808, a second set of input images rendered by MC path tracing and a second set of corresponding reference images are received. In some embodiments, the second set of input images may have different characteristics than those of the first set of input images. For example, the it may contain a different type of image content, or may be rendered by a different type of renderer.”) determining, by the first part, a second output signal for the third input signal and the fourth value (see Vogels : Fig.8, [0121], “At 812, the second neural network is trained using the second set of input images and the second set of reference images to obtain a number of optimized second parameters associated with the number of second nodes of each of the plurality of second hidden layers. During the training, the number of optimized first parameters associated with the number of first nodes of each of the plurality of first hidden layers of the first neural network may be fixed.”) adapting a plurality of the parameters of the first part according to a deviation of the second output signal to the third input signal (see Vogels : Fig.8, [0121], “At 812, the second neural network is trained using the second set of input images and the second set of reference images to obtain a number of optimized second parameters associated with the number of second nodes of each of the plurality of second hidden layers. During the training, the number of optimized first parameters associated with the number of first nodes of each of the plurality of first hidden layers of the first neural network may be fixed.”) Regarding Claim 16, Vogels and Fang teaches all the limitations of Claim 14. Vogels further teaches the method further comprising: determining, by the first part and based on the first input signal and the first value, a fifth value characterizing a classification of the type of noise characterized by the first input signal (see Vogels : Fig.15, [0196], “At 1502, an input image rendered by MC path tracing and a corresponding reference image are received.” adapting a plurality of the parameters of the first part according to a deviation of a class characterized by the fifth value and a class of noise type corresponding to the first input signal (see Vogels : Fig.15, [0200], “At 1510, a new denoised image corresponding to the new input image may be generated by passing the new input image through the generator using the optimized first set of parameters.”) Regarding Claim 17, Vogels and Fang teaches all the limitations of Claim 15. Vogels further teaches the method further comprising: determining, by the first part and based on the third input signal and the fourth value, a fifth value characterizing a classification of the type of noise characterized by the third input signal (see Vogels : Fig.8, [0119], “At 808, a second set of input images rendered by MC path tracing and a second set of corresponding reference images are received. In some embodiments, the second set of input images may have different characteristics than those of the first set of input images. For example, the it may contain a different type of image content, or may be rendered by a different type of renderer.”) adapting a plurality of the parameters of the first part according to a deviation of a class characterized by the fifth value and a class characterizing an absence of noise (see Vogels : Fig.8, [0120], “at 810, a second neural network (e.g., the second column 720 as illustrated in FIG. 7) is configured. The second neural network may include a second input layer configured to receive the second set of input images, and a plurality of second hidden layers. Each second hidden layer corresponds to a respective first hidden layer, and may have a respective number of second nodes associated with a respective number of second parameters.”) Regarding Claim 18, Vogels and Fang teaches all the limitations of Claim 15. Vogels further teaches the method further comprising: the deviation of the second output signal to the third input signal is characterized by the following formula: PNG media_image1.png 110 768 media_image1.png Greyscale See Fang : [0138], “By employing least squares loss, the personalized fashion generation system can employ the GAN to generate high quality synthesized images. To illustrate, in various embodiments, the personalized fashion generation system employs the objective functions shown below in Equation 1 using to least squares loss train the generator G and the discriminator D.”) PNG media_image2.png 328 458 media_image2.png Greyscale It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the system, of Vogels to include the system calculate the deviation of the second output signal to the third input signal by equation 1 as taught by Fang . One would have been motivated to make such a combination in order to provide effective and accurate image analysis and denoising to produce accurate image in an efficient and timely manner. Regarding independent Claim 19, Claim 19 is directed to a computer-implemented method claim and has similar/same claim limitation as claim 14 and rejected under the same rationale. Regarding Claim 20, Vogels and Fang teaches all the limitations of Claim 19. Vogels further teaches the method further comprising: the denoised signal is used as input of a control system, wherein the control system is configured to determine a control signal of an actuator based on the denoised signal (see Vogels : Fig.4A, [0084], “an exemplary denoiser 400 according to some embodiments. The denoiser 400 may include a source encoder 420 coupled to the input 410, followed by a spatial-feature extractor 430. The output of the spatial-feature extractor 430 may be fed into a KPCN kernel-prediction module 440. The scalar kernels output by the kernel-prediction module 440 may be normalized using a softmax function 450.”) Regarding Claim 21, Vogels and Fang teaches all the limitations of Claim 19. Vogels further teaches the method further comprising: the denoised signal is used as input to a virtual sensor for determining a property of the input signal that is not measured by the input signal itself (see Vogels : Fig.16, [0228], “the one or more object rendering systems 1670 can be configured to render one or more objects to produce one or more computer-generated images or a set of images over time that provide an animation. The one or more object rendering systems 1670 may generate digital images or raster graphics images.”) Regarding Claim 22, Vogels and Fang teaches all the limitations of Claim 19. Vogels further teaches the method further comprising: first input signal and/or second input signal and/or third input signal and/or the input signal are sensor signals (see Vogels : Fig., [0089], “denoiser 500 according to some embodiments. The denoiser 500 may include a first neural network 510. The first neural network 510 may include a first plurality of layers and a first number of nodes associated with a first number of parameters. An input layer of the first neural network 510 is configured to receive a first set of input images 502. The first neural network 510 may be configured to extract a set of low-level features from each of the first set of input images 502.”) Regarding independent Claim 23, Claim 23 is directed to a system claim and has similar/same claim limitation as claim 14 and rejected under the same rationale. Regarding independent Claim 24, Claim 24 is directed to a non-transitory machine-readable storage medium and has similar/same claim limitation as claim 14 and rejected under the same rationale . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. PGPUB NUMBER: INVENTOR-INFORMATION: TITLE / DESCRIPTION US 20240135235 A1 Kennel, Matthew Title : Explanatory dropout for machine learning models Description : The disclosed subject matter relates generally to the field of artificial intelligence (AI) and to technical improvements that promote the efficiency and explainability of complex machine learning models (ML Models).. US 12136197 B2 Kersch, Péter Title : Neural Network Systems And Methods For Removing Noise From Signals Description : The present disclosure is related to root cause analysis via causality-aware machine learning and more particularly to automated root cause analysis for closed-loop control of mobile networks via causality-aware machine learning explanations. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZELALEM W SHALU whose telephone number is (571)272-3003. The examiner can normally be reached M- F 0800am- 0500pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Cesar Paula can be reached at (571) 272-4128. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /Zelalem Shalu/Examiner, Art Unit 2145 /CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145 Application/Control Number: 18/548,135 Page 2 Art Unit: 2145 Application/Control Number: 18/548,135 Page 3 Art Unit: 2145 Application/Control Number: 18/548,135 Page 4 Art Unit: 2145 Application/Control Number: 18/548,135 Page 5 Art Unit: 2145 Application/Control Number: 18/548,135 Page 6 Art Unit: 2145 Application/Control Number: 18/548,135 Page 7 Art Unit: 2145 Application/Control Number: 18/548,135 Page 8 Art Unit: 2145 Application/Control Number: 18/548,135 Page 9 Art Unit: 2145 Application/Control Number: 18/548,135 Page 10 Art Unit: 2145 Application/Control Number: 18/548,135 Page 11 Art Unit: 2145 Application/Control Number: 18/548,135 Page 12 Art Unit: 2145 Application/Control Number: 18/548,135 Page 13 Art Unit: 2145 Application/Control Number: 18/548,135 Page 14 Art Unit: 2145 Application/Control Number: 18/548,135 Page 15 Art Unit: 2145 Application/Control Number: 18/548,135 Page 16 Art Unit: 2145 Application/Control Number: 18/548,135 Page 17 Art Unit: 2145 Application/Control Number: 18/548,135 Page 18 Art Unit: 2145 Application/Control Number: 18/548,135 Page 19 Art Unit: 2145 Application/Control Number: 18/548,135 Page 20 Art Unit: 2145
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