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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d).
The certified copy/copies of KR10-2023-0117238, KR10-2023-0182370, and KR10-2024-0006753 have been received on 06/28/2026.
Information Disclosure Statement
The information disclosure statement (IDS) received on 05/29/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is considered by the examiner.
Response to Amendment
This action is in response to the amendment filed 07/21/2026. Claims 1-22 remain pending in the application. Applicant’s amendments overcome the specification objections, claim objections, the 35 U.S.C. 112(b) rejection of Claim 1, and the 35 U.S.C. 101 rejection of Claim 20, and the 35 U.S.C. 103 rejections of Claims 1-20 as set forth in the Non-Final Office Action dated 04/21/2026.
Response to Arguments
Applicant's arguments filed 07/21/2026 regarding the Liu reference have been fully considered but they are not persuasive.
Applicant argues Liu does not teach of suggest at least “inputting the image and the intermediate generated image to a second generative model”, citing [0032-0034] and [Fig. 3] of the reference.
Examiner replies see MPEP § 2111.01, claims must be given their broadest reasonable interpretation in light of the specification. Within the broadest reasonable interpretation of “inputting the image and the intermediate generated image to a second generative model”, Liu has a sufficient teaching. Liu teaches inputting a first image into a diffusion model, then feeds the modified image back into the diffusion model. In [Fig. 3], it is being interpreted as image 330 and image 338 are input into diffusion model 304. See arrows feeding into diffusion model 304. While there is only one diffusion model in the multi-algorithm diffusion sampling module 110, the diffusion model can be interpreted as a second generative model because the gradient estimator model is also generative model. While this does not simultaneously input two images, it is still inputting two images, [0032-033].
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Applicant’s arguments, filed 07/21/2026, with respect to the rejection(s) of claim(s) 1 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of the combination of Kanazawa and Kim.
Applicant argues the amended claim language overcomes the 103 rejection of Claim 1.
Examiner replies Liu does teach “inputting the image and the intermediate generated image to a second generative model”, however neither the Kanazawa or Liu explicitly disclose “wherein the image and the intermediate generated image are input to the second generative model together”. Applicant' s arguments, with respect to the rejection(s) of claim(s) 1 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of the combination of Kanazawa and Kim.
Claim Objections
A series of singular dependent claims is permissible in which a dependent claim refers to a preceding claim which, in turn, refers to another preceding claim.
A claim which depends from a dependent claim should not be separated by any claim which does not also depend from said dependent claim. It should be kept in mind that a dependent claim may refer to any preceding independent claim. In general, applicant's sequence will not be changed. See MPEP § 608.01(n). Claims 21-22 depend on Claim 1 and are improper because it is separated by Claims 11-20, claims that do not depend on Claim 1.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 22 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 22 recites “… the obtaining of the final generated image comprises simultaneously inputting the image and the intermediate generated image to the second generated model”, the specification fails to support this limitation. In [0064], the specification discloses “The electronic device 1000 may input the image including the information of the partial area and the intermediate generated image to the second generative model 1200.”, while this can be interpreted to teach the two images inputted into the second generative model together, it does not teach or suggest a simultaneous input of the image including the information of the partial area and the intermediate generated image which would require the specification to specify this input happening at the same time. Further, Fig. 1 or any modified embodiments of the architecture, fail to illustrate a simultaneous input. Therefore the newly recited claim language is new matter.
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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, 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.
Claim(s) 1-2, 11-12, 20, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Kanazawa et al. (US 20250232411 A1), hereinafter referenced as Kanazawa in view of Kim et al. (US 20250029377 A1), hereinafter.
Regarding Claim 1, Kanazawa discloses
A method of generating a partial area of an image by using a generative model (Kanazawa: [Fig. 1], illustrates a flowchart <method> where a partial area of an image is generated using a machine learning model <because generation is happening, the machine learning models in this method are interpreted as generative ML models>; [0002], discloses image inpainting as a generative modification), the method comprising:
obtaining the image comprising information of the partial area (Kanazawa: [0029], discloses a computer system obtaining a lower resolution version of an input image; [Fig. 1], illustrates the lower resolution input image, reference character 16 <interpreted as the obtained input image> comprising one or more image elements, at reference character 14 <interpreted as the partial area>);
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obtaining an intermediate generated image by inputting the image into a first generative model, the intermediate generated image comprising first image information corresponding to the partial area (Kanazawa: [Fig. 1], illustrates a created intermediate image, at reference character 22, by inputting the image, at reference character 16, into a first machine learning model, the intermediate generated image comprises a generated element, at reference character 24, which corresponds with the one or more image elements, at reference character 14 <interpreted as the partial area>; [0029], discloses the undesirable image element 14 is replaced using inpainting <generative technique>);
and obtaining a final generated image comprising second image information by inputting the (Kanazawa: [Fig. 1], illustrates creating a final generated image by inputting a modified intermediate image, at reference character 26, into a second machine learning model, where the output, at reference character 30, is different than the first generated image information, at reference character 22; [0041], discloses the second-machine learned inpainting model generates the refined portion that modified at least a portion of the inpainted data),
intermediate generated image (Kanazawa: [Fig. 1], reference character 26)
second generative model (Kanazawa: [Fig. 1], reference character 28)
single operation of the second generative model (Kanazawa: [0007], discloses a second machine-learned model to generate <generative model> a refined portion <generating a refined portion is the single operation>)
Kanazawa fails to explicitly teach
inputting the image and the intermediate generated image to a second generative model
wherein the image and the intermediate generated image are input to the second generative model together as inputs for a single operation of the second generative model.
However, Kim discloses
inputting the image and the second image to a (Kim: [0043], discloses a first image and a second image inputted to a generative model)
wherein the image and the second image are input to the (Kim: [0043, 0039], discloses a first image and a second image inputted to a generative model simultaneously <narrower than together> as inputs for outputting a resultant image from the generative model).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply and/or modify the method disclosed by the combination of Kanazawa by inputting two images together as taught by Kim. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to make this modification for accurate feature retention.
Regarding Claim 11, it recites similar limitations to Claim 1, but as an electronic device. As shown in the rejection, the combination of Kanazawa and Kim disclose the method of Claim 1. The combination of Kanazawa and Kim further disclose
An electronic device (Kanazawa: [0062], discloses a computing device) comprising:
memory storing one or more instructions (Kanazawa: [0062], discloses the computing device comprising a memory; [0075], discloses a program <has instructions> loaded into a memory);
and at least one processor; wherein the at least one processor executes the one or more instructions stored in the memory to cause the electronic device (Kanazawa: [0062], discloses the computing device comprising a processor; [0075], discloses a program <has instructions> loaded into a memory and executed by one or more processors) to: …
Regarding Claim 20, it recites similar limitations to Claims 1 and 11, but as a non-transitory computer-readable recording medium. As shown in the rejection, the combination of Kanazawa and Kim disclose the method and electronic device of Claims 1 and 11 respectively. The combination of Kanazawa and Kim further disclose
A non-transitory computer-readable recording medium having recorded thereon a program for performing a method (Kanazawa: [0067, 0071, 0075], discloses a non-transitory computer-readable storage medium comprising one or more sets of computer-executable instructions) comprising:
Regarding Claims 2 and 12, the combination of Kanazawa and Kim disclose the method and electronic device of Claims 1 and 11 respectively. The combination of Kanazawa and Kim further disclose wherein obtaining the image comprising the information of the partial area comprises:
obtaining a mask map that distinguishes the partial area from an entire area of the image (Kanazawa: [0034], discloses processing <must be obtained to be processed> a mask that identifies the one or more image elements <partial area, by identifying it, it distinguishes it from the entire area of the image);
and concatenating the mask map to the image (Kanazawa: [Fig. 1], illustrates a mask, at reference character 18, and an downscaled image <interpreted as the initial image>, at reference character 16, inputted into a first machine learning model and the model outputting one image; [0034], discloses the mask is used to identified image elements with the first machine learning model to generate an augmented image modifying the mask-identified elements using inpainting <interpreted as concatenation>).
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Regarding Claim 22, the combination of Kanazawa and Kim disclose the method of Claim 1. The combination of Kanazawa and Kim further disclose(s) wherein
intermediate generated image (Kanazawa: [Fig. 1], reference character 26)
second generative model (Kanazawa: [Fig. 1], reference character 28)
the obtaining of the final generated image comprises simultaneously inputting the image and the second image to the (Kim: [0043], discloses the obtaining of the resultant image <final generated image> comprises simultaneously inputting the first and second image into the artificial intelligence model <the model generates a resultant image, therefore it is interpreted as a generative model>).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply and/or modify the method disclosed by the combination of Kanazawa and Kim by using a simultaneous input as further taught by Kim. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to make this modification for accurate feature retention.
Claims 3-4 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Kanazawa and Kim in view of Liu et al. (US 20240153194), hereinafter referenced as Liu.
Regarding Claims 3 and 13, the combination of Kanazawa and Kim disclose the method and electronic device of Claims 1 and 11 respectively. The combination of Kanazawa and Kim fail to explicitly disclose the limitations of Claims 3 and 13, however, Liu disclose(s) wherein the obtaining of the final generated image further comprises:
encoding the intermediate generated image (Liu: [Fig. 3], illustrates an initial image, at reference character 302, modified to create image, at reference character 310 <interpreted as the intermediate generated image> inputted into an image encoder, at reference character 316);
and obtaining the final generated image by inputting the image and the encoded intermediate generated image to the second generative model (Liu: [Fig. 3], illustrates obtaining an output image, at reference character 346, after inputting the initial image, at reference character 302, and updated initial image, at reference character 328, which has passed though the image encoder, at reference character 316, into the diffusion model, at reference character 304).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply and/or modify the method or electronic device disclosed by the combination of Kanazawa and Kim by encoding a modified image and inputting the encoded modified image and original image into a generative model as taught by Liu. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to make this modification to improve convergence and numeric stability.
Regarding Claims 4 and 14, the combination of Kanazawa and Kim disclose the method and electronic device of Claims 1 and 11 respectively. The combination of Kanazawa and Kim fail to explicitly disclose the limitations of Claims 4 and 14, however, Liu disclose(s) wherein the obtaining of the final generated image further comprises:
obtaining a text input (Liu: [0029], discloses receiving input text);
encoding the text input (Liu: [0029], discloses a text encoder is used to generate a text embedding using the input text);
and obtaining the final generated image by inputting the image and the intermediate generated image and the encoded text input to the second generative model (Liu: [Fig. 3], illustrates obtaining the output image <final generated image>, at reference character 346, by inputting the initial image, at reference character 302, and the updated initial image, at reference character 326, into the diffusion model, at reference character 304. The updated initial image is created using text input, at reference character 116, that is encoded by the text encoder, at reference character 318).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply and/or modify the method or electronic device taught by the combination of Kanazawa and Kim by using encoded text input as an additional input as taught by Liu. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to make this modification because text input allows a user to communicate desired image refinements, enabling precise semantic control.
Claims 5, 7-8, 15, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Kanazawa and Kim in view of Xiao et al. (US 20230095092 A1), hereinafter referenced as Xiao.
Regarding Claims 5 and 15, the combination of Kanazawa and Kim disclose the method and electronic device of Claims 1 and 11 respectively. The combination of Kanazawa and Kim further disclose(s) wherein the obtaining of the final generated image further comprises:
the intermediate generated image (Kanazawa: [Fig. 1], illustrates an intermediate image at reference character 22)
second generative model (Kanazawa: [Fig. 1], illustrates a second machine learning model at reference character 28; [0041], discloses the second-machine learned inpainting model generates the refined portion that modified at least a portion of the inpainted data <meaning the second machine learning model would be a generative model>)
and obtaining the final generated image by inputting (Kanazawa: [Fig. 1], illustrates an outputted <obtained> final generated image at reference character 32, after the intermediate image, at reference character 22 is processed and inputted into the second machine learning model, at reference character 28)
The combination of Kanazawa and Kim fail to disclose
obtaining a denoising strength for the
adding noise to the
and obtaining the final generated image by inputting the image
However, Xiao discloses
obtaining a denoising strength for the (Xiao: [0109] discloses recording the amount of noise <reads on denoising strength>)
adding noise to the (Xiao: [0110], discloses adding additional noise to a first output <intermediate generated image> to form second generated output);
and obtaining the final generated image by inputting the (Xiao: [0110], discloses adding additional noise to a first output <intermediate generated image> to form second generated output, every time noise is added to an image into a DDGAN it becomes a new input for the model; [Fig. 3B], illustrates a final generated output, at reference character 358, by inputting the image with added noise, at reference character 356, to the generative model, at reference character 206).
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply and/or modify the method or electronic device disclosed by the combination of Kanazawa and Kim by adding noise to an image before inputting it into a generative model as disclosed by Xiao. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to make this modification because to prevent overfitting, enhance and boost regularization and generalization.
Regarding Claims 7 and 17, the combination of Kanazawa, Kim, and Xiao disclose the method and electronic device of Claims 5 and 15 respectively. The combination of Kanazawa, Kim, and Xiao further disclose(s) wherein the obtaining of the final generated image further comprises:
obtaining current noise information; concatenating the image and the current noise information (Xiao: [0083], discloses a forward diffusion process where an intermediate image is generated with more noise; [Fig. 3B], illustrates the image x0, at reference character 352, with the current noise information concatenated at xt-1, reference character 354 <the current noise information must be obtained to be concatenated with the image x0>);
inputting the concatenated image to the second generative model (Xiao: [Fig. 3B], illustrates the intermediate image, at reference character 354, is further processed then inputted to the generator <second generative model>, at reference character 206);
and obtaining next noise information from the second generative model (Xiao: [Fig. 3B], illustrates performing posterior sampling on the output of the generator, at reference character 206, where the new intermediate generated image, at referenced character 360 has noise added back <noise information must be obtained to add it I to the image>).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply and/or modify the method and electronic device disclosed by the combination of Kanazawa, Kim, and Xiao by performing obtaining and adding noise as further taught by Xiao. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to make this modification to improve model robustness and prevent overfitting.
Regarding Claims 8 and 18, the combination of Kanazawa, Kim, and Xiao disclose the method and electronic device of Claims 7 and 17 respectively. The combination of Kanazawa, Kim, and Xiao further disclose(s) wherein
the current noise information corresponds to the intermediate generated image with the added noise (Xiao: [Fig. 3B], illustrates intermediate image, at reference character 354, with noise <current noise information> added to it <therefore they correspond through a direct mathematical relationship that modifies pixels based on statistical distributions>).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply and/or modify the method or electronic device as disclosed by the combination of Kanazawa, Kim, and Xiao by having the current noise information correspond to the image with said added noise as further taught by Xiao. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to make this modification to improve model robustness and prevent overfitting.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Kanazawa, Kim, and Xiao in view of Smirnov et al. (US 20230289923 A1), hereinafter referenced as Smirnov.
Regarding Claim 6, the combination of Kanazawa, Kim, and Xiao disclose the method of Claim 5. The combination of Kanazawa, Kim, and Xiao further disclose wherein the obtaining of the denoising strength for the intermediate generated image comprises:
obtaining an output from the second machine learning model is based on the intermediate generated image (Kanazawa: [Fig. 1], illustrates an intermediate image at reference character 22, the output of the second machine learning model is based on this intermediate generated image)
The combination of Kanazawa, Kim, and Xiao do not disclose
obtaining a predicted confidence value based on the
and determining the denoising strength based on at least one of the predicted confidence value, a size of the partial area, or a shape of the partial area
However, Smirnov discloses
obtaining a predicted confidence value based on the intermediate generated image (Smirnov: [0085], discloses receiving confidence value <must be calculated from a set of data> associated with a pixel of a downscaled image <interpreted as intermediate generated, therefore the predicted confidence value is based on part of the intermediate generated image>);
and determining the denoising strength based on at least one of the predicted confidence value, (Smirnov: [0085], discloses a noise reduction circuit used to obtain a denoise image, it further discloses that the noise reduction performed is based on the confidence values of the received images, where higher confidence value indicates less noise reduction).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply and/or modify the method as taught by the combination of Kanazawa, Kim, and Xiao by determining the denoising strength based on the confidence value as taught by Smirnov. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to make this modification to prevent overfitting, enhance and boost regularization and generalization.
Regarding Claim 16, the combination of Kanazawa, Kim, and Xiao disclose the electronic device of Claim 15. The combination of Kanazawa, Kim, and Xiao further disclose wherein the obtaining of the denoising strength for the intermediate generated image comprises:
obtaining an output from the second machine learning model is based on the intermediate generated image (Kanazawa: [Fig. 1], illustrates an intermediate image at reference character 22, the output of the second machine learning model is based on this intermediate generated image)
The combination of Kanazawa, Kim, and Xiao do not disclose
obtaining a predicted confidence value based on the
and determine the denoising strength based on the predicted confidence value
However, Smirnov discloses
obtaining a predicted confidence value based on the (Smirnov: [0085], discloses receiving confidence value <must be calculated from a set of data> associated with a pixel of a downscaled image <interpreted as intermediate generated, therefore the predicted confidence value is based on part of the intermediate generated image>);
and determine the denoising strength based on the predicted confidence value (Smirnov: [0085], discloses a noise reduction circuit used to obtain a denoise image, it further discloses that the noise reduction performed is based on the confidence values of the received images, where higher confidence value indicates less noise reduction).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply and/or modify the electronic device as taught by the combination of Kanazawa, Kim, and Xiao by determining the denoising strength based on the confidence value as taught by Smirnov. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to make this modification to prevent overfitting, enhance and boost regularization and generalization.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Kanazawa, Kim, and Xiao in view of Ho et al. (“Denoising Diffusion Probabilistic Models”, 2020), hereinafter referenced as Ho.
Regarding Claims 9 and 19, the combination of Kanazawa, Kim, and Xiao disclose the method and electronic device of Claims 8 and 18 respectively. The combination of Kanazawa, Kim, and Xiao further disclose(s) wherein the obtaining of the final generated image further comprises:
an output from the second machine learning model corresponds to the intermediate generated image (Kanazawa: [Fig. 1], illustrates an intermediate image at reference character 22, the output of the second machine learning model corresponds to this intermediate generated image)
The combination of Kanazawa, Kim, and Xiao do not disclose
determining a target denoising order corresponding to the
and setting a denoising order of the current noise information as the determined target denoising order
However, Ho discloses
determining a target denoising order corresponding to the (Ho: [Background and Fig. 2], illustrates a forward and reversed diffusion process using a Markov chain where noise is iteratively added or reduced, see Fig. 2. When referencing the denoising order, the forward diffusion order has already taken place, so from xt to xt-1 would be the target denoising order corresponding to the image with added noise, based on the predefined order xT, xt, xt-1, x0, based on denoising strength where xT has the most noise and x0 has the least);
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and setting a denoising order of the current noise information as the determined target denoising order (Ho: [Fig. 2], illustrates denoising from xT to x0 <interpreted as the target denoising order>, by actually denoising the image it sets the denoising order of the current noise information at any point in the process).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply and/or modify the method or electronic device taught by the combination of Kanazawa, Kim, and Xiao by having a set denoising order as further taught by Ho. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to make this modification to maintain consistency across the forward and reverse diffusion process.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Kanazawa, Kim, Xiao, and Ho in view of Wu et al. (US 20250022457 A1), hereinafter referenced as Wu.
Regarding Claim 10, the combination of Kanazawa, Kim, Xiao, and Ho disclose the method of Claim 9. The combination of Kanazawa, Kim, Xiao, and Ho disclose(s) wherein
the first generative model is a generative adversarial network (GAN) model, and the second generative model is a diffusion model (Wu: [Claim 5], recites where first model is an unsupervised diffusion generative adversarial network <GAN> model and the second model is a diffusion model).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply and or modify the method disclosed by the combination of Kanazawa, Kim, Xiao, and Ho by using a GAN followed by a diffusion model as disclosed by Wu. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to make this modification to utilize the speed of a GAN then refine the image using the diffusion model to output realistic inpaintings and maintain user satisfaction.
Claim 21 rejected under 35 U.S.C. 103 as being unpatentable over the combination of Kanazawa and Kim in view of Zhang et al. (US 20250071425 A1), hereinafter referenced as Zhang.
Regarding Claim 21, the combination of Kanazawa and Kim disclose the method of Claim 1. The combination of Kanazawa and Kim teach first and second generative models (Kanazawa: [Fig. 1]) but fail to explicitly disclose the limitations of Claim 21, however, Zhang disclose(s) wherein
the first algorithm model comprises a first number of layers and a first number of weight values (Zhang: [0078], discloses a first algorithm model finely adjusting in terms of weights, describing a tuning process where weights are updated <the first algorithm model has weight values>; [0075-0078], disclose the first algorithm model comprises layers),
and the second algorithm model comprises a second number of layers and a second number of weight values (Zhang: [0078], discloses a first algorithm model finely adjusting in terms of weights, describing a tuning process where weights are updated <the second algorithm model has weight values>; [0075-0078], disclose the second algorithm model comprises layers),
wherein the first number of layers is less than the second number of layers or the first number of weight values is less than the second number of weight values (Zhang: [0075], discloses the first algorithm model has fewer layers than the second algorithm model; [0072-0078] are also relevant).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply and/or modify the method disclosed by the combination of Kanazawa and Kim by utilizing weights and layers in the models, wherein the first model has less layers than the second model as taught by Zhang. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to make this modification to capture, compress, and reconstruct complex data distributions. By having less layers in the first model, the method supplements and checks the initial generation therefore improving accuracy.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gorban et al. (US 20220262032 A1) discloses data being input simultaneously to a model, see [0088].
Osuala et al., “Data Synthesis and Adversarial Networks: A Review and Meta-Analysis in Cancer Imaging”, 2022 discloses two generative models, where one GAN generates an ROI while another inpaints them, see [Fig. 9F].
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ISABELLA OCHSNER whose telephone number is (571)272-9322. The examiner can normally be reached 9:30 - 6:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Devona Faulk can be reached at (571) 272-7515. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/I.O./Examiner, Art Unit 2618
/DEVONA E FAULK/Supervisory Patent Examiner, Art Unit 2618
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