DETAILED ACTION
Response to Arguments
Applicant’s arguments, see application, filed 07/24/2026, with respect to the 112 rejections have been fully considered and are persuasive. The 112 rejections have been withdrawn.
Applicant’s arguments with respect to claims 16-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07/24/2026 has been entered.
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.
Claims 16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou et al. (herein after will be referred to as Zhou) (US 20210035338) in view of Amthor et al. (herein after will be referred to as Amthor) (US 20210356729).
Regarding claim 16, Zhou discloses
a computer-implemented method for image processing a source image, comprising: [See Zhou [Figs. 9-10]]
downscaling the source image to create a downscaled source image; [See Zhou [Fig. 9]] Downsample input image to create a refined image.]
inputting the downscaled source image into a first image-to-image model, which outputs a result image that differs in an image property from the downscaled source image; and [See Zhou [Fig. 9] Downsampled input image is input into a first deep image-to-image network. Also, see fig. 10, input image is input into first deep image-to-image network (1010). Also, see 0070, the input image has distortions such as artifacts, blurring, noise, quality, etc., and 0076, improve image quality such as removing artifacts, denoising, super-resolution, etc. Therefore, refined image has better quality (i.e. claimed image property).]
inputting the source image together with the result image into a second image-to-image model for calculating an output image which has a higher image resolution than the result image and resembles the result image in the image property. [See Zhou [Fig. 10 and 0069] Input image and refined image are input in second deep image-to-image network (1030). Also, see 0070, the input image has distortions such as artifacts, blurring, noise, quality, etc., and 0076, improve image quality such as removing artifacts, denoising, super-resolution, etc. Therefore, output image has greater resolution than refined image and has better quality (i.e. claimed image property) which resembles the refined image.]
Zhou does not explicitly disclose
providing training data which comprises a plurality of training source images and associated target images, and wherein the training source images and the target images differ in an image property; calculating downscaled training source images from a plurality of the training source images, and calculating downscaled target images from the associated target images; and training the first image-to-image model with downscaled training source images as inputs and downscaled target images as targets.
However, Amthor does disclose
providing training data which comprises a plurality of training source images and associated target images, and wherein the training source images and the target images differ in an image property; calculating downscaled training source images from a plurality of the training source images, and calculating downscaled target images from the associated target images; and training the first image-to-image model with downscaled training source images as inputs and downscaled target images as targets. [See Amthor [0023] The machine learning algorithm is trained by way of supervised learning wherein use is made of microscope images as input images and target images spatially registered to the microscope images.]
It would have been obvious to the person of ordinary skill in the art at the time of the effective filing date to modify the method by Zhou to add the teachings of Amthor, in order to perform a simple substitution of medical images to microscopic images and/or a simple substitution of how the machine learning is trained (i.e. utilizing supervised learning, where the benefits of supervised learning is obvious to one of ordinary skill in the art).
Regarding claim 18, Zhou (modified by Amthor) disclose the method of claim 16. Furthermore, Zhou does not explicitly disclose
wherein a training of the second image-to-image model, an input into the second image-to-image model comprises: A) one of the training source images, as well as simultaneously: B) a downscaled target image associated with the training source image, or a result image calculated from the training source image by the first image-to-image model, or a processed image based on the downscaled target image and/or on the result image calculated from the training source image; wherein the method further includes using the target image associated with said training source image as a target in the training of the second image-to-image model.
However, Amthor does disclose
wherein a training of the second image-to-image model, an input into the second image-to-image model comprises: A) one of the training source images, as well as simultaneously: B) a downscaled target image associated with the training source image, or a result image calculated from the training source image by the first image-to-image model, or a processed image based on the downscaled target image and/or on the result image calculated from the training source image; wherein the method further includes using the target image associated with said training source image as a target in the training of the second image-to-image model. [See Amthor [0023] The machine learning algorithm is trained by way of supervised learning wherein use is made of microscope images as input images and target images spatially registered to the microscope images.]
Applying the same motivation as applied in claim 16.
Regarding claim 19, Zhou (modified by Amthor) disclose the method of claim 16. Furthermore, Zhou does not explicitly disclose
wherein the source image is a microscopic image.
However, Zhou does disclose
wherein the source image is a microscopic image. [See Amthor [0023] The machine learning algorithm is trained by way of supervised learning wherein use is made of microscope images as input images and target images spatially registered to the microscope images.]
Applying the same motivation as applied in claim 16.
Regarding claim 20, see examiners rejection for claim 16 which is analogous and applicable for the rejection of claim 20.
Allowable Subject Matter
Claims 1-15 are allowed.
Claim 17 is objected to 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.
Conclusion
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/JAMES T BOYLAN/Examiner, Art Unit 2486
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