DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This Office Action is in response to Applicant’s amendment filed 07/15/2025 which has
been entered and made of record. Claims 1, 3, 7, 10, 13-14, 17, 20, 22-27, and 37-40 amended. No claim has been newly added or cancelled. Claims 1-40 are pending in the application.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-6 and 29-40 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 (due to applicant’s arguments directed to newly amend limitation(s) which is addressed by new prior art presented in this Office Action).
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) 1-3, 5-6, 31, 34-35 and 37-40 is/are rejected under 35 U.S.C. 103 as being unpatentable over Iwase et al. (U.S. Patent Application Publication No. 2021/0390696), hereinafter referenced as Iwase, in view of BOUZARAA et al. (U.S. Patent Application Publication No. 2020/0134787), hereinafter referenced as BOUZARAA, and Tutorials (How to Fix Underexposed & Overexposed Photos in Photoshop) [https://www.youtube.com/watch?v=k-YWlI57pqs].
Regarding claim 1, Iwase teaches an image processing apparatus comprising: a processor, wherein the processor is configured to: (fig. 4, reference 400 shows an image processing apparatus and paragraph 650 teaches non-transitory computer-readable storage medium with instructions to be executed by a processor) acquire a first image, which is obtained by performing first AI processing on a processing target image, (paragraphs 10-11 teaches a image in which image quality is improved using a machine learning (ML) engine); the resulting high quality image from Iwase is treated as the first image in this case, the processing is done on the original[medical image of a predetermined site of a subject detailed in paragraphs 76-77]/target image and although this is referred to as second image it can also be treated as a first image; and a second image, which is obtained without performing the first AI processing on the processing target image (paragraphs 10-11 teaches a first image obtained); although this is referred to as first image it can also be treated as a second image and since this image is used at the end when compositing, this means it does not undergo AI processing, thus also acts as the processing target image; and adjust excess and deficiency of the first AI processing by combining the first image and the second image (paragraphs 10-11 teaches compositing the first and second image).
However, Iwase fails to teach and adjust excess and deficiency of the first AI processing by combining the first image and the second image.
However, BOUZARAA teaches and adjust excess and deficiency of the first AI processing by combining the first image and the second image (BOUZARAA, paragraph 64 teaches to reduce artefacts related to under- and/or over-exposure in a final image); adjust excess/deficiency corresponds to reducing over/under exposure respectively and this is done for the HDR image which is obtained from AI processing as described above. BOUZARAA is considered to be analogous art because it is reasonably pertinent to the problem faced by the inventor of adjusting excess and deficiencies of AI processed image(s). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Iwase’s invention with the adjusting of images techniques of BOUZARAA to improve the quality of the HDR image (BOUZARAA, paragraph 34).
However, the combination of Iwase and BOUZARAA fails to teach and wherein the excess and deficiency is an excess and deficiency derived from a factor that controls a visual impression given from the processing target image.
However, Tutorials teaches and wherein the excess and deficiency is an excess and deficiency derived from a factor that controls a visual impression given from the processing target image (Tutorials, 0:12-0:20 teaches fixing exposure in under exposed and over exposed photos, 2:30-2:40 teaches lowering exposure using slider bar and 3:55-4:05 [see fig. 1 of this action] teaches increasing exposure using slider bar); factor is the exposure slider bar which is controlling visual impression and over/under exposure shows excess/deficiency. Tutorials is considered to be analogous art because it is reasonably pertinent to the problem faced by the inventor of having excess and deficiency from a factor that controls a visual impression. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Iwase and BOUZARAA invention with the excess and deficiency from a factor techniques of Tutorials to fix underexposed & overexposed photos (Tutorials, title). This would ensure a better resulting image due to being fixed.
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Regarding claim 2, the combination of Iwase, BOUZARAA and Tutorials teaches wherein the second image is an image obtained by performing non-AI method processing, which does not use a neural network, on the processing target image (Iwase, paragraph 145 teaches obtaining unit obtains an input image (first image) that is an image of a predetermined site of the subject); obtaining unit is separate from image quality improving unit-which uses ML meaning the obtaining unit itself simply obtains and does not use the ML for processing, in other words, the obtaining unit obtains the image using processing which doesn’t involve AI.
Regarding claim 3, the combination of Iwase, BOUZARAA and Tutorials teaches an image processing apparatus comprising: a processor, wherein the processor is configured to: (Iwase, fig. 4, reference 400 shows an image processing apparatus and paragraph 650 teaches non-transitory computer-readable storage medium with instructions to be executed by a processor) acquire a first image, which is obtained by performing first AI processing on a processing target image to adjust a non-noise element of the processing target image, (Iwase, paragraphs 10-11 teaches a image in which image quality is improved using a machine learning (ML) engine); the resulting high quality image from Iwase is treated as the first image in this case, the processing is done on the original[medical image of a predetermined site of a subject detailed in paragraphs 76-77]/target image and although this is referred to as second image it can also be treated as a first image, additionally, one of ordinary skill in the art would understand that improving image quality includes adjusting non-noise element such as edges and contours sharpened while preserving structural features; and a second image, which is obtained without performing the first AI processing on the processing target image (Iwase, paragraphs 10-11 teaches a first image obtained); although this is referred to as first image it can also be treated as a second image and since this image is used at the end when compositing, this means it does not undergo AI processing, thus also acts as the processing target image; and adjust the non-noise element by combining the first image and the second image (BOUZARAA, paragraph 64 teaches to reduce artefacts related to under- and/or over-exposure in a final image and Iwase, paragraph 10-11 teaches compositing the first and second image); the reduction and combining the two images would adjust the non-noise elements since they would have over/under exposure as well; and wherein the non-noise element includes an element related to a factor that controls a visual impression given from the processing target image (Tutorials, 0:12-0:20 teaches fixing exposure in under exposed and over exposed photos, 2:30-2:40 teaches lowering exposure using slider bar and 3:55-4:05 [see fig. 1 of this action] teaches increasing exposure using slider bar); factor is the exposure slider bar which is controlling visual impression and exposure is non-noise element. The same motivations used in claim 1 apply here in claim 3.
Regarding claim 5, the combination of Iwase, BOUZARAA and Tutorials teaches wherein the second image is an image in which the non-noise element is not adjusted (Iwase, fig. 5 shows a path after S530 where no changes made to the input image); no changes means non-noise element wouldn't be adjusted.
Regarding claim 6, the combination of Iwase, BOUZARAA and Tutorials teaches wherein the processor is configured to combine the first image and the second image at a ratio in which the excess and deficiency of the first AI processing is adjusted (Iwase, abstract teaches combining two images at a ratio and paragraph 254 teaches the ratio to perform correction so as to eliminate unevenness).
Regarding claim 31, the combination of Iwase, BOUZARAA and Tutorials teaches wherein the first AI processing includes a plurality of purpose-specific processing performed by using an AI method, the first image includes a multiple processed image obtained by performing the plurality of purpose-specific processing on the processing target image, (Iwase, paragraph 193 teach image quality improving engine including multiple purpose-specific processing such as smoothing filter processing and gradation conversion processing); this means when the first image goes for quality improvement (which uses ML/AI as explained in claim 1) it can also go through the other multiple purpose-specific processing mentioned and this first image originates from the processing target image so the processing is essentially applied to a version of that image as well; and the processor is configured to combine the multiple processed image and the second image at the ratio (Iwase, abstract teaches combining two images at a ratio and paragraph 254 teaches the ratio to perform correction so as to eliminate unevenness); in this scenario the two images are the multiple processed image and second image respectively.
Regarding claim 34, the combination of Iwase, BOUZARAA and Tutorials teaches wherein the ratio is defined based on a difference between the processing target image and the first image and/or a difference between the first image and the second image (Iwase, paragraph 531 teaches ratio for combining two images may be determined by using a differential value between pixel values in at least corresponding partial regions of the two images); this ratio accounts for differences in the two images which can be first and second image respectively.
Regarding claim 35, the combination of Iwase, BOUZARAA and Tutorials teaches wherein the processor is configured to adjust the ratio according to related information that is related to the processing target image (Iwase, paragraph 531 teaches a configuration may be adopted so as to change a ratio for combining an image which the image quality improving engine generated and an input image based on the brightness of the input image); the input image would be a captured image making it the same as processing target image and the brightness is information related to it.
Regarding claim 37, the method claim 37 recites similar limitations as apparatus claim 1, and thus is rejected under similar rationale.
Regarding claim 38, the method claim 38 recites similar limitations as apparatus claim 3, and thus is rejected under similar rationale.
Regarding claim 39, the non-transitory computer-readable storage medium claim 39 recites similar limitations as apparatus claim 1, and thus is rejected under similar rationale.
Regarding claim 40, the non-transitory computer-readable storage medium claim 40 recites similar limitations as apparatus claim 3, and thus is rejected under similar rationale.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Iwase, BOUZARAA and Tutorials as applied to claims 3 above, and further in view of Mao et al. (U.S. Patent Application Publication No. 2020/0279352), hereinafter referenced as Mao.
Regarding claim 4, the combination of Iwase, BOUZARAA and Tutorials fails to teach wherein the second image is an image in which the non-noise element is adjusted by performing non-AI method processing, which does not use a neural network, on the processing target image.
However, Mao teaches wherein the second image is an image in which the non-noise element is adjusted by performing non-AI method processing, (Mao, paragraph 36 and fig. 6 show image being adjusted using sharp filter); sharp filter acts on the non-noise elements and this is done using non-AI since AI is not mentioned to do this task; which does not use a neural network, on the processing target image (Mao, paragraph 36 teaches the steps done on an input image and paragraph 34 teaches many different types of filters used); some of the filters listed do not use neural networks. Mao is considered to be analogous art because it is reasonably pertinent to the problem faced by the inventor of correction of images using non-ai methods. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Iwase, BOUZARAA and Tutorials with the correction processing techniques of Mao for improving visualization applications (Mao, paragraph 37).
Claim(s) 29 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Iwase, BOUZARAA and Tutorials as applied to claim 6 above, and further in view of PAN et al. (U.S. Patent Application Publication No. 2023/0100305), hereinafter referenced as PAN.
Regarding claim 29, the combination of Iwase, BOUZARAA and Tutorials teaches and the processor is configured to adjust an element derived from the image style change processing by combining the image style changed image and the second image at the ratio (Iwase, abstract teaches combining two images at a ratio and paragraph 254 teaches the ratio to perform correction so as to eliminate unevenness); in this scenario the two images are the image style changed and second image respectively.
However, the combination of Iwase, BOUZARAA and Tutorials fails to teach wherein the first image includes an image style changed image obtained by performing image style change processing of changing an image style of the processing target image by using an AI method, as processing included in the first AI processing.
However, PAN teaches wherein the first image includes an image style changed image obtained by performing image style change processing of changing an image style of the processing target image by using an AI method, as processing included in the first AI processing, (PAN, paragraph 75 teaches a neural network trained for image and style shifting a reference image); this means the first image would include this image style change as well after undergoing this processing. PAN is considered to be analogous art because it is reasonably pertinent to the problem faced by the inventor of changing image style. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Iwase, BOUZARAA and Tutorials with the image styling techniques of PAN to enable a thinner and lighter system design as well as to improve system responsiveness (PAN, paragraph 88). This can be due to simply changing a images style instead of processing a new one.
Claim(s) 30 and 36 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Iwase, BOUZARAA and Tutorials as applied to claims 6 and 1 above, and further in view of NAKATSUKA (U.S. Patent Application Publication No. 2009/0245649), hereinafter referenced as NAKATSUKA.
Regarding claim 30, the combination of Iwase, BOUZARAA and Tutorials teaches and the processor is configured to adjust an element derived from the skin image quality adjustment processing by combining the skin image quality adjusted image and the second image at the ratio (Iwase, abstract teaches combining two images at a ratio and paragraph 254 teaches the ratio to perform correction so as to eliminate unevenness); in this scenario the two images are the skin image quality adjusted image and second image respectively.
However, the combination of Iwase, BOUZARAA and Tutorials fails to teach wherein the processing target image is an image obtained by imaging skin, the first image includes a skin image quality adjusted image obtained by performing skin image quality adjustment processing of adjusting an image quality related to the skin captured in the processing target image by using an AI method, as processing included in the first AI processing.
However, NAKATSUKA teaches wherein the processing target image is an image obtained by imaging skin, the first image includes a skin image quality adjusted image obtained by performing skin image quality adjustment processing of adjusting an image quality related to the skin captured in the processing target image by using an AI method, as processing included in the first AI processing, (NAKATSUKA, fig. 12 and paragraph 80 teach image quality adjustment for skin after acquiring image data); skin must be imaged to obtain the image data of skin and as explained in claim 1, the quality is improved using machine learning in Iwase which is an AI method. NAKATSUKA is considered to be analogous art because it is reasonably pertinent to the problem faced by the inventor of skin image quality adjustment. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Iwase, BOUZARAA and Tutorials with the skin quality adjustment techniques of NAKATSUKA to appropriately convert the image data (NAKATSUKA, paragraph 6). This ensures accurate image data once adjustments are made and if the data needs to be converted.
Regarding claim 36, the combination of Iwase, BOUZARAA, Tutorials, and NAKATSUKA teaches an imaging apparatus comprising: the image processing apparatus according to claim 1 [see Iwase and BOUZARRA teachings from claim 1]; and an image sensor, wherein the processing target image is an image obtained by performing imaging by the image sensor (NAKATSUKA, paragraph 58 teaches image data captured by digital still cameras); these cameras have an image sensor and the image data (captured image) is what's used as the processing target image as explained in claim 1. The same motivations used in claim 30 apply here in claim 36.
Claim(s) 32-33 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Iwase, BOUZARAA and Tutorials as applied to claims 31 and 32 above, and further in view of Iwabuchi (U.S. Patent Application Publication No. 2008/0118171), hereinafter referenced as Iwabuchi.
Regarding claim 32, the combination of Iwase, BOUZARAA and Tutorials fails to teach wherein the plurality of purpose-specific processing are performed in an order based on a degree of influence applied on the processing target image.
However, Iwabuchi teaches wherein the plurality of purpose-specific processing are performed in an order based on a degree of influence applied on the processing target image (Iwabuchi, paragraph 21, (50), teaches purpose-specific processing performed in order of the influence degree); the whole reason of determining degree of influence, would be so that processing is performed in an order based on a degree of influence to add efficiency. Iwabuchi is considered to be analogous art because it is reasonably pertinent to the problem faced by the inventor of processing based on an order based on degree of influence. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Iwase, BOUZARAA and Tutorials with the processing based on degree of influence techniques of Iwabuchi for improvement in that the processing burden on a computer that executes the image processing and that the processing takes long time (Iwabuchi, paragraph 8).
Regarding claim 33, the combination of Iwase, BOUZARAA, Tutorials and Iwabuchi teaches wherein the plurality of purpose-specific processing are performed stepwise from purpose-specific processing in which the degree of the influence is small to purpose-specific processing in which the degree of the influence is large (Iwabuchi, paragraph 202 teaches processing is performed in descending order of influence degrees of the filters); this shows processing going in a stepwise order dependent on the degree of influence and for the specific small to large degree of influence, descending order can correspond to the stepwise from small to large because the end goal and result is the same of influencing the error to be suppressed to ensure an accurate processing and/or correction. The same motivations used in claim 32 apply here in claim 33.
Allowable Subject Matter
Claims 7-28 allowed.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 7, the closest prior art of (or combination of) Iwase teaches an image processing apparatus comprising: a processor, wherein the processor is configured to: (Iwase, fig. 4, reference 400 shows an image processing apparatus and paragraph 650 teaches non-transitory computer-readable storage medium with instructions to be executed by a processor) acquire a first image, which is obtained by performing first AI processing on a processing target image, (Iwase, paragraphs 10-11 teaches a image in which image quality is improved using a machine learning (ML) engine); the resulting high quality image from Iwase is treated as the first image in this case, the processing is done on the original[medical image of a predetermined site of a subject detailed in paragraphs 76-77]/target image and although this is referred to as second image it can also be treated as a first image; and a second image, which is obtained without performing the first AI processing on the processing target image (Iwase, paragraphs 10-11 teaches a first image obtained); although this is referred to as first image it can also be treated as a second image and since this image is used at the end when compositing, this means it does not undergo AI processing, thus also acts as the processing target image; adjust excess and deficiency of the first AI processing by combining the first image and the second image (Iwase, paragraphs 10-11 teaches compositing the first and second image); and combine the first image and the second image at a ratio in which the excess and deficiency of the first AI processing is adjusted (Iwase, abstract teaches combining two images at a ratio and paragraph 254 teaches the ratio to perform correction so as to eliminate unevenness).
wherein the processing target image is an image obtained by performing imaging by an imaging apparatus, (Iwase, fig. 7, step 710 teaches to obtain input image from imaging apparatus); which appears in the processing target image due to a characteristic of the imaging apparatus, (Iwase, paragraph 531-532 teach configuration adopted to extract linear structures which affects certain noise extracted); this configuration along with the ability to handle certain noises acts as a characteristic of the imaging apparatus.
Iwase fails to teach adjust excess and deficiency of the first AI processing by combining the first image and the second image.
However, BOUZARAA teaches adjust excess and deficiency of the first AI processing by combining the first image and the second image (BOUZARAA, paragraph 64 teaches to reduce artefacts related to under- and/or over-exposure in a final image); adjust excess/deficiency corresponds to reducing over/under exposure respectively and this is done for the HDR image which is obtained from AI processing as described above
However, the combination of Iwase and BOUZARAA fails to teach the first AI processing includes first correction processing of correcting a phenomenon; by using an AI method, the first image includes a first corrected image obtained by performing the first correction processing, and the processor is configured to adjust an element derived from the first correction processing by combining the first corrected image and the second image at the ratio.
Furthermore, no prior art of record either alone or in combination teaches
the first AI processing includes first correction processing of correcting a phenomenon; by using an AI method, the first image includes a first corrected image obtained by performing the first correction processing, and the processor is configured to adjust an element derived from the first correction processing by combining the first corrected image and the second image at the ratio when read in light of the rest of the limitations in claim 7 and the claims to which claim 7 depends and thus claim 7 contains allowable subject matter.
Regarding claim 10, the closest prior art of (or combination of) Iwase teaches an image processing apparatus comprising: a processor, wherein the processor is configured to: (Iwase, fig. 4, reference 400 shows an image processing apparatus and paragraph 650 teaches non-transitory computer-readable storage medium with instructions to be executed by a processor) acquire a first image, which is obtained by performing first AI processing on a processing target image, (Iwase, paragraphs 10-11 teaches a image in which image quality is improved using a machine learning (ML) engine); the resulting high quality image from Iwase is treated as the first image in this case, the processing is done on the original[medical image of a predetermined site of a subject detailed in paragraphs 76-77]/target image and although this is referred to as second image it can also be treated as a first image; and a second image, which is obtained without performing the first AI processing on the processing target image (Iwase, paragraphs 10-11 teaches a first image obtained); although this is referred to as first image it can also be treated as a second image and since this image is used at the end when compositing, this means it does not undergo AI processing, thus also acts as the processing target image; adjust excess and deficiency of the first AI processing by combining the first image and the second image (Iwase, paragraphs 10-11 teaches compositing the first and second image); and combine the first image and the second image at a ratio in which the excess and deficiency of the first AI processing is adjusted (Iwase, abstract teaches combining two images at a ratio and paragraph 254 teaches the ratio to perform correction so as to eliminate unevenness).
wherein the processing target image is an image obtained by performing imaging by an imaging apparatus, (Iwase, fig. 7, step 710 teaches to obtain input image from imaging apparatus); which appears in the processing target image due to a characteristic of the imaging apparatus, (Iwase, paragraph 531-532 teach configuration adopted to extract linear structures which affects certain noise extracted); this configuration along with the ability to handle certain noises acts as a characteristic of the imaging apparatus; and the processor is configured to adjust an element derived from the first change processing by combining the first changed image and the second image at the ratio (Iwase, abstract teaches combining two images at a ratio).
Iwase fails to teach adjust excess and deficiency of the first AI processing by combining the first image and the second image.
However, BOUZARAA teaches adjust excess and deficiency of the first AI processing by combining the first image and the second image (BOUZARAA, paragraph 64 teaches to reduce artefacts related to under- and/or over-exposure in a final image); adjust excess/deficiency corresponds to reducing over/under exposure respectively and this is done for the HDR image which is obtained from AI processing as described above
However, the combination of Iwase and BOUZARAA fails to teach wherein the first AI processing includes first change processing of changing a factor that controls a visual impression given from the processing target image by using an AI method, the first image includes a first changed image obtained by performing the first change processing, and the processor is configured to adjust an element derived from the first change processing by combining the first changed image and the second image at the ratio.
However, Tutorials teaches wherein the first AI processing includes first change processing of changing a factor that controls a visual impression given from the processing target image by using an AI method, (Tutorials, 0:12-0:20 teaches fixing exposure in under exposed and over exposed photos, 2:30-2:40 teaches lowering exposure using slider bar and 3:55-4:05 [see fig. 1 of this action] teaches increasing exposure using slider bar); factor is the exposure slider bar which is controlling visual impression.
However, the combination of Iwase, BOUZARAA, and Tutorials fails to teach the first image includes a first changed image obtained by performing the first change processing, and the processor is configured to adjust an element derived from the first change processing by combining the first changed image and the second image at the ratio.
Furthermore, no prior art of record either alone or in combination teaches
the first image includes a first changed image obtained by performing the first change processing, and the processor is configured to adjust an element derived from the first change processing by combining the first changed image and the second image at the ratio; when read in light of the rest of the limitations in claim 10 and the claims to which claim 10 depends and thus claim 10 contains allowable subject matter.
Regarding claim 13, the closest prior art of (or combination of) Iwase teaches an image processing apparatus comprising: a processor, wherein the processor is configured to: (Iwase, fig. 4, reference 400 shows an image processing apparatus and paragraph 650 teaches non-transitory computer-readable storage medium with instructions to be executed by a processor) acquire a first image, which is obtained by performing first AI processing on a processing target image, (Iwase, paragraphs 10-11 teaches a image in which image quality is improved using a machine learning (ML) engine); the resulting high quality image from Iwase is treated as the first image in this case, the processing is done on the original[medical image of a predetermined site of a subject detailed in paragraphs 76-77]/target image and although this is referred to as second image it can also be treated as a first image; and a second image, which is obtained without performing the first AI processing on the processing target image (Iwase, paragraphs 10-11 teaches a first image obtained); although this is referred to as first image it can also be treated as a second image and since this image is used at the end when compositing, this means it does not undergo AI processing, thus also acts as the processing target image; adjust excess and deficiency of the first AI processing by combining the first image and the second image (Iwase, paragraphs 10-11 teaches compositing the first and second image); and combine the first image and the second image at a ratio in which the excess and deficiency of the first AI processing is adjusted (Iwase, abstract teaches combining two images at a ratio and paragraph 254 teaches the ratio to perform correction so as to eliminate unevenness); wherein the processing target image is a captured image obtained by imaging subject light, (Iwase, paragraph 79 teaches capturing subject with light); which is formed on a light-receiving surface by a lens of an imaging apparatus, by the imaging apparatus, (Iwase, paragraph 514 teaches lens characteristics); this means lens must exists in the apparatus to perform the capturing; the first image includes a first aberration corrected image obtained by performing aberration region correction processing of correcting a region of the captured image where an aberration of the lens is reflected by using an AI method, as processing included in the first AI processing, (Iwase, paragraph 514 teaches image quality improving engine may be adopted by learning the training image based on lens aberration) and the processor is configured to adjust an element derived from the aberration region correction processing by combining the first aberration corrected image and the second aberration corrected image at the ratio (Iwase, abstract teaches combining two images at a ratio and paragraph 254 teaches the ratio to perform correction so as to eliminate unevenness); in this scenario the two images can be the first and second aberration corrected images respectively. Iwase fails to teach adjust excess and deficiency of the first AI processing by combining the first image and the second image.
However, BOUZARAA teaches adjust excess and deficiency of the first AI processing by combining the first image and the second image (BOUZARAA, paragraph 64 teaches to reduce artefacts related to under- and/or over-exposure in a final image); adjust excess/deficiency corresponds to reducing over/under exposure respectively and this is done for the HDR image which is obtained from AI processing as described above
However, the combination of Iwase and BOUZARAA fails to teach the second image includes a second aberration corrected image obtained by performing processing of correcting the region of the captured image where the aberration of the lens is reflected by using a non-AI method.
Furthermore, no prior art of record either alone or in combination teaches
the second image includes a second aberration corrected image obtained by performing processing of correcting the region of the captured image where the aberration of the lens is reflected by using a non-AI method,
when read in light of the rest of the limitations in claim 4 and the claims to which claim 13 depends and thus claim 13 contains allowable subject matter.
Regarding claim 14, the closest prior art of (or combination of) Iwase teaches an image processing apparatus comprising: a processor, wherein the processor is configured to: (Iwase, fig. 4, reference 400 shows an image processing apparatus and paragraph 650 teaches non-transitory computer-readable storage medium with instructions to be executed by a processor) acquire a first image, which is obtained by performing first AI processing on a processing target image, (Iwase, paragraphs 10-11 teaches a image in which image quality is improved using a machine learning (ML) engine); the resulting high quality image from Iwase is treated as the first image in this case, the processing is done on the original[medical image of a predetermined site of a subject detailed in paragraphs 76-77]/target image and although this is referred to as second image it can also be treated as a first image; and a second image, which is obtained without performing the first AI processing on the processing target image (Iwase, paragraphs 10-11 teaches a first image obtained); although this is referred to as first image it can also be treated as a second image and since this image is used at the end when compositing, this means it does not undergo AI processing, thus also acts as the processing target image; adjust excess and deficiency of the first AI processing by combining the first image and the second image (Iwase, paragraphs 10-11 teaches compositing the first and second image); and combine the first image and the second image at a ratio in which the excess and deficiency of the first AI processing is adjusted (Iwase, abstract teaches combining two images at a ratio and paragraph 254 teaches the ratio to perform correction so as to eliminate unevenness); with respect to the processing target image in a distinguishable manner by using an AI method, as processing included in the first AI processing, (Iwase, paragraph 547 teaches the image processing apparatus may use GAN); this means the GAN/AI method can be used to perform these steps; the processor is configured to adjust an element derived from the color processing by combining the first colored image and the second colored image at the ratio (Iwase, abstract teaches combining two images at a ratio).
Iwase fails to teach and adjust excess and deficiency of the first AI processing by combining the first image and the second image.
However, BOUZARAA teaches and adjust excess and deficiency of the first AI processing by combining the first image and the second image (BOUZARAA, paragraph 64 teaches to reduce artefacts related to under- and/or over-exposure in a final image); adjust excess/deficiency corresponds to reducing over/under exposure respectively and this is done for the HDR image which is obtained from AI processing as described above.
However, the combination of Iwase and BOUZARAA fails to teach wherein the first image includes a first colored image obtained by performing color processing of coloring a first region and a second region, which is a region different from the first region, the second image includes a second colored image obtained by performing processing of changing color of the processing target image by using a non-AI method, and the processor is configured to adjust an element derived from the color processing.
Furthermore, no prior art of record either alone or in combination teaches
wherein the first image includes a first colored image obtained by performing color processing of coloring a first region and a second region, which is a region different from the first region, the second image includes a second colored image obtained by performing processing of changing color of the processing target image by using a non-AI method, and the processor is configured to adjust an element derived from the color processing, when read in light of the rest of the limitations in claim 14 and the claims to which claim 14 depends and thus claim 14 contains allowable subject matter.
Regarding claim 17, the closest prior art of (or combination of) Iwase teaches an image processing apparatus comprising: a processor, wherein the processor is configured to: (Iwase, fig. 4, reference 400 shows an image processing apparatus and paragraph 650 teaches non-transitory computer-readable storage medium with instructions to be executed by a processor) acquire a first image, which is obtained by performing first AI processing on a processing target image, (Iwase, paragraphs 10-11 teaches a image in which image quality is improved using a machine learning (ML) engine); the resulting high quality image from Iwase is treated as the first image in this case, the processing is done on the original[medical image of a predetermined site of a subject detailed in paragraphs 76-77]/target image and although this is referred to as second image it can also be treated as a first image; and a second image, which is obtained without performing the first AI processing on the processing target image (Iwase, paragraphs 10-11 teaches a first image obtained); although this is referred to as first image it can also be treated as a second image and since this image is used at the end when compositing, this means it does not undergo AI processing, thus also acts as the processing target image; adjust excess and deficiency of the first AI processing by combining the first image and the second image (Iwase, paragraphs 10-11 teaches compositing the first and second image); and combine the first image and the second image at a ratio in which the excess and deficiency of the first AI processing is adjusted (Iwase, abstract teaches combining two images at a ratio and paragraph 254 teaches the ratio to perform correction so as to eliminate unevenness); and the processor is configured to adjust an element derived from the first contrast adjustment processing by combining the first contrast adjusted image and the second contrast adjusted image at the ratio (Iwase, abstract teaches combining two images at a ratio). Iwase fails to teach adjust excess and deficiency of the first AI processing by combining the first image and the second image.
However, BOUZARAA teaches adjust excess and deficiency of the first AI processing by combining the first image and the second image (BOUZARAA, paragraph 64 teaches to reduce artefacts related to under- and/or over-exposure in a final image); adjust excess/deficiency corresponds to reducing over/under exposure respectively and this is done for the HDR image which is obtained from AI processing as described above
However, the combination of Iwase and BOUZARAA fails to teach wherein the first image includes a first contrast adjusted image obtained by performing first contrast adjustment processing of adjusting a contrast of the processing target image by using an AI method, as processing included in the first AI processing, the second image includes a second contrast adjusted image obtained by performing second contrast adjustment processing of adjusting the contrast of the processing target image by using a non-AI method.
Furthermore, no prior art of record either alone or in combination teaches
wherein the first image includes a first contrast adjusted image obtained by performing first contrast adjustment processing of adjusting a contrast of the processing target image by using an AI method, as processing included in the first AI processing, the second image includes a second contrast adjusted image obtained by performing second contrast adjustment processing of adjusting the contrast of the processing target image by using a non-AI method, when read in light of the rest of the limitations in claim 17 and the claims to which claim 17 depends and thus claim 17 contains allowable subject matter.
Regarding claim 20, the closest prior art of (or combination of) Iwase teaches an image processing apparatus comprising: a processor, wherein the processor is configured to: (Iwase, fig. 4, reference 400 shows an image processing apparatus and paragraph 650 teaches non-transitory computer-readable storage medium with instructions to be executed by a processor) acquire a first image, which is obtained by performing first AI processing on a processing target image, (Iwase, paragraphs 10-11 teaches a image in which image quality is improved using a machine learning (ML) engine); the resulting high quality image from Iwase is treated as the first image in this case, the processing is done on the original[medical image of a predetermined site of a subject detailed in paragraphs 76-77]/target image and although this is referred to as second image it can also be treated as a first image; and a second image, which is obtained without performing the first AI processing on the processing target image (Iwase, paragraphs 10-11 teaches a first image obtained); although this is referred to as first image it can also be treated as a second image and since this image is used at the end when compositing, this means it does not undergo AI processing, thus also acts as the processing target image; adjust excess and deficiency of the first AI processing by combining the first image and the second image (Iwase, paragraphs 10-11 teaches compositing the first and second image); and combine the first image and the second image at a ratio in which the excess and deficiency of the first AI processing is adjusted (Iwase, abstract teaches combining two images at a ratio and paragraph 254 teaches the ratio to perform correction so as to eliminate unevenness); wherein the first image includes a first resolution adjusted image obtained by performing first resolution adjustment processing of adjusting a resolution of the processing target image by using an AI method, as processing included in the first AI processing, (Iwase, paragraph 4 teaches image to have a low amount of noise, high resolution and spatial resolution and paragraph 208 teaches certain resolution which is performed by an image quality improving engine); and the processor is configured to adjust an element derived from the first resolution adjustment processing by combining the first resolution adjusted image and the second resolution adjusted image at the ratio (Iwase, abstract teaches combining two images at a ratio).
Iwase fails to teach adjust excess and deficiency of the first AI processing by combining the first image and the second image.
However, BOUZARAA teaches adjust excess and deficiency of the first AI processing by combining the first image and the second image (BOUZARAA, paragraph 64 teaches to reduce artefacts related to under- and/or over-exposure in a final image); adjust excess/deficiency corresponds to reducing over/under exposure respectively and this is done for the HDR image which is obtained from AI processing as described above
However, the combination of Iwase and BOUZARAA fails to teach wherein the first image includes a first resolution adjusted image obtained by performing first resolution adjustment processing of adjusting a resolution of the processing target image by using an AI method, as processing included in the first AI processing, the second image includes a second resolution adjusted image obtained by performing second resolution adjustment processing of adjusting the resolution by using a non-AI method, and the processor is configured to adjust an element derived from the first resolution adjustment processing by combining the first resolution adjusted image and the second resolution adjusted image at the ratio.
Furthermore, no prior art of record either alone or in combination teaches
wherein the first image includes a first resolution adjusted image obtained by performing first resolution adjustment processing of adjusting a resolution of the processing target image by using an AI method, as processing included in the first AI processing, the second image includes a second resolution adjusted image obtained by performing second resolution adjustment processing of adjusting the resolution by using a non-AI method, and the processor is configured to adjust an element derived from the first resolution adjustment processing by combining the first resolution adjusted image and the second resolution adjusted image at the ratio, when read in light of the rest of the limitations in claim 20 and the claims to which claim 20 depends and thus claim 20 contains allowable subject matter.
Regarding claim 22, the closest prior art of (or combination of) Iwase teaches an image processing apparatus comprising: a processor, wherein the processor is configured to: (Iwase, fig. 4, reference 400 shows an image processing apparatus and paragraph 650 teaches non-transitory computer-readable storage medium with instructions to be executed by a processor) acquire a first image, which is obtained by performing first AI processing on a processing target image, (Iwase, paragraphs 10-11 teaches a image in which image quality is improved using a machine learning (ML) engine); the resulting high quality image from Iwase is treated as the first image in this case, the processing is done on the original[medical image of a predetermined site of a subject detailed in paragraphs 76-77]/target image and although this is referred to as second image it can also be treated as a first image; and a second image, which is obtained without performing the first AI processing on the processing target image (Iwase, paragraphs 10-11 teaches a first image obtained); although this is referred to as first image it can also be treated as a second image and since this image is used at the end when compositing, this means it does not undergo AI processing, thus also acts as the processing target image; adjust excess and deficiency of the first AI processing by combining the first image and the second image (Iwase, paragraphs 10-11 teaches compositing the first and second image); and combine the first image and the second image at a ratio in which the excess and deficiency of the first AI processing is adjusted (Iwase, abstract teaches combining two images at a ratio and paragraph 254 teaches the ratio to perform correction so as to eliminate unevenness). Iwase fails to teach adjust excess and deficiency of the first AI processing by combining the first image and the second image. However, BOUZARAA teaches adjust excess and deficiency of the first AI processing by combining the first image and the second image (BOUZARAA, paragraph 64 teaches to reduce artefacts related to under- and/or over-exposure in a final image); adjust excess/deficiency corresponds to reducing over/under exposure respectively and this is done for the HDR image which is obtained from AI processing as described above; wherein the first image includes a first high dynamic range image obtained by performing expansion processing of expanding a dynamic range of the processing target image by using an AI method, as processing included in the first AI processing, (BOUZARAA, abstract teaches HDR (high dynamic range) image associated with a first dynamic range).
However, the combination of Iwase and BOUZARAA fails to teach of the processing target image by using an AI method, as processing included in the first AI processing, the second image includes a second high dynamic range image obtained by performing processing of expanding the dynamic range of the processing target image by using a non-AI method, and the processor is configured to adjust an element derived from the expansion processing by combining the first high dynamic range image and the second high dynamic range image at the ratio
Furthermore, no prior art of record either alone or in combination teaches
of the processing target image by using an AI method, as processing included in the first AI processing, the second image includes a second high dynamic range image obtained by performing processing of expanding the dynamic range of the processing target image by using a non-AI method, and the processor is configured to adjust an element derived from the expansion processing by combining the first high dynamic range image and the second high dynamic range image at the ratio when read in light of the rest of the limitations in claim 22 and the claims to which claim 22 depends and thus claim 22 contains allowable subject matter.
Regarding claim 23, the closest prior art of (or combination of) Iwase teaches an image processing apparatus comprising: a processor, wherein the processor is configured to: (Iwase, fig. 4, reference 400 shows an image processing apparatus and paragraph 650 teaches non-transitory computer-readable storage medium with instructions to be executed by a processor) acquire a first image, which is obtained by performing first AI processing on a processing target image, (Iwase, paragraphs 10-11 teaches a image in which image quality is improved using a machine learning (ML) engine); the resulting high quality image from Iwase is treated as the first image in this case, the processing is done on the original[medical image of a predetermined site of a subject detailed in paragraphs 76-77]/target image and although this is referred to as second image it can also be treated as a first image; and a second image, which is obtained without performing the first AI processing on the processing target image (Iwase, paragraphs 10-11 teaches a first image obtained); although this is referred to as first image it can also be treated as a second image and since this image is used at the end when compositing, this means it does not undergo AI processing, thus also acts as the processing target image; adjust excess and deficiency of the first AI processing by combining the first image and the second image (Iwase, paragraphs 10-11 teaches compositing the first and second image); and combine the first image and the second image at a ratio in which the excess and deficiency of the first AI processing is adjusted (Iwase, abstract teaches combining two images at a ratio and paragraph 254 teaches the ratio to perform correction so as to eliminate unevenness); wherein the first image includes a first edge emphasized image obtained by performing emphasis processing of emphasizing an edge region in the processing target image more than a non-edge region, which is a region different from the edge region, by using an AI method, as processing included in the first AI processing, (Iwase, paragraph 598 teaches superimposing on an image as long as a region is an edge); this means the edge must be checked and emphasized to use it and that a non-edge would be different
Iwase fails to teach adjust excess and deficiency of the first AI processing by combining the first image and the second image.
However, BOUZARAA teaches adjust excess and deficiency of the first AI processing by combining the first image and the second image (BOUZARAA, paragraph 64 teaches to reduce artefacts related to under- and/or over-exposure in a final image); adjust excess/deficiency corresponds to reducing over/under exposure respectively and this is done for the HDR image which is obtained from AI processing as described above.
However, the combination of Iwase and BOUZARAA fails to teach the second image includes a second edge emphasized image obtained by performing processing of emphasizing the edge region more than the non-edge region by using a non-AI method, and the processor is configured to adjust an element derived from the emphasis processing by combining the first edge emphasized image and the second edge emphasized image at the ratio.
Furthermore, no prior art of record either alone or in combination teaches
the second image includes a second edge emphasized image obtained by performing processing of emphasizing the edge region more than the non-edge region by using a non-AI method, and the processor is configured to adjust an element derived from the emphasis processing by combining the first edge emphasized image and the second edge emphasized image at the ratio
when read in light of the rest of the limitations in claim 23 and the claims to which claim 23 depends and thus claim 23 contains allowable subject matter.
Regarding claim 24, the closest prior art of (or combination of) Iwase teaches an image processing apparatus comprising: a processor, wherein the processor is configured to: (Iwase, fig. 4, reference 400 shows an image processing apparatus and paragraph 650 teaches non-transitory computer-readable storage medium with instructions to be executed by a processor) acquire a first image, which is obtained by performing first AI processing on a processing target image, (Iwase, paragraphs 10-11 teaches a image in which image quality is improved using a machine learning (ML) engine); the resulting high quality image from Iwase is treated as the first image in this case, the processing is done on the original[medical image of a predetermined site of a subject detailed in paragraphs 76-77]/target image and although this is referred to as second image it can also be treated as a first image; and a second image, which is obtained without performing the first AI processing on the processing target image (Iwase, paragraphs 10-11 teaches a first image obtained); although this is referred to as first image it can also be treated as a second image and since this image is used at the end when compositing, this means it does not undergo AI processing, thus also acts as the processing target image; adjust excess and deficiency of the first AI processing by combining the first image and the second image (Iwase, paragraphs 10-11 teaches compositing the first and second image); and combine the first image and the second image at a ratio in which the excess and deficiency of the first AI processing is adjusted (Iwase, abstract teaches combining two images at a ratio and paragraph 254 teaches the ratio to perform correction so as to eliminate unevenness); by combining the first point image adjusted image and the second point image adjusted image at the ratio (Iwase, abstract teaches combining at a ratio).
Iwase fails to teach adjust excess and deficiency of the first AI processing by combining the first image and the second image.
However, BOUZARAA teaches adjust excess and deficiency of the first AI processing by combining the first image and the second image (BOUZARAA, paragraph 64 teaches to reduce artefacts related to under- and/or over-exposure in a final image); adjust excess/deficiency corresponds to reducing over/under exposure respectively and this is done for the HDR image which is obtained from AI processing as described above.
However, combination of Iwase and BOUZARAA fails to teach wherein the first image includes a first point image adjusted image obtained by performing point image adjustment processing of adjusting a blurriness amount of a point image with respect to the processing target image by using an AI method, as processing included in the first AI processing, the second image includes a second point image adjusted image obtained by performing processing of adjusting the blurriness amount by using a non-AI method, and the processor is configured to adjust an element derived from the point image adjustment processing by combining the first point image adjusted image and the second point image adjusted image at the ratio.
Furthermore, no prior art of record either alone or in combination teaches
wherein the first image includes a first point image adjusted image obtained by performing point image adjustment processing of adjusting a blurriness amount of a point image with respect to the processing target image by using an AI method, as processing included in the first AI processing, the second image includes a second point image adjusted image obtained by performing processing of adjusting the blurriness amount by using a non-AI method, and the processor is configured to adjust an element derived from the point image adjustment processing by combining the first point image adjusted image and the second point image adjusted image at the ratio.
when read in light of the rest of the limitations in claim 24 and the claims to which claim 24 depends and thus claim 24 contains allowable subject matter.
Regarding claim 25, the closest prior art of (or combination of) Iwase teaches an image processing apparatus comprising: a processor, wherein the processor is configured to: (Iwase, fig. 4, reference 400 shows an image processing apparatus and paragraph 650 teaches non-transitory computer-readable storage medium with instructions to be executed by a processor) acquire a first image, which is obtained by performing first AI processing on a processing target image, (Iwase, paragraphs 10-11 teaches a image in which image quality is improved using a machine learning (ML) engine); the resulting high quality image from Iwase is treated as the first image in this case, the processing is done on the original[medical image of a predetermined site of a subject detailed in paragraphs 76-77]/target image and although this is referred to as second image it can also be treated as a first image; and a second image, which is obtained without performing the first AI processing on the processing target image (Iwase, paragraphs 10-11 teaches a first image obtained); although this is referred to as first image it can also be treated as a second image and since this image is used at the end when compositing, this means it does not undergo AI processing, thus also acts as the processing target image; adjust excess and deficiency of the first AI processing by combining the first image and the second image (Iwase, paragraphs 10-11 teaches compositing the first and second image); and combine the first image and the second image at a ratio in which the excess and deficiency of the first AI processing is adjusted (Iwase, abstract teaches combining two images at a ratio and paragraph 254 teaches the ratio to perform correction so as to eliminate unevenness); wherein the processing target image is an image obtained by imaging a third subject, (Iwase, fig. 14a shows multiple regions in a captured image, any of which can be considered a third subject); r1419 can be considered third subject; … combining the first blurred image and the second blurred image at the ratio (Iwase, abstract teaches combining images at a ratio).
Iwase fails to teach adjust excess and deficiency of the first AI processing by combining the first image and the second image.
However, BOUZARAA teaches adjust excess and deficiency of the first AI processing by combining the first image and the second image (BOUZARAA, paragraph 64 teaches to reduce artefacts related to under- and/or over-exposure in a final image); adjust excess/deficiency corresponds to reducing over/under exposure respectively and this is done for the HDR image which is obtained from AI processing as described above
However, the combination of Iwase and BOUZARAA fails to teach the first image includes a first blurred image obtained by performing blur processing of applying a blurriness, which is determined in accordance with the third subject, to the processing target image by using an AI method, as processing included in the first AI processing, the second image includes a second blurred image obtained by performing processing of applying the blurriness to the processing target image by using a non-AI method, and the processor is configured to adjust an element derived from the blur processing by combining the first blurred image and the second blurred image at the ratio.
Furthermore, no prior art of record either alone or in combination teaches
the first image includes a first blurred image obtained by performing blur processing of applying a blurriness, which is determined in accordance with the third subject, to the processing target image by using an AI method, as processing included in the first AI processing, the second image includes a second blurred image obtained by performing processing of applying the blurriness to the processing target image by using a non-AI method, and the processor is configured to adjust an element derived from the blur processing by combining the first blurred image and the second blurred image at the ratio
when read in light of the rest of the limitations in claim 25 and the claims to which claim 25 depends and thus claim 25 contains allowable subject matter.
Regarding claim 26, the closest prior art of (or combination of) Iwase teaches an image processing apparatus comprising: a processor, wherein the processor is configured to: (Iwase, fig. 4, reference 400 shows an image processing apparatus and paragraph 650 teaches non-transitory computer-readable storage medium with instructions to be executed by a processor) acquire a first image, which is obtained by performing first AI processing on a processing target image, (Iwase, paragraphs 10-11 teaches a image in which image quality is improved using a machine learning (ML) engine); the resulting high quality image from Iwase is treated as the first image in this case, the processing is done on the original[medical image of a predetermined site of a subject detailed in paragraphs 76-77]/target image and although this is referred to as second image it can also be treated as a first image; and a second image, which is obtained without performing the first AI processing on the processing target image (Iwase, paragraphs 10-11 teaches a first image obtained); although this is referred to as first image it can also be treated as a second image and since this image is used at the end when compositing, this means it does not undergo AI processing, thus also acts as the processing target image; adjust excess and deficiency of the first AI processing by combining the first image and the second image (Iwase, paragraphs 10-11 teaches compositing the first and second image); and combine the first image and the second image at a ratio in which the excess and deficiency of the first AI processing is adjusted (Iwase, abstract teaches combining two images at a ratio and paragraph 254 teaches the ratio to perform correction so as to eliminate unevenness); and the processor is configured to adjust an element derived from the round blurriness processing by combining the first round blurriness image and the second round blurriness image at the ratio (Iwase, abstract teaches combining images at a ratio).
Iwase fails to teach adjust excess and deficiency of the first AI processing by combining the first image and the second image.
However, BOUZARAA teaches adjust excess and deficiency of the first AI processing by combining the first image and the second image (BOUZARAA, paragraph 64 teaches to reduce artefacts related to under- and/or over-exposure in a final image); adjust excess/deficiency corresponds to reducing over/under exposure respectively and this is done for the HDR image which is obtained from AI processing as described above
However, the combination of Iwase and BOUZARAA fails to teach wherein the first image includes a first round blurriness image obtained by performing round blurriness processing of applying a first round blurriness to the processing target image by using an AI method, as processing included in the first AI processing, the second image includes a second round blurriness image obtained by performing processing of adjusting the first round blurriness from the processing target image by using a non-AI method or of applying a second round blurriness to the processing target image by using the non-AI method, and the processor is configured to adjust an element derived from the round blurriness processing by combining the first round blurriness image and the second round blurriness image at the ratio.
Furthermore, no prior art of record either alone or in combination teaches
wherein the first image includes a first round blurriness image obtained by performing round blurriness processing of applying a first round blurriness to the processing target image by using an AI method, as processing included in the first AI processing, the second image includes a second round blurriness image obtained by performing processing of adjusting the first round blurriness from the processing target image by using a non-AI method or of applying a second round blurriness to the processing target image by using the non-AI method, and the processor is configured to adjust an element derived from the round blurriness processing by combining the first round blurriness image and the second round blurriness image at the ratio when read in light of the rest of the limitations in claim 26 and the claims to which claim 26 depends and thus claim 26 contains allowable subject matter.
Regarding claim 27, the closest prior art of (or combination of) Iwase teaches an image processing apparatus comprising: a processor, wherein the processor is configured to: (Iwase, fig. 4, reference 400 shows an image processing apparatus and paragraph 650 teaches non-transitory computer-readable storage medium with instructions to be executed by a processor) acquire a first image, which is obtained by performing first AI processing on a processing target image, (Iwase, paragraphs 10-11 teaches a image in which image quality is improved using a machine learning (ML) engine); the resulting high quality image from Iwase is treated as the first image in this case, the processing is done on the original[medical image of a predetermined site of a subject detailed in paragraphs 76-77]/target image and although this is referred to as second image it can also be treated as a first image; and a second image, which is obtained without performing the first AI processing on the processing target image (Iwase, paragraphs 10-11 teaches a first image obtained); although this is referred to as first image it can also be treated as a second image and since this image is used at the end when compositing, this means it does not undergo AI processing, thus also acts as the processing target image; adjust excess and deficiency of the first AI processing by combining the first image and the second image (Iwase, paragraphs 10-11 teaches compositing the first and second image); and combine the first image and the second image at a ratio in which the excess and deficiency of the first AI processing is adjusted (Iwase, abstract teaches combining two images at a ratio and paragraph 254 teaches the ratio to perform correction so as to eliminate unevenness); wherein the first image includes a first gradation adjusted image obtained by performing first gradation adjustment processing of adjusting a gradation of the processing target image by using an AI method, as processing included in the first AI processing, (Iwase, paragraph 409 teaches image quality improving engine may include gradation conversion processing); gradation conversion indicates gradation adjustment processing would occur and that the gradation adjusted image is obtained.
Iwase fails to teach adjust excess and deficiency of the first AI processing by combining the first image and the second image.
However, BOUZARAA teaches adjust excess and deficiency of the first AI processing by combining the first image and the second image (BOUZARAA, paragraph 64 teaches to reduce artefacts related to under- and/or over-exposure in a final image); adjust excess/deficiency corresponds to reducing over/under exposure respectively and this is done for the HDR image which is obtained from AI processing as described above
However, the combination of Iwase and BOUZARAA fails to teach as processing included in the first AI processing, the second image includes a second gradation adjusted image obtained by performing second gradation adjustment processing of adjusting the gradation of the processing target image by using a non-AI method, and the processor is configured to adjust an element derived from the first gradation adjustment processing by combining the first gradation adjusted image and the second gradation adjusted image at the ratio.
Furthermore, no prior art of record either alone or in combination teaches
included in the first AI processing, the second image includes a second gradation adjusted image obtained by performing second gradation adjustment processing of adjusting the gradation of the processing target image by using a non-AI method, and the processor is configured to adjust an element derived from the first gradation adjustment processing by combining the first gradation adjusted image and the second gradation adjusted image at the ratio.
when read in light of the rest of the limitations in claim 4 and the claims to which claim 27 depends and thus claim 27 contains allowable subject matter.
Claims 8-9, 11-12, 15-16, 18-19, 21 and 28 contain allowable subject matter because they depend on a claim that contains allowable subject matter.
Conclusion
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/KEE M TUNG/Supervisory Patent Examiner, Art Unit 2611
/N.U.A./ Examiner, Art Unit 2611