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 .
Notice to Applicants
This communication is in response to the application filed on 04/18/2024.
Claims 1-20 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 04/19/2024 has been considered.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-6, 12, and 16 are rejected under 35 § U.S.C. 102(a)(1) as being anticipated by WO 2016/044465 A1 to MANDELKERN et al. (hereinafter “MANDELKERN”).
As per claim 1, MANDELKERN discloses a method, comprising: receiving an input medical image (a computing system and method of operation to enhance/improve x-ray image quality and in order to perform this process receives an input of x-ray images; abstract; figs 1A, 2B-4, 6-7, 17A-C, 27A-D and 28; paragraphs [0237], [0248-0251], [0265]); producing a noise filtrated image by applying recursive noise filtration to the received input medical image (iteratively (recursive) applying to the input x-ray images at step 2802 are ran through a noise reduction filter to remove and reduce noise of the image slices; figs 17A and 28; paragraphs [0250]); producing a contrast enhanced image by applying recursive contrast enhancement to the received input medical image (iteratively (recursively) applying an edge/contrast enhancement filter on the input x-ray images in order to enhance edges within the image; figs 17A and 28; paragraphs [0192], [0249], [0265], [0335]); producing a sharpened image by applying recursive sharpness enhancement to the received input medical image (the image enhancement model is depicted in figure 17a and 28 and is adapted to take an input x-ray image apply an enhancement including a sharpness enhancements to the image; figs 17A and 28; paragraphs [0192], [0249], [0265], [0335]); and producing an output medical image by linearly mixing the noise filtrated image and the contrast enhanced image with the sharpened image (the computing system is adapted to perform the enhancements on the selected x-ray image slices and is further adapted to output the enhanced x-ray images to a display component of the computer at step 1720 of fig 17A; fig 17A; paragraphs [0107], [0121], [0192]).
As per claim 2, MANDELKERN discloses the method of claim 1, wherein receiving the input medical image comprises receiving an X-ray image (the system using an x-ray device which is operably connected to the computing system acquires input x-ray images; fig 17A; paragraphs [0105-0108], [0154], [0192]), and producing the output medical image comprises producing an enhanced X-ray image (the system is adapted to output enhanced x-ray images to a computer display; figs 6-7, and 17A; paragraphs [0107], [0121], [0192]).
As per claim 3, MANDELKERN discloses the method of claim 2, wherein receiving the input medical image comprises receiving a sequence of computer tomography images (the input medical images are received and collected via a tomography system to receive tomography images; paragraphs [00190-00192], [00236]), and producing the output medical image comprises producing an enhanced image for each image of the sequence of computer tomography images (the tomography images undergo the same processing steps as the x-ray images and produce an enhanced tomography medical image using the same filtering and enhancement steps; abstract; fig 17A; paragraphs [00190-00192], [00236]).
As per claim 4, MANDELKERN discloses the method of claim 2, wherein receiving the X-ray image comprises receiving an X-ray dental image (the system using an x-ray device which is operably connected to the computing system acquires input x-ray images which are dental images as seen in figs provided; figs 6-7 and 17A; paragraphs [0105-0108], [0154], [0192]), and producing the enhanced X-ray image comprises producing an enhanced X-ray dental image (the system is adapted to output enhanced x-ray dental images to a computer display; figs 6-7, and 17A; paragraphs [0107], [0121], [0192]).
As per claim 5, MANDELKERN discloses the method of claim 1, further comprising performing the applications of the recursive noise filtration and the recursive contrast enhancement in parallel with performing the application of the recursive sharpness enhancement (the described processes for enhancing features such as assigning a focus factor to an image slice are run in parallel by the computer performing the enhancement steps; paragraph [00169]).
As per claim 6, MANDELKERN discloses the method of claim 1, wherein linearly mixing the noise filtrated image and the contrast enhanced image with the sharpened image comprises applying weighting coefficients to the noise filtrated image, the contrast enhanced image, and the sharpened image, the weighting coefficients determined based on requirements for the output medical image (the image enhancement model is adjustable based on operators set values and includes the ability to adjust sensitivity of the variance and gradient kernel operators to edges, and the width of the detected edges on the variance and gradient images, can be adjusted by tuning the parameters of the operators acting as weighted coefficients to effect the output of the enhanced input x-ray image; fig 17B; paragraphs [0198], [0204], [00313]).
As per claim 12, MANDELKERN discloses an apparatus for enhancing medical images, the apparatus comprising a processor configured to: receive an input medical image (a computing system which comprises a memory and processor and performs a method of operation to enhance/improve x-ray image quality and in order to perform this process receives an input of x-ray images; abstract; figs 1A, 2B-4, 6-7, 17A-C, 27A-D and 28; paragraphs [0012], [0237], [0248-0251], [0265]); apply recursive noise filtration to the received input medical image to produce a noise filtrated image (iteratively (recursive) applying to the input x-ray images at step 2802 are ran through a noise reduction filter to remove and reduce noise of the image slices; figs 17A and 28; paragraphs [0250]); apply recursive contrast enhancement to the received input medical image to produce a contrast enhanced image (iteratively (recursively) applying an edge/contrast enhancement filter on the input x-ray images in order to enhance edges within the image; figs 17A and 28; paragraphs [0192], [0249], [0265], [0335]); apply recursive sharpness enhancement to the received input medical image to produce a sharpened image (the image enhancement model is depicted in figure 17a and 28 and is adapted to take an input x-ray image apply an enhancement including a sharpness enhancements to the image; figs 17A and 28; paragraphs [0192], [0249], [0265], [0335]); and linearly mix the noise filtrated image and the contrast enhanced image with the sharpened image to produce an output medical image (the computing system is adapted to perform the enhancements on the selected x-ray image slices and is further adapted to output the enhanced x-ray images to a display component of the computer at step 1720 of fig 17A; fig 17A; paragraphs [0107], [0121], [0192]).
As per claim 16, MANDELKERN discloses a non-transitory computer-readable storage medium including instructions, which when executed by a machine, cause the machine to perform a method comprising (a computing system which comprises a memory and processor and performs a method of operation to enhance/improve x-ray image quality and in order to perform this process receives an input of x-ray images; abstract; figs 1A, 2B-4, 6-7, 17A-C, 27A-D and 28; paragraphs [0012], [0237], [0248-0251], [0265]): receiving an input medical image (the system receives an input of x-ray images; abstract; figs 1A, 2B-4, 6-7, 17A-C, 27A-D and 28; paragraphs [0237], [0248-0251], [0265]); producing a noise filtrated image by applying recursive noise filtration to the received input medical image (iteratively (recursive) applying to the input x-ray images at step 2802 are ran through a noise reduction filter to remove and reduce noise of the image slices; figs 17A and 28; paragraphs [0250]); producing a contrast enhanced image by applying recursive contrast enhancement to the received input medical image (iteratively (recursively) applying an edge/contrast enhancement filter on the input x-ray images in order to enhance edges within the image; figs 17A and 28; paragraphs [0192], [0249], [0265], [0335]); producing a sharpened image by applying recursive sharpness enhancement to the received input medical image ((the image enhancement model is depicted in figure 17a and 28 and is adapted to take an input x-ray image apply an enhancement including a sharpness enhancements to the image; figs 17A and 28; paragraphs [0192], [0249], [0265], [0335]); and producing an output medical image by linearly mixing the noise filtrated image and the contrast enhanced image with the sharpened image ((the computing system is adapted to perform the enhancements on the selected x-ray image slices and is further adapted to output the enhanced x-ray images to a display component of the computer at step 1720 of fig 17A; fig 17A; paragraphs [0107], [0121], [0192])).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 7, 13, 17 are rejected under 35 § U.S.C. 103 as being obvious over WO 2016/044465 A1 to MANDELKERN et al. (hereinafter “MANDELKERN”) in view of US 2019/0378329 A1 to KIELY (hereinafter “KIELY”).
As per claim 7, MANDELKERN discloses the method of claim 1. MANDELKERN fails to disclose wherein applying the recursive noise filtration to the received input medical image comprises applying Recursive Locally-Adaptive Wiener Filtration (RLA-WF) based on a sliding window calculated following a 2-dimensional (2D) recursive scheme.
KIELY discloses wherein applying the recursive noise filtration to the received input medical image comprises applying Recursive Locally-Adaptive Wiener Filtration (RLA-WF) based on a sliding window calculated following a 2-dimensional (2D) recursive scheme (the computing system adapted to provide optimal x-ray image parameters includes a wiener filter used to reduce noise in the x-ray images and would be easily applied recursively by applying it iteratively at the noise filtration steps of MANDELKERN; paragraphs [0174-0178]).
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 MANDELKERN to have applying the recursive noise filtration to the received input medical image comprises applying Recursive Locally-Adaptive Wiener Filtration of KIELY reference. The Suggestion/motivation for doing so would have been to provide the ability to reduce noise by employing specifically a wiener filter on the x-ray images as suggested by paragraph [0175] of KIELY. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine KIELY with MANDELKERN to obtain the invention as specified in claim 7.
As per claim 13, MANDELKERN discloses the apparatus of claim 12. MANDELKERN fails to disclose wherein the processor is configured to apply Recursive Locally-Adaptive Wiener Filtration (RLA-WF) to the received input medical image to produce the noise filtrated image based on a sliding window calculated following a 2-dimensional (2D) recursive scheme.
KIELY discloses wherein the processor is configured to apply Recursive Locally-Adaptive Wiener Filtration (RLA-WF) to the received input medical image to produce the noise filtrated image based on a sliding window calculated following a 2-dimensional (2D) recursive scheme (the computing system adapted to provide optimal x-ray image parameters includes a wiener filter used to reduce noise in the x-ray images and would be easily applied recursively by applying it iteratively at the noise filtration steps of MANDELKERN; paragraphs [0174-0178]).
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 MANDELKERN to have apply Recursive Locally-Adaptive Wiener Filtration to the received input medical image to produce the noise filtrated image of KIELY reference. The Suggestion/motivation for doing so would have been to provide the ability to reduce noise by employing specifically a wiener filter on the x-ray images as suggested by paragraph [0175] of KIELY. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine KIELY with MANDELKERN to obtain the invention as specified in claim 13.
As per claim 17, MANDELKERN discloses the non-transitory computer-readable storage medium of claim 16. MANDELKERN fails to disclose wherein applying the recursive noise filtration to the received input medical image comprises applying Recursive Locally-Adaptive Wiener Filtration (RLA-WF) based on a sliding window calculated following a 2-dimensional (2D) recursive scheme.
KIELY discloses wherein applying the recursive noise filtration to the received input medical image comprises applying Recursive Locally-Adaptive Wiener Filtration (RLA-WF) based on a sliding window calculated following a 2-dimensional (2D) recursive scheme (the computing system adapted to provide optimal x-ray image parameters includes a wiener filter used to reduce noise in the x-ray images and would be easily applied recursively by applying it iteratively at the noise filtration steps of MANDELKERN; paragraphs [0174-0178]).
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 MANDELKERN to have applying the recursive noise filtration to the received input medical image comprises applying Recursive Locally-Adaptive Wiener Filtration of KIELY reference. The Suggestion/motivation for doing so would have been to provide the ability to reduce noise by employing specifically a wiener filter on the x-ray images as suggested by paragraph [0175] of KIELY. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine KIELY with MANDELKERN to obtain the invention as specified in claim 17.
Claims 8-9, 14, and 18 are rejected under 35 § U.S.C. 103 as being obvious over WO 2016/044465 A1 to MANDELKERN et al. (hereinafter “MANDELKERN”) in view of US 2019/0378329 A1 to KIELY (hereinafter “KIELY”) in view of US 2024/0185425 A1 to ZHOU et al (hereinafter “ZHOU”).
As per claim 8, MANDELKERN in view of KIELY discloses the method of claim 7. Modified MANDELKERN fails to disclose wherein applying the recursive contrast enhancement to the received input medical image comprises applying Recursive Sliding Window Contrast Limited Adaptive Histogram Equalization (RSW-CLAHE) using a Truncation Threshold Surface (TTS) and the sliding window.
ZHOU discloses wherein applying the recursive contrast enhancement to the received input medical image comprises applying Recursive Sliding Window Contrast Limited Adaptive Histogram Equalization (RSW-CLAHE) using a Truncation Threshold Surface (TTS) and the sliding window (during contrast reconstruction of the x-ray images the computing system applies a contrast-limited adaptive histogram equalization “cLAHE” which would be applied recursively by applying it to the steps of MANDELKERN, and is compared to a grey level threshold which can have variations in weight and are applied as different threshold values based on the weight applied; paragraphs [0126], [0194-0199]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to further modify MANDELKERN to have applying the recursive contrast enhancement to the received input medical image comprises applying Recursive Sliding Window Contrast Limited Adaptive Histogram Equalization of ZHOU reference. The Suggestion/motivation for doing so would have been to provide contrast filtering via a specific contrast-limited adaptive histogram equalization filter of paragraph [0199] of ZHOU. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine ZHOU with modified MANDELKERN to obtain the invention as specified in claim 8.
As per claim 9, MANDELKERN in view of KIELY in view of ZHOU discloses the method of claim 8. Modified MANDELKERN fails to disclose comprising producing a truncation threshold by calculating the TTS to determine an individual threshold for trimming of the local histogram in the sliding window around the current pixel.
ZHOU discloses comprising producing a truncation threshold by calculating the TTS to determine an individual threshold for trimming of the local histogram in the sliding window around the current pixel (based on the provided weighted value the threshold adjusts on a sliding scale related to the weight applied to the parameter value, and refers to the variation between two or more of the thresholds such as T0 , T1 , T2 , T3, etc.., wherein the nonlinear applied enhancements include histogram equalization (which involves reduction which is synonyms with trimming) and allows for the histogram if double peaks are seen to segment (trim) the initial image; paragraphs [0106], [0118], [0126], [0199]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to further modify MANDELKERN to have a truncation threshold by calculating the TTS to determine an individual threshold for trimming of the local histogram of ZHOU reference. The Suggestion/motivation for doing so would have been to provide histogram data in order to make decisions on enhancing the initial image as suggested at paragraph [0118] of ZHOU. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine ZHOU with modified MANDELKERN to obtain the invention as specified in claim 9.
As per claim 14, MANDELKERN in view of KIELY discloses the apparatus of claim 13. Modified MANDELKERN fails to disclose wherein the processor is configured to apply Recursive Sliding Window Contrast Limited Adaptive Histogram Equalization (RSW-CLAHE) to the received input medical image to produce the contrast enhanced image using a Truncation Threshold Surface (TTS) and the sliding window.
ZHOU discloses wherein the processor is configured to apply Recursive Sliding Window Contrast Limited Adaptive Histogram Equalization (RSW-CLAHE) to the received input medical image to produce the contrast enhanced image using a Truncation Threshold Surface (TTS) and the sliding window (during contrast reconstruction of the x-ray images the computing system applies a contrast-limited adaptive histogram equalization “cLAHE” which would be applied recursively by applying it to the steps of MANDELKERN, and is compared to a grey level threshold which can have variations in weight and are applied as different threshold values based on the weight applied; paragraphs [0126], [0194-0199]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to further modify MANDELKERN to have to apply Recursive Sliding Window Contrast Limited Adaptive Histogram Equalization to the received input medical image of ZHOU reference. The Suggestion/motivation for doing so would have been to provide contrast filtering via a specific contrast-limited adaptive histogram equalization filter of paragraph [0199] of ZHOU. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine ZHOU with modified MANDELKERN to obtain the invention as specified in claim 14.
As per claim 18, MANDELKERN in view of KIELY discloses the non-transitory computer-readable storage medium of claim 17. Modified MANDELKERN fails to disclose wherein applying the recursive contrast enhancement to the received input medical image comprises applying Recursive Sliding Window Contrast Limited Adaptive Histogram Equalization (RSW-CLAHE) using a Truncation Threshold Surface (TTS) and the sliding window.
ZHOU discloses wherein applying the recursive contrast enhancement to the received input medical image comprises applying Recursive Sliding Window Contrast Limited Adaptive Histogram Equalization (RSW-CLAHE) using a Truncation Threshold Surface (TTS) and the sliding window (during contrast reconstruction of the x-ray images the computing system applies a contrast-limited adaptive histogram equalization “cLAHE” which would be applied recursively by applying it to the steps of MANDELKERN, and is compared to a grey level threshold which can have variations in weight and are applied as different threshold values based on the weight applied; paragraphs [0126], [0194-0199]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to further modify MANDELKERN to have applying the recursive contrast enhancement to the received input medical image comprises applying Recursive Sliding Window Contrast Limited Adaptive Histogram Equalization of ZHOU reference. The Suggestion/motivation for doing so would have been to provide contrast filtering via a specific contrast-limited adaptive histogram equalization filter of paragraph [0199] of ZHOU. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine ZHOU with modified MANDELKERN to obtain the invention as specified in claim 18.
Claims 10-11, 15, and 19-20 are rejected under 35 § U.S.C. 103 as being obvious over WO 2016/044465 A1 to MANDELKERN et al. (hereinafter “MANDELKERN”) in view of US 2019/0378329 A1 to KIELY (hereinafter “KIELY”) in view of US 2024/0185425 A1 to ZHOU et al (hereinafter “ZHOU”) in view of US 2015/0296193 A1 to COTE et al (hereinafter “COTE”).
As per claim 10, MANDELKERN in view of KIELY in view of ZHOU discloses the method of claim 8. Modified MANDELKERN fails to disclose wherein applying the recursive sharpness enhancement to the received input medical image comprises applying Fast Recursive Adaptive Unsharp Masking (FRA-UM) using the sliding window to obtain the sharpened image by a 2-dimensional (2D) recursive scheme.
COTE discloses wherein applying the recursive sharpness enhancement to the received input medical image comprises applying Fast Recursive Adaptive Unsharp Masking (FRA-UM) using the sliding window to obtain the sharpened image by a 2-dimensional (2D) recursive scheme (the computing system is adapted to apply an unsharp masking step and this step would be applied iteratively (recursively) if used in the steps of MANDELKERN and would result in x-ray image enhancement by sharpening the images edges, and improving the enhancement of textures and edges while also reducing noise in the output image and utilizes a 2-d lookup table; paragraph [0898-0901], [0917], [0931]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to further modify MANDELKERN to have applying Fast Recursive Adaptive Unsharp Masking (FRA-UM) using the sliding window of COTE reference. The Suggestion/motivation for doing so would have been to provide an unsharp mask which results in improving the enhancement of textures and edges while also reducing noise in the output image as suggested by paragraph [0917] of COTE. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine COTE with modified MANDELKERN to obtain the invention as specified in claim 10.
As per claim 11, MANDELKERN in view of KIELY in view of ZHOU discloses the method of claim 8. Modified MANDELKERN fails to disclose wherein applying the recursive sharpness enhancement to the received input medical image comprises applying Fast Recursive Adaptive Unsharp Masking (FRA-UM) using the sliding window to obtain the sharpened image by 1-dimensional (lD) recursive scheme based on double scanning of the input medical image.
COTE discloses wherein applying the recursive sharpness enhancement to the received input medical image comprises applying Fast Recursive Adaptive Unsharp Masking (FRA-UM) using the sliding window to obtain the sharpened image by 1-dimensional (lD) recursive scheme based on double scanning of the input medical image (the computing system is adapted to apply an unsharp masking step and this step would be applied iteratively (recursively) if used in the steps of MANDELKERN and would result in x-ray image enhancement by sharpening the images edges, and improving the enhancement of textures and edges while also reducing noise in the output image and utilizes a 1-d motion table lookup table which comprises 237 entries; paragraphs [0591-0594], [0898-0901], [0917]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to further modify MANDELKERN to have applying Fast Recursive Adaptive Unsharp Masking of COTE reference. The Suggestion/motivation for doing so would have been to provide an unsharp mask which results in improving the enhancement of textures and edges while also reducing noise in the output image as suggested by paragraph [0917] of COTE. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine COTE with modified MANDELKERN to obtain the invention as specified in claim 11.
As per claim 15, MANDELKERN in view of KIELY in view of ZHOU discloses the apparatus of claim 14. Modified MANDELKERN fails to disclose wherein the processor is configured to apply Fast Recursive Adaptive Unsharp Masking (FRA-UM) using the sliding window to obtain the sharpened image by a 2-dimensional (2D) recursive scheme or a 1-dimensional (1D) recursive scheme, the 1D recursive scheme based on double scanning of the input medical image.
COTE discloses wherein the processor is configured to apply Fast Recursive Adaptive Unsharp Masking (FRA-UM) using the sliding window to obtain the sharpened image by a 2-dimensional (2D) recursive scheme or a 1-dimensional (1D) recursive scheme, the 1D recursive scheme based on double scanning of the input medical image (the computing system is adapted to apply an unsharp masking step and this step would be applied iteratively (recursively) if used in the steps of MANDELKERN and would result in x-ray image enhancement by sharpening the images edges, and improving the enhancement of textures and edges while also reducing noise in the output image and utilizes a 1-d motion table lookup table which comprises 237 entries; paragraphs [0591-0594], [0898-0901], [0917]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to further modify MANDELKERN to have ability to apply Fast Recursive Adaptive Unsharp Masking of COTE reference. The Suggestion/motivation for doing so would have been to provide an unsharp mask which results in improving the enhancement of textures and edges while also reducing noise in the output image as suggested by paragraph [0917] of COTE. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine COTE with modified MANDELKERN to obtain the invention as specified in claim 15.
As per claim 19, MANDELKERN in view of KIELY in view of ZHOU discloses the non-transitory computer-readable storage medium of claim 18. Modified MANDELKERN fails to disclose wherein applying the recursive sharpness enhancement to the received input medical image comprises applying Fast Recursive Adaptive Unsharp Masking (FRA-UM) using the sliding window to obtain the sharpened image by a 2-dimensional (2D) recursive scheme.
COTE discloses wherein applying the recursive sharpness enhancement to the received input medical image comprises applying Fast Recursive Adaptive Unsharp Masking (FRA-UM) using the sliding window to obtain the sharpened image by a 2-dimensional (2D) recursive scheme (the computing system is adapted to apply an unsharp masking step and this step would be applied iteratively (recursively) if used in the steps of MANDELKERN and would result in x-ray image enhancement by sharpening the images edges, and improving the enhancement of textures and edges while also reducing noise in the output image and utilizes a 2-d lookup table; paragraph [0898-0901], [0917], [0931]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to further modify MANDELKERN to have applying the recursive sharpness enhancement to the received input medical image comprises applying Fast Recursive Adaptive Unsharp Masking of COTE reference. The Suggestion/motivation for doing so would have been to provide an unsharp mask which results in improving the enhancement of textures and edges while also reducing noise in the output image as suggested by paragraph [0917] of COTE. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine COTE with modified MANDELKERN to obtain the invention as specified in claim 19.
As per claim 20, MANDELKERN in view of KIELY in view of ZHOU discloses the non-transitory computer-readable storage medium of claim 18. Modified MANDELKERN fails to disclose wherein applying the recursive sharpness enhancement to the received input medical image comprises applying Fast Recursive Adaptive Unsharp Masking (FRA-UM) using the sliding window to obtain the sharpened image by 1-dimensional (ID) recursive scheme based on double scanning of the input medical image.
COTE discloses wherein applying the recursive sharpness enhancement to the received input medical image comprises applying Fast Recursive Adaptive Unsharp Masking (FRA-UM) using the sliding window to obtain the sharpened image by 1-dimensional (ID) recursive scheme based on double scanning of the input medical image (the computing system is adapted to apply an unsharp masking step and this step would be applied iteratively (recursively) if used in the steps of MANDELKERN and would result in x-ray image enhancement by sharpening the images edges, and improving the enhancement of textures and edges while also reducing noise in the output image and utilizes a 1-d motion table lookup table which comprises 237 entries; paragraphs [0591-0594], [0898-0901], [0917]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to further modify MANDELKERN to have applying the recursive sharpness enhancement to the received input medical image comprises applying Fast Recursive Adaptive Unsharp Masking of COTE reference. The Suggestion/motivation for doing so would have been to provide an unsharp mask which results in improving the enhancement of textures and edges while also reducing noise in the output image as suggested by paragraph [0917] of COTE. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine COTE with modified MANDELKERN to obtain the invention as specified in claim 20.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. These prior arts include the following:
WO 2017/203316 A1
US 2008/0063139 A1
US 2016/0100814 A1
US 2011/0150307 A1
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEVIN JACOB DHOOGE whose telephone number is (571) 270-0999. The examiner can normally be reached 7:30-5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Bee can be reached on (571) 270-5183. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800- 786-9199 (IN USA OR CANADA) or 571-272-1000.
/Devin Dhooge/
USPTO Patent Examiner
Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677