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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 9/9/2026 has been entered.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 10/1/2024, 05/28/2025, 6/16/2026, and 9/10/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
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 nonobviousness.
Claim(s) 1-3, 8, 10-13, and 15-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Du, Hao et al. “Multi-task Graph Convolutional Neural Network for Calcification Morphology and Distribution Analysis in Mammograms.” ArXiv abs/2105.06822 (2021): n. pag. in view of Dewaele et. al. (United States Patent Application Publication US 2010/0046814 A1) and Kamei et. al. (United States Patent Application Publication US 2020/0380670 A1).
Regarding claim 1, Du et. al. discloses an image processing apparatus comprising: at least one processor that is configured to (Du Figure 2): detect calcification from a radiation image obtained by imaging a breast by irradiating the breast with radiation (Du section 2.1, second sentence); and determine a type of a distribution state of the calcification based on the low-resolution image (Du, page 3, item 3 and figure 2b, the output Y_dist).
However, Du et. al. fails to disclose and generate a calcification distribution image in which only the calcification detected from the radiation image appears; generate a low-resolution image from the calcification distribution image.
Dewaele et. al. teaches generate a low-resolution image from the calcification distribution image (Dewaele et. al. [0135]-[0140]: Step 1: Computation of a Reduced-Resolution Image. Since the gross scale of the image structure, we are interested in is neither at the noise level nor in gross anatomic variations, a reduced-resolution image is derived from the original image.).
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Kamei et. al. teaches and generate a calcification distribution image in which only the calcification detected from the radiation image appears (Kamei et. al. Abstract, Fig. 2A, Fig. 4A-4E, [0002], [0025]-[0033], [0035], [0039]: more effective diagnosis is possible by displaying, with emphasis, a distribution of microcalcified regions that overlap a mammary gland.)
This is important to the claimed invention because reducing the resolution of the calcification distribution image improves the accuracy of determining the type of the distribution state of the calcification. The calcium distribution image enables a more effective diagnosis. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Du et. al., Dewaele et. al., and Kamei et. al. so that the calcification distribution images are converted to a lower resolution than the resolution of the radiation image.
Regarding claim 17, Du et. al. discloses an image processing method performed by a computer, the method comprising: detecting calcification from a radiation image obtained by imaging a breast by irradiating the breast with radiation (Du section 2.1, second sentence); and determining a type of a distribution state of the calcification based on the low-resolution image (Du, page 3, item 3 and figure 2b, the output Y_dist).
However, Du et. al. fails to teach and generating a calcification distribution image in which only the calcification detected from the radiation image appears; generating a low-resolution image from the calcification distribution image.
Dewaele et. al. teaches generating a low-resolution image from a calcification distribution image. (Dewaele et. al. [0135]-[0140]: Step 1: Computation of a Reduced-Resolution Image. Since the gross scale of the image structure, we are interested in is neither at the noise level nor in gross anatomic variations, a reduced-resolution image is derived from the original image.).
Kamei et. al. teaches and generate a calcification distribution image in which only the calcification detected from the radiation image appears (Kamei et. al. Abstract, Fig. 2A, Fig. 4A-4E, [0002], [0025]-[0033], [0035], [0039]: more effective diagnosis is possible by displaying, with emphasis, a distribution of microcalcified regions that overlap a mammary gland.).
This is important to the claimed invention because reducing the resolution of the calcification distribution image improves the accuracy of determining the type of the distribution state of the calcification. The calcium distribution image enables a more effective diagnosis. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Du et. al., Dewaele et. al., and Kamei et. al. so that the calcification distribution images are converted to a lower resolution than the resolution of the radiation image.
Regarding claim 18, Du et. al. discloses a non-transitory storage medium storing a program that causes a computer to execute image processing, the image processing comprising: detecting calcification from a radiation image obtained by imaging a breast by irradiating the breast with radiation (Du section 2.1, second sentence); and determining a type of a distribution state of the calcification based on the low-resolution image (Du, page 3, item 3 and figure 2b, the output Y_dist).
However, Du et. al. fails to teach and generating a calcification distribution image in which only the calcification detected from the radiation image appears; generating a low-resolution image from the calcification distribution image.
Dewaele et. al. teaches generating a low-resolution image from a calcification distribution image (Dewaele et. al. [0135]-[0140]: Step 1: Computation of a Reduced-Resolution Image. Since the gross scale of the image structure, we are interested in is neither at the noise level nor in gross anatomic variations, a reduced-resolution image is derived from the original image.).
Kamei et. al. teaches and generate a calcification distribution image in which only the calcification detected from the radiation image appears (Kamei et. al. Abstract, Fig. 2A, Fig. 4A-4E, [0002], [0025]-[0033], [0035], [0039]: more effective diagnosis is possible by displaying, with emphasis, a distribution of microcalcified regions that overlap a mammary gland.).
This is important to the claimed invention because reducing the resolution of the calcification distribution image improves the accuracy of determining the type of the distribution state of the calcification. The calcium distribution image enables a more effective diagnosis. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Du et. al. and Dewaele et. al. so that the calcification distribution images are converted to a lower resolution than the resolution of the radiation image.
Regarding claim 2, Du et. al., Dewaele et. al., and Kamei et. al. disclose the image processing apparatus according to claim 1, and Du et. al. further discloses wherein the calcification distribution image is a grayscale image or a binary image (Du figure 2a: each patch is a gray-scale image).
Regarding claim 3, Du et. al., Dewaele et. al., and Kamei et. al. disclose the image processing apparatus according to claim 1, and Du et. al. further discloses wherein the calcification distribution image is a mask image in which a region other than a region of the calcification is masked (Du, section 2.1, second sentence: each patch is a mask which masks other regions of the image outside the patch. The masked regions are not used in the further processing).
Regarding claim 8, Du et. al., Dewaele et. al., and Kamei et. al. disclose the image processing apparatus according to claim 1, and Dewaele et. al. further discloses wherein the at least one processor is configured to, in a case in which the calcification distribution image includes a plurality of distributions, determine the type of the distribution state for each distribution (Du, Figure 1, Figure 2 neural network, plurality of particular regions of interest of the input mammogram. Classifying multiple different regions of interest of an image is well known).
Regarding claim 10, Du et. al., Dewaele et. al., and Kamei et. al. disclose the image processing apparatus according to claim 1, and Dewaele et. al. further discloses wherein the at least one processor is configured to determine a type of a shape of the calcification based on the low-resolution image (Du figure 2b: the output y_morph determines the morphology as indicated in figure 1 morphology descriptors, that is, the shape).
Regarding claim 11, Du et. al., Dewaele et. al., and Kamei et. al. disclose the image processing apparatus according to claim 10, and Dewaele et. al. further discloses wherein the at least one processor is configured to estimate a degree of malignancy of the calcification based on the type of the distribution state of the calcification and the type of the shape of the calcification (Du, pg.2, last sentence of second paragraph. Abstract, first sentence).
Regarding claim 12, Du et. al., Dewaele et. al., and Kamei et. al. disclose the image processing apparatus according to claim 1, and Du et. al. further discloses wherein the at least one processor is configured to estimate whether the calcification is benign or malignant from the low-resolution image (Du, pg.2, last sentence of second paragraph. Abstract, first sentence).
Regarding claim 13, Du et. al., Dewaele et. al., and Kamei et. al. disclose the image processing apparatus according to claim 1, and Du et. al. further disclose wherein the at least one processor is configured to display a determination result of the type of the distribution state in a form of being superimposed on the radiation image (Du et. al. figure 1).
Regarding claim 15, Du et. al., Dewaele et. al., and Kamei et. al. disclose the image processing apparatus according to claim 1, and Du et. al. further discloses wherein the at least one processor is configured to detect the calcification from the radiation image by using a learning-based calcification detection model (Du Figure 2, this is a well-known means for calcification detection).
Regarding claim 16, Du et. al., Dewaele et. al., and Kamei et. al. disclose the image processing apparatus according to claim 1, and Du et. al. further discloses wherein the radiation image is a two-dimensional image obtained by normal imaging of the breast of a subject, a plurality of tomographic images obtained from a series of a plurality of projection images obtained by tomosynthesis imaging of the breast, or a composite two-dimensional image obtained by combining at least a part of the series of the plurality of projection images or the plurality of tomographic images (Du, abstract third sentence, mammograms).
Claim(s) 4-6 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Du, Hao et al. “Multi-task Graph Convolutional Neural Network for Calcification Morphology and Distribution Analysis in Mammograms.” ArXiv abs/2105.06822 (2021): n. pag. In view of Dewaele et. al. (United States Patent Application Publication US 2010/0046814 A1) and Kamei et. al. (United States Patent Application Publication US 2020/0380670 A1) as applied to claim 1 above, and in further view of Shi (Chinese Patent CN 107798679A)
Regarding claim 4, Du et. al., Dewaele et. al., and Kamei et. al. disclose the image processing apparatus according to claim 1. However, Du fails to disclose wherein the at least one processor is configured to dilate a region of the calcification included in the calcification distribution image and generate the low-resolution image from the calcification distribution image in which the image of the calcification is enlarged.
Shi teaches wherein the at least one processor is configured to dilate a region of the calcification included in the calcification distribution image and generate the low-resolution image from the calcification distribution image in which the image of the calcification is enlarged (Shi [0014], [0082]-[0084], Figure 6, 8). This is a standard image pre-processing operation such as dilation which is known to remove noise such as small holes. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Du, Dewaele et. al., Kamei et. al. and the teachings of Shi so that this feature is incorporated in the claimed solution.
Regarding claim 5, Du et. al., Dewaele et. al., and Kamei et. al. disclose the image processing apparatus according to claim 1. However, Du fails to disclose wherein the at least one processor is configured to: perform filtering on the calcification distribution image based on a signal value of the calcification; and generate the low-resolution image from the calcification distribution image subjected to the filtering.
Shi teaches wherein the at least one processor is configured to: perform filtering on the calcification distribution image based on a signal value of the calcification; and generate the low-resolution image from the calcification distribution image subjected to the filtering (Shi [0066]-[0067] The threshold for determining calcifications depends on the intensity of the texture image which represents the amount of the gland, because it is determined by means of gland-detection texture filters ([0059]-[0060]) in the input mammogram). This is an important aspect of the claimed invention because this is how the calcifications are detected. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Du, Dewaele et. al., Kamei et. al. and Shi so that the apparatus includes how the calcifications are detected.
Regarding claim 6, Du et. al, Dewaele et. al., Kamei et. al. and Shi disclose the image processing apparatus according to claim 5. Shi further teaches wherein the at least one processor is configured to: derive a mammary gland amount of the breast from the radiation image; and set a threshold value for the filtering based on the mammary gland amount (Shi [0066]-[0067] The threshold for determining calcifications depends on the intensity of the texture image which represents the amount of the gland, because it is determined by means of gland-detection texture filters ([0059]-[0060]) in the input mammogram). This is an important aspect of the claimed invention because this is how the calcifications are detected. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Du, Dewaele et. al., Kamei et. al. and Shi so that the apparatus includes how the calcifications are detected.
Regarding claim 14, Du et. al., Dewaele et. al., and Kamei et. al. disclose the image processing apparatus according to claim 1. However, Du fails to disclose wherein the at least one processor is configured to detect the calcification from the radiation image by using a rule-based calcification detection model.
Shi teaches wherein the at least one processor is configured to detect the calcification from the radiation image by using a rule-based calcification detection model. (Shi [0066]-[0067] The threshold for determining calcifications depends on the intensity of the texture image which represents the amount of the gland, because it is determined by means of gland-detection texture filters ([0059]-[0060]) in the input mammogram). This is an important aspect of the claimed invention because this is how the calcifications are detected. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Du, Dewaele et. al., Kamei et. al. and Shi so that the apparatus includes how the calcifications are detected.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Du, Hao et al. “Multi-task Graph Convolutional Neural Network for Calcification Morphology and Distribution Analysis in Mammograms.” ArXiv abs/2105.06822 (2021): n. pag. In view of Dewaele et. al. (United States Patent Application Publication US 2010/0046814 A1) and Kamei et. al. (United States Patent Application Publication US 2020/0380670 A1) as applied to claim 1 above, and in further view of Takeo (Japanese Patent JPh08294479 A).
Regarding claim 7, Du et. al., Dewaele et. al., and Kamei et. al. disclose the image processing apparatus according to claim 1. However, Du fails to disclose wherein the at least one processor is configured to detect at least one of a skin line or a nipple of the breast from the radiation image, and the calcification distribution image includes at least one of the detected skin line or nipple.
Takeo teaches wherein the at least one processor is configured to detect at least one of a skin line or a nipple of the breast from the radiation image, and the calcification distribution image includes at least one of the detected skin line or nipple (Takeo [0123]-[0126] and [0157]-[0166], figure 2 and 14-15, etc.) This is important to the claimed invention because of the delineation of where the radiation image is taken from the breast. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Du, Dewaele et. al., Kamei et. al. and Takeo so that this example is included in the region of interest.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Du, Hao et al. “Multi-task Graph Convolutional Neural Network for Calcification Morphology and Distribution Analysis in Mammograms.” ArXiv abs/2105.06822 (2021): n. pag. In view of Dewaele et. al. (United States Patent Application Publication US 2010/0046814 A1) and Kamei et. al. (United States Patent Application Publication US 2020/0380670 A1) as applied to claim 1 above, and in further view of Gaborski (United State Patent 5,857,030).
Regarding claim 9, Du, Dewaele et. al., Kamei et. al. disclose the image processing apparatus according to claim 1. However Du fails to disclose wherein the radiation image includes a left breast radiation image obtained by imaging a left breast of a subject and a right breast radiation image obtained by imaging a right breast of the subject, and the at least one processor is configured to: detect calcification from each of the left breast radiation image and the right breast radiation image; generate a left breast low resolution image from a left breast calcification distribution image representing a detection result of the calcification of the left breast; generate a right breast low resolution image from a right breast calcification distribution image representing a detection result of the calcification of the right breast; and determine a type of a distribution state of the calcification based on the left breast low resolution image and the right breast low resolution image.
Gaborski teaches wherein the radiation image includes a left breast radiation image obtained by imaging a left breast of a subject and a right breast radiation image obtained by imaging a right breast of the subject, and the at least one processor is configured to (Gaborski System, Figure 9, col 8, lines 60-67): detect calcification from each of the left breast radiation image and the right breast radiation image; generate a left breast low resolution image from a left breast calcification distribution image representing a detection result of the calcification of the left breast; generate a right breast low resolution image from a right breast calcification distribution image representing a detection result of the calcification of the right breast; and determine a type of a distribution state of the calcification based on the left breast low resolution image and the right breast low resolution image (Gaborski col 9 lines 3-26, col 10 lines 1-18 neural networks are utilized to determine whether suspect candidates identified are true areas of abnormality, and the data is defined by small blocks (32 x 32 pixels) centered on suspect pixels by processing unit 190). This is important to the claimed invention so that both right and left breasts of the subject are screened for calcification accurately. Thus, it would have been obvious to a person skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Du, Dewaele et. al., Kamei et. al. and Gaborski so that the component processing unit is incorporated as part of the solution with the teachings of Du and Dewaele.
Conclusion
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/JESSICA YIFANG LIN/Examiner, Art Unit 2668 September 24, 2026
/ANAND P BHATNAGAR/
Primary Examiner, Art Unit 2668
September 28, 2026
