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 .
Information Disclosure Statement
Information Disclosure Statements (IDS)s submitted on 07/31/2025 and 03/04/2026 have been entered and fully considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3, 5, 9, 11, 12, 15, 19 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 of the subject matter eligibility test (see MPEP 2106.03).
Claim 1 is directed to an “apparatus” which describes one of the four statutory categories of patentable subject matter, i.e., a machine.
Claim 20 is directed to a “method” which describes one of the four statutory categories of patentable subject matter, i.e., a process.
Step 2A of the subject matter eligibility test (see MPEP 2106.04).
Prong One:
Claims 1 and 20 recite (“sets forth” or “describes”) the abstract idea of a mental process, substantially as follows: “generate a breast schematic diagram divided into a plurality of regions, display the breast schematic diagram, determine, based a plurality of ultrasonic images, a category of a glandular tissue component in each of the plurality of positions of the breast, determining whether all plurality of positions are completed and repeat a determination until all of the plurality of positions are completed in determining.”
In claims 1 and 20, the above recited steps can be practically performed in the human mind, with the aid of a pen and paper or with a generic computer, in a computer environment, or merely using the generic computer as a tool to perform the steps. If a person were to visually examine, i.e., perform an observation of the ultrasound image data either in a printout or an electronic format, he/she would be able to identify the category of the glandular tissue by experience. Therefore, a person would be able to perform the identification and selection mentally or with a generic computer.
Prong Two: Claims 1 and 2- do not include additional elements that integrate the mental process into a practical application.
This judicial exception is not integrated into a practical application. In particular, the claims recites (1) captured a ultrasound images corresponding to a plurality of predetermined positions and (2) outputting the determined category of the tissue by a processor.
The step in (1) corresponds to data gathering as a pre-solution activity and is recited at a high level of generality and step in (2) represents merely notification outputting by a processor as a post-solution activity and is recited at a high level of generality.
As a whole, the additional elements merely serve to gather and feed information to the abstract idea and to output a notification based on the abstract idea, while generically implementing it on conventionally used tools. There is no practical application because the abstract idea is not applied, relied on, or used in a meaningful way. No improvement to the technology is evident, and the estimated bio-information is not outputted in any way such that a practical benefit is realized. Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application.
Step 2B of the subject matter eligibility test (see MPEP 2106.05).
Claims 1 and 20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Accordingly, The claims hence are considered to be patent ineligible.
Dependent Claims
The following dependent claims merely further define the abstract idea and are, therefore, directed to an abstract idea for similar reasons:
Extracting the mammary gland region and performing the determination step based on the extraction (claims 3 and 9)
Analyzing the image (claim 5, 12, 15, 19)
Calculating a mathematical ratio (claim 11)
Taken alone and in combination, the additional elements do not integrate the judicial exception into a practical application at least because the abstract idea is not applied, relied on, or used in a meaningful way. They also do not add anything significantly more than the abstract idea. Their collective functions merely provide computer/electronic implementation and processing, and no additional elements beyond those of the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. There is no indication that the combination of elements improves the functioning of a computer, output device, improves technology other than the technical field of the claimed invention, etc. Therefore, the claims are rejected as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
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.
Claims 1, 19 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated Nagase et al. (JP 2008/154833) hereinafter “Nagase.
Regarding claim 1, Nagase discloses an ultrasonic diagnostic apparatus [apparatus of Nagase; see abstract] comprising:
a monitor; [display unit 46; see page 7, paragraph before last of Kobayashi] and
a processor [display processing unit 29 and signal processing unit 26 together make up the processor of Nagase] configured to:
generate a breast schematic diagram divided into a plurality of regions; [see FIG. 4 and page 9, third paragraph]
display the breast schematic diagram on the monitor; [see FIG. 5 and page 9, fourth paragraph]
determine, based on a plurality of ultrasonic images including a mammary gland region in a breast of a subject captured at a plurality of predetermined positions on the breast of the subject [a plurality of ultrasound images 114, 116, 118 from the breast tissue in different locations; see page 9, paragraph before last and FIG. 5] corresponding to the plurality of regions of the breast schematic diagram, [see page 9, paragraph before last and FIG. 5] a category of a glandular tissue component in each of the plurality of positions of the breast; [see page 8, paragraph before last, step S103; 3D diagnosis is repeated performed on each part of the breast and a disease or a pathological condition is detected or not ]
output the determined category of the glandular tissue component in each of the plurality of positions; [see page 8, last paragraph continued in page 9]
determine whether outputs in all of the plurality of positions are completed; and repeat a determination of the category and an output of the category until that the outputs in all of the plurality of positions are completed is determined. [see page 8, paragraph before last, step S103; 3D diagnosis is repeated performed on each part of the breast and a disease or a pathological condition is detected or not ]
Regarding claim 19, Nagase further discloses wherein the ultrasonic image is a three-dimensional ultrasonic image, [see FIG. 5, paragraph before last disclosing that the probe is a 3D probe providing 3D images] and the processor is configured to determine the category of the glandular tissue component based on the three-dimensional ultrasonic image [a plurality of ultrasound images 114, 116, 118 (3D ultrasound images) from the breast tissue in different locations; see page 9, paragraph before last and FIG. 5]
Regarding claim 20, Nagase discloses a method of controlling an ultrasonic diagnostic apparatus, [see abstract of Nagase] the method comprising:
generating a breast schematic diagram divided into a plurality of regions; [see FIG. 4 and page 9, third paragraph]
displaying the breast schematic diagram [see FIG. 5 and page 9, fourth paragraph]
on a monitor; [display unit 46; see page 7, paragraph before last of Kobayashi]
determining, based on a plurality of ultrasonic images including a mammary gland region in a breast of a subject [a plurality of ultrasound images 114, 116, 118 from the breast tissue in different locations; see page 9, paragraph before last and FIG. 5] corresponding to the plurality of regions of the breast schematic diagram, [see page 9, paragraph before last and FIG. 5] a category of a glandular tissue component in each of the plurality of positions of the breast; [see page 8, paragraph before last, step S103; 3D diagnosis is repeated performed on each part of the breast and a disease or a pathological condition is detected or not ] and
outputting the determined category of the glandular tissue component in each of the plurality of positions; [see page 8, last paragraph continued in page 9]
determining whether outputs in all of the plurality of positions are completed; and repeating a determination of the category and an output of the category until that the outputs in all of the plurality of positions are completed is determined. [see page 8, paragraph before last, step S103; 3D diagnosis is repeated performed on each part of the breast and a disease or a pathological condition is detected or not]
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 2 is rejected under 35 U.S.C. 103 as being obvious over Nagase et al. (JP 2008/154833) hereinafter “Nagase in view of Kobayashi (U.S. Publication No. 2021/0052247) hereinafter “Kobayashi”.
Regarding claim 2, Nagase discloses all the limitations of claim 1 [see rejection of claim 1 above]
Nagase does not disclose wherein the processor is configured to determine the category of the glandular tissue component using a trained model that has been trained through machine learning based on a plurality of training data each including the ultrasonic image in which the breast is imaged and the category of the glandular tissue component in the breast.
Kobayashi, directed towards categorizing breast tissue based on ultrasound images [see abstract of Kobayashi] further discloses wherein the processor is configured to determine the category of the glandular tissue component using a trained model that has been trained through machine learning based on a plurality of training data each including the ultrasonic image in which the breast is imaged and the category of the glandular tissue component in the breast. [see [0120]-[0121] of Kobayashi]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the design of Nagase further such that the processor is configured to determine the category of the glandular tissue component using a trained model that has been trained through machine learning based on a plurality of training data each including the ultrasonic image in which the breast is imaged and the category of the glandular tissue component in the breast according to the teachings of Kobayashi in order to increase the accuracy of the categorization of each section [see [0122] of Kobayshi]
Claims 3-10 and 12-18 are rejected under 35 U.S.C. 103 as being obvious over Nagase et al. (JP 2008/154833) hereinafter “Nagase in view of Xu et al. (“Medical breast ultrasound image segmentation by machine learning”, ultrasonics, 91, 2019) hereinafter “Xu” and Kobayashi (U.S. Publication No. 2021/0052247) hereinafter “Kobayashi.
Regarding claim 3, Nagase discloses all the limitations of claim 1 [see rejection of claim 1 above]
Nagase does not disclose wherein the processor is configured to: extract the mammary gland region from the ultrasonic image in which the breast of the subject is imaged; and determine the category of the glandular tissue component in the breast based on the mammary gland region.
Xu, directed towards segmentation of ultrasound images of breast to determine different types of tissue [see abstract of Xu] further discloses that the processor is configured to: extract the mammary gland region from the ultrasonic image in which the breast of the subject is imaged; [see page 2, right column, first paragraph disclosing: “the CNNs take image blocks centered at a pixel as inputs and produced the tissue class of the center pixel as the output. Taking the images segmented by clinicians as the ground truth, we trained the CNN models to distinguish skin, fibroglandular tissues, and masses.”]
Kobayashi further discloses that the processor is configured to determine the category of the glandular tissue component in the breast based on the mammary gland region. [see [0120]-[0121] of Kobayashi]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the design of Nagase further such that the processor is configured to: extract the mammary gland region from the ultrasonic image in which the breast of the subject is imaged according to the teachings of Xu in order to help discriminate between different functional tissues, providing more precise and consistent tumor localization and diagnosis of breast cancer [see page 1, section under “Introduction”, left column, last two lines continued in right column of Xu]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the design of Nagase further such that the processor is configured to determine the category of the glandular tissue component in the breast based on the mammary gland region according to the teachings of Kobayashi in order to increase the accuracy of the categorization of each section [see [0122] of Kobayashi]
Regarding claim 4, Nagase in view of Xu and Kobayashi discloses all the limitations of claim 3 [see rejection of claim 3 above]
Xu further discloses that wherein the processor is configured to determine the category of the glandular tissue component using a trained model that has been trained through machine learning based on a plurality of training data each including the ultrasonic image including the mammary gland region in the breast, the extracted mammary gland region, and the category of the glandular tissue component in the breast [see page 2, right column, first paragraph disclosing that the trained CNN models are used to distinguish between different types of tissues]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the design of Nagase further such that the processor is configured to extract the mammary gland region by image-analyzing the ultrasonic image according to the teachings of Xu in order to help discriminate between different functional tissues, providing more precise and consistent tumor localization and diagnosis of breast cancer [see page 1, section under “Introduction”, left column, last two lines continued in right column of Xu]
Regarding claim 5, Nagase in view of Xu Kobayashi discloses all the limitations of claim 3 [see rejection of claim 3 above]
Xu further discloses wherein the processor is configured to extract the mammary gland region by image-analyzing the ultrasonic image. [see page 6, right column, first paragraph disclosing that the training data are ultrasound images of the breast]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the design of Nagase further such that the processor is configured to extract the mammary gland region by image-analyzing the ultrasonic image according to the teachings of Xu in order to help discriminate between different functional tissues, providing more precise and consistent tumor localization and diagnosis of breast cancer [see page 1, section under “Introduction”, left column, last two lines continued in right column of Xu]
Regarding claim 6, Nagase in view of Xu and Kobayashi discloses all the limitations of claim 3 [see rejection of claim 4 above]
Xu further discloses wherein the processor is configured to extract the mammary gland region by image- analyzing the ultrasonic image. [see page 6, right column, first paragraph disclosing that the training data are ultrasound images of the breast]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the design of Nagase further such that the processor is configured to extract the mammary gland region by image-analyzing the ultrasonic image according to the teachings of Xu in order to help discriminate between different functional tissues, providing more precise and consistent tumor localization and diagnosis of breast cancer [see page 1, section under “Introduction”, left column, last two lines continued in right column of Xu]
Regarding claim 7, Nagase in view of Xu and Kobayashi discloses all the limitations of claim 3 [see rejection of claim 3 above]
Xu further discloses wherein the processor is configured to extract the mammary gland region using a trained model that has been trained through machine learning based on a plurality of training data each including the ultrasonic image in which the breast is imaged and the mammary gland region in the breast. [see page 2, right column, first paragraph disclosing that the trained CNN models are used to distinguish between different types of tissues]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the design of Nagase further such that the processor is configured to extract the mammary gland region by image-analyzing the ultrasonic image according to the teachings of Xu in order to help discriminate between different functional tissues, providing more precise and consistent tumor localization and diagnosis of breast cancer [see page 1, section under “Introduction”, left column, last two lines continued in right column of Xu]
Regarding claim 8, Nagase in view of Xu and Kobayashi discloses all the limitations of claim 4 [see rejection of claim 4 above]
Xu further discloses wherein the processor is configured to extract the mammary gland region using a trained model that has been trained through machine learning based on a plurality of training data each including the ultrasonic image in which the breast is imaged and the mammary gland region in the breast. [see page 2, right column, first paragraph disclosing that the trained CNN models are used to distinguish between different types of tissues]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the design of Nagase further such that the processor is configured to extract the mammary gland region by image-analyzing the ultrasonic image according to the teachings of Xu in order to help discriminate between different functional tissues, providing more precise and consistent tumor localization and diagnosis of breast cancer [see page 1, section under “Introduction”, left column, last two lines continued in right column of Xu]
Regarding claim 9, Nagase discloses all the limitations of claim 1 [see rejection of claim 1 above]
Nagase does not disclose that diagnostic apparatus according to wherein the processor is configured to: extract the mammary gland region from the ultrasonic image in which the breast of the subject is imaged; extract a glandular tissue component region including a mammary duct, a lobule, and perilobular stroma in the mammary gland region, from the extracted mammary gland region and determine the category of the glandular tissue component in the breast based on the extracted glandular tissue component region.
Xu further discloses that diagnostic apparatus according to wherein the processor is configured to: extract the mammary gland region from the ultrasonic image in which the breast of the subject is imaged; [see page 6, right column, first paragraph disclosing that the training data are ultrasound images of the breast] extract a glandular tissue component region including a mammary duct, a lobule, and perilobular stroma in the mammary gland region, from the extracted mammary gland region; [see page 3, right column, first 3 paragraphs discloses that the glandular tissue is being distinguished; it is inherent that the glandular tissue includes a duct, a lobule and perilobar stroma]
Kobayashi further discloses to determine the category of the glandular tissue component in the breast based on the extracted glandular tissue component region. [see [0120]-[0121] of Kobayashi]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the design of Nagase further such that the processor is configured to that diagnostic apparatus according to wherein the processor is configured to: extract the mammary gland region from the ultrasonic image in which the breast of the subject is imaged; extract a glandular tissue component region including a mammary duct, a lobule, and perilobular stroma in the mammary gland region, from the extracted mammary gland region according to the teachings of Xu in order to help discriminate between different functional tissues, providing more precise and consistent tumor localization and diagnosis of breast cancer [see page 1, section under “Introduction”, left column, last two lines continued in right column of Xu]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the design of Nagase further such that the processor is configured to determine the category of the glandular tissue component in the breast based on the extracted glandular tissue component region according to the teachings of Kobayashi in order to increase the accuracy of the categorization of each section [see [0122] of Kobayashi]
Regarding claim 10, Nagase in view of Kobayashi discloses all the limitations of claim 9 [see rejection of claim 9 above]
Kobayashi further discloses wherein the processor is configured to determine the category of the glandular tissue component using a trained model that has been trained through machine learning based on a plurality of training data each including the ultrasonic image in which the glandular tissue component region is imaged and the category of the glandular tissue component in the breast. [see [0120]-[0121] of Kobayashi]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the design of Nagase further such that the processor is configured to determine the category of the glandular tissue component using a trained model that has been trained through machine learning based on a plurality of training data each including the ultrasonic image in which the glandular tissue component region is imaged and the category of the glandular tissue component in the breast according to the teachings of Kobayashi in order to increase the accuracy of the categorization of each section [see [0122] of Kobayashi]
Regarding claim 12, Nagase in view of Kobayashi discloses all the limitations of claim 9 [see rejection of claim 9 above]
Xu further discloses wherein the processor is configured to extract the glandular tissue component region by image-analyzing the ultrasonic image in which the mammary gland region is imaged [see page 6, right column, first paragraph disclosing that the training data are ultrasound images of the breast]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the design of Nagase further such that the processor is configured to extract the mammary gland region by image-analyzing the ultrasonic image according to the teachings of Xu in order to help discriminate between different functional tissues, providing more precise and consistent tumor localization and diagnosis of breast cancer [see page 1, section under “Introduction”, left column, last two lines continued in right column of Xu]
Regarding claim 13, Nagase in view of Kobayashi discloses all the limitations of claim 10 [see rejection of claim 10 above]
Xu further discloses wherein the processor is configured to extract the glandular tissue component region by image-analyzing the ultrasonic image in which the mammary gland region is imaged. [see page 6, right column, first paragraph disclosing that the training data are ultrasound images of the breast]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the design of Nagase further such that the processor is configured to extract the mammary gland region by image-analyzing the ultrasonic image according to the teachings of Xu in order to help discriminate between different functional tissues, providing more precise and consistent tumor localization and diagnosis of breast cancer [see page 1, section under “Introduction”, left column, last two lines continued in right column of Xu]
Regarding claim 14, Nagase in view of Kobayashi discloses all the limitations of claim 11 [see rejection of claim 11 above]
Xu further discloses wherein the processor is configured to extract the glandular tissue component region by image-analyzing the ultrasonic image in which the mammary gland region is imaged. [see page 6, right column, first paragraph disclosing that the training data are ultrasound images of the breast]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the design of Nagase further such that the processor is configured to extract the mammary gland region by image-analyzing the ultrasonic image according to the teachings of Xu in order to help discriminate between different functional tissues, providing more precise and consistent tumor localization and diagnosis of breast cancer [see page 1, section under “Introduction”, left column, last two lines continued in right column of Xu]
Regarding claim 15, Nagase in view of Kobayashi discloses all the limitations of claim 9 [see rejection of claim 9 above]
Xu further discloses wherein the processor is configured to extract the mammary gland region by image- analyzing the ultrasonic image. [see page 6, right column, first paragraph disclosing that the training data are ultrasound images of the breast]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the design of Nagase further such that the processor is configured to extract the mammary gland region by image-analyzing the ultrasonic image according to the teachings of Xu in order to help discriminate between different functional tissues, providing more precise and consistent tumor localization and diagnosis of breast cancer [see page 1, section under “Introduction”, left column, last two lines continued in right column of Xu]
Regarding claim 16, Nagase in view of Kobayashi discloses all the limitations of claim 9 [see rejection of claim 9 above]
wherein the processor is configured to extract the mammary gland region using a trained model that has been trained through machine learning based on a plurality of training data each including the ultrasonic image in which the breast is imaged and the mammary gland region in the breast. [see page 6, right column, first paragraph disclosing that the training data are ultrasound images of the breast]
wherein the processor is configured to extract the mammary gland region using a trained model that has been trained through machine learning based on a plurality of training data each including the ultrasonic image in which the breast is imaged and the mammary gland region in the breast.
Regarding claim 17, Nagase in view of Xu and Kobayashi discloses all the limitations of claim 10 [see rejection of claim 10 above]
Xu further discloses wherein the processor is configured to extract the mammary gland region using a trained model that has been trained through machine learning based on a plurality of training data each including the ultrasonic image in which the breast is imaged and the mammary gland region in the breast. [see page 2, right column, first paragraph disclosing that the trained CNN models are used to distinguish between different types of tissues]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the design of Nagase as modified by Xu and Kobayashi further such that wherein the processor is configured to extract the mammary gland region using a trained model that has been trained through machine learning based on a plurality of training data each including the ultrasonic image in which the breast is imaged and the mammary gland region in the breast according to the teachings of Xu in order to help discriminate between different functional tissues, providing more precise and consistent tumor localization and diagnosis of breast cancer [see page 1, section under “Introduction”, left column, last two lines continued in right column of Xu]
Regarding claim 18, Nagase in view of Xu and Kobayashi discloses all the limitations of claim 11 [see rejection of claim 11 above]
Xu further discloses wherein the processor is configured to extract the mammary gland region using a trained model that has been trained through machine learning based on a plurality of training data each including the ultrasonic image in which the breast is imaged and the mammary gland region in the breast. [see page 2, right column, first paragraph disclosing that the trained CNN models are used to distinguish between different types of tissues]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the design of Nagase further such that wherein the processor is configured to extract the mammary gland region using a trained model that has been trained through machine learning based on a plurality of training data each including the ultrasonic image in which the breast is imaged and the mammary gland region in the breast according to the teachings of Xu in order to help discriminate between different functional tissues, providing more precise and consistent tumor localization and diagnosis of breast cancer [see page 1, section under “Introduction”, left column, last two lines continued in right column of Xu]
Claim 11 is rejected under 35 U.S.C. 103 as being obvious over Nagase et al. (JP 2008/154833) hereinafter “Nagase in view of Xu et al. (“Medical breast ultrasound image segmentation by machine learning”, ultrasonics, 91, 2019) hereinafter “Xu” and Kobayashi (U.S. Publication No. 2021/0052247) hereinafter “Kobayashi as applied to claim 9 above, and further in view of Lee et al. (“Glandular tissue component on breast ultrasound in dense breast: a new imaging biomarker for breast cancer risk”, Radiology, 2022) hereinafter “Lee”.
Regarding claim 11, Nagase in view of Xu and Kobayashi discloses all the limitations of claim 9 [see rejection of claim 9 above]
Nagase in view of Xu and Kobayashi does not expressly discloses wherein the processor is configured to: calculate a ratio of the glandular tissue comment to the mammary gland region and determine the category of the glandular tissue component of the subject based on the calculated ratio of the glandular tissue component region to the mammary gland region.
Lee, directed towards categorizing breast tissue using ultrasound imaging [see abstract of Lee] further discloses wherein the processor is configured to: calculate a ratio of the glandular tissue comment to the mammary gland region and determine the category of the glandular tissue component of the subject based on the calculated ratio of the glandular tissue component region to the mammary gland region.[see page 576, left column, last paragraph continued in right paragraph disclosing the ratio of GTC in the breast determining the likelihood of cancer ; see Also FIG. 3A-B and caption]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the design of Nagase as modified by Xu and Kobayashi further such that wherein the processor is configured to: calculate a ratio of the glandular tissue comment to the mammary gland region and determine the category of the glandular tissue component of the subject based on the calculated ratio of the glandular tissue component region to the mammary gland region according to the teachings of Lee in order to diagnose the breast abnormalities with more accuracy and consistency; [see introduction section of Lee]
Conclusion
No claim is allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARJAN - SABOKTAKIN whose telephone number is (303)297-4278. The examiner can normally be reached M-F 9 am-5pm CT.
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/MARJAN SABOKTAKIN/Examiner, Art Unit 3797
/MICHAEL J CAREY/Supervisory Patent Examiner, Art Unit 3795