DETAILED ACTION
Notice of 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 .
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.
(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.
Claim(s) 1, 5, 9, 17, 18, and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by “Breast ultrasound image segmentation: a survey” by Q. Huang et al. Int J CARS (2017) 12:493-507. (Huang).
Regarding claims 1 and 20, Huang discloses an ultrasonic diagnostic apparatus and method comprising: a processor configured to detect a suspected lesion region in a mammary gland region of a subject based on an ultrasonic image where the mammary gland region is imaged; create mask data of the suspected lesion region; set an exclusion region to be excluded from a target of a glandular tissue component evaluation based on the mask data; and perform the glandular tissue component evaluation on an evaluation target region obtained by excluding the exclusion region from the mammary gland region (Fig. 1: “BUS” – Breast ultrasound, “thresholding segmentation method”).
Regarding claim 17, Huang discloses that the processor is configured to detect the suspected lesion region using a trained model that has been trained through machine learning based on a plurality of training data each of which includes the ultrasonic image where the mammary gland region including the suspected lesion region is imaged (p.502: “Neural network(NN)-bases segmentation methods”).
Regarding claims 5, 9, and 18, Huang discloses a monitor and a processor configured to display the ultrasonic image on the monitor, and highlight the exclusion region on the monitor, detecting a suspected lesion region by image-analyzing the ultrasonic image (Fig. 1).
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 may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claim(s) 2, 6, 11, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Breast ultrasound image segmentation: a survey” by Q. Huang et al. Int J CARS (2017) 12:493-507. (Huang).
Regarding claim 2, Huang does not explicitly disclose that the processor is configured to classify the evaluation target region into a low- echo region and a high-echo region based on a predetermined brightness threshold value, and outputs a ratio between the number of pixels occupied by the low-echo region and the number of pixels occupied by the high-echo region as a result of the glandular tissue component evaluation. However, Huang does teach performing a binary classification of pixels based upon a thresholding of intensity values that are based upon an intensity of echoes (p.495). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to apply the claimed ratio of pixels, as to provide a means of comparing pixel values.
Regarding claim 6, Huang discloses a monitor and a processor configured to display the ultrasonic image on the monitor, and highlight the exclusion region on the monitor, detecting a suspected lesion region by image-analyzing the ultrasonic image (Fig. 1).
Regarding claim 11, Huang does not explicitly disclose that the processor is configured to display a dialog for confirming with a user whether to correct or delete the exclusion region on the monitor. However, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to prompt a user’s input on an action, as to provide a common and routine manner of prompting the acquisition of user input.
Regarding claim 19, Huang does not explicitly disclose explicitly disclose that the ultrasonic image is a three-dimensional ultrasonic image, and the processor is configured to perform the glandular tissue component evaluation based on the three-dimensional ultrasonic image. However, Huang does teach that 3D ultrasound segmentation methods are being performed and explored (p.505). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to apply the segmentation to 3D ultrasound, as to provide segmentation techniques to a diverse data set.
Claim(s) 10 and 12-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Breast ultrasound image segmentation: a survey” by Q. Huang et al. Int J CARS (2017) 12:493-507. (Huang), as applied to claims 1, 2, and 5 above, in view of “DSEU-net: A novel deep supervision SEU-net for medical ultrasound image segmentation” by G. Chen et al. Expert Systems with Applications. 223. March 22, 2023 (Chen).
Regarding claim 10, Huang does not explicitly disclose that the processor is configured to display the exclusion region on the monitor in a color or a form in accordance with a reliability degree of the detection of the suspected lesion region. However, Chen teaches displaying an image mask in a black/white color scheme that demonstrates the reliability/accuracy of the mask (Fig. 4). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to apply the visualization of Chen to the image mask of Huang, as to provide robust visualization of a mask’s reliability.
Regarding claims 12-15, Huang does not explicitly disclose that the processor is configured to: determine whether the mask data is smaller/larger than a predetermined first size threshold value; and upon determining that the mask data is smaller than the predetermined first size threshold value, skip setting the exclusion region/performing the glandular tissue component. However, Chen teaches that the characteristic of ultrasound image masks would include consideration of their sizes (Fig. 4 demonstrates that the size of a mask would be considered in evaluating the accuracy of the mask). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to apply the mask selection as taught by Chen to the system of Huang, as to provide accurate and reliable image masks.
Regarding claim 16, Huang does not explicitly disclose that the processor is configured to: perform the glandular tissue component evaluation on the mammary gland region that does not exclude the exclusion region, in addition to the glandular tissue component evaluation on the evaluation target region; and display, on the monitor, a result of the glandular tissue component evaluation on the evaluation target region and a result of the glandular tissue component evaluation on the mammary gland region where the exclusion region is not excluded. However, Chen teaches displaying both included and excluded regions of a tissue component (Fig. 4). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to apply the mask visualization as taught by Chen to the system of Huang, as to provide accurate and reliable image masks.
Claim(s) 3, 4, 7, and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Breast ultrasound image segmentation: a survey” by Q. Huang et al. Int J CARS (2017) 12:493-507. (Huang), as applied to claim 1 above, in view of “Automated 3D ultrasound image segmentation to aid breast cancer image interpretation” by P. Gu et al. Ultrasonics. 65(2016) 51-58 (Gu).
Regarding claim 3, Huang does not explicitly disclose that the processor is configured to determine a category of a glandular tissue component in the mammary gland region based on the ultrasonic image including the evaluation target region, and outputs the category as a result of the glandular tissue component evaluation. However, Gu teaches determining categorizing segmented portions of an ultrasound image (Abstract: “we propose an automated algorithm to segment 3D ultrasound volumes into three major tissue types”). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to apply the categorization of Gu to the segmentation of Huang, as to provide fine segmentation.
Regarding claim 4, Huang discloses 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 of which includes the ultrasonic image where the mammary gland region is imaged and the category of the glandular tissue component in the mammary gland region (p.502: “Neural network(NN)-bases segmentation methods”).
Regarding claims 7 and 8, Huang discloses a monitor and a processor configured to display the ultrasonic image on the monitor, and highlight the exclusion region on the monitor, detecting a suspected lesion region by image-analyzing the ultrasonic image (Fig. 1).
Conclusion
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/JASON M IP/Primary Examiner, Art Unit 3793
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