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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 November 20, 2025 has been entered.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-5, 7-9, 11-17, 21-22 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
In re claims 1, it is unclear what the metes and bounds of “brightness value” are. Throughout the application, Applicant only describes “pixel values” with respect to a brightness distribution. So is “brightness value” the same as “pixel values”? For the purpose of this examination, the Examiner will interpret it as “pixel values.” Furthermore, a “pixel” value could be any of these: intensity, or any values assigned to the pixel. Hence, furthermore, a “brightness distribution” is a pixel value distribution.
In re claim 13, it is depending on claim 10 which is now cancelled by Applicant. The Examiner interpret that claim 13 is now depending from claim 1.
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.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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(s) 21-22 is/are rejected under 35 U.S.C. 102(a)(1) as anticipated by or, in the alternative, under 35 U.S.C. 103 as obvious over Guo et al. (US 2021/0158511, hereinafter Guo ‘511)
In re claim 21, Guo ‘511 teaches a magnetic resonance imaging apparatus comprising a computer configured to:
collect magnetic resonance signals of an examination target (0013) and reconstruct an image (note that it is conventional, common practice, hence, obvious and/or inherent that MRI images are a reconstructed image from K-space data that MRI system collected, basic MRI book would explain this basic reconstruction of image from K-space data collected by MRI system);
process the image reconstructed based on the magnetic resonance signals (see explanation above), and specify a designated region in the image; and
highlight the designated region (fig.1, 0013, 0014) based on shape information of the designated region (0015) and spatial information of the designated region (0018), the spatial information of the designated region including a brightness distribution of the designated region, and the brightness distribution (0014, brightness changes in the displayed image is brightness distribution with a brightness value) of the designated region having been acquired by a CNN (convolutional neural network) (0028; figs. 2-4).
In re claim 22, Guo ‘511 teaches the computer being further configured to select and apply one of multiple CNNs (0003, 0004, 0014, 0015, 0017, 0025, 0028), in response to an imaging condition under which the image that is acquired is to be processed (note that the teaching of ‘511 provides on CNN that is in response to at least an imaging condition because the MRI image is already collected under a condition and processed under a condition; 0014, 0015; 0025, )
Claim(s) 1-3, 7, 9, 11, 12, 14, 16, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guo ‘511 in view of Kadomura et al. (US 2011/0033099, hereinafter Kadomura ‘099).
In re claim 1, Guo ‘511 teaches a magnetic resonance imaging apparatus comprising a computer configured to:
collect magnetic resonance signals of an examination target (0013) and reconstruct an image (note that it is conventional, common practice, hence, obvious and/or inherent that MRI images are a reconstructed image from K-space data that MRI system collected, basic MRI book would explain this basic reconstruction of image from K-space data collected by MRI system);
process the reconstructed image (see explanation above), and specify a designated region in the image (fig. 1, 0013, 0014); and
highlight the designated region (fig.1, 0013, 0014) based on shape information of the designated region (0015) and spatial information of the designated region (0018);
Guo ‘511 fails to teach generate a probability map indicating, for each pixel of the designated region, a presence probability that the pixel of the designated region exists in a specific organ or tissue of the examination target, wherein the spatial information of the designated region includes the probability map and a brightness distribution of the designated region, the brightness distribution indicating changes in brightness value within the designated region.
Kadomura ‘099 teaches that [0160] In the above-described embodiment, the X-ray CT image has been described. However, the present invention may be applied to diagnosis of medical image information acquired by a medical imaging apparatus such as an X-ray fluoroscopic imaging apparatus, an MRI apparatus, an ultrasonic diagnosis apparatus or the like.
Hence it would have been obvious with the teaching of Kadomura ‘009 to collect magnetic resonance signals of an examination target and reconstruct an image; process the reconstructed image because all these are traditional and normal and ordinary part of diagnostic imaging methods when using and when applying diagnosis of medical image information acquired by an MRI apparatus.
Kadomura ‘099 then teaches generate a probability map (0099-0119) indicating, for each pixel of the designated region, a presence probability that the pixel of the designated region exists in a specific organ or tissue of the examination target 0135-0137, etc.), wherein the spatial information of the (0091-0097, note coordinate data of tissue is the spatial information, fig. 9, 0099, 0102, boundary of each tissue is spatial information; 0103, 0106, distance map is spatial information, etc.) designated region includes the probability map and a brightness distribution of the designated region, the brightness distribution indicating changes in brightness value (0140-0147), within the designated region (fig. 9, 0153, fig. 18).
It would have been prima facie obvious to one of ordinary skills in the art at the time of invention to modify the method/device of Guo ‘511 to include the features of Kadomura ‘099 in order to create an identification probability map for determining the identification degree of the biomedical tissue on the basis of the statistic amount information and the identification map.
In re claim 2, Kadomura ‘099 teaches wherein the spatial information additionally includes a tissue distribution of the designated region in tissues of the examination target (0076-0083).
In re claim 3, Guo ‘511 teaches the computer being further configured to create as a candidate image (0003), an image of only a predetermined shape based on the shape information of the designated region (0024), and discriminate between the designated region and other regions, the designated region being discriminated based on the spatial information of the candidate image (0024-0029).
In re claim 7, Guo ‘511 teaches create segmentation images of the organ or the tissue of the examination target (0013-0015, etc.).
Kadomura ‘099 teaches the computer being further configured to create segmentation images of the organ or the tissue of the examination target, wherein the probability of the designated region (0134-0140 etc.) is calculated with respect to the segmentation images (fig. 18, 0130-0131).
In re claim 9, Guo ‘511 teaches the computer being further configured to discriminate between the designated region and other tissue, a CNN (convolutional neural network) trained in advance with features of the images including the designated region and the surrounding region thereof, being used to discriminate the designated region (0017, 0028).
In re claim 11, Guo ‘511 teaches the computer being further configured to use a CNN (convolutional neural network), which was trained in advance with multiple images having different brightness distributions of a predetermined shape (0016, 0024), to acquire the brightness distribution of the designated region (0014, 0017, 0028).
In re claim 12, Guo ‘511 teaches the computer being further configured to select and apply one of multiple trained CNNs (convolutional neural networks), which were trained in advance each under multiple imaging conditions (0003, 0004, 0014, 0015, 0017, 0025, 0028), in response to an imaging condition under which the image that is acquired is to be processed (note that the teaching of ‘511 provides on CNN that is in response to at least an imaging condition because the MRI image is already collected under a condition and processed under a condition; 0014, 0015).
In re claim 14, Guo ‘511 (0013, 0014, fig. 1, images with pixel is a 2D image)/Kadomura ‘099 (fig. 18) teaches wherein the reconstructed image that is processed is a two-dimensional image.
In re claim 16, Guo ‘511 (figs. 2-4)/ Kadomura ‘099 (fig. 18) teaches the computer being further configured to display on a display device, a processing result of the highlighting, together with the image.
In re claim 17, Kadomura ‘099 teaches the computer being further configured to display a GUI configured to accept user's modification on the result displayed on the display device, and pass the contents modified via the GUI, to further processing (figs. 4, 5).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guo ‘511 and Kadomura ‘099 in view of Gosche; Karen (US 6,430,430, hereinafter Gosche ‘430)
In re claim 8, Guo ‘511 and Kadomura ‘099 fail to teach wherein an image of the examination target is a brain image, and the segmentation images include a brain parenchyma image and a cerebrospinal fluid image.
Gosche ‘430 teaches wherein an image of the examination target is a brain image, and the segmentation images include a brain parenchyma image and a cerebrospinal fluid image (col. 2, line 8-12; col. 9, lines 28-59; col. 18, lines 1-65).
It would have been prima facie obvious to one of ordinary skills in the art at the time of invention to modify the method/device of Guo ‘511 and Kadomura ‘099 to include the features of Gosche ‘430 in order to identify suspected lesions in a brain.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guo ‘511 and Kadomura ‘099 in view of Sakata et al. (US 2014/0334708, hereinafter Sakata ‘708).
In re claim 13, Guo ‘511 and Kadomura ‘099 fails to teach the computer being further configured to use a brightness gradient on an outline of the designated region, as the brightness distribution.
Sakata ‘708 teaches the computer being further configured to use a brightness gradient on an outline of the designated region, as the brightness distribution (0066).
It would have been prima facie obvious to one of ordinary skills in the art at the time of invention to modify the method/device of Guo ‘511 and Kadomura ‘099 to include the features of Sakata ‘708 in order to perform a template matching process that employs an image pattern of the surroundings of the control points in boundary detecting process.
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guo ‘511 and Kadomura ‘099 in view of Chesebro et al. (Chesebro, A.G., Amarante, E., Lao, P.J. et al. Automated detection of cerebral microbleeds on T2*-weighted MRI. Sci Rep 11, 4004 (2021), published Feb. 17, 2021, cited in IDS filed on August 15, 2023, hereinafter Chesebro ‘2021).
In re claim 15, Guo ‘511 and Kadomura ‘099 fails to teach wherein the image that is processed is at least one of a T2* weighted image and a susceptibility-weighted image.
Chesebro ‘2021 teaches wherein the image that is processed is at least one of a T2* weighted image and a susceptibility-weighted image (title, abstract, page 2, MRI acquisition).
It would have been prima facie obvious to one of ordinary skills in the art at the time of invention to modify the method/device of Guo ‘511 and Kadomura ‘099 to include the features of Chesebro ‘2021 in order to ensure a high standard of clinical care and provide reliable data to uncover biological causes of microbleeds and how they relate to diseases.
Allowable Subject Matter
Claims 4-5 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and if all 112 rejections are resolved.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-5, 7-9, 11-17, 21-22 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BO JOSEPH PENG whose telephone number is (571)270-1792. The examiner can normally be reached Monday thru Friday: 8:00 AM-5:00 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ANNE M KOZAK can be reached at (571) 270-0552. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BO JOSEPH PENG/Primary Examiner, Art Unit 3797