DETAILED ACTION
Response to Arguments
Interpretation under 35 USC § 112(f) is agreed upon.
Rejection under 35 U.S.C. 101 is withdrawn in light of amendment.
Rejection under 35 USC § 102 is withdrawn.
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.
Claim(s) 1, 3-5, 7-9, 12-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over OGINO( US 20200175675) in view of KOPPARAPU ( US 20190156159) .
Regarding claim 1, OGINO teaches an image processing apparatus that processes a medical image, the image processing apparatus comprising:
an image group conversion unit that calculates a value of a predetermined feature for each image( 220 in Fig. 2) constituting an input first image group( 210 in Fig. 2), selects an image from the first image group on the basis of the value of the feature ( 230 in Fig. 2), and sets the image as an image of a second image group( 240 in Fig. 2); and
a feature extraction unit that performs learning on the second image group generated ( 240 in Fig. 2) by the image group conversion unit using a feature generation network and extracts a new feature( 250 in Fig. 2);
wherein the feature extraction unit includes a second feature extraction unit that extracts a patient information feature of input patient information( 240 in Fig. 2) n;
and wherein a feature integration unit that integrates the new feature extracted for the second image group by the feature extraction unit and the patient information feature extracted by the second feature extraction unit( 240 in Fig. 2).
OGINO does not expressly teach input patient information including at least one of blood test information or genomic variation information.
However, KOPPARAPU teaches extraction input patient information including at least one of blood test information or genomic variation information([0008], the biopsy image to indicate the extent of tumor biological features, such as tumor proliferation, necrosis, and hyperplastic blood vessels in tissues).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of OGINO and KOPPARAPU, by substitute the training data set in OGINO with that taught by KOPPARAPU, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of claimed invention would have recognized that the results of the combination were predictable.
Regarding claim 3, OGINO in view of KOPPARAPU teaches the image processing apparatus according to claim 1, wherein the predetermined feature includes a plurality of features(OGINO, [0038], the number of feature amounts may be three or more) , and the image group conversion unit selects an image based on a value of each of the plurality of features and generates a second image group for each of the plurality of features([0038], each patch to a space having a feature amount).
Regarding claim 4, OGINO in view of KOPPARAPU teaches the image processing apparatus according to claim 3, wherein the feature extraction unit has a network structure having configurations different for the plurality of features(OGINO, [0041], The CNN extracts the features of the input image).
Regarding claim 5, OGINO in view of KOPPARAPU teaches the image processing apparatus according to claim 3, further comprising a feature integration unit that integrates new features extracted by the feature extraction unit for each of a plurality of second image groups(OGINO, [0056], the information to pass the image data of a portion not set to the ROI not to the classification unit 220 and the selection unit 230 but to the integration unit 250 through the blank. Thus, the image data in which only the ROI is converted to a high-quality image is obtained).
Regarding claim 7, OGINO in view of KOPPARAPU teaches the image processing apparatus according to claim 6, wherein the second feature extraction unit receives the first image group as the patient information and extracts features of the first image group(OGINO, [0057], the user sets the ROI, the image processing unit 200 uses the information to pass the image data of a portion not set to the ROI not to the classification unit 220 and the selection unit 230 but to the integration unit 250 through the blank).
Regarding claim 8, OGINO in view of KOPPARAPU teaches the image processing apparatus according to claim 7, wherein the feature integration unit compares the features of the first image group with the new feature, and includes a feature selection unit that excludes a redundant feature(OGINO, [0057], the selection unit 230 but to the integration unit 250 through the blank. Thus, the image data in which only the ROI is converted to a high-quality image is obtained).
Regarding claim 9, OGINO in view of KOPPARAPU teaches the image processing apparatus according to claim 6, wherein the second feature extraction unit receives information other than an image as the patient information ( OGINO, [0057], the user sets the ROI, the image processing unit 200 uses the information to pass the image data of a portion not set to the ROI not to the classification unit 220 and the selection unit 230 but to the integration unit 250 through the blank).
Regarding claim 12, OGINO in view of KOPPARAPU teaches the image processing apparatus according to claim 1, further comprising a prediction unit that receives the new feature and outputs a prediction result regarding a lesion ( intended use does not further limit the scope of the claim).
Claims 13-14 recite the method in the apparatus of claims 1 and 3-9, thus they are also rejected.
Claims 15 recites the medium for the method in claim 13, and is also rejected.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over OGINO in view of KOPPARAPU, further in view of MATSUNAGA(JP2017045341).
Regarding claim 2, OGINO in view of KOPPARAPU teaches the image processing apparatus according to claim 1.
OGINO in view of KOPPARAPU does not expressly teach wherein the image group conversion unit generates a machine learning discriminator for the predetermined feature, evaluates identification performance of the discriminator, and sets a feature threshold for selecting an image based on the identification performance.
However, MATSUNAGA teaches generates a machine learning discriminator for the predetermined feature([0027], the unit discriminator … may be subjected to learning in advance ), evaluates identification performance of the discriminator( [0038], the features extracted by the unit
Discriminators … and performs discrimination), and sets a feature threshold for selecting an image based on the identification performance([0058], In order to obtain a steeper boundary,
binarization may be performed with a desired threshold).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of OGINO in view of KOPPARAPU and MATSUNAGA, by substituting the Classification unit in QGINO with the discriminator as taught by MATSUNAGA, with motivation that “generates class information so that a disease to be diagnosed can be identified” ( MATSUNAGA, [0026]).
Claim(s) 10-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over OGINO in view of KOPPARAPU, further in view of YAGUCHI (JP 2020018705).
Regarding claim 10, OGINO in view of KOPPARAPU teaches the image processing apparatus according to claim 1. OGINO in view of KOPPARAPU does not expressly teach wherein the predetermined feature includes a degree of a spicula of a contour of a tumor .
However, YAGUCHI teaches the predetermined feature includes a degree of a spicula of a contour of a tumor ( [0075], set of training samples in which the composite image is input and the volume of the nodule region … A correct output for classifying or detecting a nodule region, for example, a name such as a partially filled nodule or a spicule may be used).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of OGINO in view of KOPPARAPU and YAGUCHI , by preparing the training data in OGINO following the teaching of YAGUCHI, with motivation “a lesion with a small number of cases is reproduced, and thus an improvement in accuracy of machine learning can be expected” ( YAGUCHI, [0078]).
Regarding claim 11, OGINO in view of KOPPARAPU and YAGUCHI teaches the image processing apparatus according to claim 10, wherein the feature extraction unit uses, as a feature of the degree of the spicula of the contour of the tumor, at least one of a frequency calculated from an amplitude of the contour shape and a grade evaluation by an expert (YAGUCHI, [0053], a partially filled nodule may be generated as a composite image. The solid shadow in the partially-filled nodule is formed inside the nodule… and has various sizes and shapes … deforming the contour of the ground-glass nodule, and a pseudo-solid shadow obtained by filling the inside of the solid region mask with the CT value of the solid shadow is synthesized with the original ground-glass nodule) .
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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JIANGENG SUN
Examiner
Art Unit 2661
/Jiangeng Sun/Examiner, Art Unit 2671