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 .
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in Japan on 3/13/23. It is noted, however, that applicant has not filed a certified copy of the foreign application as required by 37 CFR 1.55.
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.
Claim(s) 1-3, 5, and 7-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Giger et al. (US 5931780; hereinafter Giger).
Giger shows an image processing apparatus and method (abstract) comprising at least one processor, wherein the processor is configured to: acquire two radiation images acquired by imaging a subject including a bone part and a soft part with radiation having different energy distributions (column 7, line 35-column 8, line 30); derive a bone part image including a microstructure inside the bone part based on the two radiation images (column 7, line 35-column 8, line 30); and derive a likelihood of osteoporosis based on the bone part image or a partial image including a bone of interest in the bone part image (assess osteoporosis and risk of fracture; column 7, lines 20-34; column 10, lines 35-42).
Giger also shows wherein the bone part image includes a trabecular structure of a cancellous bone included in the bone part (column 4, lines 1-5); wherein the trabecular structure of the cancellous bone is depicted in the bone part image (column 10, lines 20-25); extract the partial image from at least one of the bone part image or the two radiation images (column 7, line 35-column 8, line 30); wherein the processor is configured to function as a trained model that has undergone machine learning to output the likelihood of osteoporosis in response to an input of the bone part image or the partial image (neural network to yield likelihood of future risk of fracture; column 6, lines 49-55; column 11, lines 38-45); wherein the processor is configured to function as a trained model that has undergone machine learning to output the likelihood of osteoporosis in response to an input of imaging conditions of the two radiation images in addition to the bone part image or the partial image (neural network to yield likelihood of future risk of fracture; column 6, lines 49-55; column 11, lines 38-45); wherein the processor is configured to derive the likelihood of osteoporosis based on a statistical value of pixel values of the bone of interest included in the bone part image or the partial image (column 15, lines 15-50); wherein the statistical value is at least one of an average value, a standard deviation, a maximum value, or a minimum value of pixel values in each of the bone of interest and a peripheral region of the bone of interest (column 15, lines 15-50); wherein the processor is configured to derive the likelihood by performing a weighting operation on a plurality of statistical values among an average value, a standard deviation, a maximum value, and a minimum value of pixel values in each of the bone of interest and the peripheral region of the bone of interest (column 9, lines 24-50; column 12, lines 5-10); wherein the weight of a weighting operation is derived by machine learning (column 12, lines 5-10); wherein the processor is configured to issue a notification of the likelihood (column 10, lines 35-45); wherein the processor is configured to issue a warning in a case where the likelihood is equal to or greater than a predetermined threshold value (column 18, lines 60-65); determine a priority of a treatment for osteoporosis based on the likelihood (following progress of patients on therapy for osteoporosis, column 1, lines 30-35; neural network to yield a likelihood of future fracture risk, column 6, lines 50-55) and issue a notification of the priority (column 10, lines 35-45); wherein the processor is configured to remove scattered ray components included in the two radiation images to derive the bone part image (column 8, lines 60-65); wherein the processor is configured to: derive, in a soft region including only the soft part of the two radiation images, a characteristic of the soft part related to an attenuation of the radiation based on the two radiation images; derive, in a bone region including the bone part of the two radiation images, a characteristic of the soft part based on the characteristic of the soft part derived in the soft region around the bone region; and derive, as the bone part image, an image in which the bone part is emphasized based on the characteristic of the soft part in at least a region of the subject in the two radiation images (column 8, lines 55-65); wherein the bone of interest is a femur or a vertebra (column 4, lines 20-30).
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) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Giger et al. (US 5931780; hereinafter Giger) in view of Fehre et al. (US 7174000; hereinafter Fehre).
Giger shows the invention substantially as described in the 102 rejection above.
Giger fails to show wherein the bone part image has a spatial resolution of 0.2 mm/pixel or less.
Fehre discloses methods for measurement of density distribution in bones. Fehre teaches wherein the bone part image has a spatial resolution of 0.2 mm/pixel or less (column 2, lines 60-65).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of Giger to utilize a spatial resolution of 0.2 mm/pixel or less as taught by Fehre, as Fehre teaches that such a spatial resolution is sufficient for determining the density distribution in a volume of bone with an excellent precision (column 2, lines 60-65).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Giger et al. (US 5931780; hereinafter Giger) in view of Yang et al. (US 2018/0260951; hereinafter Yang).
Giger shows the invention substantially as described in the 102 rejection above.
Giger fails to show wherein the processor is configured to extract at least one bone of interest from at least one of the bone part image or the two radiation images, and extract the partial image based on a centroid of the bone of interest.
Yang discloses automatic vertebral labeling in CT imaging. Yang teaches wherein the processor is configured to extract at least one bone of interest from at least one of the bone part image or the two radiation images, and extract the partial image based on a centroid of the bone of interest ([0031]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of Giger to utilize an image processing application including extracting based on a centroid as taught by Yang, as the centroid represents a critical point corresponding to the structure of interest and may be utilized in image processing applications to more accurately localize the structures of interest, such as the bone, within the images.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Kim (US 2024/0130703) discloses neural networks for analyzing x-ray images to determine osteoporosis information ([0045]);
Massie (US 2009/0124882) discloses dual energy x-ray imaging techniques for bone densitometry techniques ([0053]).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN CWERN whose telephone number is (571)270-1560. The examiner can normally be reached Monday - Friday, 8:00 am - 5:00 pm.
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/JONATHAN CWERN/ Primary Examiner, Art Unit 3797
