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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 03/25/2026 has been entered.
Response to Arguments
Applicant's arguments filed 03/25/2026 have been fully considered but they are moot in view of the new grounds of rejection.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph 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.
Claim(s) 1-7, 9, 12, and 14-20 is/are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Regarding claims 1 and 19, it is unclear as to whether “generate at least one pair of images below” refers to choosing at least two items from (a), (b), and (c).
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) 1, 3-5, 12, and 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bergner (US 2023/0394630, of record) in view of Park (US 2019/0156524, of record) in view of Dey (US 2022/0107378).
Regarding claims 1, 19, and 20, Bergner discloses an imaging apparatus and computer-executable method comprising a processor and memory with instructions that cause the processor to obtain a plurality of images relating to different radiation energies ([0026]: “CT images”; [0029]: “radiation source 208, such as an X-ray tube” – X-ray radiation comprise a band of energies, the CT images are thus ‘related to’ this band of energies), generate at least one of an image indicating thickness tissue as energy-subtraction images (CT images are considered energy-subtraction images because the radiation is attenuated as it passes through tissue), and obtaining a learned model using a first image obtained using a radiation and a second image obtained by adding a noise which has been artificially calculated to the first image (Figs. 6-7: first image is noiseless, second image is first image + synthetic noise). Bergner does not explicitly disclose that the energy-subtraction images indicate a thickness of a bone and that of soft tissue. However, Park teaches abdominal CT images which would reveal both soft tissue and bone thickness (spine) ([0051]). Park is directed to denoising images using deep learning, which renders it within the same field of endeavor. 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 tissue thickness taught by Park to the system of Bergner, as to provide robust visualization of tissue. Neither Bergner nor Park explicitly disclose using pairs of images in training a learned model. However, Dey teaches using pairs of medical images to train a noise-reduction model ([0111]: “computed tomography”; [0196]: “paired clean and noisy images”). Dey also teaches subtracting images to reduce noise, which may be considered a form of generating energy-subtraction images if one assumes the energy being subtracted is noise ([0042], [0043]). 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 paired images of Dey to the Bergner and Park, as to provide corresponding images suitable for proper and robust training.
Regarding claim 3, Bergner discloses obtaining, by inputting the plurality of images into the learned model, the at least one of energy-subtraction images as output data from the learned model (Fig. 7).
Regarding claim 4, Bergner does not explicitly disclose obtaining, by inputting the plurality of images into the learned model, a plurality of images with reduced noise than the plurality of images as output data from the learned model; and generate the at least one of energy-subtraction images from the plurality of images obtained as the output data from the learned model. However, Bergner teaches removing noise from an image and thus producing a higher quality image (Figs. 6 and 7). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have a higher quality image due to the removal of noise.
Regarding claim 5, while Bergner does not explicitly disclose generating at least one of first energy-subtraction images from the plurality of images; and obtain, by inputting the at least one of first energy-subtraction images into the learned model, at least one of second energy-subtraction images with reduced noise than the at least one of first energy-subtraction images as output data from the learned model, Bergner does teach a machine learning model that removes noise from an image, and thus it would have been obvious that inputting a denoised image into the model would further remove any detected noise and thus improving image quality.
Regarding claim 12, while Bergner does not explicitly disclose that the at least one of energy-subtraction images includes a plurality of material decomposition images discriminating a plurality of materials and a respective plurality of images indicating an effective atomic number and area density, Bergner does teach CT imaging which segments an image with respect to material, and thus atomic traits, and density ([0026]: “CT images”). 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 discrimination of materials so as to elucidate a composition of a subject.
Regarding claim 15, Bergner discloses obtaining a plurality of images obtained by irradiating radiations of different energies in an inclined direction with respect to an object to be examined; and the plurality of images includes a plurality of projection images or a plurality of tomographic images reconstructed from the plurality of projection images ([0026]: “CT images”).
Regarding claim 16, Bergner does not explicitly disclose displaying the at least one of energy-subtraction images, wherein: generating, using the learned model, a plurality of energy-subtraction images based on a plurality of tomographic images corresponding to at least two cross-sections of the object to be examined; displaying the plurality of energy-subtraction images side by side. However, Bergner teaches performing CT imaging scan with a rotating gantry and using a learned model to remove noise from acquired CT images ([0028]). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to use two cross-sectional images of a subject and comparing the results visually, as to provide a robust sampling of image data.
Regarding claim 17, Bergner does not explicitly disclose collectively switching, according to an instruction from an operator, display between at least one of energy-subtraction images generated from a plurality of tomographic images corresponding to the at least two cross-sections without using the learned model and the plurality of energy-subtraction images generated using the learned model. However, Bergner teaches performing CT imaging scan with a rotating gantry and using a learned model to remove noise from acquired CT images ([0028]). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to compare between a noisy and denoised image, so as to visualize the effect of the denoising operation.
Regarding claim 18, Bergner disclose that the cross-sections relating to the plurality of tomographic images are set according to at least an initial setting ([0026]: “CT scanning unit” would have initial settings).
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bergner (US 2023/0394630, of record) in view of Park (US 2019/0156524, of record) in view of Dey (US 2022/0107378), as applied to claim 1 above, in view of “Tradeoffs in CT Image Quality and Dose” by M.F. NcNitt-Gray. Medical Physics. 2006 (McNitt, of record).
Regarding claim 2, neither Bergner, Park, nor Dey, explicitly disclose that the second image is obtained using a dose higher than a dose used to obtain the first image or that the image is obtained by averaging. However, McNitt teaches that reduced radiation dose correlates to image noise (Section I: “Tradeoffs between Radiation Dose and Image Quality”). A higher radiation dose would correlate with lower noise and a “higher-quality” image. Averaging is known in the art to cancel noise components and thus improve the quality of an image. 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 a higher dose image for the second image, as to provide an image with less noise relative to a lower dose image for the purposes of training a model.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jason Ip whose telephone number is (571) 270-5387. The examiner can normally be reached Monday - Friday 9a-5p PST.
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/JASON M IP/
Primary Examiner, Art Unit 3793
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