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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 02/09/2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1 and 7 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.
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 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 of this title, 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.
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.
Claims 1, 2, 7, 9, and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Takahashi (US 20210030374 A1) in view of Iijima (US 20160029992 A1) and Lints (US 20200160122 A1).
With regards to claims 1, 2, and 7, Takahashi discloses an X-ray imaging apparatus comprising:
an X-ray irradiation unit 21 configured to irradiate a subject with X-rays [0050];
an X-ray detection unit 32, 33 configured to detect X-rays emitted from the X-ray irradiation unit [0050];
an X-ray image generation unit 81 configured to generate an X-ray image based on a detection signal of X-rays detected by the X-ray detection unit [0080-0084]; and
a control unit 70,
wherein the control unit includes:
an enhanced image generation unit configured to generate, as an enhanced image, at least one of an output image of a trained model in which a position of remain is emphasized and a composite image generated such that the position of the remain is emphasized based on the output image and the X-ray image, based on the trained model that detects a region of the remain in a body of the subject in the X-ray image when the X-ray image generated by the X-ray image generation unit is input, wherein the enhanced image generation unit is configured to generate, as the composite image, a superimposed image in which the output image or an image generated based on the output image is superimposed on the X-ray image [0019-0021, 0083-0092, 0099-0109] (discloses a machine learning section configured to generate enhanced images and composite images based on a trained model that detects anatomical regions in an image, wherein the regions include bone portion, organs, vessels, and stents and constitute “remains”).
Takahashi discloses a display unit 17, but does not explicitly teach an image output unit configured to make a display unit display the X-ray image and at least one of the output image and the composite image simultaneously or switchably, the output image and the composite image being generated by the enhanced image generation unit. However, Iijima also teaches an X-ray imaging apparatus and explains such a configuration was known [0081-0083]. It would have been well known, obvious, and predictably suitable to one with ordinary skill in the art to modify Takahashi with the claimed configuration in order to easily compare images to locate a desired remain.
The combination of Takahashi and Iijima does not teach wherein the enhanced image generation unit is configured to generate the enhanced image based on the output image as a heatmap image wherein the region containing the remain in the X-ray region is colored according to the degree of correspondence with the remain. However, Lints teaches a medical scan analysis system and method comprising: generating heat map visualization data based on probability matrix data generated as the output of an interference function (trained model) and assigning color values corresponding to probability values of the interference output, such that regions of an image are colored according to the likelihood associated with the detected feature [0272-0276]. As such, it would have been well known, obvious, and predictably suitable to one with ordinary skill in the art to modify the combination of Takahashi and Iijima with the claimed configuration as taught by Lints to improve the ability of users to visually identify the detected regions or objects of interest.
With regards to claims 9 and 10, Takahashi discloses wherein the remain is a foreign object remaining in the body of the subject after the completion of a medical procedure [0021].
Claim 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Takahashi in view of Iijima and Hamauzu (US 20210015440 A1).
With regards to claim 3, the combination of Takahashi and Iijima disclose the claimed invention according to claim 1, but does not teach the enhanced image generation unit as claimed. However, Hamauzu teaches an imaging apparatus and method comprising emphasizing a region of a surgical tool detected from a radiographic image by a detection unit to which a trained discriminator is applied, and displaying the radiographic image on a display unit, wherein a colored mask is applied to the region of the surgical tool included in the radiographic image [0051-0052]. It would have been well known, obvious, and predictably suitable to one with ordinary skill in the art to modify Takahashi with the teachings of Hamauzu to arrive at the configuration recited in claim 3 in order to quickly identify a desired object.
Claim 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Takahashi in view of Iijima, Lints, and Matsumoto (US 20200074623 A1).
With regards to claim 6, the combination of Takahashi and Iijima disclose the claimed invention according to claim 1, but does not teach the enhanced image generation unit as claimed. However, Matsumoto teaches an imaging apparatus and method for generating an output layer image and an intermediate layer image comprising a deep learning processor 13 includes: a learning device 131 that, by taking a medical image and an image (structure image) expressing signal components of a prescribed structure in the medical image as a set of learning data, extracts the signal components of the prescribed structure from a thinned image (low-resolution image) acquired by performing (i.e., executing) thinning processing for decreasing the pixel count on the inputted medical image by using a large number of learning data sets, and learns parameters of a convolution neural network having a plurality of convolution layers optimized to generate a structure image expressing the signal components (including high-frequency components) of the prescribed structure in the original medical image with the same pixel count as that of the original medical image; and a structure extractor 132 that, by taking the thinned image of the inputted medical image by the convolution neural network using the parameters learned by the learning device 131 as the input image, extracts the signal components of the prescribed structure from the input image, and outputs the structure image expressing the signal components of the prescribed structure in the medical image with the same pixel count (pixel spacing) as that of the medical image [0024]. Therefore, in order to extract structural image data for a particular structure of interest, it would have been well known, obvious, and predictably suitable to one with ordinary skill in the art to modify the combination of Takahashi and Iijima with the claimed configuration.
Allowable Subject Matter
Claim 11 is allowed.
Claims 4 and 5 are 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.
The following is a statement of reasons for the indication of allowable subject matter:
Prior art does not teach the enhanced generation unit as claimed in claims 4, 5, and 11.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARCUS H TANINGCO whose telephone number is (571)272-1848. The examiner can normally be reached Monday-Friday 9am-6pm EST.
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/MARCUS H TANINGCO/Primary Examiner, Art Unit 2884