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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 4/28/2026 has been entered.
All claims canceled and claims 12-18 are added that cover a similar scope to previous claims.
Response to Arguments
Applicant's arguments filed 4/28/2026 with respect to claim(s) 12-18 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. [applicant has not written any challenges].
Priority
Priority is claimed to foreign application JP2021-157099 filed 09/27/2021. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Claims are given a priority date of 09/27/2021.
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) 12-15, 17-18 are rejected under 35 U.S.C. 103 as unpatentable over US 20220383466 A1 Tsukuda; Akira et al. in view of US 20230097849 A1 TAKAHASHI; Wataru et al.
Consider Claims 12, 17, and 18
In the language of claim 12:
Tsukuda teaches An image processing device (Tsukuda Fig. 1 111 computer/image processing apparatus [0033], Fig. 2 method) comprising:
at least one processor (Tsukuda Fig. 1, [0033], [0041]: “one or more processors of the computer 111”),
wherein the processor
acquires a first radiation image and a second radiation image acquired by imaging the subject including three or more compositions ([0050] “..It is assumed that in FIGS. 3A and 3B, the image contains three types of materials, namely a guide wire (which may alternatively be a stent, a coil etc.) serving as a target object, a bone, and a soft tissue ..” ) with radiation having different energy distributions (Fig. 1, Fig. 2, step S207, [0048] “ the planar distribution obtaining unit 115 generates a decomposition image representing the planar distribution related to materials through material decomposition or material identification from two or more X-ray images obtained by imaging an object that contains the target object by means of radiation of different levels of energy.”)
derives a first composition image representing a first composition ( Fig. 2 step S208 [0048] “..The frequency decomposition unit 116 performs frequency decomposition on the bone image and thus decomposes the bone image into an image of the target object (a band limitation image in which the target object is enhanced) ..”) included in the subject from the first radiation image acquired or the second radiation image ( [0104] “..according to the first to fifth embodiments, when the human body contains three or more materials, such as a contrast agent, a stent, and a guide wire, in addition to bones and soft tissues when spectral imaging of two-dimensional X-ray images is performed, for example, decomposition into these three or more materials can be enabled with a smaller number of times of imaging.”),
derives a first removal radiation image and a second removal radiation image by weighting subtraction of the first composition image ([0036]-[0037] “..The planar distribution obtaining unit 115 obtains a decomposition image representing the planar distribution by means of energy subtraction..”) from the first radiation image and the second radiation image (Fig. 2, step s208 [0048] “..The frequency decomposition unit 116 performs frequency decomposition on the bone image and thus decomposes the bone image into.. a bone image (a band limitation image in which the bone is enhanced..”; [0052] Next, the frequency decomposition unit 116 performs frequency decomposition on the soft tissue removal image 303 to divide the soft tissue removal image 303 into a plurality of frequency components, and generates a plurality of band limitation images (step S208). According to an example, here, the highest-frequency band limitation image is an image that mainly contains noise components (a noise image 305), and the next highest-frequency band limitation image is an image that most strongly contains guide wire components (a wire image 306). The band limitation image of low frequency components is an image that strongly contains the bone components (a bone image 307);
derives a plurality of other composition images representing a plurality of other compositions different from the first composition included in the subject by using the first removal radiation image and the second removal radiation image (Tsukuda [0093 “ the image obtaining unit 112 obtains a high tube voltage image 1005 and a low tube voltage image 1006 that contain the bone, the soft tissue, the stent, and the contrast agent (step S903). ..a soft tissue image 1008 corresponding to the soft tissue image and a soft tissue removal image 1007 corresponding to the bone and the target objects (the contrast agent and the stent) are generated. [0094] Of the bone, the contrast agent, and the stent, the bone moves only slightly and therefore appears at the same position in the soft tissue removal image 1004 and the soft tissue removal image 1007. Therefore, the planar distribution obtaining unit 115 obtains a subtraction image 1009 from which the bone has been removed, by subtracting the soft tissue removal image 1004 from the soft tissue removal image 1007 (step S905)..”; see also Fig. 2 step S207-s208 [0048]: “..the planar distribution obtaining unit 115 calculates and generates a decomposition image representing the planar distribution related to the materials from the high tube voltage image and the low tube voltage image he planar distribution obtaining unit 115 obtains two decomposition images, which are a bone image and a soft tissue image, as the planar distributions related to the materials…” [0048] “..The frequency decomposition unit 116 performs frequency decomposition on the bone image and thus decomposes the bone image into an image of the target object (a band limitation image in which the target object is enhanced) and a bone image (a band limitation image in which the bone is enhanced..”), and
derives a composite image obtained by synthesizing the first composition image and the plurality of other composition images (Tsukuda Fig. 2 S209, Fig. 6, [0073]- [0074] “..plurality of band limitation images are combined instead of extracting one of a plurality of band limitation images in step S209. FIG. 6 is a flowchart illustrating combination processing in step S209. Images 507, 508, and 509 are band limitation images obtained by performing frequency decomposition described with reference to FIG. 5. The output image generation unit 117 multiplies the image 508 by a weighting coefficient g.sub.3 through weighting processing 603, …multiplies the image 509 by a weighting coefficient g.sub.2 through weighting processing to obtain a weighted image. … to obtain a combined image 604. As a result, the mixing ratio between the image 509 and the image 508 can be changed in the combination to obtain the combined image 604, and a specific frequency band can then be enhanced. Similarly, the output image generation unit 117 performs addition processing to add a weighted image obtained by multiplying the image 508 by a weighting coefficient g.sub.1 through weighting processing to an enlarged image obtained by enlarging the combined image 604, and thus obtains a combined image 605..”) at a predetermined ratio (Tsukuda Fig. 2 S209, Fig. 6, [0073]- [0074] “…The mixing ratio between the image 509 and the combined image 604 can also be changed in the combination to obtain the combined image 605, and a specific frequency band can thus be enhanced….” See also weighting coefficients; Fig. 6, [0073]- [0074] “.. a specific frequency band can thus be enhanced….” And ¶72 “a plurality of frequency bands are used, enables a target object to be stably extracted” thus purpose of imaging to extract target object),
wherein the first composition is an artificial object (Tsukuda [0050] “..It is assumed that in FIGS. 3A and 3B, the image contains … a guide wire (which may alternatively be a stent, a coil etc.)..”), and
wherein the plurality of other compositions are a bone part image and a soft part image (Tsukuda ¶48 “obtains two decomposition images, which are a bone image and a soft tissue image, as the planar distributions related to the materials…” and ¶50).
In the same analysis and grounds cited for claim 1, Tsukuda teaches An image processing method of claim 9 and a non-transitory computer-readable storage medium that stores an image processing program of claim 10.
Tsukuda does not teach using a derivation model that has been subjected to machine learning to derive the first composition image from a radiation image.
Examiner notes the claim limitation “derivation model that has been subjected to machine learning” broadly describes the intended environment and does not limit the derivation model. Nonetheless, TAKAHASHI teaches using a derivation model that has been subjected to machine learning to derive the first composition image from a radiation image (TAKAHASHI Fig. 4, Fig. 5, 7, [0066]-[0068] “…The machine learning is performed for each image element of the plurality of image elements 50 to be extracted. That is, the training data 66 is prepared for each image element 50 to be extracted… As shown in FIG. 4, the plurality of image elements 50 include a first element 51, which is the biological tissue, and a second element 52, which is a non-biological tissue. In addition, the plurality of image elements 50 include at least a plurality of image elements of a bone 53, a blood vessel 54, a device 55 introduced into the body, clothing 56, a noise 57, and a scattered ray component 58 of the X-rays. Among these, the bone 53 and the blood vessel 54 correspond to the first element 51. The first element 51 may include the biological tissue other than the bone 53 and the blood vessel 54. Among these, the device 55 introduced into the body, the clothing 56, the noise 57, and the scattered ray component 58 of the X-rays correspond to the second element 52. The …In the example shown in FIG. 5, one trained model 40 is configured to separately extract the plurality of image elements 50…”).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the invention of Tsukuda to include the noted teachings of TAKAHASHI in order to enable even on a plurality of image elements to improve the visibility of medical images in various usage scenes (TAKAHASHI [0005]).
Consider Claim 13
Tsukuda teaches The image processing device according to claim 12,
wherein the processor derives a first removal radiation image and a second removal radiation image obtained by removing the first composition from the first radiation image and the second radiation image by using the first composition image (Tsukuda Fig. 3, [0093] Next, the image obtaining unit 112 obtains a high tube voltage image 1005 and a low tube voltage image 1006 that contain the bone, the soft tissue, the stent, and the contrast agent (step S903). The planar distribution obtaining unit 115 derives a planar distribution related to the materials (material decomposition in this example) for the high tube voltage image 1005 and the low tube voltage image 1006 (step S904). Thus, a soft tissue image 1008 corresponding to the soft tissue image and a soft tissue removal image 1007 corresponding to the bone and the target objects (the contrast agent and the stent) are generated. [0094] Of the bone, the contrast agent, and the stent, the bone moves only slightly and therefore appears at the same position in the soft tissue removal image 1004 and the soft tissue removal image 1007.), and
derives the plurality of other composition images by performing weighting subtraction on the first removal radiation image and the second removal radiation image (Tsukuda Fig. 3, [0094] “Therefore, the planar distribution obtaining unit 115 obtains a subtraction image 1009 from which the bone has been removed, by subtracting the soft tissue removal image 1004 from the soft tissue removal image 1007 (step S905)”).
Consider Claim 14
Tsukuda teaches the processor derives the bone part image and the soft part image from the first removal radiation image and the second removal radiation image (See claim 13 rejection).
Tsukuda does not teach by using a second derivation model that has been subjected to machine learning to derive the bone part image and the soft part image from the first removal radiation image and the second removal radiation image.
Examiner notes the claim limitation “derivation model that has been subjected to machine learning” broadly describes the intended environment and does not limit the derivation model.
Nonetheless, TAKAHASHI teaches by using a second derivation model that has been subjected to machine learning to derive the bone part image and the soft part image from the first removal radiation image and the second removal radiation image. (TAKAHASHI Fig. 4, Fig. 5, 7, [0066]-[0068] “…The machine learning is performed for each image element of the plurality of image elements 50 to be extracted. That is, the training data 66 is prepared for each image element 50 to be extracted… As shown in FIG. 4, the plurality of image elements 50 include a first element 51, which is the biological tissue, and a second element 52, which is a non-biological tissue. In addition, the plurality of image elements 50 include at least a plurality of image elements of a bone 53, a blood vessel 54, a device 55 introduced into the body, clothing 56, a noise 57, and a scattered ray component 58 of the X-rays. Among these, the bone 53 and the blood vessel 54 correspond to the first element 51. The first element 51 may include the biological tissue other than the bone 53 and the blood vessel 54. Among these, the device 55 introduced into the body, the clothing 56, the noise 57, and the scattered ray component 58 of the X-rays correspond to the second element 52. The …In the example shown in FIG. 5, one trained model 40 is configured to separately extract the plurality of image elements 50…”; [0053], Fig. 29, [0192]: “..The extraction processing unit 20 extracts the image element 50 using one or a plurality of trained models 40…”).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the invention of Tsukuda to include the noted teachings of TAKAHASHI in order to enable even on a plurality of image elements to improve the visibility of medical images in various usage scenes (TAKAHASHI [0005]).
Consider Claim 15
Tsukuda teaches The image processing device according to claim 12, wherein the processor is able to change the predetermined ratio (Tsukuda Fig. 2 S209, Fig. 6, [0073]- [0074] “..The mixing ratio between the image 509 and the combined image 604 can also be changed in the combination to obtain the combined image 605, and a specific frequency band can thus be enhanced….” See also Weighting coefficents).
Allowable Subject Matter
Claim 16 is 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.
Closest prior art US 20210118193 A1 Torii; Sota et al. teaches wherein the first composition is an artificial object, and the other compositions are a bone part, fat, and muscle (Torii Fig. 10, [0109] FIG. 12 is a diagram exemplarily showing regions of interest in a radiation image. As shown in FIG. 12, it is permissible to perform location recognition by executing image processing with respect to the radiation image, extract a first material (e.g., body fat), a second material (e.g., bones), and a third material (e.g., a medical device, such as a catheter and a stent), and use the values of corresponding effective atomic numbers of the materials as effective atomic numbers in the energy table…”).
Claim 16 recites “wherein the processor derives a muscle image and a fat image from the soft part image by using a third derivation model that has been subjected to machine learning to derive the muscle image and the fat image from the soft part image, and derives the composite image by using a predetermined ratio of the muscle image and the fat image as the soft part image” which is not found in the prior art of record and examiner’s updated search.
Thus claim 16 contains allowable subject matter, in combination with the remaining limitations of parent claims.
Pertinent Prior Art(s)
The prior art made of record though not relied upon in the current rejection is considered pertinent to applicant's disclosure:
US 20240081761 A1 TAKAHASHI; Tomoyuki
Claim 1. An image processing device comprising: at least one processor, wherein the processor specifies a target bone, which is a target of evaluation, by excluding a fracture and an artificial object in a bone part image in which at least a bone component of a subject is extracted, and derives an evaluation result indicating a state of a bone of the subject based on the target bone.
b. US 20090080755 A1 KAWAMURA; Takahiro et al.
[0020] Fig. 2, 4 composition types given in Fig. 2 (soft tissue bone,plaster catheter) and also [0022] where additional composition types are listed glass, plastic, metal, or the like
[0020] The image processor 20 performs extraction or removal of a specific object within the subject 12 from a radiation image by carrying out weighted subtraction using a plurality of pieces of radiation image information obtained at different radiation energies. The weighted subtraction is computed as
S=.alpha.S.sub.1+S.sub.2
where S is a resultant piece of radiation image information, S.sub.1 and S.sub.2 are pieces of radiation image information obtained with first and second image capturing conditions, respectively, and .alpha. is a weighting coefficient.
c. US 20220092787 A1 CUI; Kai et al.
CUI [0127]: “..the trained metal detection model may be a model used for determining a metal image with respect to the X-ray image. For example, the trained metal detection model may be trained from a neural network model for category semantic perception. The neural network model for category semantic perception may be a deep neural network model capable to recognize different types of target objects..”
Conclusion
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/UMAIR AHSAN/Primary Examiner, Art Unit 2647
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