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 .
Response to Applicant’s Arguments
Applicant’s arguments filed 02/12/2026 have been fully considered, but they are not deemed to be persuasive.
Applicant argues that the cited references do not teach the claimed “identify an attention grid included in the grid point cloud data on the basis of a display condition related to a display range of the medical image data, wherein the display condition related to the display range is a display condition related to cross-sectional position of the displayed medical image data.” Ooida teaches the claimed “identify an attention grid included in the grid point cloud data on the basis of a display condition related to a display range of the medical image data” (Ooida, [0047]-[0048] - the first acquisition function 155a may specify the target region based on designation of the target region that is input by the user via the input interface 153. Specifically, the first acquisition function 155a may specify, as the target region used at Step S3 and succeeding processing, the region designated as the target region by the user. The first acquisition function 155a may also specify the target region based on an anatomical structure delineated in the CT image by a known region extraction technique. Examples of the known region extraction technique include, for example, Otsu's binarization method based on a CT value, a region expansion method, a snake method, a graph cut method, a mean shift method, and the like). Noted that Ooida’s graph cut method defines a cutting plane of cross-sectional position of the displayed medical image data (see Hu, 3. Any angle extraction of virtual slice - Figure 7 Virtual slice extraction of 3D CT image). Accordingly, the claimed invention as represented in the claims does not represent a patentable distinction over the art of record.
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 (i.e., changing from AIA to pre-AIA ) 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, 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, 3-5, and 8-12 are rejected under 35 U.S.C. 103 as being unpatentable over OOIDA et al (2022/0139564) in view of HU et al (Extraction of Any Angle Virtual Slice on 3D CT Image) and ESSA et al (Automatic segmentation of cross-sectional coronary arterial images).
As per claim 1, Ooida teaches the claimed "medical information processing apparatus" comprising processing circuitry configured "to acquire medical image data that includes a target organ" (Ooida, [0043] - the first acquisition function 155a acquires a CT image of the subject from the X-ray CT device 110 or the medical image storage device 120); "identify an area of the target organ that indicates an anatomical structure of the target organ" (Ooida, Figure 3 - e.g., organs 11-16; [0046] - the first acquisition function 155a acquires, as the target region, coordinate information of each pixel in the CT image indicated by the mitral valve on the CT image; [0077] - For example, the first acquisition function 155a acquires the region 21 of the mitral valve at the time point t1 illustrated in FIG. 3. The first acquisition function 155a then sets a plurality of calculation grids (meshes) based on conditions such as the number, a size, a shape, and an element determined in advance for the acquired region 21 of the mitral valve. The first acquisition function 155a then applies a mathematical model or a physical model that is set based on the parameter set at Step S3 to each of the calculation grids, and estimates shapes (forms) of the mitral valve at five time points from the time point t2 to the time point t6); "based on the identified area of the target organ, acquire grid point cloud data that is associated with the medical image data and that is related to the target organ" (Ooida, [0044] - a four-dimensional CT image including three-dimensional data corresponding to six phases (six time phases) obtained by imaging a mitral valve at six time points assuming that the mitral valve of the subject delineated in the CT image is the target organ); "display the medical image data" (Ooida, [0049] - the first acquisition function 155a specifies a region that is related to the target organ, and is larger than the target organ but smaller than the entire medical image).
Applicant’s arguments filed 02/12/2026 have been fully considered, but they are not deemed to be persuasive.
Applicant argues that the cited references do not teach the claimed “identify an attention grid included in the grid point cloud data on the basis of a display condition related to a display range of the medical image data, wherein the display condition related to the display range is a display condition related to cross-sectional position of the displayed medical image data.” Ooida teaches the claimed “identify an attention grid included in the grid point cloud data on the basis of a display condition related to a display range of the medical image data” (Ooida, [0047]-[0048] - the first acquisition function 155a may specify the target region based on designation of the target region that is input by the user via the input interface 153. Specifically, the first acquisition function 155a may specify, as the target region used at Step S3 and succeeding processing, the region designated as the target region by the user. The first acquisition function 155a may also specify the target region based on an anatomical structure delineated in the CT image by a known region extraction technique. Examples of the known region extraction technique include, for example, Otsu's binarization method based on a CT value, a region expansion method, a snake method, a graph cut method, a mean shift method, and the like). Noted that Ooida’s graph cut method defines a cutting plane of cross-sectional position of the displayed medical image data (see Hu, 3. Any angle extraction of virtual slice - Figure 7 Virtual slice extraction of 3D CT image) see also Essa, Abstract - We present a novel approach to segment coronary cross-sectional images acquired using catheterization imaging techniques, i.e. intra- vascular ultrasound (IVUS) and optical coherence tomography (OCT). The proposed approach combines cross-sectional segmentation with longitudinal tracking in order to tackle various forms of imaging artifacts and to achieve consistent segmentation. A node-weighted directed graph is constructed on two consecutive cross-sectional frames with embedded shape constraints within individual cross-sections or frames and between consecutive frames). Thus, it would have been obvious, in view of Hu and Essa, to configure Ooida's apparatus as claimed by using a slice cutting including slice direction and display angle of the medical image. The motivation is to enhance the image representation by allowing to view the object in any angle related to the cutting slice and to optimize feature selection (Essa, Abstract).
Claim 3 adds into claim 1 "wherein the display condition related to the display range includes a display angle of the displayed medical image data and a position in a slice direction" which would have been obvious in view of Ooida's performed image processing (e.g., Ooida, [0047] - Examples of the known region extraction technique include, for example, Otsu's binarization method based on a CT value, a region expansion method, a snake method, a graph cut method, a mean shift method, and the like; [0049] - If the first acquisition function 155a performs processing such as a graph cut method on the entire medical image, calculation cost may become excessively high. Thus, the first acquisition function 155a specifies a region that is related to the target organ, and is larger than the target organ but smaller than the entire medical image. Hereinafter, the region that is related to the target organ, and is larger than the target organ but smaller than the entire medical image is referred to as a relevant region). Furthermore, Ooida's snake method which is also known as the active contour model, has been extensively used to segment cross-sectional magnetic resonance (MR) images (see also Essa, Abstract - We present a novel approach to segment coronary cross-sectional images acquired using catheterization imaging techniques, i.e. intra- vascular ultrasound (IVUS) and optical coherence tomography (OCT). The proposed approach combines cross-sectional segmentation with longitudinal tracking in order to tackle various forms of imaging artifacts and to achieve consistent segmentation. A node-weighted directed graph is constructed on two consecutive cross-sectional frames with embedded shape constraints within individual cross-sections or frames and between consecutive frames) (see also Hu, 3. Any angle extraction of virtual slice - Figure 7 Virtual slice extraction of 3D CT image). Thus, it would have been obvious, in view of Hu and Essa, to configure Ooida's apparatus as claimed by using a slice cutting including slice direction and display angle of the medical image. The motivation is to enhance the image representation by allowing to view the object in any angle related to the cutting slice and to optimize feature selection (Essa, Abstract).
Claim 4 adds into claim 3 "wherein the processing circuitry identifies the attention grid on the basis of the display condition related to the display range and a size of a treatment device" (Ooida, Figure 3 - the elements 11-16; [0102] - The second acquisition function 155c then applies, to each of the calculation grids set to the region 21 of the mitral valve, a mathematical model or a physical model that is set based on a parameter related to the clip set in advance based on a known size, weight, tension, or the like of the clip, and the parameter related to the shape determined at Step S5; [0104] - the second acquisition function 155c may set, as the parameter, the size of the clip or the position on the mitral valve at which the clip is disposed that is designated by the user via the user interface. The second acquisition function 155c may then estimate the shape 51 of the mitral valve after the treatment at the time point ta1 using the size of the clip or the position on the mitral valve at which the clip is disposed that is set as the parameter).
Claim 5 adds into claim 1 "wherein the display condition related to the display range includes a center position of the displayed medical image data" (Ooida, [0046] - the first acquisition function 155a acquires, as the target region, coordinate information of each pixel in the CT image indicated by the mitral valve on the CT image - Ooida's coordinate information of the target region indicates a center position of the target).
Claim 8 adds into claim 1 "wherein the processing circuitry is further configured to display a plurality of images based on the medical image data, select an attention image from among the plurality of displayed images, and identify the attention grid on the basis of the display condition of the selected attention image" (Ooida, [0021] - the X-ray CT device 110 collects projection data representing distribution of X-rays transmitted through the subject by turning and moving an X-ray tube and an X-ray detector on a circular orbit surrounding the subject. Examples of such a target organ include biological organs such as a mitral valve, a heart, and lungs. The target organ is not limited thereto, but may be any biological organ. The X-ray CT device 110 then generates a CT image based on the collected projection data).
Claim 9 adds into claim 1 "wherein the processing circuitry is further configured to perform a physical simulation by using the identified attention grid as calculation condition" (Ooida, [0043] - The medical image as the processing target may also be a four-dimensional image that is obtained by taking a plurality of images in a time direction; [0058] - the parameter set by the calculation function 155b may be any parameter that can be used for a simulation of a living body.. The parameter related to movement of the biological organ used in the present embodiment may be a parameter indicating at least one of hardness, a volume, and a degree of smoothness of a surface of the biological organ, and viscosity, a flow speed, and a total quantity of a fluid (for example, the blood) flowing in the biological organ; [0075] - the first acquisition function 155a performs the pretreatment simulation based on the parameters set at Step S3).
Claims 10-12 claim a method, a computer-readable non-transitory recording medium, and an information processing apparatus based on the apparatus of claim 1; therefore, they are rejected under a similar rationale.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over OOIDA et al (2022/0139564) in view of HU et al (Extraction of Any Angle Virtual Slice on 3D CT Image) and ESSA et al (Automatic segmentation of cross-sectional coronary arterial images), and further in view of TAGARE et al (Medical image databases: A content- based retrieval approach).
Claim 6 adds into claim 4 "wherein the display condition related to the display range includes an enlargement percentage of the displayed medical image data" which would have been obvious in view of Ooida's performed image processing ([0027] - The medical data processing device 150 performs various kinds of image processing on the medical image) which performs an image editing such as enlargement region on display (see also Tagare, page 194, 15 column, 2nd paragraph - the key here is to control the field of view and enlarge it sequentially as the user iteratively refines his or her formalization). Thus, it would have been obvious, in view of Hu and Essa, to configure Ooida's apparatus as claimed by performing an image editing such as enlargement region on display. The motivation is enhancing the image representation by allowing to view the object in more detail through enlargement.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHU K NGUYEN whose telephone number is (571)272-7645. The examiner can normally be reached M-F 8-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Daniel F. Hajnik can be reached at (571) 272-7642. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PHU K NGUYEN/ Primary Examiner, Art Unit 2616
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