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 Amendment
Claims 1-12 has been amended.
Claim 13 has been newly added.
Claims 1-13 are still pending for consideration.
Response to Arguments
Applicant’s arguments 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 (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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-2, 8-9, and 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Lay et al. (US 20160328855 A1) in view of Song et al. (US 20190392584 A1).
Regarding claim 1, Lay et al. teaches a medical information processing device comprising a processor (see para [0073]; “The above-described methods for bone removal, bone segmentation, vessel segmentation, and bone removal editing may be implemented on a computer using well-known computer processors”) configured to: execute bone segmentation of an image (see para [0007]; “bone structures are segmented in a 3D medical image, resulting in a bone mask of the 3D medical image”). However, Lay et al. does not teach to acquire first data indicating a bone type and a bone size of a bone, and execute, based on the first data, organ segmentation of the image, by using the bone type and the bone size as reference criteria for determining organ information, and acquire second data.
In the same field of endeavor, Song et al. teaches to acquire first data indicating a bone type and a bone size of a bone (see para [0022]; “The detected skeletal region of the body of the human subject 110 may comprise a rib region, a spine region, and other body portions different from the rib region and the spine region of the body of the human subject 110”, see also para [0023]; “a center of the bounding region that covers a maximum area of the detected skeletal region in the CT images”, see also para [0025]; “a first number of voxels from only the determined rib region and the spine region (e.g., bone voxels) present in the first region of the 3D representation….. a second number of voxels (e.g., bone voxels) from only the determined rib region and the spine region”); and execute, based on the first data, organ segmentation of the image (see para [0032]; “The liver bounding box may include the liver organ 112 of the human subject 102 and may be further utilized to segment the liver organ 112 from the CT images”) by using the bone type and the bone size as reference criteria for determining organ information, and acquire second data (see para [0081]; “an area and positioning of the liver bounding box on the CT images by a reference to the first bottom portion of the right lung corresponds to a top edge of the liver bounding box of the liver organ and the second bottom portion of the rib region”, Note: using skeletal/rib-region information, including bone type and geometry extent, as a reference criteria for determining liver information and segmenting the liver). Accordingly, it would have been obvious to one ordinary skill in the art before the effective filling date of the claimed invention to modify a method for whole body bone removal and vasculature visualization in medical image data of Lay et al. in view of a method for organ localization, includes storing a 3D representation and CT images of an anatomical portion of the body of a subject of Song et al. in order to improve segmentation accuracy and robustness (see para [0022]).
Regarding claim 2, the rejection of claim 1 is incorporated herein.
Song et al. in the combination further teach wherein the processor is further configured to acquire, from information on the segmented bone in the first data, the organ information on an organ existing around the bone based on the bone type and acquire information on the segmented organ as the second data (see para [0020]; “the CT images includes representation of both hard (e.g., bone including rib cage) and soft tissues (e.g. liver or other abdominal organs). The CT images may be used to generate 3D volumetric data that may be visualized via a set of views that may comprise at least an axial view, a coronal view, and a sagittal view of the body or a portion of the body of the human subject 110”, see para [0032]; “The liver bounding box may include the liver organ 112 of the human subject 102 and may be further utilized to segment the liver organ 112 from the CT images”).
Regarding claim 8, the rejection of claim 1 is incorporated herein.
Lay et al. in the combination further teach further comprising a display configured to display and output information, wherein the display is configured to display: information on the segmented bone in the first data acquired by the processor (see para [0043]; “FIG. 4 illustrates exemplary results of whole body bone segmentation in a CT volume using the method of FIG. 3. In FIG. 4, segmented bone structures 402 are shown in three views of a CT volume. The body cavity mesh 404 is also shown in the three views of the CT volume”); and a candidate region of an organ acquired processor based on the first data (see para [0015]; “FIG. 8 illustrates exemplary vessel segmentation results using the method of FIG. 6”, see also para [0059]; “the resulting visualization of the vessels can be output, for example, by displaying the visualization of the vessels (i.e., the image with the bone voxels removed) on a display device of a computer system”).
Regarding claim 9, the rejection of claim 8 is incorporated herein.
Lay et al. in the combination further teach wherein the processor is further configured to receive a request from a user, the processor receiving the request from the user with respect to the candidate region of the organ displayed on the display, and execute the organ segmentation of the organ based on the request received (see para [0004]; “a system and method for editing medical image data in which bone removal has been performed”, see also para [0075]; “A client computer may transmit requests for data… to display specified data on a screen, etc. For example, the server may transmit a request adapted to cause a client computer to perform one or more of the method steps described herein, including one or more of the steps of FIGS. 1, 2, 3, 5, 6, 9, 10, and 13”).
Regarding claim 11, the scope of claim 11 is fully incorporated in claim 1, and the rejection of claim 1 analysis is equally applicable here.
Regarding claim 12, the scope of claim 12 is fully incorporated in claim 1, and the rejection of claim 1 analysis is equally applicable here.
Regarding claim 13, the rejection of claim 1 is incorporated herein.
Song et al. in the combination further teach wherein the processor is further configured to execute the organ segmentation of the image by estimating the organ information including at least one of a position or a size of an organ by using the bone type and the bone size indicated by the first data as reference criteria, and by performing the organ segmentation based on the estimated organ information to acquire the second data (see para [0022]; “The detected skeletal region of the body of the human subject 110 may comprise a rib region, a spine region, and other body portions different from the rib region and the spine region of the body of the human subject 110”, see also para [0023]; “a center of the bounding region that covers a maximum area of the detected skeletal region in the CT images”, see also para [0025]; “a first number of voxels from only the determined rib region and the spine region (e.g., bone voxels) present in the first region of the 3D representation….. a second number of voxels (e.g., bone voxels) from only the determined rib region and the spine region”, see also para [0032]; “The liver bounding box may include the liver organ 112 of the human subject 102 and may be further utilized to segment the liver organ 112 from the CT images” and para [0081]; “an area and positioning of the liver bounding box on the CT images by a reference to the first bottom portion of the right lung corresponds to a top edge of the liver bounding box of the liver organ and the second bottom portion of the rib region”, Note: using skeletal/rib-region information, including bone type and geometry extent, as a reference criteria for determining liver information and segmenting the liver).
Claims 3-7, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Lay et al. in view of Song et al. as applied in claim 1 and 2, and further in view of Zhu et al. (US 20180211419 A1).
Regarding claim 3, the rejection of claim 2 is incorporated herein.
Lay et al. in the combination further teach wherein the processor is further configured to set a first region of interest based on the bone size of the bone in the first data (see para [0065]; “the segmentation algorithm (e.g., graph cut or random walker) can use the following input information to perform the segmentation for each voxel in the 3D image…. (2) The distance map of all nearby bones within the multi-label atlas… the distance map can be the size of a region of interest”). The combination of Lay et al. and Song et al. as a whole does not teach to acquire the information on the organ in the first region of interest based on a dictionary, and to generate the second data.
In the same field of endeavor Zhu et al. teach to acquire the organ information on the organ in the first region of interest based on a dictionary, and to generate the second data (see para [0059]; “the anatomic image may be segmented into a plurality of regions based on the voxel segmentation information of the reference image”, see also para [0059]; “On the basis of the plurality of regions and the topological information of the voxels of the anatomic image, the voxel segmentation information of a dictionary element may include corresponding attenuation coefficient. For example, some voxels of the anatomic image may be segmented and designated as a bone region, and the attenuation coefficient of a bone of the dictionary element may be assigned to the bone region of the anatomic image. As another example, some voxels of an anatomic image may be segmented and designated as a lung tissue, and the attenuation coefficient of lung tissue of the dictionary element may be assigned to the lung tissue of the anatomic image”). Accordingly, it would have been obvious to one ordinary skill in the art before the effective filling date of the claimed invention to modify a method for whole body bone removal and vasculature visualization in medical image data of Lay et al. in view of a method for organ localization, includes storing a 3D representation and CT images of an anatomical portion of the body of a subject of Song et al. and a method for generating an attenuation map by assigning attenuation coefficients to the plurality of regions of Zhu et al. in order to reconstruct the imaging data (see para [0059]).
Regarding claim 4, the rejection of claim 3 is incorporated herein.
Lay et al. in the combination further teach set a size of the first region of interest based on the selected dictionary (see para [0065]; “the segmentation algorithm (e.g., graph cut or random walker) can use the following input information to perform the segmentation for each voxel in the 3D image…. (2) The distance map of all nearby bones within the multi-label atlas… the distance map can be the size of a region of interest”).
Zhu et al. in the combination further teach wherein the processor is further configured to select the dictionary from a plurality of dictionaries according to a type of the organ (see para [0054]; “An anatomic image (of a dictionary element) may be selected to be used as the reference image based on, for example, a degree of matching with respect to the anatomic image. In some embodiments, the anatomic image (of a dictionary element) that has a highest degree of matching, among the dictionary elements, with respect to the anatomic image may be selected and be designated as the reference image”), and acquire information on segmentation from information on the bone included in the first data using the selected dictionary (see para [0041]; “the database may include one or more anatomic images, voxel segmentation information, or the like, or any combination thereof. In some embodiments, the database may include a plurality of dictionary elements (D.sub.1, D.sub.2, D.sub.3, . . . , D.sub.X, . . . , D.sub.Y-1, D.sub.Y), in which Y may denote the total number of dictionary elements”).
Regarding claim 5, the rejection of claim 4 is incorporated herein.
Song et al. in the combination further teach wherein the processor is further configured to set a second region of interest for the organ information on the organ acquired based on the first region of interest, and further acquire information on another organ (see para [0021]; “the assistive apparatus 102 may be configured to segment different regions that correspond to different internal organs or structures present in CT images. For the segmentation of the different regions that correspond to different internal organs or structures present in CT images, a defined sequence of segmentation or organ identification of the different regions that correspond to different internal organs or structures may be followed… the defined sequence of segmentation may start with lungs segmentation followed by skeletal region (e.g. spine and ribs region) identification to enhance accuracy of a specific internal organ localization, such as liver localization”, see also para [0022]; “The first thresholding operation may be an image segmentation method that may be utilized to segment a pair of lung regions from the CT images).
Regarding claim 6, the rejection of claim 5 is incorporated herein.
Song et al. in the combination further teach wherein the processor is further configured to set the second region of interest as a region of interest related to the other organ whose positional relationship with the organ is graspable (see para [0031]; “The area and positioning of the liver bounding box may be determined by a reference to the first bottom portion of the right lung that may correspond to a top edge of the liver bounding box of the liver organ and the second bottom portion of the rib region that may correspond to a bottom edge of the liver bounding box of the liver organ”, see also para [0032]; “the assistive apparatus 102 may be configured to localize the liver organ in the liver bounding box based on the first bottom portion of the right lung and the second bottom portion of the rib region of the body of the human subject 110”).
Regarding claim 7, the rejection of claim 4 is incorporated herein.
Song et al. in the combination further teach wherein the processor is further configured to set a second region of interest for the organ information on the organ acquired based on the first region of interest, and further acquire information on an inside of the organ (see para [0060]; “The liver bounding box 354 may include the liver organ 112 of the human subject 102 and may be further utilized to segment the liver organ 112 from the CT images 302”).
Regarding claim 10, the rejection of claim 1 is incorporated herein.
Song et al. in the combination further teach determine whether an abnormality has occurred based on a result of the organ segmentation, and specify and output a position of the abnormality when the abnormality has occurred (see para [00327]; “The healthcare provider 114 may utilize the localized liver organ in the CT images for computer-aided diagnosis and therapy of the liver organ 112 of the human subject 110”).
Zhu et al. in the combination further teach wherein the processor is further configured to estimate a sex or an age based on a result of the bone segmentation (see para [0042]; “The characteristic information of the subject indicated by a reference image may include height, weight, gender, age, medical conditions of the subject, medical history of the subject, birthplace of the subject, an area of the subject to imaging, or the like, or any combination thereof”). Accordingly, it would have been obvious to one ordinary skill in the art before the effective filling date of the claimed invention to modify a method for whole body bone removal and vasculature visualization in medical image data of Lay et al. in view of a method and system for automatically detecting lesions in a 3D medical image of Suehling et al. and a method for generating an attenuation map by assigning attenuation coefficients to the plurality of regions of Zhu et al. in order to reconstruct the imaging data (see para [0042]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/WINTA GEBRESLASSIE/ Examiner, Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677
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