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 .
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, 5-6, 8-10, 15-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gosche (US 6430430 B1) and Chen (US 20120313942 A1) in view of DeCarli et al. NPL “Anatomical Mapping of White Matter Hyperintensities (WMH) Exploring the Relationships Between Periventricular WMH, Deep WMH, and Total WMH Burden”.
Regarding claim1, Gosche teaches a method for providing diagnostic auxiliary information based on a plurality of images performed by a diagnostic auxiliary information providing device (see col. 9, lines 20-27; “provide a method and/or system for processing the information and/or data provided by MRI images to detect and identify hyperintensities for disease screening, and thereby serve as a diagnostic aid in helping neuroradiologists verify disease diagnosis and severity, to arrive at a prognosis, or to follow the possible effects of therapy (e.g., drug therapy)”); labeling the plurality of cells included in the first and second slice images with a class value reflecting at least one of white matter, ventricle, or white matter hyperintensity signal (WMH) (see col. 2, lines 12-13; “the EM classifier is used to classify each pixel of each image as either WM, GM, CSF, or lesion tissue”); wherein the first cell is selected from among the plurality of cells labeled with a class value reflecting white matter, white matter hyperintensity signal, or ventricle (see sol. 24, lines 13-18; “all pixels identified as lateral ventricle, thalamus, caudate nuclei, lenticular nuclei, or the preiventricular ring are removed leaving only the WM tissue and possible hyperintense lesions embedded within it. To isolate these lesions, all pixels identifiable as WM are removed”), and the second cell is labeled with a class value reflecting a brain region different from the class value labeled in the first cell (see col. 22, lines 25-35; “CSF pixels identified by the VGC segmentation are grouped into regions by a region-labeling operation… The ventricular CSF comprising the lateral ventricles is identified and isolated”, Note; the first cell corresponds to a pixel identified as a white matter or WMH, and a pixel identified as ventricular CSF/lateral ventricle corresponds to the second cell having a class value reflecting a brain region different from that of the first cell). However, Gosche does not teach comprising: obtaining a first slice image and a second slice image—wherein the first and second slice images are related to the brain and each includes a plurality of unit cells; and providing diagnostic auxiliary information based on positional correlation between a first cell included in the first slice image and a second cell included in the second slice image.
In the same field of endeavor, Chen teaches comprising: obtaining a first slice image and a second slice image—wherein the first and second slice images are related to the brain and each includes a plurality of unit cells (see para [0056]; “each tile corresponding to one slice in the original 3D volume”, see also para [0043]; “For a pixel at a first coordinate in a subject tile, there is a next adjacent pixel at the same first coordinate in the next tile”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to modify a method for identifying suspected lesions in a brain of Gosche in view of a method for processing digital volume image data of Chen in order to address schemes needed to harness the capability of the GPU for high-level image processing such as registration, filtering, and segmentation (see para [0056]).
However, the combination of Gosche and Chen as a whole does not teach and providing diagnostic auxiliary information based on positional correlation between a first cell included in the first slice image and a second cell included in the second slice image.
In the same field of endeavor, DeCarli et al teaches and providing diagnostic auxiliary information based on positional correlation between a first cell included in the first slice image and a second cell included in the second slice image (see page 51, right col, “Data Analysis”; “We used this characteristic of the method to determine the exact distance between each WMH voxel and the ventricular ependymal surface for all subjects”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to modify a method for identifying suspected lesions in a brain of Gosche in view of method for processing digital volume image data of Chen and further in view a method for anatomical mapping of white matter hyperintensities (WMH) exploring the relationships between periventricular WMH, Deep WMH, and Total WMH Burden of DeCarli et al. in order to accurately reflect total WMH volume relationships (see page 51, right col, “Data Analysis”).
Regarding claim 5, the rejection of claim 1 is incorporated herein.
Gosche in the combination further teach wherein the plurality of images include a plurality of consecutive cross-sectional images related to one photographed object (see col. 10, lines 39-40; “the at least one imaging scan includes a plurality of consecutive imaging scans”, see also col. 50, lines 20-22; “a user performs a series of standard, consecutive imaging scans of a patient's head”), and the first slice image and the second slice image are two images selected from the plurality of consecutive cross-sectional images (see col. 7, 1-3; “method of MS lesion detection that is limited to slices occurring adjacent to the central axial slice”, see also col. 6, lines 16-19; “The algorithm relies exclusively on intensity class relationships derived from the T2 slice and is restricted to those 5 slices lying immediately above or below the central axial slice in a image set”).
Regarding claim 6, the rejection of claim 5 is incorporated herein.
Chen in the combination further teach wherein the first slice image and the second slice image are two adjacent images among the plurality of consecutive cross-sectional images (see Abstract; “a stack of two-dimensional image slices of voxels. A 1:1 mapping of each of the slices is formed, in sequential order…… a neighborhood is defined that has, relative to each voxel, four or more adjacent voxels that are within the corresponding tile of each voxel, and one or more adjacent voxels that are within the preceding tile, and one or more adjacent voxels that are within the next tile”, see para [0029]; “the terms "tile" and slice are interchangeable”, and para [0032]; “Information must propagate in multiple directions, not only within a slice (intra-slice), but between slices (inter-slice)”).
Regarding claim 8, the rejection of claim 1 is incorporated herein.
Gosche in the combination further teach wherein the diagnostic auxiliary information includes the area of the WMH region (see col. 2, lines 23-27; “two manually selected thresholds to perform white matter lesion (WML) detection. The first is set to separate brain tissue from the skull, while the second is set to identify all areas definable as lesions having intensities greater than the brain tissue”).
Regarding claim 9, the rejection of claim 1 is incorporated herein.
Chen in the combination further teach further comprising: determining a reference cell that reflects the positional information of the first cell within the second slice image (see para [0043]; “For a pixel at a first coordinate in a subject tile, there is a next adjacent pixel at the same first coordinate in the next tile. Similarly, for a pixel at the first coordinate in a subject tile, there is a previous or preceding adjacent pixel at the same first coordinate in the preceding tile”, see also para [0035]; “This is a 1:1 ordered mapping, so that image slice 1 maps to tile 1, slice 2 to maps to tile 2, and generally slice n maps to tile n….. the terms "slice" and "tile" are used equivalently”).
DeCarli et al. in the combination further teach wherein the positional correlation is derived from the relationship between the reference cell and the second cell (see page 51, right col, “Data Analysis”; “We used this characteristic of the method to determine the exact distance between each WMH voxel and the ventricular ependymal surface for all subjects”).
Regarding claim 10, the scope of claim 10 is fully incorporated in claim 1, and the
rejection of claim 1 is equally applicable here.
Regarding claim 15, the rejection of claim 10 is incorporated herein.
Gosche in the combination further teach wherein the plurality of images include a plurality of consecutive cross-sectional images related to one photographed object (see col. 10, lines 39-40; “the at least one imaging scan includes a plurality of consecutive imaging scans”, see also col. 50, lines 20-22; “a user performs a series of standard, consecutive imaging scans of a patient's head”), and the first slice image and the second slice image are two images selected from the plurality of consecutive cross-sectional images (see col. 7, 1-3; “method of MS lesion detection that is limited to slices occurring adjacent to the central axial slice”, see also col. 6, lines 16-19; “The algorithm relies exclusively on intensity class relationships derived from the T2 slice and is restricted to those 5 slices lying immediately above or below the central axial slice in a image set”).
Regarding claim 16, the rejection of claim 15 is incorporated herein.
Chen in the combination further teach wherein the first slice image and the second slice image are two adjacent images among the plurality of consecutive cross-sectional images (see Abstract; “a stack of two-dimensional image slices of voxels. A 1:1 mapping of each of the slices is formed, in sequential order…… a neighborhood is defined that has, relative to each voxel, four or more adjacent voxels that are within the corresponding tile of each voxel, and one or more adjacent voxels that are within the preceding tile, and one or more adjacent voxels that are within the next tile”, see para [0029]; “the terms "tile" and slice are interchangeable”, and para [0032]; “Information must propagate in multiple directions, not only within a slice (intra-slice), but between slices (inter-slice)”).
Regarding claim 18, the rejection of claim 10 is incorporated herein.
Gosche in the combination further teach wherein the diagnostic auxiliary information includes the area of the WMH region (see col. 2, lines 23-27; “two manually selected thresholds to perform white matter lesion (WML) detection. The first is set to separate brain tissue from the skull, while the second is set to identify all areas definable as lesions having intensities greater than the brain tissue”).
Regarding claim 19, the rejection of claim 10 is incorporated herein.
Chen in the combination further teach wherein the controller determines a reference cell reflecting the positional information of the first cell within the second slice image (see para [0043]; “For a pixel at a first coordinate in a subject tile, there is a next adjacent pixel at the same first coordinate in the next tile. Similarly, for a pixel at the first coordinate in a subject tile, there is a previous or preceding adjacent pixel at the same first coordinate in the preceding tile”, see also para [0035]; “This is a 1:1 ordered mapping, so that image slice 1 maps to tile 1, slice 2 to maps to tile 2, and generally slice n maps to tile n….. the terms "slice" and "tile" are used equivalently”).
DeCarli et al. in the combination further teach and the positional correlation is derived from the relationship between the reference cell and the second cell (see page 51, right col, “Data Analysis”; “We used this characteristic of the method to determine the exact distance between each WMH voxel and the ventricular ependymal surface for all subjects”).
Regarding claim 20, the rejection of claim 1 is incorporated herein.
Gosche in the combination further teach non-transitory computer-readable storage medium storing a program for executing the method (see col. 13, lines 2-5; “a computer readable medium including instructions being executed by a computer is provided. The instructions instruct the computer to execute an interpretation of at least one imaging scan of a patient”).
Claims 2, 4, 11-12, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Gosche and Chen in view of DeCarli et al. as applied in claim 1 above, and further in view of Brickman et al. NPL “Quantitative approaches for assessment of white matter hyperintensities in elderly populations”.
Regarding claim 2, the rejection of claim 1 is incorporated herein. The combination of Gosche, Chen and DeCarli et al. as a whole does not teach wherein the positional correlation includes a distance between the first cell and the second cell, and the distance between the first cell and the second cell is determined in consideration of the distance between the first slice image and the second slice image.
In the same field of endeavor, Brickman et al. teach wherein the positional correlation includes a distance between the first cell and the second cell, and the distance between the first cell and the second cell is determined in consideration of the distance between the first slice image and the second slice image (see page 103, 3rd para; “To determine whether WMH-labeled voxels falls within the periventricular or deep cortical area, we calculate the distance, in x, y, and z directions, of the closest voxel of each nearest generated WMH cluster to the ventricles using a Euclidian distance formula”, see also page 102, right col. last para; “Fluid-attenuated inversion recovery (FLAIR) images: 36 contiguous 3 mm thick sections”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to modify a method for identifying suspected lesions in a brain of Gosche in view of method for processing digital volume image data of Chen and further in view a method for anatomical mapping of white matter hyperintensities (WMH) exploring the relationships between periventricular WMH, Deep WMH, and Total WMH Burden of DeCarli et al. and quantitative approaches for assessment of white matter hyperintensities in elderly populations of Brickman et al. in order to provide evidence for good reliability and criterion validity for two approaches for WMH volume determination (see page 103, 3rd para).
Regarding claim 4, the rejection of claim 2 is incorporated herein.
Gosche in the combination further teach wherein the first cell is located at a boundary of a region formed by cells labeled as the ventricles (see col. 23, lines 52-55; “applying a morphological dilation operator to the previously defined edge of the lateral ventricle. Each dilation generates a single pixel ring around the previous single layer”, see also col. 42, lines 51-55; “These lesions are identified by iteratively applying a morphological dilation operator (Haralick et al, 1987) to the previously defined exterior edge of the lateral ventricle. Each dilation generates a single pixel ring around the previous single layer”, and col. 22, lines 63-64; “the x-coordinates of the pixels comprising the lateral ventricle”).
Regarding claim 11, the rejection of claim 10 is incorporated herein.
Brickman et al. in the combination further teach wherein the positional correlation includes the distance between the first cell and the second cell (see page 103, 3rd para; “To determine whether WMH-labeled voxels falls within the periventricular or deep cortical area, we calculate the distance, in x, y, and z directions, of the closest voxel of each nearest generated WMH cluster to the ventricles using a Euclidian distance formula”, see also page 102, right col. last para; “Fluid-attenuated inversion recovery (FLAIR) images: 36 contiguous 3 mm thick sections”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to modify a method for identifying suspected lesions in a brain of Gosche in view of method for processing digital volume image data of Chen and further in view a method for anatomical mapping of white matter hyperintensities (WMH) exploring the relationships between periventricular WMH, Deep WMH, and Total WMH Burden of DeCarli et al. and quantitative approaches for assessment of white matter hyperintensities in elderly populations of Brickman et al. in order to provide evidence for good reliability and criterion validity for two approaches for WMH volume determination (see page 103, 3rd para).
Regarding claim 12, the rejection of claim 11 is incorporated herein.
Brickman et al. in the combination further teach wherein the distance between the first cell and the second cell is determined in consideration of the distance between the first slice image and the second slice image (see page 103, 3rd para; “To determine whether WMH-labeled voxels falls within the periventricular or deep cortical area, we calculate the distance, in x, y, and z directions, of the closest voxel of each nearest generated WMH cluster to the ventricles using a Euclidian distance formula”, see also page 102, right col. last para; “Fluid-attenuated inversion recovery (FLAIR) images: 36 contiguous 3 mm thick sections”).
Regarding claim 14, the rejection of claim 11 is incorporated herein.
Gosche in the combination further teach wherein the first cell is located at a boundary of a region formed by cells labeled as the ventricles (see col. 23, lines 52-55; “applying a morphological dilation operator to the previously defined edge of the lateral ventricle. Each dilation generates a single pixel ring around the previous single layer”, see also col. 42, lines 51-55; “These lesions are identified by iteratively applying a morphological dilation operator (Haralick et al, 1987) to the previously defined exterior edge of the lateral ventricle. Each dilation generates a single pixel ring around the previous single layer”, and col. 22, lines 63-64; “the x-coordinates of the pixels comprising the lateral ventricle”).
Claims 3, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Gosche and Chen in view of DeCarli et al. and Brickman et al. as applied in claims 1 and 2 above, and further in view of Ekin et al. (US 20110194741 A1).
Regarding claim 3, the rejection of claim 2 is incorporated herein. The combination of Gosche, Chen, DeCarli et al. and Brickman et al. as a whole does not teach wherein the first cell is located at a central part of a region formed by cells labeled as the ventricle.
In the same field of endeavor, Ekin et al. teach wherein the first cell is located at a central part of a region formed by cells labeled as the ventricle (see para [0012]; “a central point detector for identifying a central point of at least part of the brain ventricle”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to modify a method for identifying suspected lesions in a brain of Gosche in view of method for processing digital volume image data of Chen and further in view a method for anatomical mapping of white matter hyperintensities (WMH) exploring the relationships between periventricular WMH, Deep WMH, and Total WMH Burden of DeCarli et al. and system for analyzing a brain ventricle of Ekin et al. in order to assess brain structure in healthy and diseased brains (see para [0012]).
Regarding claim 13, the rejection of claim 11 is incorporated herein.
Brickman et al. in the combination further teach wherein the first cell is located at a central part of a region formed by cells labeled as the ventricles (see para [0012]; “a central point detector for identifying a central point of at least part of the brain ventricle”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to modify a method for identifying suspected lesions in a brain of Gosche in view of method for processing digital volume image data of Chen and further in view a method for anatomical mapping of white matter hyperintensities (WMH) exploring the relationships between periventricular WMH, Deep WMH, and Total WMH Burden of DeCarli et al. and system for analyzing a brain ventricle of Ekin et al. in order to assess brain structure in healthy and diseased brains (see para [0012]).
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Gosche and Chen in view of DeCarli et al. as applied in claim 1 above, and further in view of Okuda et al. (US 20190197347 A1).
Regarding claim 7, the rejection of claim 1 is incorporated herein. The combination of Gosche, Chen and DeCarli et al. as a whole does not teach wherein the diagnostic auxiliary information includes the thickness of the WMH region.
In the same field of endeavor, Okuda et al. teach wherein the diagnostic auxiliary information includes the thickness of the WMH region (see para [0027]; “Examples of these brain lesion characteristics may include shape or geometric characteristics, size characteristics”, see also para [0050]; “The percentage of lesions was significantly different between patients with MS and patients with NSWM disease for lesions that were asymmetric…. and length of the longest side (9.47 mm [6.22] vs. 5.21 mm [2.84], P<0.001) was also observed to be higher in MS than in NSWM disease patients”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to modify a method for identifying suspected lesions in a brain of Gosche in view of method for processing digital volume image data of Chen and further in view a method for anatomical mapping of white matter hyperintensities (WMH) exploring the relationships between periventricular WMH, Deep WMH, and Total WMH Burden of DeCarli et al. and implementations for creating 3-dimensional (3D) representations exhibiting geometric and surface characteristics of brain lesions of Okuda et al. in order to enable a more efficient and accurate way of determining brain lesion etiologies (see para [0027]).
Regarding claim 17, the rejection of claim 10 is incorporated herein.
Okuda et al. in the combination further teach wherein the diagnostic auxiliary information includes the thickness of the WMH region (see para [0027]; “Examples of these brain lesion characteristics may include shape or geometric characteristics, size characteristics”, see also para [0050]; “The percentage of lesions was significantly different between patients with MS and patients with NSWM disease for lesions that were asymmetric…. and length of the longest side (9.47 mm [6.22] vs. 5.21 mm [2.84], P<0.001) was also observed to be higher in MS than in NSWM disease patients”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to modify a method for identifying suspected lesions in a brain of Gosche in view of method for processing digital volume image data of Chen and further in view a method for anatomical mapping of white matter hyperintensities (WMH) exploring the relationships between periventricular WMH, Deep WMH, and Total WMH Burden of DeCarli et al. and implementations for creating 3-dimensional (3D) representations exhibiting geometric and surface characteristics of brain lesions of Okuda et al. in order to enable a more efficient and accurate way of determining brain lesion etiologies (see para [0027]).
Conclusion
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