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 .
Priority
Receipt is acknowledged that application claims priority to foreign application with application number KR10-2023-0096213 dated 7/24/2023. Copies of certified papers required by 37 CFR 1.55 have been received. Priority is acknowledged under 35 USC 119(e) and 37 CFR 1.78.
Information Disclosure Statement
The IDS dated 3/10/2025 has been considered and placed in the application file.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim(s) 2-4, 9, 11-13, 18 are rejected under 35 U.S.C. 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 2 recites “wherein the sub volume data includes data regarding a moving direction of voxels which configure the sub volume data with respect to any one axis.” It is unclear what the Applicant is claiming when stating “a moving direction of voxels”, as voxels are simply just 3d pixels with inherently no directions involved. For examination purposes, the examiner will interpret this limitation as the sub volume data including data with respect to any one axis.
Claim 3 recites “wherein the step of generating sub volume data is a step of generating the plurality of sub volume data by laminating a plurality of slices which configures the medical image at a predetermined height and segmenting the slices in a direction perpendicular to a plane of the slice”. It is unclear what the Applicant is claiming when stating the sub volume data is “laminated”, as by definition, laminating means covering a surface with a plastic layer. It is also unclear what the medical image is being configured with its predetermined height, and how the plane is being produced to segment the slices. For examination purposes, the examiner will interpret this limitation as generating the plurality of sub volume data by determining sub-images in the medical image.
Claim 4 recites “determining the type of the target area included in each of the plurality of slices which configures the medical image”. There is insufficient antecedent basis for “the plurality of slices”. For examination purposes, the examiner will interpret this limitation as “each of a plurality of slices”.
Claim 9 recites “prior to the step of acquiring a medical image, acquiring learning data having a different slice thickness according to a type of the medical image”. There is insufficient antecedent basis for “a medical image”. For examination purposes, the examiner will interpret this as “the medical image”. It is also unclear how learning data can be acquired prior to acquiring the medical images, as the learning data consists of attributes from the medical image (i.e. “learning data having a different slice thickness according to a type of the medical image…”). For examination purposes, the examiner will assume this takes place prior to acquiring the sub volume data.
Claims 11-13, 18 corresponds to claims 2-4, 9 respectively and thus are rejected for the same reasons of indefiniteness.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites:
“acquiring a medical image of a subject” is a well-understood, routine, and conventional insignificant extra-solution activity of data gathering.
“generating a plurality of sub volume data using the medical image” which can be reasonably interpreted as a human observer mentally determining data associated with an image, such as height, width, resolution, etc.
“determining an area corresponding to a target area from the plurality of sub volume data by inputting the plurality of sub volume data to a prediction model which is trained to predict any one target area with a 3D medical image as an input” which can be reasonably interpreted as a human observer mentally determining a target region in the sub volume data. The prediction model is a well-understood, routine, and conventional insignificant extra-solution activity of a learning model.
“And providing the area corresponding to the target area in the medical image of the subject” is a well-understood, routine, and conventional insignificant extra-solution activity of data outputting.
Claim 2 recites “wherein the sub volume data includes data regarding a moving direction of voxels which configure the sub volume data with respect to any one axis”. A person with the aid of pen and paper can determine sub volume data by drawing axis’.
Claim 3 recites “wherein the step of generating sub volume data is a step of generating the plurality of sub volume data by laminating a plurality of slices which configures the medical image at a predetermined height and segmenting the slices in a direction perpendicular to a plane of the slice”. A person with the aid of pen and paper can generate slices by drawing lines across an image/paper and segment them by outlining certain regions within these boxes.
Claim 4 recites “prior to the step of generating the plurality of sub volume data, inputting the medical image to a classification model which is trained to classify a type of the target area with the medical image as an input; determining the type of the target area included in each of the plurality of slices which configures the medical image; and grouping the plurality of slices for every target area, according to a classification result”. Inputting the medical image into a classification model is a well-understood, routine, and conventional insignificant extra-solution activity of data inputting. A human can mentally determine the type of the target area within the slices and classify what an image region is. A human can also mentally group the slices based on their classification.
Claim 5 recites “wherein the step of providing the area corresponding to the target area is providing a user interface screen including a segmentation model selection area to select the target area to be segmented in the medical image and a segmentation result display area to display the area corresponding to the target area”. A person can mentally select a target area to be segmented and displaying the area is a well-understood, routine, and conventional insignificant extra-solution activity of data outputting.
Claim 6 recites “wherein the step of providing the area corresponding to the target area further includes: a step of combining volume data having a probability value corresponding to the target area which is equal to or higher than a predetermined value from the plurality of sub volume data using the prediction model and displaying the combined volume data on the user interface screen”. A person can mentally determine how likely a target area in an image contains a region of interest. Displaying the data is a well-understood, routine, and conventional insignificant extra-solution activity of data outputting.
Claim 7 recites “wherein the step of providing the area corresponding to the target area further includes: a step of displaying each of the plurality of sub volume data on the user interface screen with a different transparency according to a probability value corresponding to the target area, using the prediction model”. This is a well-understood, routine, and conventional insignificant extra-solution activity of data outputting.
Claim 8 recites “wherein in the step of generating the sub volume data, the sub volume data is generated with respect to an axis including any one plane, among axial, coronal, and sagittal planes, and the step of providing the area corresponding to the target area further includes: a step of acquiring any one reference plane to display the target area, among the axial, coronal, and sagittal planes, through the user interface screen; and a step of rendering the area corresponding to the target area based on the reference plane”. A person with the aid of pen and paper can draw the axial/coronal/sagittal planes across a body. Displaying the target area on a plane and rendering it is a well-understood, routine, and conventional insignificant extra-solution activity of data outputting.
Claim 9 recites “prior to the step of acquiring a medical image, acquiring learning data having a different slice thickness according to a type of the medical image; generating a learning dataset by bootstrapping a different number of times for every thickness of the learning data; and generating the prediction model which is configured to predict any one target area based on the learning dataset”. Acquiring learning data having a different slice thickness is a well-understood, routine, and conventional insignificant extra-solution activity of data gathering.
Claims 10-18 corresponds to claims 1-9 respectively, additionally reciting an image segmentation apparatus. These parts are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 5, 10-12, 14 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Zhang (US 20220301224 A1).
Regarding claim 1, Zhang discloses a medical image segmentation method performed by a processor of a medical image segmentation apparatus (Zhang, paragraph [0186], "For example, the process 800 may be stored in the storage device 150 and/or the storage (e.g., the storage 220, the storage 390) as a form of instructions, and invoked and/or executed by the processing device 110 (e.g., the processor 210 of the computing device 200 as illustrated in FIG. 2, the CPU 340 of the mobile device 300 as illustrated in FIG. 3)") comprising the steps of:
acquiring a medical image of a subject (Zhang, paragraph [0094], "In 510, the processing device 110 (e.g., the first obtaining module 410) may obtain an initial image."),
generating a plurality of sub volume data using the medical image (Zhang, paragraph [0144], "The target image determination module 420 may obtain the plurality of sub-images by extracting a plurality of 3D image blocks from a 3D initial image B."),
determining an area corresponding to a target area from the plurality of sub volume data by inputting the plurality of sub volume data to a prediction model which is trained to predict any one target area with a 3D medical image as an input (Zhang, paragraph [0176], "As shown in FIG. 6B, the target image determination module 420 may input the plurality of image blocks of the initial image B to the trained positioning model, and output a determination result “1” corresponding to the image blocks B.sub.1, B.sub.2, B.sub.3, and B.sub.4, indicating that the image blocks B.sub.1, B.sub.2, B.sub.3, and B.sub.4 include a portion of the target region, respectively."),
And providing the area corresponding to the target area in the medical image of the subject (Zhang, paragraph [0135], "In some embodiments, after the target region in each target image is segmented, the first segmentation result of the target region may be displayed directly in the target image.").
Regarding claim 2, Zhang discloses the medical image segmentation method according to claim 1, wherein the sub volume data includes data regarding a moving direction of voxels which configure the sub volume data with respect to any one axis (Zhang, paragraph [0158],"As shown in FIG. 7A, an extraction interval corresponding to 3D image blocks of an initial image may be one image block evenly spaced in an X-axis direction (indicated by dashed lines).").
Regarding claim 3, Zhang discloses the medical image segmentation method according to claim 1, wherein the step of generating sub volume data is a step of generating the plurality of sub volume data by laminating a plurality of slices which configures the medical image at a predetermined height and segmenting the slices in a direction perpendicular to a plane of the slice (Zhang, paragraph [0007], Fig. 6a/6b below, "In some embodiments, the determining, based on the initial image, at least one target image using a positioning model may include determining a plurality of sub-images of the initial image", as shown in the figures below, the image is segmented into blocks and perpendicular to the X-axis).
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Regarding claim 5, Zhang discloses the medical image segmentation method according to claim 1, wherein the step of providing the area corresponding to the target area is providing a user interface screen including a segmentation model selection area to select the target area to be segmented in the medical image (Zhang, paragraph [0068], "For instance, the information that the user controls the performance of the imaging device may include a record that the user manually adjusts parameters of the imaging device, a user recognition of the output image of the imaging device, a part of the imaging device that is used to image by the user, a positioning region of a region of interest in the output image of the imaging device, or the like, or any combination thereof."),
and a segmentation result display area to display the area corresponding to the target area (Zhang, paragraph [0135], "In some embodiments, after the target region in each target image is segmented, the first segmentation result of the target region may be displayed directly in the target image.").
Claims 10-12, 14 corresponds to claims 1-3, 5 respectively, additionally reciting a medical image segmentation apparatus (Zhang, paragraph [0246], “The image segmentation system 1200 and its modules in the present disclosure may be implemented by a hardware circuit in a programmable hardware device in a ultra large scale integrated circuit, a gate array chip, a semiconductor such as a transistor, a field programmable gate array, a programmable logic device, a software performed by various processors, or a combination thereof (e.g., firmware)”), a communication interface (Zhang, paragraph [0081], “A computer with user interface elements may be used to implement a personal computer (PC) or another type of work station or terminal device, although a computer may also act as a server if appropriately programmed”), a memory and a processor which is operably connected to the communication interface and the memory (Zhang, paragraph [0246], “Those skilled in the art can understand that, the methods and systems described in the present disclosure may be implemented by the executable instructions of a computer and/or by control code in the processor, for example, the code supplied in a carrier medium such as a disk, a CD, a DVD-ROM, in a programmable storage such as a read-only memory (firmware), or in a data carrier such as optical signal carrier or electric signal carrier.”). Thus, claims 10-12, 14 are rejected for the same reasons of anticipation as claims 1-3, 5.
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.
Claim(s) 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang (US 20220301224 A1) in view of Bu (US 11253324 B1).
Regarding claim 4, Zhang discloses the medical image segmentation method according to claim 1, further comprising the steps of: prior to the step of generating the plurality of sub volume data, inputting the medical image to a classification model which is trained to classify a type of the target area with the medical image as an input (Zhang, paragraph [0154], "In some embodiments, the first segmentation module 430 may automatically determine the type of the target region based on the initial image"),
determining the type of the target area included in each of the plurality of slices which configures the medical image (Zhang, paragraph [0151], "In some embodiments, the feature of the target region may include a type of the target region, a size of the target region, information of a target subject, or the like, or any combination thereof. The type of the target region may include a type (e.g., the target volume, a regular OAR, a complex OAR) of the ROI in the target region and/or a type (e.g., a lung, a heart, a brain) of the tissue which the target region belongs to").
Zhang does not teach “And grouping the plurality of slices for every target area, according to a classification result”.
However, Bu teaches and grouping the plurality of slices for every target area, according to a classification result (Bu, Col. 7, Lines 12-15, "In an embodiment, each slice of an image that is being processed in a system may be classified and grouped with similar slices of an image based on features within the slice").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to group Zhang’s images based on their classified type, as taught by Bu.
The suggestion/motivation for doing so would have been to provide an organized view that allows users to recognize what part is being segmented.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Zhang in view of Bu to obtain the invention as specified in claim 4.
Claim 13 corresponds to claim 4, additionally reciting the medical image segmentation apparatus (Zhang, paragraph [0246], “The image segmentation system 1200 and its modules in the present disclosure may be implemented by a hardware circuit in a programmable hardware device in a ultra large scale integrated circuit, a gate array chip, a semiconductor such as a transistor, a field programmable gate array, a programmable logic device, a software performed by various processors, or a combination thereof (e.g., firmware)”) and the processor (Zhang, paragraph [0246], “Those skilled in the art can understand that, the methods and systems described in the present disclosure may be implemented by the executable instructions of a computer and/or by control code in the processor, for example, the code supplied in a carrier medium such as a disk, a CD, a DVD-ROM, in a programmable storage such as a read-only memory (firmware), or in a data carrier such as optical signal carrier or electric signal carrier.”). Thus, claim 13 is rejected for the same reasons of obviousness as claim 4.
Claim(s) 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang (US 20220301224 A1) in view of Carnell (US 20220222816 A1).
Regarding claim 6, Zhang discloses the medical image segmentation method according to claim 5, wherein the step of providing the area corresponding to the target area further includes: a step of combining volume data having a probability value corresponding to the target area which is equal to or higher than a predetermined value from the plurality of sub volume data using the prediction model (Zhang, paragraph [0176], "As shown in FIG. 6B, the target image determination module 420 may input the plurality of image blocks of the initial image B to the trained positioning model, and output a determination result “1” corresponding to the image blocks B.sub.1, B.sub.2, B.sub.3, and B.sub.4, indicating that the image blocks B.sub.1, B.sub.2, B.sub.3, and B.sub.4 include a portion of the target region, respectively. A determination result “0” corresponding to other image blocks may indicate that the other image blocks include no portion of the target region and are not determined as the target image").
Zhang does not teach “and displaying the combined volume data on the user interface screen”.
However, Carnell teaches and displaying the combined volume data on the user interface screen (Carnell, paragraph [0092], "The image display (105) can be configured to display one more output images—e.g., output images 107a and 107b shown in FIG. 6, which each include an overlay 108 of the data of the lesion probability map generator over the normalized MRI image data set 109.").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to display the determination result of Zhang on a display screen, as taught by Carnell.
The suggestion/motivation for doing so would have been to allow users to recognize what blocks contain a segmentation result and what does not, thereby providing feedback for analysis.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Zhang in view of Carnell to obtain the invention as specified in claim 6.
Claim 15 corresponds to claim 6, additionally reciting the medical image segmentation apparatus (Zhang, paragraph [0246], “The image segmentation system 1200 and its modules in the present disclosure may be implemented by a hardware circuit in a programmable hardware device in a ultra large scale integrated circuit, a gate array chip, a semiconductor such as a transistor, a field programmable gate array, a programmable logic device, a software performed by various processors, or a combination thereof (e.g., firmware)”) and the processor (Zhang, paragraph [0246], “Those skilled in the art can understand that, the methods and systems described in the present disclosure may be implemented by the executable instructions of a computer and/or by control code in the processor, for example, the code supplied in a carrier medium such as a disk, a CD, a DVD-ROM, in a programmable storage such as a read-only memory (firmware), or in a data carrier such as optical signal carrier or electric signal carrier.”). Thus, claim 15 is rejected for the same reasons of obviousness as claim 6.
Claim(s) 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang (US 20220301224 A1) in view of Nonaka (US 20200098090 A1).
Regarding claim 7, Zhang discloses the medical image segmentation method according to claim 5.
Zhang does not teach “wherein the step of providing the area corresponding to the target area further includes: a step of displaying each of the plurality of sub volume data on the user interface screen with a different transparency according to a probability value corresponding to the target area, using the prediction model”.
However, Nonaka teaches wherein the step of providing the area corresponding to the target area further includes: a step of displaying each of the plurality of sub volume data on the user interface screen with a different transparency according to a probability value corresponding to the target area, using the prediction model (Nonaka, paragraph [0124], "The object image generation step S20 may include a degree-of-transparency setting step of setting a degree of transparency in the pixels in the edge area of the object image according to the probability of being an object area, which has been set in the pixel in the boundary area 116 of the mask image 110.").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to have an image displayed with a transparency in accordance to Zhang’s probability, as taught by Nonaka.
The suggestion/motivation for doing so would have been to provide more visibility and focus for images that contain a target region.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Zhang in view of Nonaka to obtain the invention as specified in claim 7.
Claim 16 corresponds to claim 7, additionally reciting the medical image segmentation apparatus (Zhang, paragraph [0246], “The image segmentation system 1200 and its modules in the present disclosure may be implemented by a hardware circuit in a programmable hardware device in a ultra large scale integrated circuit, a gate array chip, a semiconductor such as a transistor, a field programmable gate array, a programmable logic device, a software performed by various processors, or a combination thereof (e.g., firmware)”) and the processor (Zhang, paragraph [0246], “Those skilled in the art can understand that, the methods and systems described in the present disclosure may be implemented by the executable instructions of a computer and/or by control code in the processor, for example, the code supplied in a carrier medium such as a disk, a CD, a DVD-ROM, in a programmable storage such as a read-only memory (firmware), or in a data carrier such as optical signal carrier or electric signal carrier.”). Thus, claim 16 is rejected for the same reasons of obviousness as claim 7.
Claim(s) 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang (US 20220301224 A1) in view of Prasad (US 20200202515 A1).
Regarding claim 8, Zhang discloses the medical image segmentation method according to claim 5.
While Zhang teaches generating the sub volume data with respect to an axis (Zhang, paragraph [0158], "As shown in FIG. 7A, an extraction interval corresponding to 3D image blocks of an initial image may be one image block evenly spaced in an X-axis direction (indicated by dashed lines)."), they do not teach the “axis including any one plane, among axial, coronal, and sagittal planes”.
However, Prasad teaches the axis including any one plane, among axial, coronal, and sagittal planes (Prasad, paragraph [0025], "A method for automatically labeling functional image volumes, such as the method depicted in FIG. 6, may include outputting labeled and segmented registered functional spine images in one or more of the coronal, axial, oblique, and sagittal, views, as requested by a user.").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to generate and display Zhang’s sub volume data using the coronal, axial, and sagittal planes, as taught by Prasad.
The suggestion/motivation for doing so would have been to provide perfect symmetry for each sub-image and distinguish the axes and rotations.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Zhang in view of Prasad discloses and the step of providing the area corresponding to the target area further includes: a step of acquiring any one reference plane to display the target area, among the axial, coronal, and sagittal planes, through the user interface screen (Prasad, paragraph [0025], "A method for automatically labeling functional image volumes, such as the method depicted in FIG. 6, may include outputting labeled and segmented registered functional spine images in one or more of the coronal, axial, oblique, and sagittal, views, as requested by a user.", as modified earlier, the planes are displayed),
and a step of rendering the area corresponding to the target area based on the reference plane (Zhang, paragraph [0158], "As shown in FIG. 7A, an extraction interval corresponding to 3D image blocks of an initial image may be one image block evenly spaced in an X-axis direction (indicated by dashed lines).", using the modified planes, Zhang’s images are rendered based on the planes).
Therefore, it would have been obvious to combine Zhang in view of Prasad to obtain the invention as specified in claim 8.
Claim 17 corresponds to claim 8, additionally reciting the medical image segmentation apparatus (Zhang, paragraph [0246], “The image segmentation system 1200 and its modules in the present disclosure may be implemented by a hardware circuit in a programmable hardware device in a ultra large scale integrated circuit, a gate array chip, a semiconductor such as a transistor, a field programmable gate array, a programmable logic device, a software performed by various processors, or a combination thereof (e.g., firmware)”) and the processor (Zhang, paragraph [0246], “Those skilled in the art can understand that, the methods and systems described in the present disclosure may be implemented by the executable instructions of a computer and/or by control code in the processor, for example, the code supplied in a carrier medium such as a disk, a CD, a DVD-ROM, in a programmable storage such as a read-only memory (firmware), or in a data carrier such as optical signal carrier or electric signal carrier.”). Thus, claim 17 is rejected for the same reasons of obviousness as claim 8.
Claim(s) 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang (US 20220301224 A1) in view of Wels (US 20120183193 A1).
Regarding claim 9, Zhang discloses the medical image segmentation method according to claim 1, further comprising the steps of: prior to the step of acquiring a medical image, acquiring learning data having a different slice thickness according to a type of the medical image (Zhang, paragraph [0143], "Merely by way of example, taking the multi-scale sliding-window as an example, if the initial image is a 2D image with 200×200 pixels, the target image determination module 420 may first obtain 190×190 image blocks from the initial image by sliding in a step of 1 through a sliding window with a scale of 10×10 pixels, then obtain 180×180 image blocks from the initial image by sliding in a step of 2 through a sliding window with a scale of 20×20 pixels, . . . , and finally obtain 190×190+180×180+ . . . image blocks. As used herein, the scale of the sliding window, the step, and/or the count (or number) of image blocks may be preset parameters.").
Zhang does not teach “generating a learning dataset by bootstrapping a different number of times for every thickness of the learning data; And generating the prediction model which is configured to predict any one target area based on the learning dataset”.
However, Wels teaches generating a learning dataset by bootstrapping a different number of times for every thickness of the learning data; and generating the prediction model which is configured to predict any one target area based on the learning dataset (Wels, paragraph [0023], " At step 108, bone lesion candidates are detected in the regions of interest in the current resolution pyramid level image using a cascade of trained detectors. In an advantageous embodiment for spinal bone lesion detection, a cascade of five detectors is used to narrow the range of possible lesion candidates during translation and scale detection. All of the detectors are trained from annotated training data in a bootstrapping matter.").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to generate training data for Zhang’s model by bootstrapping Zhang’s images, as taught by Wels.
The suggestion/motivation for doing so would have been to improve stability and accuracy of Zhang’s models.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Zhang in view of Wels to obtain the invention as specified in claim 9.
Claim 18 corresponds to claim 9, additionally reciting the medical image segmentation apparatus (Zhang, paragraph [0246], “The image segmentation system 1200 and its modules in the present disclosure may be implemented by a hardware circuit in a programmable hardware device in a ultra large scale integrated circuit, a gate array chip, a semiconductor such as a transistor, a field programmable gate array, a programmable logic device, a software performed by various processors, or a combination thereof (e.g., firmware)”) and the processor (Zhang, paragraph [0246], “Those skilled in the art can understand that, the methods and systems described in the present disclosure may be implemented by the executable instructions of a computer and/or by control code in the processor, for example, the code supplied in a carrier medium such as a disk, a CD, a DVD-ROM, in a programmable storage such as a read-only memory (firmware), or in a data carrier such as optical signal carrier or electric signal carrier.”). Thus, claim 18 is rejected for the same reasons of obviousness as claim 9.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WAYNE ZHANG whose telephone number is (571) 272-0245. The examiner can normally be reached Monday-Friday 10:00-6:00 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ms. Sumati Lefkowitz can be reached on (571) 272-3638. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WAYNE ZHANG/Examiner, Art Unit 2672
/SUMATI LEFKOWITZ/Supervisory Patent Examiner, Art Unit 2672
