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 .
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 2 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2 recites “MRCP”; however, this acronym has not been defined in the claims.
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 (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 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 & 5-6 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Cho (KR 2023/0163770, citations according to attached translation).
Regarding claim 1, Cho teaches a method for creating a three-dimensional (3D) representation of the biliary tract, the method comprising the steps of:
reconstructing abdominal image data (Page 3, Paragraph 5) including the biliary tract (Page 3, Paragraph 9) captured by magnetic resonance cholangiopancreatography (Page 3, Paragraph 4);
Although CT images of the abdomen are input into the AI model (Page 4, Paragraphs 11-13), both CT and MRCP images are used to train the model (Page 3, Paragraph 4). Additionally, the Background-Art section teaches that MRI can also be performed, and that MRI results in higher-contrast images of the biliary tract than CT images. Thus, it is expected that an MRCP image can also be input into the model with expected results.
inputting the reconstructed image data into trained artificial intelligence (AI) (Page 3, Paragraph 4);
identifying the biliary tract from the reconstructed abdominal image data using the trained AI (Page 3, Paragraph 9); and
converting the identified biliary tract into a 3D representation (Background-Art, Paragraph 3 & Page 3, Paragraph 9), wherein the trained AI identifies and outputs the biliary tract from the input abdominal image data including the biliary tract (Page 3, Paragraph 9).
In addition to Background-Art teaching that MRI can create three-dimensional reconstructed images, Page 3, Paragraph 9 teaches that “texture and shape features” are extracted from the images. Texture and shape features would only be present in a 3D image.
Regarding claim 5, Cho teaches a computer-readable recording medium (processing device, Page 3, Paragraph 4) recording a computer program (artificial intelligence model, Page 3, Paragraph 7) for executing the method according to claim 1 (See rejection of claim 1).
Regarding claim 6, Cho teaches an electronic apparatus (processing device, Page 3, Paragraph 4) for executing the method according to claim 1 (See rejection of claim 1).
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 factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Cho, as applied to claim 1, above, in view of Stuber (US 2009/0027051).
Regarding claim 2, Cho teaches the method for creating a three-dimensional (3D) representation of the biliary tract according to claim 1.
However, Cho fails to disclose that the abdominal image data including the biliary tract captured by the magnetic resonance cholangiopancreatography is MRCP GRASE (gradient and spin echo) abdominal image data.
Stuber teaches that the abdominal image data is GRASE (gradient and spin echo) abdominal image data ([0122]).
Although Stuber does not explicitly teach performing MRCP, [0120] teaches imaging the pancreas. Nevertheless, MRCP is a specific type of MRI, so GRASE (a well-known MRI method) is expected to apply to MRCP as well.
It would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method of Cho such that the abdominal image data is GRASE (gradient and spin echo) abdominal image data, as taught by Stuber. GRASE is a well-known MRI method that can reduce scan times and provide increased image quality.
Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Cho, as applied to claim 1, above, in view of Wang (US 2022/0138933).
Regarding claim 3, Cho teaches the method for creating a three-dimensional (3D) representation of the biliary tract according to claim 1.
However, Cho fails to disclose that the trained AI is a U-Net model based on a deep learning model.
Wang teaches that the trained AI (ML/DL computer model 610, [0133]) is a U-Net model based on a deep learning model (U-Net neural network model 612, [0133]).
It would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method of Cho such that the trained AI is a U-Net model based on a deep learning model, as taught by Wang. U-Net models are well-known AI models used for image segmentation.
Regarding claim 4, Cho teaches the method for creating a three-dimensional (3D) representation of the biliary tract according to claim 1.
However, Cho fails to disclose: reconstructing the voxel resolution of the image data to 1.0 x 1.0 x 1.0 mm; clipping the arbitrary unit (AU) values of the reconstructed image data within a predetermined range; and converting the AU values of the reconstructed image data into values between 0 and 1.
Wang teaches:
reconstructing the voxel resolution of the image data to 1.0 x 1.0 x 1.0 mm (“The lesion segmentation stage 140…uses a watershed technique and corresponding ML/DL computer model 142 to partition the detection map to generate image element, e.g., voxel, partitioning of the medical images (slices) of the input volume 105”, [0102]);
Paragraph [0102] teaches partitioning the image into voxels. Although no dimensions are specified, the voxels will inherently have a size, and the claimed 1.0 x 1.0 x 1.0 mm size represents a non-critical value. If so desired, or under routine optimization, the voxels of Wang can be the claimed size. Paragraph [0126] suggests limiting pixel size to a minimum 0.55 mm. One having ordinary skill in the art would understand that a smaller voxel size results in greater image quality and diagnostic capabilities, albeit at the cost of additional computing power.
clipping the arbitrary unit (AU) values of the reconstructed image data within a predetermined range ([0126]); and
converting the AU values of the reconstructed image data into values between 0 and 1 ([0126]).
It would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method of Cho to include: reconstructing the voxel resolution of the image data to 1.0 x 1.0 x 1.0 mm; clipping the arbitrary unit (AU) values of the reconstructed image data within a predetermined range; and converting the AU values of the reconstructed image data into values between 0 and 1, as taught by Wang. Reconstructing the data into voxels can help in segmenting the image, and clipping and normalizing the values can simplify thresholding of the data, allowing for an improved extraction of a region of interest.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADAM KOLKIN whose telephone number is (571)272-5480. The examiner can normally be reached Monday-Friday 1:00PM-10:00PM EDT.
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/ADAM D. KOLKIN/Examiner, Art Unit 3798
/KEITH M RAYMOND/Supervisory Patent Examiner, Art Unit 3798
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