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 § 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(s) 1-4, 6, 8, 10, 12, 14, 16, 18, 22, and 24-33 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guachi et al., “Automatic Colorectal Segmentation with Convolutional Neural Network” in view of Chen et al., “A 3D Convolutional Neural Network Framework for Polyp Candidates Detection on the Limited Dataset of CT Colonography”.
Regarding claim 1, Guachi discloses a method of analyzing an abdominal computed tomography (CT) scan (Fig. 1b; Section 1. Introduction, Last Paragraph; method for automatic colon tissues segmentation based on spatial features learned with by using CNN; abdominal CT images), the method comprising:
applying a colon segmentation machine learning procedure to the CT scan (Fig. 1b; CNN is processing the input CT image), and receiving from said procedure an output indicative of a plurality of colon segments (Fig. 1b; the final segmented image is outputted from the CNN); and
(Section 1. Introduction, Third Paragraph; the colon segmentation in human abdominal CT images is the base of analysis and identification of cancer nidus, providing powerful information in a CAD, such as early polyp detection, which can reduce the incidence of colon cancer).
Guachi discloses claim 1 as enumerated above, but Guachi does not explicitly disclose feeding said output into a colon lesion detection machine learning procedure as claimed.
However, Chen discloses the colon segmentation is inputted into the CNN (Fig. 1; Section III. Methods; A. Overview).
Therefore, taking the combined disclosures of Guachi and Chen as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the colon segmentation is inputted into the CNN as taught by Chen into the invention of Guachi for the benefit of providing a better solution to polyp detection (Chen: Section V. Discussion and Conclusion).
Regarding claim 2, the method according to claim 1, Guachi in the combination further disclose wherein said output of said colon segmentation machine learning procedure comprises a colon segmentation binary mask (Fig. 2).
Regarding claim 3, the method according to claim 1, Chen in the combination further disclose wherein said output of said colon lesion detection machine learning procedure comprises a colon lesion binary mask (Fig. 3).
Regarding claim 4, the method according to claim 1, Chen in the combination further disclose comprising feeding the CT scan also into said colon lesion detection machine learning procedure (Fig. 1; Section III. Methods; A. Overview).
Regarding claim 6, the method according to claim 1, Guachi in the combination further disclose comprising defining a plurality of patches over the CT scan, wherein said colon segmentation machine learning procedure is applied separately to each patch (Fig. 1b; Section 2.3 Feature Learning).
Regarding claim 8, the method according to claim 6, Guachi in the combination further disclose comprising feeding a position of each patch to said colon segmentation machine learning procedure (Fig. 1b; Section 2.3 Feature Learning).
Regarding claim 10, the method according to claim 1, Guachi in the combination further disclose wherein said colon segmentation machine learning procedure comprises a convolutional neural network (CNN) having convolutional layers (Fig. 1a; Section 2.1 CNN architecture).
Regarding claim 12, the method according to claim 8, Guachi in the combination further disclose wherein said colon segmentation machine learning procedure comprises a convolutional neural network (CNN) having convolutional layers and a fully connected layer receiving said position together with an output from said convolutional layers (Fig. 1a; Section 2.1 CNN architecture).
Regarding claim 14, the method according to claim 1, Chen in the combination further disclose comprising defining a plurality of patches over the CT scan, wherein said colon lesion detection machine learning procedure is applied separately to each patch (Section III, A. Overview-B. Sample Generation).
Regarding claim 16, the method according to claim 1, Chen in the combination further disclose wherein said colon lesion detection machine learning procedure comprises a convolutional neural network (CNN) having convolutional layers (Figs. 1-2; Section D. Flat Residual FCN).
Regarding claim 18, the method according to claim 1, Guachi in the combination further disclose comprising acquiring said CT scan from a subject having non-empty and un-insufflated colon (Abstract; Section 1. Introduction, Third-Fourth Paragraphs).
Regarding claim 22, this claim recites substantially the same limitations that are performed by claim 1 above, and it is rejected for the same reasons.
Regarding claim 24, this claim recites substantially the same limitations that are performed by claim 1 above, and it is rejected for the same reasons.
Regarding claim 25, this claim recites substantially the same limitations that are performed by claim 2 above, and it is rejected for the same reasons.
Regarding claim 26, this claim recites substantially the same limitations that are performed by claim 3 above, and it is rejected for the same reasons.
Regarding claim 27, this claim recites substantially the same limitations that are performed by claim 4 above, and it is rejected for the same reasons.
Regarding claim 28, this claim recites substantially the same limitations that are performed by claim 6 above, and it is rejected for the same reasons.
Regarding claim 29, this claim recites substantially the same limitations that are performed by claim 8 above, and it is rejected for the same reasons.
Regarding claim 30, this claim recites substantially the same limitations that are performed by claim 10 above, and it is rejected for the same reasons.
Regarding claim 31, this claim recites substantially the same limitations that are performed by claim 12 above, and it is rejected for the same reasons.
Regarding claim 32, this claim recites substantially the same limitations that are performed by claim 14 above, and it is rejected for the same reasons.
Regarding claim 33, this claim recites substantially the same limitations that are performed by claim 16 above, and it is rejected for the same reasons.
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guachi et al., “Automatic Colorectal Segmentation with Convolutional Neural Network” in view of Chen et al., “A 3D Convolutional Neural Network Framework for Polyp Candidates Detection on the Limited Dataset of CT Colonography” and further in view of Glenn Jr., et al., US 2005/0245803.
Regarding claim 20, the method according to claim 18, Guachi and Chen in the combination do not explicitly disclose transmitting said CT scan to a remote location, wherein said applying and said feeding is executed by a computer at said remote location as claimed.
However, Glenn Jr. discloses transmitting the CT scan data to a remote computer for processing and display (Abstract; Fig. 1; para 0039, 0062-0064, and 0133).
Therefore, taking the combined disclosures of Guachi, Chen and Glenn Jr. as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate transmitting the CT scan data to a remote computer for processing and display as taught by Glenn Jr. into the inventions of Guachi and Chen for the benefit of providing for rapid evaluation of the tissue structures of tubular structure and the accurate determination of any abnormalities present (Glenn Jr.: para 0003).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Evans et al., US 2021/0398676 discloses systems for preparing, training, and deploying a machine learning algorithm for making medical condition state determinations include at least one processing unit that includes the machine learning algorithm.
Collins et al., US 2010/0021026 discloses methods and systems are presented that improve a radiologist's ability to identify polyps by automatically and more accurately detecting and displaying colonic residue such as tagged or untagged stool or colonic fluid in medical images of the colorectal region.
Gilinsky et al., US 11,934,491 discloses a method for image classification includes accessing a plurality of images of at least a portion of a gastrointestinal tract (GIT) captured by a capsule endoscopy device and for each image of the plurality of images.
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/VAN D HUYNH/Primary Examiner, Art Unit 2665