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 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.
Claims 1, 16 are rejected under 35 U.S.C. 103 as being unpatentable over D11 and further in view of D22.
With regard to claim 16, D1 teach method of processing a medical image based on a neural network, in which a small bowel region is classified from medical images acquired by capsule endoscopy (see abstract: capsule endoscopy images, deep learning neural network), the method comprising: inputting the medical images to an organ classification algorithm (see abstract, fig. 4, 11: inputting images into deep learning neural network); and classifying the small blow region from the medical images by the organ classification algorithm (see abstract, fig. 4, 11, ¶¶ 81-82: classifying the small bowel region), the organ classification algorithm comprising: a convolutional neural network algorithm configured to distinguish the small bowel region by classifying organs contained in the medical image into a stomach, a small bowel and a colon (see abstract, fig. 4, 11, ¶¶ 81-82, 94: classifying the regions into pre-small bowel or stomach, small bowel and colon).
D1 fails to explicitly teach a temporal filtering algorithm linked to the convolutional neural network algorithm and configured to reduce images misclassified by the convolutional neural network algorithm. However, D2 teach the missing feature (see D2 abstract, fig. 1: temporal convolution neural network for segmentation).
One skilled in the art before the effective filing date would have found it obvious to combine the teachings to arrive at the claimed invention. In particular, D1 is related to segmentation of small bowel and colon structures from images of captured using a capsule endoscopy. Separately, D2 teaches using temporal filtering to segment images. It would have been obvious for one skilled in the art to modify the neural network of D1 with the neural network of D2 for segmenting sequence of temporal images captured by the endoscopy yielding predictable and enhanced results using temporal information.
With regard to claim 1, see discussion of claim 16. D1 inherently uses a memory and processor (see fig. 3).
Claims 2-19 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Pertinent Art
Saito et al.3 is related to classification of endoscopic images captured using a capsule endoscopy.
Conclusion
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/AVINASH YENTRAPATI/Primary Examiner, Art Unit 2672
1 US Publication No. 2023/0148834.
2 Farha, Yazan Abu, and Jurgen Gall. "Ms-tcn: Multi-stage temporal convolutional network for action segmentation." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019.
3 US Publication No. 2022/0020496.
Prosecution Insights