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 .
Election/Restrictions
Claims 1-14 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected Group, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 06/04/2026.
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.
Claims 16-20 are 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.
Regarding claim 16, the claim limitations render the claim indefinite because it is unclear and confusing what the limitation “collecting system of a kidney” refers to. The applicant’s invention appears to be segmenting kidney and kidney stones/tumors, instead of a particular portion of a kidney. Please amend the claim for clarification.
Claim Rejections - 35 USC § 102
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.
Claims 15-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Stoebner et al. (“Segmentation of kidney stones in endoscopic video feeds”).
Regarding claim 15, Stoebner discloses:
generating training, validation, and testing datasets, wherein the training, validation, and testing datasets each include a plurality of images, wherein one or more images of the plurality of images are annotated with a location of one or more anatomical features of an anatomical region in the image (see section 2.2, training, validation, and testing datasets each including multiple endoscopic images, wherein some of the multiple endoscopic images are annotated with location and boundary of kidney stones of a kidney);
training a computational model, including: training the computational model on the training dataset using forward propagation and loss computation to adjust one or more parameters of the computational model, and validating the computational model on the validation dataset (see section 2.2, training a segmentation model on the training dataset using forward propagation and loss computation, adjusting parameters by back propagation, with frequent validation checks);
receiving a frame of a video feed from an endoscope; inputting the frame into the computational model; generating, via the computational model, an output, wherein the output includes data indicating one or more locations of the frame containing an anatomical feature (see section 3.3, a frame of an endoscopic video is inputted into the trained segmentation model, which generates an output indicating locations of the frame containing kidney stones); and
adjusting a visual display that includes the frame with an overlay based on the data indicating one or more locations of the frame containing the anatomical feature (see fig 4, highlighting the kidney stones in the frame for display).
Regarding claim 16, Stoebner further discloses: wherein the anatomical region is a collecting system of a kidney, and the feature of the anatomical region is a kidney phenomenon (see rejection of claim 15, kidney and kidney stones).
Regarding claim 17, Stoebner further discloses: wherein the kidney phenomenon is one of: a kidney stone; a kidney stone fragment; or a tumor (see rejection of claim 15, kidney stones).
Regarding claim 18, Stoebner further discloses: wherein generating the training, validation, and testing datasets includes:
receiving the plurality of images (see section 2.1, receiving video frames); and
for each image in the plurality of images, using a computer vision library to determine one or more contours, selecting a closed contour of the one or more contours, and annotating the image based on a bounding box associated with the selected closed contour (see section 2.1, for each video frame, using OpenCV to select closed contour with the greatest area to identify a bounding box and annotating such as kidney stones).
Regarding claim 19, Stoebner further discloses: wherein validating the computational model on the validation dataset includes computing one or more Sorenson-Dice coefficients (see sections 2.3 and 3.2 and Table 1, the Dice score computed as a validation statistics for U-Net, U-Net++, and DenseNet).
Regarding claim 20, Stoebner further discloses: wherein the computational model includes at least one of: U-Net; U-Net++; or DenseNet (see rejection of claim 19, U-Net, U-Net++, and DenseNet).
Claim 15 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by Negassi et al. (“Application of artificial neural networks for automated analysis of cystoscopic images: a review of the current status and future prospects”).
Regarding claim 15, Negassi discloses:
generating training, validation, and testing datasets, wherein the training, validation, and testing datasets each include a plurality of images, wherein one or more images of the plurality of images are annotated with a location of one or more anatomical features of an anatomical region in the image (see 2nd paragraph on right column of p2355, generating training, validation, and test datasets each including multiple endoscopic images; and see 1st para on left col of p2355, wherein the multiple endoscopic images are annotated with location of abnormalities of an organ, as shown in fig 6, middle column);
training a computational model, including: training the computational model on the training dataset using forward propagation and loss computation to adjust one or more parameters of the computational model, and validating the computational model on the validation dataset (see 2nd para on right col on p2351, 1st para on left col on p2352, 1st para on right col on p2353, and 1st para on right col on p2355, training a segmentation network based on the training dataset using forward propagation and loss to adjust weight parameters of the segmentation network via back propagation; and see 2nd para on right col of p2355, the validation dataset is used for validation);
receiving a frame of a video feed from an endoscope; inputting the frame into the computational model; generating, via the computational model, an output, wherein the output includes data indicating one or more locations of the frame containing an anatomical feature (see 2nd para on right col of p2355, a test image from the test dataset is inputted into the trained segmentation network, which generates an output indicating a location of abnormalities in the test image); and
adjusting a visual display that includes the frame with an overlay based on the data indicating one or more locations of the frame containing the anatomical feature (see right col of fig 6, highlighting the outputted abnormalities in the test image for display).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Luo et al. (US 2021/0406591) and Rivlin et al. (US 12,217,449) discloses a segmentation network trained to segment endoscopic frames.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SJ PARK whose telephone number is (571)270-3569. The examiner can normally be reached M-F 8:00 AM - 5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, EMILY TERRELL can be reached at 571-270-3717. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SJ Park/Primary Examiner, Art Unit 2675
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