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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zur (US 20200387706) in view of Refai et al. (US 20200281454).
Regarding claim 1, Zur discloses a method of analyzing and displaying medical videos during an endoscopy procedure using a camera endoscope having camera (imaging probe 212 – see include, but are not limited to, figures 2, 6-7, 16-17, paragraphs 0045, 0047, 0049-0051), comprising:
receiving a first medical video from a camera (receiving a first medical video/images captured by endoscopic camera – see include, but are not limited to, figures 1-2, 6-7, 16-17, paragraphs 0049-0051);
receiving a second medical video from a camera (receiving a second medical video/images captured by endoscopic camera – see include, but are not limited to, figures 1-2, 6-7, 16-17, paragraphs 0049-0051);
detecting one or more target features of the second medical video using a pre-trained target feature detector comprising a neural network (detecting one more target features of polyp of the second video images using a pretrained target feature detector/polyp detector comprising a neural network – figures 1-2, 8-9, 15, paragraphs 0005-0006, 0010-0011, 0014, 0020, 0051, 0058);
generating an alert indicating the detection of the one or more target features in the second medical video (generating an alert/indicator/box/ROI indicating the detection of one or more targets feature of polyp in the second media video/images – see include ,but are not limited to, figures 1, 6, 16-17, paragraphs 0054, 0059, 0208, 0213, 0216, 0219); and
displaying the first medical video and the alert on a display (displaying the video image and the alert/indicator/box/ROI of polyp on a display – see include ,but are not limited to, figures 1, 6, 16-17, paragraphs 0054, 0059, 0208, 0213, 0216, 0219, 0221-0223) .
Zur does not explicitly disclose the camera endoscope comprises multi-camera endoscope having a first camera and a second camera; wherein first medical video from a first camera, second video from a second camera.
Refai discloses a method of analyzing and displaying medial videos during an endoscopy procedure using a multi-camera endoscope having a firs camera (e.g., 20a) and a second camera (20b) – see include, but are not limited to, figures 1, 11-15b, 17-23, 25, 36b-36l, paragraphs 0058, 0093-0094);
receiving a first medical video from a first camera (receiving a first video from one camera (e.g., 20a) - – see include, but are not limited to, figures 1, 11-15b, 17-23, 25, 36b-36l, paragraphs 0058, 0093-0094) ;
receiving a second medical video from a second camera (receiving a second video from a second camera such as camera 20b – see include, but are not limited to, figures 1, 11-15b, 17-23, 25, 36b-36l, paragraphs 0058, 0093-0094),
detecting one or more target features of second medical video using a pre-trained target feature detector (see paragraphs 0133-0136);
generating an alert indicating the detection of the one or more target features in the second medical video (generating an alert/indicator indicating the detection of the one or more feature of polyp in video image – see include, but are not limited to, figures 11-15, 25, 36c-36d, paragraphs 0042-0043,0051, 0093, 0121, 0133-0134, 0139, 0175, 0177) ;
display media video and the alert on a display (see for example, figure 36d, paragraphs 0175, 0177, 0182).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zur with the teaching comprising multi-camera endoscope having a first camera and a second camera; wherein first medical video from a first camera, second video from a second camera as taught by Refai in order to yield predictable result of improving accuracy and success rate (see paragraph 0002-0004, 0062).
Regarding claim 2, Zur in view of Refai discloses the method of claim 1, wherein the endoscopy procedure is a colonoscopy and wherein the one or more target features comprises a polyp (polyp 604/1904 – see for example, Zur: figures 6, 16, paragraphs 0009-0010, 0115, 0125; Refai: figure 36d).
Regarding claim 3, Zur in view of Refai discloses the method of claim 1, wherein the first medical video comprises a first field of view and the second medical video comprises a second field of view (see include, but are not limited to, Refai: figures 12-14).
Regarding claim 4, Zur in view of Refai discloses the method of claim 3, wherein the first field of view is different than the second field of view (see include, but are not limited to, Refai: figures 12-14).
Regarding claim 5, Zur in view of Refai discloses the method of claim 4, further comprising determining a location of the second field of view relative to the first field of view (see include, but are not limited to, Refai: figures 12-19, paragraphs 0103-0104).
Regarding claim 6, Zur in view of Refai discloses the method of claim 5, wherein detecting one or more target features comprises detecting a location of the one or more target features in the second field of view (detecting a location of the one or more features of polyp in the second field of view – see include, but are not limited to, Zur: figures 1, 4-6, 15-17; Refai: figures 12-16, 36b-36d, paragraphs 0051, 0055, 0093, 0108).
Regarding claim 7, Zur in view of Refai discloses the method of claim 6, wherein the alert indicates a location of the one or more target features relative to the first field of view (the alert/indicator/marked area/ROI indicate location of one or more features relative to the first field of view of the capture image – see include, but are not limited to, Zur: figures 1, 6, 15-17, paragraphs 0208-0211, 0221-0223; Refai: figures 12-16, 36b-36d, paragraphs 0055, 0093, 0108).
Regarding claim 8, Zur in view of Refai discloses the method of claim 1, wherein the alert comprises a part of the second medical video comprising the one or more target features (part of second video/frame comprising one or more target feature of polyp– see include, but are not limited to, Zur: figures 1, 6, 15-17, paragraphs 0208-0211, 0221-0223; Refai: figures 12-16, 36b-36d, paragraphs 0055, 0093, 0108).
Regarding claim 9, Zur in view of Refai discloses the method of claim 8, wherein the alert comprises a box encompassing one or more of the one or more target features (box for polyp such as 604/1904 – see include, but are not limited to, Zur: figures 6, 16-17, paragraphs 0208-0210, 0221-0223, or see Refai: figure 16d).
Regarding claim 10, Zur in view of Refai discloses the method of claim 1, wherein the first camera and the second camera are coupled to an endoscope (camera 20s, 20a, 20b, are coupled to an endoscope - see include, but are not limited to, Refai: figures 1-2a, 11-14, paragraphs 0004, 0040, 0093-0094).
Regarding claim 11, Zur in view of Refai discloses the method of claim 1, wherein the target feature detector comprises a domain- specific pre-trained machine learning model (see include, but are not limited to, Zur: paragraphs 0058, 0132-0136, 0140; Refai: paragraphs 0132-0136).
Regarding claim 12, limitations of a system that correspond to the limitations of a method of claim 1 are analyzed as discussed in the rejection of claim 1. Particularly, Zur in view of Refai discloses a system for analyzing and displaying medical videos during an endoscopy procedure, comprising:
an endoscope comprising a first camera and a second camera (see discussion in the rejection of claim 1 and include, but are not limited to, Zur: figures 1-2; Refai: figures 1-2, 12-16d);
a display (see discussion in the rejection of claim 1, and Zur: figures 2, 6, 16-17; Refai: figures 1-2, 11-16d); and
a processor configured to:
receive a first medical video from the first camera;
receive a second medical video from the second camera;
detect one or more target features of the second medical video using a target feature detector;
generate an alert indicating the detection of the one or more target features in the second medical video; and
display the first medical video and the alert on the display (see similar discussion in the rejection of claim 1).
Regarding claims 13-17, the additional limitations of the system that correspond to the additional limitations of the method of claims 3-7 are analyzed as discussed in the rejection of claims 3-7.
Regarding claim 18, limitations of a system that correspond to the limitations of claims 1, 3, are analyzed as discussed in the rejection of claims 12. In particular, Zur in view of Refai discloses a system for analyzing and displaying medical videos during an endoscopy procedure, comprising:
a first camera configured to receive a first medical video comprising a first field of view;
a second camera configured to receive a second medical video comprising a second field of view different from the first field of view;
a display configured to display at least the first medical video;
a processor configured to:
receive the second medical video;
detect a target feature of the second medical video using a pre-trained target feature detector comprising a neural network;
generate an alert indicating the detection of the target feature in the second medical video; and
display the alert on the display (see similar discussion in the rejection of claims 1, 3 and include, but are not limited to, Zur: figures 1-2, 6, 15-17; Refai: figures 1-3, 12-16d).
Regarding claim 19, Zur in view of Refai discloses the system of claim 18, wherein the alert is displayed adjacent to the first medical video (see include, but are not limited Zur: figures 6, 16, 17; Refai: figures 36b-36d).
Regarding claim 20, Zur in view of Refai discloses the system of claim 18, wherein the alert comprises a part of the second medical video comprising the target feature (see include, but are not limited Zur: figures 6, 16, 17; Refai: figures 36b-36d).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Evans et al. (US 20210398676) discloses machine learning algorithm for detecting medical conditions, related systems, and methods.
Tearney et al. (US 20160150944) discloses imaging apparatus comprises multiple cameras.
Levy (US 10674897) discloses dynamic field of view endoscope.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AN SON P HUYNH whose telephone number is (571)272-7295. The examiner can normally be reached 9:00 am-6:00 pm.
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/AN SON P HUYNH/Primary Examiner, Art Unit 3795
October 3, 2026
