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 .
Applicant’s Response
In Applicant’s Response dated 4/7/26, the Applicant amended Claims 1, 11 and 16, and argued claims previously rejected in the Office Action dated 1/13/26. Claims 1-20 are pending examination.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/7/26 has been entered.
Claim Rejections - 35 USC § 103
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 1-3, 11-13 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Barral et al., United States Patent No. 9833254 (hereinafter “Barral”), in view of Yin et al., "DeepRayburst for Automatic Shape Analysis of Tree-Like Structures in Biomedical Images," (hereinafter “Kimia”), in further view of Rhoads et al., United States Patent Publication 20130273968 (hereinafter “Rhoads”).
Claim 1:
Barral discloses:
A computationally rapid method of identifying and avoiding blood vessels in robotic surgery, the method comprising:
receiving a two-dimensional image of biological tissue having visible blood vessels (see column 4 lines 62 – column 5 line 22). Barral teaches receiving a 2D image of tissue including blood vessels.;
generating a difference of the image (see column 5 lines 22-59). Barral teaches generating difference between the locations of the incisions vs the location of the tissue based on the image;
segmenting, in at least one computer processor, the image in order to identify a vascular segment (see column 6 lines 33-68). Barral teaches segmenting the image into regions to identify the vascular segments;
selecting a minimum distance from the distances, the minimum distance identified as a local diameter of the vascular segment (see column 10 lines 38-63). Barral teaches using different sensors to calculate location between the tissue and surgical system, finding the best way to safely perform the surgery;
labeling pixels within the vascular segment based on the diameter (see column 5 lines 3-22). Barral teaches identifying and labeling regions of the images that include vascular segments;
receiving coordinates in the image to be targeted by a surgical robotic end effector (see column 4 lines 62-67). Barral teaches receiving coordinates/locations in the image to be targets/desired dissection for the surgical robot;
determining whether any pixel within a predetermined distance of the coordinates is labeled based on the diameter (see column 5 lines 3-22). Barral teaches determining the target tissue and vascular areas of the images and determining if during the surgery, if any of the identified vascular data is nearby; and
halting or redirecting the end effector based on the determining (see column 5 lines 3-22 and column 18 lines 1-17, 56-62). Barral teaches redirecting the path based on the location of the blood vessels.
Barral fails to expressly disclose calculating a Gaussian difference within the image.
Yin discloses:
projecting multiple rays from a side of the segment to an opposite side of the segment in the difference of Gaussians of the image to determine distances across the segment (see page 2 2nd paragraph). Yin teaches projecting multiple rays parallel rays from one side to another to determine the distances;
segmenting, in at least one computer processor, the difference of Gaussians of the image in order to identify a vascular segment (see page 4, 2nd column). Yin teaches using the calculated Gaussian differences to identify vascular segments within the images.
Accordingly, it would have been obvious to one having ordinary skill in the art before the claimed invention was made to modify the method taught by Barral to include generating Gaussian differences and projecting rays across the images to help identify vascular segments within the image for the purpose effectively identifying and confirming the shapes of the vascular segments within the images, as taught by Yin.
Barral and Yin fail to expressly disclose applying Gaussian blurs to an image.
Rhoads discloses:
generating a difference of Gaussians of the image by applying a first gaussian blur to the image to produce a first blurred image, applying a second gaussian blur to the image to produce a second blurred image, and subtracting the second blurred image from the first blurred image (see paragraphs [0815] and [1351]). Rhoads teaches
Accordingly, it would have been obvious to one having ordinary skill in the art before the claimed invention was made to modify the method taught by Barral and Yin to include generating Gaussian differences by subtracting the difference in blurred images for the purpose of effectively identifying features to extract vessels, as taught by Rhoads.
Claim 2:
Barral discloses:
comparing the diameter labeled in a pixel to a predetermined threshold, wherein the halting or redirecting is based on the diameter being greater than a threshold diameter (see paragraph [0018]). Barral teaches comparing the location and shape targeted in the image to a threshold to determining if the surgery path needs to be changed.
Claim 3:
Barral fails to expressly disclose generating diameters of the vascular segment.
Yin discloses:
determining multiple diameters of the vascular segment (see pages 5-6, Neural Shape Reconstruction, Section: Soma Shape Reconstruction). Yin teaches determining multiple diameters/distances of the vascular segment; and
selecting a maximum of the diameters for the labeling of all of the pixels within the vascular segment (see pages 5-6, Neural Shape Reconstruction, Section: Soma Shape Reconstruction). Yin teaches selecting the value having the max distance for identifying the vascular segment.
Accordingly, it would have been obvious to one having ordinary skill in the art before the claimed invention was made to modify the method taught by Barral to include determining distances for vascular segments for the purpose effectively identifying and confirming the shapes of the vascular segments within the images, as taught by Yin.
Claims 11-13:
Although Claims 11-13 are medium claims, they are interpreted and rejected for the same reasons as the method of Claims 1-3.
Claims 16-18:
Although Claims 16-18 are system claims, they are interpreted and rejected for the same reasons as the method of Claims 1-3.
Claims 4, 14 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Barral, in view of Yin and Rhoads, in further view of LV et al., United States Patent Publication 2020/0234445 (hereinafter “LV”).
Claim 4:
Barral, Yin and Rhoads fail to expressly disclose extracting green channel of the image.
LV discloses:
extracting a green channel of the received image for the generating of the difference of Gaussians of the image, whereby the green channel efficiently contrasts red blood vessels from surrounding tissue (see paragraph [0049]). LV discloses extracting a green channel of the image and using Gaussian differences to denoise the image contrast other colors in the images.
Accordingly, it would have been obvious to one having ordinary skill in the art before the claimed invention was made to modify the method taught by Barral, Yin and Rhoads to include extracting green channel to contrast particular colors for the purpose effectively denoising images to obtain a correct image, as taught by LV.
Claim 14, 19:
Although Claim 14 is a medium claim and Claim 19 is a system claim, they are interpreted and rejected for the same reasons as the method of Claim 4.
Claims 5, 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Barral, in view of Yin and Rhoads, in further view of Matsuoka et al., United States Patent Publication 2007/0008498 (hereinafter “Matsuoka”).
Claim 5:
Barral, Yin and Rhoads fail to expressly disclose projecting rays at fixed angles.
Matsuoka discloses:
selecting a point on the side of the segment; and determining a direction that is normal to the side of the segment at the point, wherein the rays are projected at a fixed set of angles around the normal (see Claim 8). Matsuoka discloses determining a normal side to an image and projecting rays at set angles such as 45 degrees.
Accordingly, it would have been obvious to one having ordinary skill in the art before the claimed invention was made to modify the method taught by Barral, Yin and Rhoads to include projecting rays on a normal side at 45 degrees for the purpose correctly projecting rays onto an image, as taught by Matsuoka.
Claim 6:
Barral, Yin and Rhoads fail to expressly disclose projecting rays at 45 degrees.
Matsuoka discloses:
wherein the rays are projected at a maximum of +/-30° or +/-45° from the normal direction (see Claim 8). Matsuoka discloses projecting rays at set angles such as 45 degrees.
Accordingly, it would have been obvious to one having ordinary skill in the art before the claimed invention was made to modify the method taught by Barral, Yin and Rhoads to include projecting rays on a normal side at 45 degrees for the purpose correctly projecting rays onto an image, as taught by Matsuoka.
Claim 15, 20:
Although Claim 15 is a medium claim and Claim 20 is a system claims, they are interpreted and rejected for the same reasons as the method of Claim 5.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Barral, in view of Yin, in further view of Marion and Rhoads, United States Patent Publication 20060045330.
Claim 7:
Barral, Yin and Rhoads fail to expressly disclose a HSV filter to remove shadows.
Marion discloses:
thresholding the image using a hue-saturation-value (HSV) filter in order to remove a cast shadow from the end effector (see Claim 8). Marion discloses using a HSV filter on an image to remove a shadow.
Accordingly, it would have been obvious to one having ordinary skill in the art before the claimed invention was made to modify the method taught by Barral, Yin and Rhoads to include an HSV filter to remove shadows from images for the purpose efficiently removing shadows from images, as taught by Marion.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Barral, Yin, and Rhoads, in view of Kimia et al., United States Patent Publication 2010/0159497 (hereinafter “Kimia”).
Claim 8:
Barral, Yin and Rhoads fail to expressly disclose generating diameters of the vascular segment.
Kimia discloses:
cropping the image from a larger image; and downsampling the image (see paragraphs [0048] and [0049]). Kimia teaches specific regions of the cross-section may be divided into smaller areas to obtain more granular spatiotemporal information. Also, the cross-section may be segmented and/or labeled before being binned and/or spatially divided.
Accordingly, it would have been obvious to one having ordinary skill in the art before the claimed invention was made to modify the method taught by Barral, Yin and Rhoads to include downsampling images for the purpose effectively analyzing specific regions of the images, as taught by Kimia.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Barral, in view of Yin and Rhoads, in further view of Braido et al, United States Patent Publication 20210369394 (hereinafter “Braido”).
Claim 9:
Barral, Yin and Rhoads fail to expressly disclose performing this method quickly.
Braido discloses:
wherein method is optimized to be executed on the computer processor within 20 milliseconds (see paragraph [0256]). Braido discloses performing this method in milliseconds. Braido teaches a method that merges imaging and other sensor data to adjust device configurations, settings, and/or implementations in real-time (or near-real-time (e.g., within milliseconds or seconds, etc.)) based on any updates or changes to the sensor data, imaging data, tracking data, and/or recommendations obtained during the Intra-Operative Adjustments Stage.
Accordingly, it would have been obvious to one having ordinary skill in the art before the claimed invention was made to modify the method taught by Barral, Yin and Rhoads to include performing adjusting to the surgery in milliseconds for the purpose efficiently identifying performing pre-operative measures for navigation, as taught by Braido.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Barral, in view of Yin and Rhoads, in further view of Wang et al, United States Patent Publication 2017/033687 (hereinafter “Wang”).
Claim 10:
Barral, Yin and Rhoads fail to expressly disclose using a particular segmentation process.
Wang discloses:
wherein the segmenting is performed by UNet 2 semantic segmentation, ENet semantic segmentation, or Hessian segmentation (see paragraph [0139]). Wang discloses using a Hessian segmentation on image to identify blood vessels.
Accordingly, it would have been obvious to one having ordinary skill in the art before the claimed invention was made to modify the method taught by Barral, Yin and Rhoads to include a Hessian segmentation to identify blood vessels for the purpose efficiently identifying blood vessels within images, as taught by Wang.
Response to Arguments
Applicant’s arguments, see REM, filed 4/7/26, with respect to the rejections of claims 1-20 under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of Barral, Yin and Rhoads.
Applicant argues However, no combination of Barral and Kimia teaches or suggests, at least, the features of generating a difference of Gaussians of the image by applying a first gaussian blur to the image to produce a first blurred image, applying a second gaussian blur to the image to produce (I second blurred image, and subtracting the second blurred image from the first blurred image; segmenting, in at least one computer processor, the difference of Gaussians of the image in order to identify a vascular segment; and projecting multiple rays from a side of the segment to an opposite side of the segment in the difference of Gaussians of the image to determine distances across the segment, as is recited in Applicant's Claim 1.
The Examiner agrees that Barral and Yin does not teach the amended limitation.
The Examiner introduced new art, Rhoads, to teach generating a difference of gaussians by subtracting two images with applied gaussian blurs. See the above rejections of Claims 1, 11 and 16.
Applicant argues Furthermore, nothing in Wang, Matsuoka, Braido, LV, Marion, and Kimia can cure the aforementioned deficiencies in Barral and Yin.
The Examiner agrees that Wang, Matsuoka, Braido, LV, Marion, and Kimia does not disclose the amended limitations.
The combination of Barral, Yin and Rhoads teaches the limitations of Claims 1, 11 and 16.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TIONNA M BURKE whose telephone number is (571)270-7259. The examiner can normally be reached M-F 8a-4p.
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/TIONNA M BURKE/Examiner, Art Unit 2178 4/18/26
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