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 .
Response to Amendment
The Amendment filed May 4th 2026 has been entered. Claims 1 and 4-6 are pending in the application. Claims 2-3 were previously cancelled. Applicant’s amendments to the Claims 1, 4, and 6 have overcome the rejections previously set forth in the Final Office Action mailed December 2nd 2025. A further search has been performed to address the material amended in the aforementioned claims. Newly found reference Fraz: (NPL: Blood vessel segmentation methodologies in retinal images–A survey) was used for the newly amended claim limitations.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1 and 4-6 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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, 4, 5, and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Mei (US 20210319551 A1) in view of Tanabe (WO 2019203309 A1; hereinafter Tanabe ’09), Tanabe (WO 2019203311 A1, hereinafter Tanabe ‘11) and Fraz: (NPL: Blood vessel segmentation methodologies in retinal images–A survey).
Regarding claim 1:
Mei teaches:
An image processing method (Mei: a three dimensional (3D) quantification method [0005]) performed by a processor (Mei: The above-described aspects are envisioned to be implemented via hardware and/or software by a processor [0036]), the image processing method comprising:
acquiring OCT volume data (Mei: acquiring 3D optical coherence tomography (OCT) volumetric data of an object of a subject [0005]) including a choroid (see Note 1A);
extracting a first choroidal vessel based on the OCT volume data and
generating a first three-dimensional image of the first choroidal vessel (Mei: performing a first segmentation technique on the pre-processed data, thereby producing first segmented data, the first segmentation technique being configured to segment the physiological component from the pre-processed data [0006]; see Note 1B);
extracting a second choroidal vessel based on the OCT volume data and
generating a second three-dimensional image of the second choroidal vessel (Mei: performing a second segmentation technique on the pre-processed data, thereby producing second segmented data, the second segmentation technique being configured to segment the physiological component from the pre-processed data [0006]; see Note 1B); and
generating a three-dimensional image of a choroidal vessel by combining the first three- dimensional image and the second three-dimensional image (Mei: producing the 3D segmented data by combining the first segmented data and second segmented data [0006]).
Note 1A: Mei teaches: “Briefly, the analysis is performed on, and the visualizations are created by, segmenting OCT data for a component of interest (e.g., choroidal vasculature)” [0014]. The analysis of choroidal vasculature requires that a choroid be present.
Note 1B: Mei teaches that a “physiological component” may be a choroidal vessel: “the physiological component is choroidal vasculature” [0006]. Furthermore, Mei teaches that the “pre-processed data” may be OCT volume data, which is used to generate 3D data based on the vessel: “acquiring 3D optical coherence tomography (OCT) volumetric data of an object of a subject, […] pre-processing the volumetric data, thereby producing pre-processed data; segmenting a physiological component of the object from the pre-processed data, thereby producing 3D segmented data;” [0005].
Mei fails to explicitly teach:
acquiring OCT volume data including a choroid by scanning a region of a fundus including a vortex vein;
extracting a first choroidal vessel that is a line shaped portion of the vortex vein by extracting linearly extending blood vessel regions by line shaped extraction processing and binarization processing based on the OCT volume data
extracting a second choroidal vessel that is a bulge portion of the vortex vein by binarization processing based on the OCT volume data
The image processing unit 182 of the management server 140 extracts retinal blood vessels from the second fundus image (G color fundus image) by performing black hat filter processing on the second fundus image (G color fundus image).
Tanabe ’09 teaches:
acquiring OCT volume data including a choroid by scanning a region of a fundus including a vortex vein (Tanabe ‘09: Various fundus images are used to generate choroidal blood vessel images, (Pg. 4, par. 4); Tanabe ’09: the fundus image may be […] an image obtained by OCT angiography (Pg. 8, par. 3, Fifth Modification); see Note 1D);
Note 1C: The English translated documents supplied may have missing figure numbers due to OCR error. The Figure numbers cited by the Examiner were found by cross-referencing with the original Japanese document, which is included after the OCR English translation.
Note 1D: Tanabe ’09 teaches “choroidal blood vessel images” generated from “fundus images” which in turn are acquired by an OCT camera. Additionally, Tanabe teaches: “there are many cases where VVs exist at the four corners in the choroidal blood vessel image as shown in FIG. In FIG. 9, 246N1, 246N2, 246N3, and 246N4 indicate frames for specifying the VV position.” (Pg. 4, par. 6). That is, Figure 9 depicts multiple vortex veins “VV” in a choroidal blood vessel image. Therefore, it must be that originally, the OCT camera “acquir[ed] OCT volume data including a choroid by scanning a region of a fundus including a vortex vein”. In Note 1B above, it was shown that Mei teaches that the OCT data may be OCT volume data. When the teachings of Tanabe ’09 are combined with Mei, it would be obvious to one of ordinary skill in the art to generate a choroidal blood vessel image from the OCT volume data.
Note 1F: Mei teaches in [0006] cited above that three-dimensional images may be generated based on “choroidal vasculature” above. It would be obvious to one of ordinary skill in the art to generate a three-dimensional image based on a selection of said choroidal vasculature.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Tanabe ‘09 with Mei. Acquiring OCT volume data including a choroid by scanning a region of a fundus including a vortex vein; extracting a first choroidal vessel that is a line shaped portion of the vortex vein by line shaped extraction processing and binarization processing based on the OCT volume data and generating a first three-dimensional image of the first choroidal vessel, as in Tanabe ‘09, would benefit the Mei teachings by ensuring that a segmented blood vessel is choroidal instead of retinal or otherwise.
Mei in view of Tanabe ’09 still fails to teach:
extracting a first choroidal vessel that is a line shaped portion of the vortex vein by extracting linearly extending blood vessel regions by line shaped extraction processing and binarization processing based on the OCT volume data
extracting a second choroidal vessel that is a bulge portion of the vortex vein by binarization processing based on the OCT volume data
Tanabe ‘11 teaches:
a extracting a second choroidal vessel that is a bulge portion of the vortex vein by binarization processing (Tanabe ’11: In step 1240, the image processing unit 182 sets a circle 404 having a predetermined radius centered on the VV position 402 in the generated binarized image, as shown in FIG. 17, (Pg. 7, par. 9); see Note 1G) based on the OCT volume data (see Note 1H) and generating a second three-dimensional image of the second choroidal vessel (Tanabe: a choroidal blood vessel image is generated from the first fundus image, Pg. 3, par. 8; see Note 1F);
Note 1G: Tanabe ’11 teaches: “In step 1238, the image processing unit 182 generates a binarized image from the extracted image of the predetermined area. In step 1240, the image processing unit 182 sets a circle 404 having a predetermined radius centered on the VV position 402 in the generated binarized image, as shown in FIG. 17” (Pg. 7, par. 9). In Fig. 17, the “circle 404” is centered on the intersection, or “bulge” created by the veins – Tanabe ‘11 teaches that “The radius of the circle 404 may be set based on the blood vessel traveling pattern around the VV position” (Pg. 7, par. 9). Therefore, as best understood by the Examiner, when Tanabe ‘11 sets the circle 404, Tanabe effectively extracts a position of the “bulge” of a vortex vein.
Note 1H: Tanabe ‘11 teaches that the OCT data may be 3D OCT data: “In each of the above embodiments, a choroidal blood vessel image is analyzed. However, the technology of the present disclosure is not limited to this, and for example, an OCT-En Face image (fundus image constructed from 3D OCT data), […] etc. Also good,” (Pg. 8, First Modification)
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Tanabe ‘11 with Mei in view of Tanabe ‘09. Extracting a second choroidal vessel that is a bulge portion of the vortex vein by binarization processing based on the OCT volume data and generating a second three-dimensional image of the second choroidal vessel, as in Tanabe ‘11, would benefit the Mei in view of Tanabe ’09 teachings by enabling detection of the positions where choroidal veins intersect.
Mei in view of Tanabe ’09 and Tanabe ’11 still fails to teach:
extracting a first choroidal vessel that is a line shaped portion of the vortex vein by extracting linearly extending blood vessel regions by line shaped extraction processing and binarization processing based on the OCT volume data
Fraz teaches:
extracting (Fraz: Fraz et al. [61] have proposed a unique combination of vessel centerlines detection and morphological bit plane slicing to extract the blood vessel tree from the retinal images, Pg. 13, col. 2, par. 4 – Pg. 14, col. 1, par. 1) a first choroidal vessel that is a line shaped portion of the vortex vein (see Note 1I) by extracting linearly extending (Fraz: The orientation and gray level of a vessel does not change abruptly; they are locally linear, Pg. 2, 2.2, par. 1) blood vessel regions by line shaped extraction processing (Fraz: Staal [32] presented a ridge based vessel segmentation methodology from colored images of the retina which exploits the intrinsic property that vessels are elongated structures. […] The image is partitioned into convex set regions by assigning each image pixel to the closest line element from these sets, Pg. 8, col. 2, par. 3, emphasis added) and binarization processing (Fraz: The highest response of filter is selected for each pixel and is thresholded to provide a binary vessel image. Further post processing is then applied to prune and identify the vessel segments, Pg. 11, col. 2, par. 1) based on the OCT volume data (Fraz: Three-dimensional optical coherence tomography (3D OCT) imaging is used to obtain detailed images from within the retina, Pg. 23, col 1, par. 4) and
Note 1I: Fraz teaches segmentation of “retinal vasculature [that] is composed of arteries and veins appearing as elongated features” (Pg. 2, Section 2.2: Retinal vessel segmentation, par. 1). When combined with the teachings of Mei in view of Tanabe ’09 which teaches blood vessel images containing choroid and vortex veins, it would be obvious to one of ordinary skill in the art to “extracting a first choroidal vessel that is a line shaped portion of the vortex vein”.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Fraz with Mei in view of Tanabe ’09 in view of Tanabe ‘11. Extracting a first choroidal vessel that is a line shaped portion of the vortex vein by extracting linearly extending blood vessel regions, as in Tanabe ‘11, would benefit the Mei in view of Tanabe ’09 and Tanabe ‘11 teachings because “vessels usually have a limited curvature and may be approximated by piecewise linear segments” (Fraz: 3.2: Matched filtering).
Regarding claim 4:
Mei in view of Tanabe ’09, Tanabe ’11, and Fraz teaches:
The image processing method of claim 1 (as shown above) further comprising:
extracting choroid OCT volume data of the choroid from the OCT volume data (Mei: The present disclosure relates to clinically valuable analyses and visualizations of three-dimensional (3D) volumetric OCT data […] the analysis is performed on, and the visualizations are created by, segmenting OCT data for a component of interest (e.g., choroidal vasculature) in three dimensions following a series of pre-processing techniques [0014]), wherein the generating the three-dimensional image includes
generating the three-dimensional image based on the choroid OCT volume data (Mei: The segmentation can be applied to the data following pre-processing, and then combined to produce a final full 3D segmentation of the desired component [0014]; see Note 4A).
Note 4A: Mei teaches that a full 3D segmentation of a desired component may be produced from segmented data in [0014]. The segmented data may be choroid OCT volume data: “segmenting OCT data for a component of interest (e.g., choroidal vasculature)” [0014].
Regarding claim 5:
Claim 5 is substantially similar to claim 1, and is therefore rejected for similar reasons. Claim 5 contains the following notable differences:
Claim 5 claims an image processing device instead of an image processing method. Mei teaches a image processing device:
An image processing device, comprising: a memory; and a processor connected to the memory (Mei: The processor may be able to execute software instructions stored in some form of memory, either volatile or non-volatile, such as random access memories, flash memories, digital hard disks, and the like [0036]), wherein the processor is configured to perform processing comprising:
Regarding claim 6:
Claim 6 is substantially similar to claim 1, and is therefore rejected for similar reasons. Claim 6 contains the following notable differences:
Claim 6 claims a non-transitory storage medium instead of a image processing method. Mei teaches a non-transitory storage medium:
A non-transitory storage medium storing a program executable by a computer to perform (Mei: The processor may be able to execute software instructions stored in some form of memory, either volatile or non-volatile, such as random access memories, flash memories, digital hard disks, and the like. The processor may […] be part of a computer used for operations other than processing image data [0036]):
Conclusion
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/VINCENT ALEXANDER PROVIDENCE/Examiner, Art Unit 2617 /KING Y POON/Supervisory Patent Examiner, Art Unit 2617
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