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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 09/04/2025 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 5-9, 13-20 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.
Claim(s) 1, 5-9, 13-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Boyd et al. (US2021/0279874) in view of Lee et al. (US2022/0400943).
To claim 1, Boyd teach a computer-implemented method for diagnosing age-related macular degeneration (AMD), the method (Figs. 4-6, paragraph 0057) consisting of:
receiving one or more optical coherence tomography angiography (OCTA) image of a subject, wherein the one or more OCTA image each is an en-face OCT image (302 of Fig. 4, 402 of Fig. 5; paragraphs 0074, 0079);
pre-processing the one or more OCTA image to obtain image data, comprising segmenting an image of deep capillary plexus (DCP) from the OCTA image to obtain image data comprising data of the DCP image (404 of Fig. 5; paragraph 0099, 0143);
inputting the image data to a trained deep learning (DL) network (paragraphs 0007, 0210-0211, using deep learning network for image analysis; paragraph 0206, presence of the features in the subject's images may be determined using a classifier algorithm, which may be trained using the images and analysis from the cohorts), and
wherein a plurality of training DCP images is used in training the DL network, each training DCP images being segmented from a training OCTA image (paragraphs 0124, 0126, 0206, 0209, 0224);
generating, using the trained DL network, an output that characterizes the health of the subject with respect to AMD (paragraphs 0120, 0199-0205); and
generating, based on the output, a diagnostic result comprising an indication of presence of neovascularization (NV) or presence of NV activity in the subject, an identification of a location of NV or NV activity or a feeder vessel supplying for an NV exudation in the one or more OCTA image (408 of Fig. 5; paragraphs 0006, 0103, 0219, 0221, 0241, 0245, 0275, 0341).
But, Boyd do not expressly disclose wherein the DL network comprises one or more dense block layer.
Lee teach a computer-implemented method for diagnosing age-related macular degeneration (AMD) (paragraph 0045) with en-face OCTA images (paragraphs 0040, 0045, 0052, 0082, 0086-0087, 0099), wherein dense convolutional neural network may be applied for image analysis (paragraphs 0057-0058).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Lee into the Boyd, in order to implement deep learning network by design preference.
To claim 9, Boyd and Lee teach a system comprising one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to perform operations (as explained in response to claim 1 above).
To claim 17, Boyd and Lee teach one or more non-transitory computer-readable storage media encoded with instructions that when executed by one or more computers cause the one or more computers to perform operations (as explained in response to claim 1 above).
To claims 5 and 13, Boyd and Lee teach claims 1 and 9.
Boyd and Lee teach wherein the image data further comprises data of the image of outer retinal layer segmented from the OCTA image (Boyd, paragraphs 0224, 0393).
To claims 6, 14 and 18, Boyd and Lee teach claims 1, 9 and 17.
Boyd and Lee teach wherein a plurality of training OCTA images is used in training the DL network, each training OCTA images being pre-processed by segmenting training OCTA image to obtain at least one of an image of superficial capillary plexus, an image of deep capillary plexus, an image of outer retinal layer, and an image of choroid capillary layer (obvious as explained in response to claim 1 above, such as teachings in Boyd).
To claims 7, 15 and 19, Boyd and Lee teach claims 6, 14 and 18.
Boyd and Lee teach wherein the DL network is trained with image data of the image of superficial capillary plexus, the image of deep capillary plexus, the image of outer retinal layer, and the image of choroid capillary layer (obvious as explained in response to claim 1 above, such as teachings in Boyd).
To claims 8, 16 and 20, Boyd and Lee teach claims 1, 9 and 17.
Boyd and Lee teach wherein the classification of AMD classifies the subject as having no AMD, wet AMD or dry AMD (teaching in Boyd as explained in response to claim 1 above).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZHIYU LU whose telephone number is (571)272-2837. The examiner can normally be reached Weekdays: 8:30AM - 5:00PM.
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ZHIYU . LU
Primary Examiner
Art Unit 2669
/ZHIYU LU/Primary Examiner, Art Unit 2665 November 18, 2025