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 Arguments
Applicant's arguments filed 1/14/2026 have been fully considered but they are not persuasive..
Applicant argues:
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Examiner’s Response:
While Jia does not explicitly recite “in backscatter light” in determining bulk motion, the Examiner contends that backscatter light is inherent required for determining bulk motion artifacts. As evidence, the Examiner provides “Time interval optimized optical coherence tomographic angiography for bulk motion suppression on human skin” which states:
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And section 1
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Additionally, the Examiner has provided another rejection of the independent claims which could be extended to all the dependent claims which were rejected under Jia.
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.
Claim(s) 1, and 12 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hormel et al. (“Artifacts and artifact removal in optical coherence tomographic angiography”)
Hormel discloses 1. A method comprising:
receiving an optical coherence tomography angiography (OCTA) dataset; (Hormel, Abstract)
determining, for respective voxels of the OCTA dataset, a proportion of in-situ flow signal based on a strength of attenuated projection artifacts in backscatter light; (Hormel, Section pg. 1121-1122,
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and
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, EQN 2 discloses generating a flow signal; EQN 4 indicates that the flow signal acquires depth resolution based artifacts determined from scattering/backscattering light)
adjusting respective values of the voxels based on the respective proportions to generate a signal attenuation-compensated projection-resolved OCTA (sacPR-OCTA) dataset; (Hormel, pg. 1123, “
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, see suppress background level)
and generating a flow image based on the sacPR-OCTA dataset. (Hormel, See Fig. 1 & 2)
Claim 12 is rejected under similar grounds as claim 1.
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.
Claim(s) 1, 5, 7, 11, 12, 16, 18 and 22 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jia (PGPub 20180317851).
Jia discloses 1. A method comprising:
receiving an optical coherence tomography angiography (OCTA) dataset; (Jia, paragraph 47, OCTA dataset”)
determining, for respective voxels of the OCTA dataset, a proportion of in-situ flow signal based on a strength of attenuated projection artifacts in backscatter light; (Jia, “[0047] At 112, the method 100 may include identifying vascular voxels of the OCTA dataset and/or the OCT dataset based on the estimated bulk motion. For example, the vascular voxels may be identified as voxels of the OCTA dataset having decorrelation values greater than a bulk motion decorrelation threshold and/or voxels of the OCT dataset having reflectance values greater than a bulk motion reflectance threshold. The voxels that do not meet the threshold may be considered nonvascular, and as such only contain bulk motion. The nonvascular voxels may be set to a decorrelation value of zero in the OCTA dataset.”, decorrelation values reads on the signal; Examiner Note: Bulk Motion artifacts occur in backscatter light)
adjusting respective values of the voxels based on the respective proportions to generate a signal attenuation-compensated projection-resolved OCTA (sacPR-OCTA) dataset; (Jia, “[0047] At 112, the method 100 may include identifying vascular voxels of the OCTA dataset and/or the OCT dataset based on the estimated bulk motion. For example, the vascular voxels may be identified as voxels of the OCTA dataset having decorrelation values greater than a bulk motion decorrelation threshold and/or voxels of the OCT dataset having reflectance values greater than a bulk motion reflectance threshold. The voxels that do not meet the threshold may be considered nonvascular, and as such only contain bulk motion. The nonvascular voxels may be set to a decorrelation value of zero in the OCTA dataset. [0048] At 114, the method 100 may include subtracting estimated bulk motion velocity from the measured velocity in vascular voxels. The subtraction may be done in the velocity domain rather than the decorrelation domain.”)
and generating a flow image based on the sacPR-OCTA dataset. (Jia, “[0049] At 116, the method 100 may include generating a flow image that is corrected for the estimated bulk motion. For example, as discussed above, the nonvascular voxels may be set to zero in the OCTA dataset. Additionally, the bulk motion velocity may be subtracted from the velocity of vascular voxels, and the corrected velocity may then be converted back to the decorrelation domain to generate a corrected decorrelation value. Alternatively, the flow image may be presented in the velocity domain.”)
Jia discloses 5. The method of claim 1, further comprising:
estimating a signal strength attenuation based on a reflectance strength; and
determining the strength of attenuated projection artifacts based on the estimated signal strength attenuation. (Jia, “[0047] At 112, the method 100 may include identifying vascular voxels of the OCTA dataset and/or the OCT dataset based on the estimated bulk motion. For example, the vascular voxels may be identified as voxels of the OCTA dataset having decorrelation values greater than a bulk motion decorrelation threshold and/or voxels of the OCT dataset having reflectance values greater than a bulk motion reflectance threshold. The voxels that do not meet the threshold may be considered nonvascular, and as such only contain bulk motion. The nonvascular voxels may be set to a decorrelation value of zero in the OCTA dataset.”)
Jia discloses 7. The method of claim 1, further comprising: detecting voxels that correspond to vessels; and
compensating the values of the detected voxels based on the intensity of respective surrounding voxels prior to determining the proportion of in-situ flow signal for the respective voxels.(Jia, paragraph 82, “[0082] At each segment, a group of A-lines devoid of inner retinal flow voxels was selected. For this purpose, A-lines crossing large vessels were first identified by a binary large vessel mask of the en face angiogram and excluded from the following analysis. The mask was constructed by successively applying amplitude thresholding to remove the majority of capillary pixels, a morphological opening (erosion followed by dilation) to cleanup dispersed pixels, a Gaussian convolution filter to prevent holes in the middle of large vessels and a final binarization step. From the remaining A-lines, the first 10 percentile with the lowest decorrelation signal were selected, assuming these are composed by non-flow voxels only”)
Jia discloses 11. The method of claim 1, further comprising obtaining the OCTA dataset by measuring motion contrast using an amplitude or a phase of repeated structural B-scans acquired at a same scan location.(Jia, paragraph 6, “0006] Optical coherence tomography angiography (OCTA) is a noninvasive blood flow imaging technique based on OCT that allows depth-resolved visualization of vascular maps with capillary resolution. Flow detection using OCT was first demonstrated by Doppler OCT, a technique that images blood flow by evaluating phase differences between adjacent depth profiles. More recent OCTA algorithms on the other hand, rely on mathematical operations (e.g. decorrelation, variance, difference, or ratio) that quantify variations in the phase and/or amplitude of OCT signal between at least two consecutive B-scans acquired at the same raster position. For example, the speckle variance method computes the amplitude decorrelation between consecutive cross-sectional OCT images to identify variations in the intensity of the OCT signal due to the motion of blood cells. An improved version of the speckle variance method is the split-spectrum amplitude-decorrelation angiography (SSADA) algorithm, which splits the interferogram before computing the amplitude decorrelation and later averages the flow images generated by all spectral splits, enhancing the signal-to-noise ratio. SSADA uses only two B-scans at each lateral position, which is beneficial for a reduced scanning time. Another OCTA method referred to as split-spectrum amplitude and phase-gradient angiography (SSAPGA) exploits the phase of the OCT signal besides the amplitude to further improve the flow image quality. Yet another alternative that utilizes the complex OCT signal is known as optical microangiography and relies on a modified Hilbert transform to distinguish moving scatterers from static scatterers. All of these modalities have one common purpose, to exploit the larger variation in OCT signal between scans due to moving scatterers in order to represent the pixels corresponding to blood vessels brighter than the pixels representing the surrounding tissue.”)
Claim 12 is rejected under similar grounds as claim 1.
Claim 16 is rejected under similar grounds as claim 5.
Claim 18 is rejected under similar grounds as claim 7.
Claim 22 is rejected under similar grounds as claim 11.
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) 9-10 20-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jia in view of An (CN (114387174))
Jia discloses 9. The method of claim 1,
But does not disclose “further comprising performing wavelet composition on the sacPR-OCTA dataset to suppress background noise”
An discloses “further comprising performing wavelet composition on the sacPR-OCTA dataset to suppress background noise”.(An, “Therefore, the solution adopts double-tree complex wavelet transform. using double-tree complex wavelet transform noise reduction algorithm to reduce noise to the OCTA image. Specifically, firstly using double-tree complex wavelet transformation to the OCTA image to transform the image data into the wavelet domain, according to the set wavelet decomposition scale to obtain the corresponding number of multi-resolution scale layer, in the wavelet domain, the resolution ratio of each lifting layer corresponding to the wavelet coefficient will be reduced by 4 times, and for the wavelet coefficient in accordance with the direction characteristic distribution, the wavelet coefficient amplitude value will be increased. the noise-containing image increases with the resolution layer; the small wave coefficient value with directivity characteristic signal will become larger, and the small wave coefficient value corresponding to the randomly distributed noise is far less than the signal. The signal can be separated from the noise in the form of a threshold value to achieve the effect of image noise reduction.”)
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to use An’s method to remove noise before applying Jia’s algorithm.
The suggestion/motivation for doing so would have been a better quality input image..
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Jia with An to obtain the invention as specified in claim 9..
Jia in view of An discloses 10. The method of claim 9, wherein the wavelet decomposition is performed sequentially along a horizontal direction, a vertical direction, and a depth direction. (An, “Therefore, the solution adopts double-tree complex wavelet transform. using double-tree complex wavelet transform noise reduction algorithm to reduce noise to the OCTA image. Specifically, firstly using double-tree complex wavelet transformation to the OCTA image to transform the image data into the wavelet domain, according to the set wavelet decomposition scale to obtain the corresponding number of multi-resolution scale layer, in the wavelet domain, the resolution ratio of each lifting layer corresponding to the wavelet coefficient will be reduced by 4 times, and for the wavelet coefficient in accordance with the direction characteristic distribution, the wavelet coefficient amplitude value will be increased. the noise-containing image increases with the resolution layer; the small wave coefficient value with directivity characteristic signal will become larger, and the small wave coefficient value corresponding to the randomly distributed noise is far less than the signal. The signal can be separated from the noise in the form of a threshold value to achieve the effect of image noise reduction.”)
Claim 20 is rejected under similar grounds as claim 9.
Claim 21 is rejected under similar grounds as claim 10.
Allowable Subject Matter
Claims 2-4 , 6, 8, 13-15, 17 and 19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GANDHI THIRUGNANAM whose telephone number is (571)270-3261. The examiner can normally be reached M-F 8:30-5PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sumati Lefkowitz can be reached at 571-272-3638. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GANDHI THIRUGNANAM/ Primary Examiner, Art Unit 2672
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