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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schurer-Waldheim et al., “Robust Fovea Detection in Retinal OCT Imaging Using Deep Learning” in view of Mukherjee et al., “Retinal layer segmentation in optical coherence tomography (OCT) using a 3D deep-convolutional regression network for patients with age-related macular degeneration”.
Regarding claim 1, Schurer-Waldheim discloses a method (Section 1. Introduction, Second paragraph; methods for fovea detection) comprising:
receiving an optical coherence tomography (OCT) volume for a retina of a subject, the optical coherence tomography (OCT) volume comprising a plurality of OCT B-scans of the retina (Section I. Introduction, Second paragraph; Fig. 3, box 2. Application; Section II. Method, b) Application; acquire OCT volumes/scans (3D volume) of the retina comprising B-scans);
generating a three-dimensional image input for a model using the OCT volume, the model comprising a PRE U-Net (Fig. 3, box 2. Application; Section II. Method, b) Application; the OCT volume/scan (3D volume) and the corresponding spatial location prior are the input of the PRE U-Net); and
generating, via the model, a foveal center position comprising three-dimensional coordinates for a foveal center of the retina based on the three-dimensional image input (Fig. 3, box 2. Application; Section II. Method, First paragraph, b) Application, A. Spatial Location Prior, and B. Target Map Creation; the model predicts a fovea distance volume (i.e., foveal center position). The coordinates of the 3D volume center/coordinates of the ground truth landmark),
wherein the three-dimensional coordinates of the foveal center position include a transverse coordinate, an axial coordinate, and a lateral coordinate with respect to a selected coordinate system for the OCT volume (Figs. 1 and 3; Section II. Method, A. Spatial Location Prior; axial, lateral and transversal coordinates).
Schurer-Waldheim discloses claim 1 as enumerated above, but Schurer-Waldheim does not explicitly disclose a three-dimensional convolutional neural network as claimed.
However, Mukherjee discloses 3D spatial context from the volumetric OCT data by implementing a 3D UNet via the use of 3D convolutional features (3D deep neural network) (Abstract; Section 1. Introduction, 1.2. Brief overview).
Therefore, taking the combined disclosures of Schurer-Waldheim and Mukherjee as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate 3D spatial context from the volumetric OCT data by implementing a 3D UNet via the use of 3D convolutional features (3D deep neural network) as taught by Mukherjee into the invention of Schurer-Waldheim for the benefit of learning the surface positions of the layers in the retinal volumes (Mukherjee: Abstract).
Regarding claim 2, the method of claim 1, Schurer-Waldheim in the combination discloses further comprising:
generating a central subfield thickness measurement using the three-dimensional coordinates of the foveal center position (Section I. Introduction, First paragraph, Section II. Method, b) Application, and Section V. discussion, Last paragraph).
Regarding claim 3, the method of claim 1, Schurer-Waldheim in the combination discloses further comprising:
determining a retinal grid that divides the retina into regions based on the three-dimensional coordinates of the foveal center position (Section I. Introduction, First paragraph and Section V. discussion, Fourth paragraph).
Regarding claim 4, the method of claim 3, Schurer-Waldheim in the combination discloses wherein the retinal grid is an Early Treatment Diabetic Retinopathy Study (ETDRS) grid that divides the retina into nine regions centered with respect to the three-dimensional coordinates of the foveal center position (Section I. Introduction, First paragraph and Section V. discussion, Fourth paragraph).
Regarding claim 5, the method of claim 1, Schurer-Waldheim in the combination discloses further comprising:
modifying a segmentation of at least one OCT B-scan of the plurality of OCT B-scans of the retina based on the three-dimensional coordinates of the foveal center position Section II. Method, b) Application, A. Spatial Location Prior, and B. Target Map Creation).
Regarding claim 6, the method of claim 1, Mukherjee in the combination discloses wherein the model further comprises a regression layer that is used to convert an output of the three-dimensional convolutional neural network into the three-dimensional coordinates (Abstract; Section 1. Introduction, 1.2. Brief overview and 2. Methods, 2.1. Proposed work: 3D aggregation regression network (3D AggRegNet) to learn spatial locations of surfaces, Last paragraph).
Regarding claim 7, the method of claim 1, Schurer-Waldheim in the combination discloses wherein generating the three-dimensional image input comprises:
performing a set of preprocessing operations on the OCT volume to form the three-dimensional image input, the set of preprocessing operations including at least one of a normalization operation, a scaling operation, a resizing operation, a horizontal flipping operation, a vertical flipping operation, a cropping operation, a rotation operation, or a noise filtering operation (Section III. Experiment Setup, B. Training Details).
Regarding claim 8, the method of claim 1, Schurer-Waldheim in the combination discloses further comprising:
transforming the three-dimensional coordinates of the foveal center position from a first selected coordinate system associated with the OCT volume to a second selected coordinate system associated with the retina or the subject (Fig. 9; Section II. Method, B. Target Map Creation and Section III. Experiment Setup, B. Training Details).
Regarding claim 9, the method of claim 1, Schurer-Waldheim in the combination discloses wherein the retina of the subject is a healthy retina, or is diagnosed with age-related macular degeneration (AMD), neovascular age-related macular degeneration (nAMD), diabetic retinopathy, macular edema, or geographic atrophy (Abstract; Section IV. Results, A. Quantitative and Qualitative Evaluation).
Regarding claim 10, the method of claim 1, Schurer-Waldheim in the combination discloses wherein one coordinate of the three-dimensional coordinates of the foveal center position corresponds to a particular B-scan of the plurality of OCT B-scans of the retina (Fig. 3, box 2. Application; Section II. Method, First paragraph, b) Application, A. Spatial Location Prior, and B. Target Map Creation).
Regarding claim 11, the method of claim 1, Schurer-Waldheim in the combination discloses further comprising:
rounding a transverse coordinate of the three-dimensional coordinates of the foveal center position to a value corresponding to an index associated with a particular OCT B-scan of the plurality of OCT B-scans of the retina (Fig. 7; Section IV. Results, A. Quantitative and Qualitative Evaluation, Last paragraph).
Regarding claim 12, the method of claim 1, Schurer-Waldheim and Mukherjee in the combination disclose wherein generating, via the model comprising the three-dimensional convolutional neural network (Mukherjee: Abstract; Section 1. Introduction, 1.2. Brief overview), the foveal center position comprises: rounding an initial value for a transverse coordinate of the foveal center position to a rounded value that corresponds to an index associated with a particular OCT B-scan of the plurality of OCT B-scans of the retina, wherein the rounded value is one of the three-dimensional coordinates of the foveal center position (Schurer-Waldheim: Fig. 7; Section IV. Results, A. Quantitative and Qualitative Evaluation, Last paragraph).
Regarding claim 13, Schurer-Waldheim discloses a method for training a model (Fig. 3, box 1. Training; a method to train a model to predict the distance to the fovea centralis for every pixel of the input image), the method comprising:
receiving a training dataset that includes a plurality of optical coherence tomography (OCT) volumes for a plurality of retinas, wherein each of the plurality of OCT volumes includes a plurality of OCT B-scans (Fig. 3, box 1. Training; Section II. Methods, a) Training and Sampling Strategy; training dataset of OCT volumes; For training the PRE U-Net, B-scans are sampled from each OCT volume);
generating training three-dimensional image input for a model using the plurality of OCT volumes in the training dataset, the model comprising a PRE U-Net and a regression task (Fig. 3, box 1. Training; Section II. Methods, a) Training and Sampling Strategy and B. Target Map Creation; the OCT volume/scan (3D volume) and the corresponding spatial location prior are inputted into the PRE U-Net and the pixel-wise regression task); and
training the model to generate a foveal center position comprising three-dimensional coordinates for a foveal center of a retina in a selected OCT volume based on the training three-dimensional image input (Fig. 3, box 1. Training; Section II. Methods, a) Training and Sampling Strategy, A. Spatial Location Prior, and B. Target Map Creation; train a model to predict the distance to the fovea centralis for every pixel of the input image; The coordinates of the ground truth landmark),
wherein the three-dimensional coordinates of the foveal center position include a transverse coordinate, an axial coordinate, and a lateral coordinate for the foveal center in the OCT volume (Figs. 1 and 3; Section II. Method, A. Spatial Location Prior; axial, lateral and transversal coordinates).
Schurer-Waldheim discloses claim 13 as enumerated above, but Schurer-Waldheim does not explicitly disclose a three-dimensional convolutional neural network and a regression later as claimed.
However, Mukherjee discloses 3D spatial context from the volumetric OCT data by implementing a 3D UNet via the use of 3D convolutional features (3D deep neural network) and utilizing 3D autoencoder based regression networks (Abstract; Section 1. Introduction, 1.2. Brief overview).
Therefore, taking the combined disclosures of Schurer-Waldheim and Mukherjee as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate 3D spatial context from the volumetric OCT data by implementing a 3D UNet via the use of 3D convolutional features (3D deep neural network) and utilizing 3D autoencoder based regression networks as taught by Mukherjee into the invention of Schurer-Waldheim for the benefit of learning the surface positions of the layers in the retinal volumes (Mukherjee: Abstract).
Regarding claim 14, this claim recites substantially the same limitations that are performed by claim 7 above, and it is rejected for the same reasons.
Regarding claim 15, this claim recites substantially the same limitations that are performed by claim 9 above, and it is rejected for the same reasons.
Regarding claim 16, this claim recites substantially the same limitations that are performed by claim 10 above, and it is rejected for the same reasons.
Regarding claim 17, this claim recites substantially the same limitations that are performed by claim 1 above, and it is rejected for the same reasons.
Regarding claim 18, this claim recites substantially the same limitations that are performed by claim 2 above, and it is rejected for the same reasons.
Regarding claim 19, this claim recites substantially the same limitations that are performed by claim 3 above, and it is rejected for the same reasons.
Regarding claim 20, this claim recites substantially the same limitations that are performed by claim 5 above, and it is rejected for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Stetson et al., US 2013/0229621 discloses methods for finding the lateral position of the fovea in an OCT image volume.
Russakoff et al., US 2021/0369195 discloses optical coherence tomography (OCT) data can be analyzed with neural networks trained on OCT data and known clinical outcomes to make more accurate predictions about the development and progression of retinal diseases, central nervous system disorders, and other conditions.
Joskowicz et al., US 2024/0296555 discloses using the matched corresponding first and second pixel column patches as input for a trained machine learning model, wherein an output of the trained machine learning model classifies the second pixel column patches as representing retinal atrophy or not representing retinal atrophy.
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/VAN D HUYNH/Primary Examiner, Art Unit 2665
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