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
Rejections under 35 USC 101 are withdrawn in view of the amendments.
Applicant's arguments filed 06/11/2026, regarding 35 USC 102 have been fully considered but they are not persuasive. On Pages 12-13, Applicant argues that the Bogunovic reference does not disclose “extracting retinal feature data for a plurality of retinal features using the SD-OCT imaging data, the plurality of retinal features comprising one or more retinal features associated with at least one of a set of retinal fluids or a set of retinal layers, wherein the plurality of retinal features comprises at least one or more retinal features associated with pigment epithelial detachment (PED);”: Applicant states, “The Office Action at Pages 7-8 cites to Bogunovic as teaching retinal layer segmentation of features associated with intraretinal cystoid fluid (IRF) and subretinal fluid (SRF). However, Bogunovic does not teach or suggest use of a retinal feature associated with "a retinal fluid associated with pigment epithelial detachment (PED)" as recited in amended claim 1. In fact, Bogunovic does not mention PED at all. Thus, the rejection of claim 1 is not supported by Bogunovic and claim 1 is respectfully submitted to be allowable.”
Firstly, note that the Applicant’s argument is inconsistent with the claim amendment. Applicant argues above that “Bogunovic does not teach or suggest use of a retinal feature associated with "a retinal fluid associated with pigment epithelial detachment (PED)" as recited in amended claim 1.” Amended claim 1 does not recite "a retinal fluid associated with pigment epithelial detachment (PED)", but rather “at least one or more retinal features associated with pigment epithelial detachment (PED);”. This distinction is important because the amended claim does not limit the feature associated with the PED to be a fluid. Secondly, the retinal features extracted by Bogunovic do expressly include “at least one or more retinal features associated with pigment epithelial detachment (PED);”. Figure 2 and Section “Retinal Layer Segmentation” illustrate and describe the segmentation of the “photoreceptor outer segments with retinal pigment epithelium (OR)”. This layer is directly associated with pigment epithelial detachment. Moreover, under an even broader interpretation, any eye feature is inherently “associated with” pigment epithelial detachment (PED), by virtue of being associated with the same organ.
Further, On Page 13, Applicant argues that the claims 2-11 and 13-14 are allowable as dependent on claim 1, claim 19 is allowable as analogous to claim 1, and claim 20 is allowable as dependent on claim 19. Applicant further argues that claim 12 is allowable because Kumari does not cure the deficiencies of claim 1. These arguments are all moot in view of the above explanation for maintaining the rejection of claim 1 using Bogunovic.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-14 and 19-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation “the SD-OCT imaging data" in the extracting step. There is improper antecedence for this limitation. In particular, the training step recites “spectral domain optical coherence tomography (SD-OCT) imaging data of subjects receiving anti-VEGF treatment” and the receiving step recites, “SD-OCT imaging data of a retina of the subject”. As written, it is unclear which imaging data is being referred to in the extracting step (of subjects receiving anti-VEGF treatment or of a retina of the subject). The claim is being interpreted under the latter interpretation.
Claim 19 is rejected for the same reasons as claim 1.
Claims 2-14 and 20 are rejected as dependent on the above claims.
Claim Rejections - 35 USC § 102
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 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) 1-11, 13-14, and 19-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bogunovic (Prediction of Anti-VEGF Treatment Requirements in Neovascular AMD Using a Machine Learning Approach).
Regarding claim 1, Bogunovic teaches “A method for managing an anti-vascular endothelial growth factor (anti-VEGF) treatment for a subject diagnosed with neovascular age-related macular degeneration (nAMD), “the method comprising: training a machine learning model using training input data to predict a treatment level for the anti-VEGF treatment,” (Bogunovic, Introduction Paragraph 3, “The aim of this pilot study is to predict, on an individual patient level, low and high anti-VEGF injection requirements during a PRN treatment regimen of patients with neovascular AMD. Our hypothesis suggests that these requirement categories can be predicted by observing retinal morphology and treatment response as early as during the standardized initiation phase of the treatment course. Using automated computational analysis of OCT, a set of spatiotemporal features was extracted from imaging series, characterizing the retina and its anatomic response to the initial anti-VEGF treatment. Machine learning methods were then applied to build a predictive model of the future therapeutic requirements during the PRN regimen. The model was trained and validated on 2- year data from a large-scale prospective randomized controlled trial in treatment-naive AMD patients.”)
“wherein the training input data comprises spectral domain optical coherence tomography (SD-OCT) imaging data of subjects receiving anti-VEGF treatment during at least one of an initial phase of treatment or a phase of treatment after the initial phase of treatment;” (Bogunovic, Discussion, Paragraph 1, “In this pilot study, we presented and evaluated a computer-based method to learn and predict low and high anti-VEGF treatment requirements of neovascular AMD patients from a longitudinal series of OCT scans acquired during the initiation phase. The predictions were based on fully automated image analysis and obtained by machine learning using a random forest as a nonlinear predictive classification model. Development and validation were performed on data from the HARBOR clinical trial, which is particularly appropriate due to its large cohort size, standardized OCT imaging, and an effective PRN retreatment protocol.” The HARBOR trial data expressly maps to the claimed training input data limitations. The Specification of the instant Application also recites the HARBOR trial as an example of training data for the model.)
“receiving SD-OCT imaging data of a retina of the subject;” (Bogunovic, “OCT Image Processing and Analysis” Paragraph 1, “The proposed methodology is based on a fully automated image processing and analysis pipeline available at the Vienna Reading Center (VRC), Vienna, Austria. No manual corrections have been performed in this study. All images were acquired with Cirrus HD-OCT III (Carl Zeiss Meditec, Inc., Dublin, CA, USA) presenting 512 X 128 X 1024 voxels, with a size of 11.7 X 47.2 X 2.0 um3, covering a volume of 6 X 6 X 2 mm3.” Note that the Cirrus HD-OCT III is an SD-OCT imager. Additionally, Figures 2 and 3 show the obtained imaging data of a retina of the subject.)
“extracting retinal feature data for a plurality of retinal features using the SD-OCT imaging data, the plurality of retinal features comprising one or more retinal features associated with at least one of a set of retinal fluids or a set of retinal layers; wherein the plurality of retinal features comprises at least one or more retinal features associated with pigment epithelial detachment (PED);” (Bogunovic, “Retinal Layer Segmentation” Section, “Intraretinal and Subretinal Fluid Segmentation” Section, and Figures 2-3, “Automated retinal layer segmentation is performed with a graph-theoretic method, part of the Iowa Reference Algorithms.10,11 The method transforms the problem into a multiscale 3D graph search to optimally and efficiently segment a set of surfaces according to image-based cost function and satisfying a priori hard constraints on surface smoothness and intersurface distances. As the a priori constraints are valid for healthy retinas, only a subset of layer interfaces is well segmented in neovascular AMD population. Thus, the following four principle layer thickness maps were extracted, which were empirically found to be robustly segmented: inner retina (IR), outer nuclear layer (ONL), photoreceptor outer segments with retinal pigment epithelium (OR), and total retinal thickness (TRT). An example of segmented surfaces denoting those layers is shown in Figure 2.”; “Segmentation of intraretinal cystoid fluid (IRF) and subretinal fluid (SRF) was performed per B-scan using a validated segmentation algorithm based on deep learning.12 First, based on the top and the bottom retinal layer, a mask is computed denoting the retina extending from the inner limiting membrane (ILM) to the RPE. Then, every voxel within the mask is classified with a multiscale convolutional neural network (CNN) as belonging to one of the three classes: Normal retina, IRF, or SRF (Fig. 3). The CNN had been trained in a supervised manner using a training set of 157 OCT volumes with » 20,000 manually annotated B-scans, acquired with the same OCT device model (Cirrus; Zeiss) and having the same pathology (neovascular AMD), which were disjoint from the set of images in the HARBOR trial.” Note that while the claim only requires either retinal fluid or retinal layer extraction, the above reference discloses both. Further note that the “photoreceptor outer segments with retinal pigment epithelium (OR)” is mapped to the feature associated with the PED. Alternatively, under a broader interpretation, all features recited above by Bogunovic are associated with the retina, and are therefore inherently “associated with” PED as being directed to the same organ.)
“sending input data formed using the retinal feature data for the plurality of retinal features into the machine learning model; and predicting, via the machine learning model, a treatment level for the anti-VEGF treatment to be administered to the subject based on the input data.” (Bogunovic, Figure 4(a-b) and “Predictive Model of Treatment Requirements” Section Paragraphs 1 and 3, “For each eye, from its longitudinal series of three OCT volumes (baseline, month 1, and month 2) and the derived segmentations, we extracted a set of quantitative features characterizing the underlying retinal pathomorphology. For the imaging features to correspond across subjects, before the feature extraction, all scans of left eyes were mirrored to conform to scans of a right eye. From the image segmentations 2D maps were computed corresponding to the thickness maps of the four layers, as well as volume and en face area maps of both IRF and SRF, resulting in eight 2D maps in total, with examples shown in Figure 4a. Analyzing data in high-dimensional OCT volumes is affected by the so-called ‘‘Curse of Dimensionality,’’ where learning is very difficult and prone to overfitting. To limit the dimensionality of the feature vector and facilitate the machine learning, we summarized the A-scan properties spatially across the regions defined by the Early Treatment Diabetic Retinopathy Study (ETDRS) grid as depicted in Figure 4b. The ETDRS grid was placed at the center of the scan, and the mean feature values per ETDRS subregions were computed. In addition to the nine ETDRS grid cells, we additionally included the central 3 mm, central 6 mm, and the rings corresponding to the parafoveal and perifoveal bands, resulting in 13 spatial regions in total. Such ETDRS-related features have the additional advantage of being easier to interpret than Ascan related ones, due to widespread use of ETDRS grid in ophthalmology. To this set of imaging features, we added the measured BCVA. To measure the rate of change of the longitudinal features, the differences between the corresponding features of the consecutive time points (month 1month 0 and month 2 month 1) were further included. This resulted in the number of local spatio-temporal features being 525, computed as follows: (8 feature maps X 13 spatial regions + 1 BCVA) 3 5 temporal elements. Last, demographic features were added: sex, race, age, and smoking status together with the fluorescein angiogram pattern type, for a total of 530 features.”; “Finally, a machine learning approach based on the random forest classifier13 was used to obtain a predictive model of the low and high treatment requirements from the set of the above features. Random forest was grown with 1000 trees for which the out of bag mean squared error was observed to have converged. The number of features to randomly sample as candidates at each split of a tree was chosen to be the square root of the number of features (√530), which is the default setting for a classification task13.”)
Regarding claim 2, Bogunovic teaches “The method of claim 1,”
“wherein the retinal feature data includes a value associated with a corresponding retinal fluid of the set of retinal fluids, the value selected from a group consisting of a volume, a height, and a width of the corresponding retinal fluid.” (Bogunovic, Figure 4(a) and “Predictive Model of Treatment Requirements” Section Paragraph 1, “For each eye, from its longitudinal series of three OCT volumes (baseline, month 1, and month 2) and the derived segmentations, we extracted a set of quantitative features characterizing the underlying retinal pathomorphology. For the imaging features to correspond across subjects, before the feature extraction, all scans of left eyes were mirrored to conform to scans of a right eye. From the image segmentations 2D maps were computed corresponding to the thickness maps of the four layers, as well as volume and en face area maps of both IRF and SRF, resulting in eight 2D maps in total, with examples shown in Figure 4a. Analyzing data in high-dimensional OCT volumes is affected by the so-called ‘‘Curse of Dimensionality,’’ where learning is very difficult and prone to overfitting. To limit the dimensionality of the feature vector and facilitate the machine learning, we summarized the A-scan properties spatially across the regions defined by the Early Treatment Diabetic Retinopathy Study (ETDRS) grid as depicted in Figure 4b. The ETDRS grid was placed at the center of the scan, and the mean feature values per ETDRS subregions were computed. In addition to the nine ETDRS grid cells, we additionally included the central 3 mm, central 6 mm, and the rings corresponding to the parafoveal and perifoveal bands, resulting in 13 spatial regions in total. Such ETDRS-related features have the additional advantage of being easier to interpret than Ascan related ones, due to widespread use of ETDRS grid in ophthalmology. To this set of imaging features, we added the measured BCVA. To measure the rate of change of the longitudinal features, the differences between the corresponding features of the consecutive time points (month 1month 0 and month 2 month 1) were further included. This resulted in the number of local spatio-temporal features being 525, computed as follows: (8 feature maps X 13 spatial regions + 1 BCVA) 3 5 temporal elements. Last, demographic features were added: sex, race, age, and smoking status together with the fluorescein angiogram pattern type, for a total of 530 features.”)
Regarding claim 3, Bogunovic teaches “The method of claim 1,”
“wherein the retinal feature data includes a value for a corresponding retinal layer of the set of retinal layers, the value selected from a group consisting of a minimum thickness, a maximum thickness, and an average thickness of the corresponding retinal layer.” (Bogunovic, Figure 2 and “Retinal Layer Segmentation” Section, “Automated retinal layer segmentation is performed with a graph-theoretic method, part of the Iowa Reference Algorithms.10,11 The method transforms the problem into a multiscale 3D graph search to optimally and efficiently segment a set of surfaces according to image-based cost function and satisfying a priori hard constraints on surface smoothness and intersurface distances. As the a priori constraints are valid for healthy retinas, only a subset of layer interfaces is well segmented in neovascular AMD population. Thus, the following four principle layer thickness maps were extracted, which were empirically found to be robustly segmented: inner retina (IR), outer nuclear layer (ONL), photoreceptor outer segments with retinal pigment epithelium (OR), and total retinal thickness (TRT). An example of segmented surfaces denoting those layers is shown in Figure 2.”)
Regarding claim 4, Bogunovic teaches “The method of claim 1,”
“wherein a retinal fluid of the set of retinal fluids is selected from a group consisting of an intraretinal fluid (IRF), a subretinal fluid (SRF), a fluid associated with pigment epithelial detachment (PED), or a subretinal hyperreflective material (SHRM).” (Bogunovic, “Intraretinal and Subretinal Fluid Segmentation” Section, and Figure 3, “Segmentation of intraretinal cystoid fluid (IRF) and subretinal fluid (SRF) was performed per B-scan using a validated segmentation algorithm based on deep learning.12 First, based on the top and the bottom retinal layer, a mask is computed denoting the retina extending from the inner limiting membrane (ILM) to the RPE. Then, every voxel within the mask is classified with a multiscale convolutional neural network (CNN) as belonging to one of the three classes: Normal retina, IRF, or SRF (Fig. 3). The CNN had been trained in a supervised manner using a training set of 157 OCT volumes with » 20,000 manually annotated B-scans, acquired with the same OCT device model (Cirrus; Zeiss) and having the same pathology (neovascular AMD), which were disjoint from the set of images in the HARBOR trial.”)
Regarding claim 5, Bogunovic teaches “The method of claim 1,”
“wherein a retinal layer of the set of retinal layers is selected from a group consisting of an internal limiting membrane (ILM) layer, an outer plexiform layer-Henle fiber layer (OPL-HAL), an inner boundary- retinal pigment epithelial detachment (IB-RPE), an outer boundary-retinal pigment epithelial detachment (OB-RPE), or a Bruch's membrane (BM).” (Bogunovic, “Intraretinal and Subretinal Fluid Segmentation” Section, “Segmentation of intraretinal cystoid fluid (IRF) and subretinal fluid (SRF) was performed per B-scan using a validated segmentation algorithm based on deep learning.12 First, based on the top and the bottom retinal layer, a mask is computed denoting the retina extending from the inner limiting membrane (ILM) to the RPE. Then, every voxel within the mask is classified with a multiscale convolutional neural network (CNN) as belonging to one of the three classes: Normal retina, IRF, or SRF (Fig. 3). The CNN had been trained in a supervised manner using a training set of 157 OCT volumes with » 20,000 manually annotated B-scans, acquired with the same OCT device model (Cirrus; Zeiss) and having the same pathology (neovascular AMD), which were disjoint from the set of images in the HARBOR trial.”)
Regarding claim 6, Bogunovic teaches “The method of claim 1,”
“further comprising: forming the input data using the retinal feature data for the plurality of retinal features and clinical data for a set of clinical features, the set of clinical features including at least one of a best corrected visual acuity, a pulse, a diastolic blood pressure, or a systolic blood pressure.” (Bogunovic, Figure 4(a-b) and “Predictive Model of Treatment Requirements” Section Paragraph 1, “For each eye, from its longitudinal series of three OCT volumes (baseline, month 1, and month 2) and the derived segmentations, we extracted a set of quantitative features characterizing the underlying retinal pathomorphology. For the imaging features to correspond across subjects, before the feature extraction, all scans of left eyes were mirrored to conform to scans of a right eye. From the image segmentations 2D maps were computed corresponding to the thickness maps of the four layers, as well as volume and en face area maps of both IRF and SRF, resulting in eight 2D maps in total, with examples shown in Figure 4a. Analyzing data in high-dimensional OCT volumes is affected by the so-called ‘‘Curse of Dimensionality,’’ where learning is very difficult and prone to overfitting. To limit the dimensionality of the feature vector and facilitate the machine learning, we summarized the A-scan properties spatially across the regions defined by the Early Treatment Diabetic Retinopathy Study (ETDRS) grid as depicted in Figure 4b. The ETDRS grid was placed at the center of the scan, and the mean feature values per ETDRS subregions were computed. In addition to the nine ETDRS grid cells, we additionally included the central 3 mm, central 6 mm, and the rings corresponding to the parafoveal and perifoveal bands, resulting in 13 spatial regions in total. Such ETDRS-related features have the additional advantage of being easier to interpret than Ascan related ones, due to widespread use of ETDRS grid in ophthalmology. To this set of imaging features, we added the measured BCVA. To measure the rate of change of the longitudinal features, the differences between the corresponding features of the consecutive time points (month 1 - month 0 and month 2 - month 1) were further included. This resulted in the number of local spatio-temporal features being 525, computed as follows: (8 feature maps X 13 spatial regions + 1 BCVA) X 5 temporal elements. Last, demographic features were added: sex, race, age, and smoking status together with the fluorescein angiogram pattern type, for a total of 530 features.”)
Regarding claim 7, Bogunovic teaches “The method of claim 1,”
“wherein predicting the treatment level comprises predicting a classification for the treatment level as either a high or a low treatment level.” (Bogunovic, “Predictive Model of Treatment Requirements” Section Paragraph 3, “Finally, a machine learning approach based on the random forest classifier13 was used to obtain a predictive model of the low and high treatment requirements from the set of the above features. Random forest was grown with 1000 trees for which the out of bag mean squared error was observed to have converged. The number of features to randomly sample as candidates at each split of a tree was chosen to be the square root of the number of features (√530), which is the default setting for a classification task13.”)
Regarding claim 8, Bogunovic teaches “The method of claim 7,”
“wherein the high treatment level indicates sixteen or more injections of the anti-VEGF treatment during a selected time period after an initial phase of treatment.” (Bogunovic, “Predictive Model of Treatment Requirements” Section Paragraph 2, “The maximum number of injections during the 2-year PRN regimen is 21 (months 3 to 23). We defined the category of “low” requirements to consist of patients in lower quartile of the number of injections, which corresponded to receiving no more than five injections. Analogously, the category of “high” requirements was defined to consist of patients in upper quartile, which corresponded to receiving ≥16 injections. The remaining eyes in the interquartile range were assigned to the “medium” requirements category. We aim to discriminate the patients in the low requirement group from the medium and high requirement groups, and analogously, the ones in the high requirement group from the medium and low requirement groups. Thus, we pose the problem as a multiclass one-versus-all classification.”)
Regarding claim 9, Bogunovic teaches “The method of claim 7,”
“wherein the low treatment level indicates five or fewer injections of the anti-VEGF treatment during a selected time period after an initial phase of treatment.” (Bogunovic, “Predictive Model of Treatment Requirements” Section Paragraph 2, “The maximum number of injections during the 2-year PRN regimen is 21 (months 3 to 23). We defined the category of “low” requirements to consist of patients in lower quartile of the number of injections, which corresponded to receiving no more than five injections. Analogously, the category of “high” requirements was defined to consist of patients in upper quartile, which corresponded to receiving ≥16 injections. The remaining eyes in the interquartile range were assigned to the “medium” requirements category. We aim to discriminate the patients in the low requirement group from the medium and high requirement groups, and analogously, the ones in the high requirement group from the medium and low requirement groups. Thus, we pose the problem as a multiclass one-versus-all classification.”)
Regarding claim 10, Bogunovic teaches “The method of claim 1,”
“wherein the extracting comprises: extracting the retinal feature data for the plurality of retinal features from segmented images generated using a second machine learning model that automatically segments the SD-OCT imaging data, wherein the plurality of retinal features is associated with at least one of a set of retinal fluid segments or a set of retinal layer segments identified in the segmented images.” (Bogunovic, “Intraretinal and Subretinal Fluid Segmentation” Section, “Segmentation of intraretinal cystoid fluid (IRF) and subretinal fluid (SRF) was performed per B-scan using a validated segmentation algorithm based on deep learning.12 First, based on the top and the bottom retinal layer, a mask is computed denoting the retina extending from the inner limiting membrane (ILM) to the RPE. Then, every voxel within the mask is classified with a multiscale convolutional neural network (CNN) as belonging to one of the three classes: Normal retina, IRF, or SRF (Fig. 3). The CNN had been trained in a supervised manner using a training set of 157 OCT volumes with ≈ 20,000 manually annotated B-scans, acquired with the same OCT device model (Cirrus; Zeiss) and having the same pathology (neovascular AMD), which were disjoint from the set of images in the HARBOR trial.”)
Regarding claim 11, Bogunovic teaches “The method of claim 10,”
“wherein the second machine learning model comprises a deep learning model.” (Bogunovic, “Intraretinal and Subretinal Fluid Segmentation” Section, “Segmentation of intraretinal cystoid fluid (IRF) and subretinal fluid (SRF) was performed per B-scan using a validated segmentation algorithm based on deep learning.12 First, based on the top and the bottom retinal layer, a mask is computed denoting the retina extending from the inner limiting membrane (ILM) to the RPE. Then, every voxel within the mask is classified with a multiscale convolutional neural network (CNN) as belonging to one of the three classes: Normal retina, IRF, or SRF (Fig. 3). The CNN had been trained in a supervised manner using a training set of 157 OCT volumes with ≈ 20,000 manually annotated B-scans, acquired with the same OCT device model (Cirrus; Zeiss) and having the same pathology (neovascular AMD), which were disjoint from the set of images in the HARBOR trial.”)
Regarding claim 13, Bogunovic teaches “The method of claim 1”
“wherein the plurality of retinal features includes at least one feature associated with subretinal fluid (SRF) and at least one feature associated with pigment epithelial detachment (PED).” (Bogunovic, “Intraretinal and Subretinal Fluid Segmentation” Section, “Segmentation of intraretinal cystoid fluid (IRF) and subretinal fluid (SRF) was performed per B-scan using a validated segmentation algorithm based on deep learning.12 First, based on the top and the bottom retinal layer, a mask is computed denoting the retina extending from the inner limiting membrane (ILM) to the RPE. Then, every voxel within the mask is classified with a multiscale convolutional neural network (CNN) as belonging to one of the three classes: Normal retina, IRF, or SRF (Fig. 3). The CNN had been trained in a supervised manner using a training set of 157 OCT volumes with ≈ 20,000 manually annotated B-scans, acquired with the same OCT device model (Cirrus; Zeiss) and having the same pathology (neovascular AMD), which were disjoint from the set of images in the HARBOR trial.” Note that the RPE layer is associated with potential PED.)
Regarding claim 14, Bogunovic teaches “The method of claim 1,”
“wherein the SD-OCT imaging data comprises an SD-OCT image captured during a single clinical visit.” (Bogunovic, “Predictive Model of Treatment Requirements” Section Paragraph 1, “For each eye, from its longitudinal series of three OCT volumes (baseline, month 1, and month 2) and the derived segmentations, we extracted a set of quantitative features characterizing the underlying retinal pathomorphology. For the imaging features to correspond across subjects, before the feature extraction, all scans of left eyes were mirrored to conform to scans of a right eye. From the image segmentations 2D maps were computed corresponding to the thickness maps of the four layers, as well as volume and en face area maps of both IRF and SRF, resulting in eight 2D maps in total, with examples shown in Figure 4a. Analyzing data in high-dimensional OCT volumes is affected by the so-called ‘‘Curse of Dimensionality,’’ where learning is very difficult and prone to overfitting. To limit the dimensionality of the feature vector and facilitate the machine learning, we summarized the A-scan properties spatially across the regions defined by the Early Treatment Diabetic Retinopathy Study (ETDRS) grid as depicted in Figure 4b. The ETDRS grid was placed at the center of the scan, and the mean feature values per ETDRS subregions were computed. In addition to the nine ETDRS grid cells, we additionally included the central 3 mm, central 6 mm, and the rings corresponding to the parafoveal and perifoveal bands, resulting in 13 spatial regions in total. Such ETDRS-related features have the additional advantage of being easier to interpret than Ascan related ones, due to widespread use of ETDRS grid in ophthalmology. To this set of imaging features, we added the measured BCVA. To measure the rate of change of the longitudinal features, the differences between the corresponding features of the consecutive time points (month 1 - month 0 and month 2 - month 1) were further included. This resulted in the number of local spatio-temporal features being 525, computed as follows: (8 feature maps X 13 spatial regions + 1 BCVA) X 5 temporal elements. Last, demographic features were added: sex, race, age, and smoking status together with the fluorescein angiogram pattern type, for a total of 530 features.” Note that Bogunovic teaches collection of data from three single visits. The claim as written does not require that all of the image data be acquired from only one single visit.)
Regarding claims 19 and 20, these claims recite a system with memory, machine executable code, and processor with elements corresponding to the steps recited in Claims 1 and 10. Therefore, the recited elements of these claims are mapped to the analogous steps in the corresponding method claims. Additionally, Bogunovic describes a deep learning approach using algorithms for image processing and diagnostic prediction, which necessarily require the claimed system features. Further, these features are generic and well-known in the art and are therefore not considered novel.
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) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bogunovic in view of Kumari (Automated Diabetic Retinopathy Screening With Montage Fundus Images).
Regarding claim 12, Bogunovic teaches “The method of claim 1,”
While Bogunovic discloses the machine learning model comprising a random forest classifier (see claim 1 rejection), Bogunovic does not expressly disclose that the model comprises an Extreme Gradient Boosting (XGBoost) algorithm.
Kumari discloses a predictive model comprising an Extreme Gradient Boosting (XGBoost) algorithm (Kumari, Section 2A, Paragraphs 5-6 and Figure 4, “Classification model was built using available popular ML classification models such as K Nearest Neighbors, Naive Bayes, XGBoost, Random Forest Classifier and Support Vector Machine Classifier [4]. As shown in Fig. 4, XGBoost and Random Forest Classifier were the best models. After doing a cross validation for the selected models, XGBoost got selected as the best model. It showed a higher mean and lower standard deviation than the Random Forest Classifier.”)
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 replace the random forest classifier of Bogunovic with an XGBoost algorithm, as taught by Kumari.
The motivation for doing so would have been to improve model accuracy, as described above by Kumari. 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 Bogunovic in view of Kumari to fully disclose, “wherein the machine learning model comprises an Extreme Gradient Boosting (XGBoost) algorithm.”
Conclusion
THIS ACTION IS MADE FINAL. 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 AARON JOSEPH SORRIN whose telephone number is (703)756-1565. The examiner can normally be reached Monday - Friday 9am - 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.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/AARON JOSEPH SORRIN/Examiner, Art Unit 2672
/SUMATI LEFKOWITZ/Supervisory Patent Examiner, Art Unit 2672
Prosecution Insights