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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11/24/2025 has been entered.
Applicant' s arguments, filed 11/24/2025, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Applicants have amended their claims, filed 11/24/2025, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Claims 1-4, 7, 9-15, and 17-21 are the currently pending claims hereby under examination. Claims 6 and 8 have been canceled. Claims 1, 7, 9, 15, and 19 have been amended.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4, 6-14, and 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims -4, 6-14, and 21 are directed to a method of diagnosing diabetic retinopathy using a computational algorithm, which is an abstract idea. Claims -4, 6-14, and 21 do not include additional elements that integrate the exception into a practical application or that are sufficient to amount to significantly more than the judicial exception for the reasons provided below which are in line with the 2014 Interim Guidance on Patent Subject Matter Eligibility (Federal Register, Vol. 79, No. 241, p 74618, December 16, 2014), the July 2015 Update on Subject Matter Eligibility (Federal Register, Vol. 80, No. 146, p. 45429, July 30, 2015), the May 2016 Subject Matter Eligibility Update (Federal Register, Vol. 81, No. 88, p. 27381, May 6, 2016), and the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 4, page 50, January 7, 2019).
The analysis of claim 1 is as follows:
Step 1: Claim 1 is drawn to a process.
Step 2A – Prong One: Claim 1 recites an abstract idea. In particular, claim 1 recites the following limitations:
A1: Receiving image data including an image of the retina of a subject
B1: Processing the image data to segment the image of the retina
C1: Extracting at least one feature from the segmented image of the retina
D1: Receiving demographic data and clinical data associated with the subject
E1: Generating, using a machine learning classifier, a diagnosis based at least in part on the at least one feature, the demographic data, and the clinical data
These elements A1-E1 of claim 1 are drawn to an abstract idea since (1) they involve mathematical concepts in the form of mathematical relationships, mathematical formulas or equations, and/or mathematical calculations; and/or (2) they involve a mental process that can be practically performed in the human mind including observation, evaluation, judgment, and opinion and using pen and paper.
Step 2A – Prong Two: Claim 1 recites the following limitations that are beyond the judicial exception:
A2: a computer
The element A2 of claim 1 does not integrate the exception into a practical application of the exception. In particular, the element A2 is merely an instruction to implement an abstract idea on a computer, as it does not provide a technological improvement to machine learning models beyond generic classification - see MPEP 2106.04(d) and MPEP 2106.05(f).
Under Step 2A, Prong 2, it is determined whether the claim as a whole integrates the judicial exception into a practical application. In view of the updated guidance and Desjardins, this analysis considers the description in the specification to determine whether the claim reflects an improvement in the functioning of a computer, an improvement to another technology or technical field in the Enfish / McRO / Desjardins sense, or merely uses generic computer components as tools.
The specification describes using a conventional OCT/OCTA imaging system to acquire retinal images of a subject, employing known image processing and segmentation techniques to partition OCT images into multiple retinal layers and OCTA images into vascular regions, extracting numerical image-based and clinical features (including thickness, reflectivity, vessel density, foveal avascular zone metrics, and counts of vascular bifurcations or crossovers), and then applying a supervised learning classifier, such as a random forest, to classify subjects as normal or having various grades of diabetic retinopathy. The asserted advantages are improved accuracy and objectivity of diabetic-retinopathy diagnosis and grading compared to manual clinical evaluation.
However:
The claim does not recite any improvement to the functioning of the computer itself (e.g., a new memory structure, processor operation, or data storage scheme), nor does the specification describe such an improvement.
The claim does not recite a new or improved structure or operation of the OCT or OCTA hardware (e.g., no new scanning protocol, hardware configuration, or acquisition technique). The imaging system is treated as a conventional data source.
The machine-learning classifier is recited at a high level as a “random forest” operating on extracted features. The specification does not describe a new random-forest architecture, training procedure, or representation that solves a specific technical problem in machine learning or computer technology (such as storage reduction, mitigation of catastrophic forgetting, or improved computational efficiency), in contrast to the type of improvements discussed in Desjardins. Instead, the classifier is used as a conventional tool to map input features to a DR diagnosis or grade.
The additional elements beyond the abstract idea (e.g., “one or more processors”, a “computing system”, an OCT/OCTA imaging device, and storage of data) are recited at a generic, functional level and perform their basic, expected functions of data acquisition, data manipulation, and execution of mathematical/ML algorithms. They merely apply the abstract diagnostic and mathematical concepts using conventional imaging equipment and general-purpose computing components.
Thus, when considered in light of the specification and as a whole, the claim does not reflect an improvement in computer functionality or an improvement to another technology in the Enfish / McRO / Desjardins sense. Instead, it uses generic imaging and computing technology as tools to implement the abstract diagnostic idea. The claim therefore does not integrate the judicial exception into a practical application under Step 2A, Prong 2. See MPEP §§ 2106.04(d), 2106.05(a)–(c), (f).
Step 2B: Claim 1 does not recite additional elements that amount to significantly more than the judicial exception itself. In particular, the element A2 requires no more than a generic computer to perform routine classification tasks that are well-understood, routine, and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
Claims 2-4, 6-14, and 21 depend from claim 1 and recite the same abstract idea as claim 1. Furthermore, these claims only contain recitations that further limit the abstract idea (that is, the claims only refine the classification and data processing without adding a specific technological improvement).
Claims 15 and 17-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 15-18 are directed to a method of diagnosing diabetic retinopathy using a computational algorithm, which is an abstract idea. Claims 15-18 do not include additional elements that integrate the exception into a practical application or that are sufficient to amount to significantly more than the judicial exception for the reasons provided below which are in line with the 2014 Interim Guidance on Patent Subject Matter Eligibility (Federal Register, Vol. 79, No. 241, p 74618, December 16, 2014), the July 2015 Update on Subject Matter Eligibility (Federal Register, Vol. 80, No. 146, p. 45429, July 30, 2015), the May 2016 Subject Matter Eligibility Update (Federal Register, Vol. 81, No. 88, p. 27381, May 6, 2016), and the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 4, page 50, January 7, 2019).
The analysis of claim 15 is as follows:
Step 1: Claim 15 is drawn to a process.
Step 2A – Prong One: Claim 15 recites an abstract idea. In particular, claim 15 recites the following limitations:
A1: Processing OCT/OCTA image data including an image of the subject retina to segment the image of the subject retina
B1: Extracting at least one feature from the segmented image of the retina
C1: Receiving demographic data and clinical data associated with the subject retina
D1: Classifying, using a machine learning classifier, the subject retina as normal or indicative of diabetic retinopathy based at least in part on the at least one feature, the demographic data, and the clinical data
These elements A1-D1 of claim 15 are drawn to an abstract idea since (1) they involve mathematical concepts in the form of mathematical relationships, mathematical formulas or equations, and/or mathematical calculations; and/or (2) they involve a mental process that can be practically performed in the human mind including observation, evaluation, judgment, and opinion and using pen and paper.
Step 2A – Prong Two: Claim 15 recites the following limitations that are beyond the judicial exception:
A2: a computer.
The element A2 of claim 15 does not integrate the exception into a practical application of the exception. In particular, the element A2 is merely an instruction to implement an abstract idea on a computer, as it does not provide a technological improvement to machine learning models beyond generic classification - see MPEP 2106.04(d) and MPEP 2106.05(f).
Under Step 2A, Prong 2, it is determined whether the claim as a whole integrates the judicial exception into a practical application. In view of the updated guidance and Desjardins, this analysis considers the description in the specification to determine whether the claim reflects an improvement in the functioning of a computer, an improvement to another technology or technical field in the Enfish / McRO / Desjardins sense, or merely uses generic computer components as tools.
The specification describes using a conventional OCT/OCTA imaging system to acquire retinal images of a subject, employing known image processing and segmentation techniques to partition OCT images into multiple retinal layers and OCTA images into vascular regions, extracting numerical image-based and clinical features (including thickness, reflectivity, vessel density, foveal avascular zone metrics, and counts of vascular bifurcations or crossovers), and then applying a supervised learning classifier, such as a random forest, to classify subjects as normal or having various grades of diabetic retinopathy. The asserted advantages are improved accuracy and objectivity of diabetic-retinopathy diagnosis and grading compared to manual clinical evaluation.
However:
The claim does not recite any improvement to the functioning of the computer itself (e.g., a new memory structure, processor operation, or data storage scheme), nor does the specification describe such an improvement.
The claim does not recite a new or improved structure or operation of the OCT or OCTA hardware (e.g., no new scanning protocol, hardware configuration, or acquisition technique). The imaging system is treated as a conventional data source.
The machine-learning classifier is recited at a high level as a “random forest” operating on extracted features. The specification does not describe a new random-forest architecture, training procedure, or representation that solves a specific technical problem in machine learning or computer technology (such as storage reduction, mitigation of catastrophic forgetting, or improved computational efficiency), in contrast to the type of improvements discussed in Desjardins. Instead, the classifier is used as a conventional tool to map input features to a DR diagnosis or grade.
The additional elements beyond the abstract idea (e.g., “one or more processors”, a “computing system”, an OCT/OCTA imaging device, and storage of data) are recited at a generic, functional level and perform their basic, expected functions of data acquisition, data manipulation, and execution of mathematical/ML algorithms. They merely apply the abstract diagnostic and mathematical concepts using conventional imaging equipment and general-purpose computing components.
Thus, when considered in light of the specification and as a whole, the claim does not reflect an improvement in computer functionality or an improvement to another technology in the Enfish / McRO / Desjardins sense. Instead, it uses generic imaging and computing technology as tools to implement the abstract diagnostic idea. The claim therefore does not integrate the judicial exception into a practical application under Step 2A, Prong 2. See MPEP §§ 2106.04(d), 2106.05(a)–(c), (f).
Step 2B: Claim 15 does not recite additional elements that amount to significantly more than the judicial exception itself. In particular, the element A2 does not qualify as significantly more because this limitation requires no more than a generic computer to perform routine classification tasks that are well-understood, routine, and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
Claims 16-18 depend from claim 15 and recite the same abstract idea as claim 1. Furthermore, these claims only contain recitations that further limit the abstract idea (that is, the claims only refine the classification and data processing without adding a specific technological improvement).
Claims 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 19-20 are directed to a method of diagnosing diabetic retinopathy using a computational algorithm, which is an abstract idea. Claims 19-20 do not include additional elements that integrate the exception into a practical application or that are sufficient to amount to significantly more than the judicial exception for the reasons provided below which are in line with the 2014 Interim Guidance on Patent Subject Matter Eligibility (Federal Register, Vol. 79, No. 241, p 74618, December 16, 2014), the July 2015 Update on Subject Matter Eligibility (Federal Register, Vol. 80, No. 146, p. 45429, July 30, 2015), the May 2016 Subject Matter Eligibility Update (Federal Register, Vol. 81, No. 88, p. 27381, May 6, 2016), and the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 4, page 50, January 7, 2019).
The analysis of claim 19 is as follows:
Step 1: Claim 19 is drawn to a machine.
Step 2A – Prong One: Claim 19 recites an abstract idea. In particular, claim 19 recites the following limitations:
A1: receiving at least one feature extracted from OCA image data of a subject retina
B1: receiving at least one feature extracted from OCTA image data of the subject retina
C1: receiving demographic data and clinical data associated with the subject retina
D1: classifying the subject retina as normal or indicative of diabetic retinopathy based at least in part on the at least one feature, the demographic data, and the clinical data
These elements A1-D1 of claim 19 are drawn to an abstract idea since (1) they involve mathematical concepts in the form of mathematical relationships, mathematical formulas or equations, and/or mathematical calculations; and/or (2) they involve a mental process that can be practically performed in the human mind including observation, evaluation, judgment, and opinion and using pen and paper.
Step 2A – Prong Two: Claim 19 recites the following limitations that are beyond the judicial exception:
A2: A non-transitory computer readable storage medium having computer program instructions stored thereon that, when executed by a processor, cause the processor to perform the following instructions
The element A2 of claim 19 do not integrate the exception into a practical application of the exception. In particular, the element A2 is merely an instruction to implement an abstract idea on a computer, as it does not provide a technological improvement to machine learning models beyond generic classification - see MPEP 2106.04(d) and MPEP 2106.05(f).
Under Step 2A, Prong 2, it is determined whether the claim as a whole integrates the judicial exception into a practical application. In view of the updated guidance and Desjardins, this analysis considers the description in the specification to determine whether the claim reflects an improvement in the functioning of a computer, an improvement to another technology or technical field in the Enfish / McRO / Desjardins sense, or merely uses generic computer components as tools.
The specification describes using a conventional OCT/OCTA imaging system to acquire retinal images of a subject, employing known image processing and segmentation techniques to partition OCT images into multiple retinal layers and OCTA images into vascular regions, extracting numerical image-based and clinical features (including thickness, reflectivity, vessel density, foveal avascular zone metrics, and counts of vascular bifurcations or crossovers), and then applying a supervised learning classifier, such as a random forest, to classify subjects as normal or having various grades of diabetic retinopathy. The asserted advantages are improved accuracy and objectivity of diabetic-retinopathy diagnosis and grading compared to manual clinical evaluation.
However:
The claim does not recite any improvement to the functioning of the computer itself (e.g., a new memory structure, processor operation, or data storage scheme), nor does the specification describe such an improvement.
The claim does not recite a new or improved structure or operation of the OCT or OCTA hardware (e.g., no new scanning protocol, hardware configuration, or acquisition technique). The imaging system is treated as a conventional data source.
The machine-learning classifier is recited at a high level as a “random forest” operating on extracted features. The specification does not describe a new random-forest architecture, training procedure, or representation that solves a specific technical problem in machine learning or computer technology (such as storage reduction, mitigation of catastrophic forgetting, or improved computational efficiency), in contrast to the type of improvements discussed in Desjardins. Instead, the classifier is used as a conventional tool to map input features to a DR diagnosis or grade.
The additional elements beyond the abstract idea (e.g., “one or more processors”, a “computing system”, an OCT/OCTA imaging device, and storage of data) are recited at a generic, functional level and perform their basic, expected functions of data acquisition, data manipulation, and execution of mathematical/ML algorithms. They merely apply the abstract diagnostic and mathematical concepts using conventional imaging equipment and general-purpose computing components.
Thus, when considered in light of the specification and as a whole, the claim does not reflect an improvement in computer functionality or an improvement to another technology in the Enfish / McRO / Desjardins sense. Instead, it uses generic imaging and computing technology as tools to implement the abstract diagnostic idea. The claim therefore does not integrate the judicial exception into a practical application under Step 2A, Prong 2. See MPEP §§ 2106.04(d), 2106.05(a)–(c), (f).
Step 2B: Claim 19 does not recite additional elements that amount to significantly more than the judicial exception itself. In particular, the element A2 requires no more than a generic computer to perform routine classification tasks that are well-understood, routine, and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
Claim 20 depends from claim 19 and recite the same abstract idea as claim 1. Furthermore, these claims only contain recitations that further limit the abstract idea (that is, the claims only refine the classification and data processing without adding a specific technological improvement).
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.
Claims 1-4, 7, 9-10, 13-14, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Sandhu et al. (Sandhu, Harpal Singh et al. “Automated Diagnosis and Grading of Diabetic Retinopathy Using Optical Coherence Tomography.” Investigative ophthalmology & visual science 59.7 (2018): 3155–3160. Web.), hereto referred as Sandhu, and further in view of Choi et al. (Choi, WooJhon et al. ULTRAHIGH SPEED SWEPT SOURCE OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY OF RETINAL AND CHORIOCAPILLARIS ALTERATIONS IN DIABETIC PATIENTS WITH AND WITHOUT RETINOPATHY. Retina 37(1):p 11-21, January 2017), hereto referred as Choi, and further in view of Huang et al. (US 20160284085 A1), hereto referred as Huang, and further in view of Bhuiya et al. (Bhuiyan et al. “Detection and Classification of Bifurcation and Branch Points on Retinal Vascular Network.” 2012 International Conference on Digital Image Computing: Techniques and Applications. IEEE, 2012. 1–8. Web.), hereto referred as Bhuiyan.
Regarding claim 1, Sandhu teaches that a computer-implemented method for diagnosing diabetic retinopathy(Sandhu, Abstract: "A computer-assisted diagnostic (CAD) system to diagnose and grade nonproliferative, diabetic retinopathy (NPDR) from optical coherence tomography (OCT) images", outlines a computer-implemented method for diagnosing diabetic retinopathy, including differentiation and grading) comprises: receiving image data including an image of a retina of a subject (Sandhu, Abstract: "All patients underwent a... spectral-domain OCT of a 6 x 6 mm area of the macula in both eyes. These images then were analyzed by a novel CAD system", describing the acquisition of image data, specifically OCT scans of the retina); processing the image data to segment the image of the retina(Sandhu, Abstract: "these images then were analyzed by a novel CAD system that segments the retina into 12 layers", describing processing of image data to segment the retina); said processing includes processing the OCT image data to segment the image of the retina into a plurality of retinal layers (Sandhu, "The CAD System" 11, Page 3156: "A novel noninvasive framework for early diagnosis of DR using OCT images then was developed"; "Methods", 13, Page 3156:....First, 12 distinct retinal layers are segmented using previously described methods", describing segmentation of OCT image data into multiple retinal layers for diagnostic purposes); extracting at least one feature from the segmented image of the retina (Sandhu, "Abstract: "These images then were analyzed by a novel CAD system that... quantifies the reflectivity, curvature, and thickness of each layer", describing the extraction of features from segmented retina layers for analysis); receiving demographic data and clinical data associated with the subject (Sandhu, "The CAD System", 12, Page 3156: "First, a set of 12 normal OCTs (from six males and six females) was used as the template for automated segmentation. Second, OCTs from 200 normal patients, aged 18 to 75, were segmented by four different retinal specialists and used as a gold standard, or "ground truth, "for normal OCT images", and "Methods", 111, Page 3156: "All patients were diagnosed clinically as having no DR or DR based on dilated fundus exam", describing the receipt of demographic data, including patient age, and clinical data, as the segmentation by specialists provides an expert-validated standard for diagnosing retinal conditions); generating, using a machine learning classifier, a diagnosis for the subject based at least in part on the at least one feature, the demographic data, and the clinical data (Sandhu, "The CAD System", 15, Page 3157: "In the final step, after segmenting the 12 retinal layers and extracting the three key features, the CAD system classified normal and DR subjects... we used a form of a neural network called a deep learning network that had the ability to learn these features and fuse them together. To learn characteristics of normal and DR subjects, CDFs were calculated for each feature and fed into the proposed network", in conjunction with the previously established dataset "The CAD System", 12, Page 3156: "First, a set of 12 normal OCTs (from six males and six females) was used as the template for automated segmentation. Second, OCTs from 200 normal patients, aged 18 to 75, were segmented by four different retinal specialists and used as a gold standard, or 'ground truth, 'for normal OCT images", and "Methods", 11, Page 3156: "All patients were diagnosed clinically as having no DR or DR based on dilated fundus exam", describing the use of a machine learning classifier to generate a diagnosis based on extracted OCT features, demographic data (age and gender), and clinical data (diabetes status, prior diagnosis, and treatment history); and the machine learning classifier is a random forest classifier (Sandhu, “Results”, Table 1, Page 3158: “The proposed deep fusion classification network (DFCN) was compared to other commonly used classifiers, including Random Forest, Support Vector Machine (SVM), and k-Nearest Neighbors (k-NN)”, describing Random Forest as a standard classifier for diabetic retinopathy classification; it would have been prima facie obvious before the effective filing date of the claimed invention to substitute the explicitly-taught Random Forest classifier for the deep learning classifier in Sandhu’s CAD system as a known, conventional alternative performing the same supervised classification function).
Also regarding claim 1, Sandhu teaches that the image data includes optical coherence tomography (OCT) image data (Sandhu, Abstract: "...to diagnose and grade nonproliferative diabetic retinopathy (NPDR) from optical coherence tomography (OCT) images", describing OCT as a relevant imaging modality for DR analysis), but does not teach that the image data includes optical coherence tomography angiography (OCTA) image data. Sandhu recognizes the need to combine OCT and OCTA data to improve the accuracy and robustness of DR classification systems (Sandhu, Discussion, 16, Page 3159). However, Sandhu does not provide a working example that integrates both OCT and OCTA data for diagnosis and grading.
Choi, however, teaches the acquisition and use of both OCT and OCTA image data together for visualizing and analyzing retinal and choriocapillaris microvascular alterations in diabetic patients (Choi, Abstract, 'Introduction', 12, p. 2-3: "since OCTA is an extension of standard OCT imaging, structural information is obtained at the same time, and is intrinsically co-registered to angiographic information". 'Nonproliferative diabetic retinopathy', 12, p. 6). Choi specifically describes OCTA imaging and analysis techniques applied to the study of diabetic retinopathy combined with OCT image data, thus filling the gap left by Sandhu.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sandhu in view of Choi to use both OCT and OCTA image data for diagnosing and grading diabetic retinopathy. The integration of OCTA data with OCT imaging would be possible as both imaging modalities can be acquired together, and Sandhu explicitly motivates combining the two for improved diagnostic accuracy (Sandhu, "Discussion", 16, Page 3159). The benefit of the combination would be improved diagnostic accuracy and more robust grading of diabetic retinopathy by integrating structural and vascular imaging data (Choi, Introduction).
Also regarding claim 1, the modified Sandhu does not fully teach that said processing includes processing the OCTA image data to segment a vasculature of the image of the retina. Rather, it discusses the use of OCT angiography (OCTA) to assess microvascular changes in diabetic retinopathy, particularly through the analysis of capillary density in different plexuses (Sandhu, “Discussion”, 16, Page 3159). However, Sandhu does not explicitly disclose segmenting vasculature from OCTA image data.
Huang, who investigates choroidal neovascularization (CNV) and retinal vasculature using OCT angiography, describes OCTA processing for generating en face angiograms to quantify retinal vasculature, including processing and segmentation techniques to extract blood vessel data (Huang, ¶[0028]: “Once generated, the en face angiogram image may be used to quantify various features of the retinal vasculature”, describing vessel-level extraction from OCTA images). This disclosure directly relates to processing OCTA image data to segment a vasculature of the image of the retina, filling the gap in Sandhu’s disclosure.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combined Sandhu and Choi in view of Huang to process the OCTA image data to segment a vasculature of the retina as recited, because Sandhu already teaches the desirability of using OCTA to analyze microvascular changes, and Huang provides explicit OCTA vasculature segmentation techniques. The benefit of this modification would be improved diabetic retinopathy diagnosis via direct segmentation of retinal vasculature from OCTA images, enabling detailed characterization of capillary density and vascular alterations as taught by Huang.
Also regarding claim 1, the modified Sandhu does not teach that said extracting includes extracting from the segmented vasculature at least one of bifurcation points and crossover points.
Bhuiyan, however, teaches detecting and classifying retinal vascular bifurcation, branch, and crossover points from segmented vessel images. In particular, Bhuiyan describes that “identifying the vascular bifurcation, branch and crossover points in the retinal image is helpful for predicting many cardiovascular diseases” and proposes “a novel method to detect and classify the vascular bifurcation, branch and crossover points (landmarks) based on the vessel geometrical features. We utilize the vessel’s centerline and width information to detect and classify these landmarks, which can be used for image matching in medical diagnosis…” (Bhuiyan, Abstract); further, the method “segment[s] the blood vessels” and then performs “vessel segmentation, skeletonization and vessel geometrical features in the junction area” to detect these landmarks (Bhuiyan, p. 2-3; Sec. 2). This disclosure directly relates to processing a segmented retinal vasculature image to extract vascular bifurcation and crossover points as quantitative features.
It would have been prima facie obvious before the effective filing date of the claimed invention to modify the combined Sandhu, Choi, and Huang system in view of Bhuiyan so that said extracting includes extracting from the segmented vasculature at least one of bifurcation points and crossover points as recited. Sandhu, Choi, and Huang already teach using OCT and OCTA imaging to obtain segmented retinal vasculature for diabetic retinopathy analysis, while Bhuiyan teaches that bifurcation and crossover points are important retinal vascular landmarks whose locations can be automatically detected from segmented vessel images using vessel centerlines and geometrical features. Because both sets of references operate on segmented images of the retinal vasculature for medical analysis, one of ordinary skill in the art would have found it obvious to apply Bhuiyan’s bifurcation and crossover detection techniques to the segmented OCTA vasculature in Sandhu/Choi/Huang and to use the resulting bifurcation and crossover points as additional extracted vascular features for classification, thereby improving characterization of retinal vascular architecture.
Regarding claim 2, the modified Sandhu teaches that the diagnosis is one of normal and diabetic retinopathy (Sandhu, "The CAD System", ¶5, Page 3157: "In the final step, after segmenting the 12 retinal layers and extracting the three key features, the CAD system classified normal and DR subjects...", describing the classification (i.e. diagnosis) of the subject as normal or having diabetic retinopathy (DR)).
Regarding claim 3, the modified Sandhu teaches that the diagnosis is one of normal, mild nonproliferative diabetic retinopathy, moderate nonproliferative diabetic retinopathy, severe nonproliferative diabetic retinopathy, and proliferative diabetic retinopathy (Sandhu, ‘The CAD System’, ¶5, Page 3157: “the CAD system classified normal and DR subjects”; ‘The CAD System’, ¶1, Page 3156: “used to classify the test subject as normal, or having subclinical or mild/moderate DR”, discloses a system for classifying diabetic retinopathy using a deep fusion classification network that distinguishes between normal and mild or moderate NPDR cases).
Regarding claim 4, the modified Sandhu teaches that the diagnosis is one of normal, mild nonproliferative diabetic retinopathy, and moderate nonproliferative diabetic retinopathy (Sandhu, ‘The CAD System’, ¶5, Page 3157: “the CAD system classified normal and DR subjects”; ‘The CAD System’, ¶1, Page 3156: “used to classify the test subject as normal, or having subclinical or mild/moderate DR”, discloses a system for classifying diabetic retinopathy using a deep fusion classification network that distinguishes between normal and mild or moderate NPDR cases).
Regarding claim 7, the modified Sandhu teaches that the at least one feature further includes at least one of retinal layer thickness, reflectivity, and curvature (Sandhu, "The CAD System", ¶5, Page 3157: "Second, three global features are measured based on curvature, reflectivity, and thickness of the segmented retinal layers", describing the extraction of retinal layer thickness, reflectivity, and curvature as features for classification).
Regarding claim 9, the modified Sandhu does not teach that the at least one feature further includes at least one of a distance map of a foveal avascular zone, blood vessel density, and blood vessel caliber. Sandhu discusses the importance of analyzing microvascular changes in diabetic retinopathy using OCT angiography (OCTA) and notes that factors such as blood vessel density and the foveal avascular zone (FAZ) are correlated with DR severity (Sandhu, "Discussion", ¶6, Page 3159), but does not teach or extract features such as, a distance map of a foveal avascular zone, blood vessel density, or blood vessel caliber. Choi teaches the extraction and assessment of such OCTA features by visualizing and analyzing vascular abnormalities including blood vessel density, blood vessel caliber, and foveal avascular zone enlargement (Choi, Abstract; 'Nonproliferative diabetic retinopathy', ¶2, p. 6). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combined Sandhu, Choi, Huang, and Bhuiyan in view of Choi to use least one feature selected from a distance map of a foveal avascular zone, blood vessel density, and blood vessel caliber from OCTA image data. The combination would be possible because both references discuss the importance of microvascular analysis and feature extraction for improved diabetic retinopathy classification, and the benefit would be enhanced diagnostic accuracy and earlier detection by utilizing additional vascular biomarkers (Sandhu, "Discussion", 16, Page 3159; Choi, Abstract; Choi, Discussion, ¶8, p. 8–9).
Regarding claim 10, the modified Sandhu teaches that the demographic data includes at least one of sex and age (Sandhu, "The CAD System", ¶2, Page 3156: "First, a set of 12 normal OCTs (from six males and six females) was used as the template for automated segmentation. Second, OCTs from 200 normal patients, aged 18 to 75, were segmented by four different retinal specialists and used as a gold standard, or ‘ground truth,’ for normal OCT images", describing the receipt of demographic data, including patient sex and age, as part of the dataset used in the analysis).
Regarding claim 13, the modified Sandhu teaches that the classifier is a two-stage classifier (Sandhu, "The CAD System", Page 3157: "To build the classification model, a deep neural network with two stages of autoencoders was used. The first stage consisted of several deep networks built with the encoders for each input feature, one autoencoder for each of the three features per each segmented layer for a total of 36 (12 × 3 = 36). In the second stage, detailed classification of subjects with DR was performed to determine the grade of DR using the deep fusion classification network", describing a two-stage classification approach where the first stage extracts feature representations via autoencoders, and the second stage applies the deep fusion classification network to categorize the level of diabetic retinopathy).
Regarding claim 14, the modified Sandhu teaches that the two-stage classifier includes a first stage which generates the diagnosis, the diagnosis being one of normal or diabetic retinopathy (Sandhu, "Results", ¶3, Page 3157: "For the first stage (normal vs. DR), the proposed CAD system showed a total diagnostic accuracy of 93.8% (150/160 subjects, describing a first-stage classification where the first stage distinguishes between normal and diabetic retinopathy); and a second stage which, if the first stage diagnoses diabetic retinopathy, differentiates between grades of diabetic retinopathy (Sandhu, "The CAD System", Page 3157: "In the second stage, detailed classification of subjects with DR was performed to determine the grade of DR using the deep fusion classification network", describing a second-stage classification where it grades the diabetic retinopathy).
Regarding claim 21, the modified Sandhu teaches that the machine learning classifier is trained using (i) OCT image data of retina from individuals with different grades of diabetic retinopathy and from individuals without a diagnosis of diabetic retinopathy (Sandhu, "The CAD System", 15, Page 3157: “a set of 12 normal OCTs (from six males and six females) was used as the template for automated segmentation. Second, OCTs from 200 normal patients, aged 18 to 75, were segmented... All patients were diagnosed clinically as having no DR or DR based on dilated fundus exam”; “the CAD system classified normal and DR subjects... To learn characteristics of normal and DR subjects, CDFs were calculated for each feature and fed into the proposed network”, showing the classifier was trained on OCT image data from both DR and non-DR individuals).
Also regarding claim 21, Sandhu does not teach (ii) OCTA image data of retina from individuals different grades of diabetic retinopathy and from individuals without a diagnosis of diabetic retinopathy. While Sandhu discusses the importance of OCTA in the analysis of microvascular changes and suggests future integration of OCT and OCTA data (Sandhu, "Discussion", 16, Page 3159), Sandhu does not disclose training the classifier with OCTA image data or with a dataset that includes OCTA images from DR and non-DR patients. Choi, however, teaches the acquisition and analysis of OCTA image data from individuals with different grades of diabetic retinopathy and from individuals without a diagnosis of diabetic retinopathy (Choi, 'Methods', ¶2, p. 3). Choi’s study included three distinct clinical subject groups: (1) normal controls, (2) diabetic patients without retinopathy, and (3) diabetic patients with nonproliferative or proliferative diabetic retinopathy, showing that OCTA images were acquired and analyzed from different grades of disease and from normal subjects (Choi, 'Methods', ¶2, p. 3). Choi compares the retinal vasculature of these groups using OCTA, describing the observed microvascular abnormalities in both normal and diabetic retinopathy subjects, but does not itself perform automated grading or classification of the images. Choi also states, “OCTA can be rapidly acquired and ultimately can be automatically graded without reader intervention” (Choi, 'Discussion', p. 8–9), indicating that automated grading and classification using such datasets is anticipated as a logical next step. A person of ordinary skill in the art would recognize that Choi’s acquisition of OCTA data for well-defined clinical grades provides labeled datasets suitable for use in supervised machine learning approaches, such as those employed by Sandhu. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combined Sandhu and Choi in view of Choi to train the machine learning classifier using OCTA image data from individuals with different grades of diabetic retinopathy and from individuals without a diagnosis of diabetic retinopathy. The combination would be possible because Sandhu and Choi both discuss acquiring imaging data from both patient and control populations for the analysis of diabetic retinopathy, and Choi demonstrates the feasibility and clinical value of including OCTA data from both groups. The benefit would be a more robust and accurate machine learning classifier for diabetic retinopathy diagnosis and grading by integrating both structural and vascular imaging data (Sandhu, 'Discussion, ¶6, Page 3159; Choi, Abstract; 'Introduction', ¶2, p. 2–3).
Also regarding claim 21, Sandhu partially teaches (iii) demographic data from the individuals in (i) and (ii), and (iv) clinical data from the individuals in (i) and (ii). Specifically, Sandhu teaches including demographic and clinical data from individuals with and without diabetic retinopathy for OCT images (Sandhu, 'The CAD System', ¶2, Page 3156). However, Sandhu does not disclose collecting demographic or clinical data for individuals who received OCTA imaging, since OCTA was not performed. Choi teaches the acquisition, via a specialist, and use of clinical data, complete ophthalmic examination, for all individuals undergoing OCTA imaging, including both diabetic patients and normal subjects (Choi, Methods, ¶2, p. 3). Choi also reports the ages of the subjects as demographic data (Choi, 'Results', p. 5-6), but does not explicitly use age as an input for assessment or classification. While the collection of demographic data in Choi is limited to age, a person of ordinary skill in the art would recognize that in a combined system using both OCT and OCTA imaging modalities, the demographic and clinical data would naturally be included for all subjects, since both imaging types would typically be acquired from the same patient cohorts and the machine learning classifier in Sandhu is already designed to incorporate such data. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combined Sandhu and Choi in view of Choi to collect and use demographic and clinical data for all individuals undergoing OCTA imaging—both those with and without diabetic retinopathy—when training the machine learning classifier. The combination would be possible because both Sandhu and Choi disclose the clinical value of using comprehensive patient datasets, and the classifier is already structured to accept such data. The benefit would be a more accurate, clinically relevant classifier for diabetic retinopathy diagnosis, reflecting the true spectrum of patient presentations (Sandhu, "The CAD System", 12, Page 3156; Choi, Methods, p. 3–4).
Claims 15, 17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sandhu et al. (Sandhu, Harpal Singh et al. “Automated Diagnosis and Grading of Diabetic Retinopathy Using Optical Coherence Tomography.” Investigative ophthalmology & visual science 59.7 (2018): 3155–3160. Web.), hereto referred as Sandhu, and further in view of Choi et al. (Choi, WooJhon et al. ULTRAHIGH SPEED SWEPT SOURCE OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY OF RETINAL AND CHORIOCAPILLARIS ALTERATIONS IN DIABETIC PATIENTS WITH AND WITHOUT RETINOPATHY. Retina 37(1):p 11-21, January 2017), hereto referred as Choi, and further in view of Bhuiya et al. (Bhuiyan et al. “Detection and Classification of Bifurcation and Branch Points on Retinal Vascular Network.” 2012 International Conference on Digital Image Computing: Techniques and Applications. IEEE, 2012. 1–8. Web.), hereto referred as Bhuiyan.
Regarding claim 15, Sandhu teaches that a computer-implemented method for classifying a retina (Sandhu, Abstract, Page 3155: "We determine the feasibility and accuracy of a computer-assisted diagnostic (CAD) system to diagnose and grade nonproliferative diabetic retinopathy", describing a computer-implemented method for classifying a retina), comprises: processing OCT image data including an OCT image of a subject retina to segment the OCT image of the subject retina (Sandhu, Abstract: "these images then were analyzed by a novel CAD system that segments the retina into 12 layers", describing processing of image data to segment the retina); extracting at least one OCT feature from the segmented OCT image of the subject retina (Sandhu, "Abstract: "These images then were analyzed by a novel CAD system that... quantifies the reflectivity, curvature, and thickness of each layer", describing the extraction of features from segmented retina layers for analysis); and receiving demographic data and clinical data associated with the subject retina (Sandhu, "The CAD System", ¶2, Page 3156: "First, a set of 12 normal OCTs (from six males and six females) was used as the template for automated segmentation. Second, OCTs from 200 normal patients, aged 18 to 75, were segmented by four different retinal specialists and used as a gold standard, or ‘‘ground truth,’’ for normal OCT images", and "Methods", ¶1, Page 3156: "All patients were diagnosed clinically as having no DR or DR based on dilated fundus exam", describing the receipt of demographic data, including patient age, and clinical data, as the segmentation by specialists provides an expert-validated standard for diagnosing retinal conditions).
Also regarding claim 15, Sandhu does not teach processing OCTA image data includi