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 Amendment
Claims 1, 2, 9, 10, 15, and 16 has been amended.
Claims 3, 11, and 17 has been cancelled.
Claims 1-2, 4-10, 12-16, and 18-20 are still pending for consideration.
Response to Arguments
Applicant's arguments filed on Dec 19, 2025 have been fully considered but they are not persuasive.
Applicant amended independent claim 1 to incorporate the subject matter of former claim 3, thereby narrowing claim 1 and newly requiring specific DR severity interval classifications. The revised rejection set forth herein necessitated by applicant’s amendment because the amendment changed the scope of the independent claim and required additional prior-art treatment of the newly incorporated interval limitation. Accordingly, this action is properly made final under MPEP 706.07(a).
Applicant on page 2 of the “Remarks” assert “Here, the Examiner states on page 4 of the Office Action that Sahlsten's use of the NRDR/RDR system for grading, which "considers the cases with no diabetic retinopathy and mild diabetic retinopathy as nonreferable diabetic retinopathy, and the cases with moderate or worse diabetic retinopathy as referable diabetic retinopathy" as teaching the subject matter of now- cancelled claim 3, whose subject matter has now been incorporated into claim 1. However, Sahlsten does not teach grading using DR severity classifications which denote "a moderate to moderately severe DR, a moderately severe to severe DR, or a moderate to severe DR," as each of those three separate classifications as presently claimed would all fall under the "referable" DR category per Sahlsten's teaching”.
Response: Sahlsten’s teaches evaluating diabetic retinopathy severity from color fundus images using a deep-learning classifier. Sahlsten also teaches a five-level PIRC grading system in which the classes are (see Table 6; “PIRC classes (0 = no apparent DR, 1 = mild NPDR, 2 = moderate NPDR, 3 = severe NPDR, 4 = PDR), PIMEC classes (0 = no apparent DME, 1 = mild DME, 2 = moderate DME, 3 = severe DME) and QRDR classes (0 = ungradable, 1 = NRDR, 2 = RDR)”). This, Sahlsten teaches classifying the eye into DR severity classifications.
The argument is persuasive only to the extent that Sahlesten’s NRDR/RDR disclosure alone does not expressly set forth the narrower interval classifications now recited in amended claim. However, applicant’s amended claim 1, to newly require those interval classifications. In response to that amendment, the rejection additionally relies on the Staurenghi et al. ETDRS/DRSS severity scale reference for the meanings of severity levels 43, 47, and 53, while continuing rely on Sahlsten for fundus image DR grading and Zhang for the probability-based metric/system implementation.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-2, 4-10, 12-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sahlsten et al. NPL “Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Grading” in view of Zhang et al. (US 20190110753 A1) and further in view of Staurenghi et al. MPL “Impact of baseline Diabetic Retinopathy Severity Scale scores on visual outcomes in the VIVID-DME and VISTA-DME studies”.
Regarding claim 1, Sahlsten et al. teaches a method for evaluating diabetic retinopathy (DR) severity, the method comprising: determining one or more DR severity scores, each score associated with a DR severity level (see Abstract; “including state-of-the-art results for accurately classifying images according to clinical five-grade diabetic retinopathy” see also page 7th para; “Here the neural network can be constructed in such a way, that it receives an input which is used in calculating an output12, such as class or grade of diabetic retinopathy”); determining a plurality of DR severity classifications, each classification denoted by a range or a set of DR severity threshold scores (see Abstract; “ We also provide novel results for five different screening and clinical grading systems for diabetic retinopathy and macular edema classification”, see also page 2, 4th para; “The NRDR/ RDR system considers the cases with no diabetic retinopathy and mild diabetic retinopathy as nonreferable diabetic retinopathy, and the cases with moderate or worse diabetic retinopathy as referable diabetic retinopathy”); receiving input data comprising at least color fundus imaging data for an eye of a subject (see page 2, 1st para; “Two 45 degree color fundus photographs, centered on fovea and optic disc were taken from the patient’s both eyes”); and classifying the eye of the received input data into a DR severity classification of the plurality of DR severity classifications based on the metric (see Abstract; “including state-of-the-art results for accurately classifying images according to clinical five-grade diabetic retinopathy”). However, Sahlsten et al. des not teach wherein at least one DR severity classification denotes a moderate to moderately severe DR, a moderately severe to severe DR, or a moderate to severe DR, determining, from the received input data, a metric indicating a probability that a score for DR severity in the eye of the subject falls within a selected range.
In the same field of endeavor Zhang et al. teaches determining, from the received input data, a metric indicating a probability that a score for DR severity in the eye of the subject falls within a selected range (see Fig. 3, para [0084]; “the application provides a breakdown of the diagnosis such as generated using softmax probabilities” see also para [0163]; “The highest drop in the probability represents the region of interest that contributed the highest importance to the deep learning algorithm”). Accordingly, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify a deep learning fundus image analysis for diabetic retinopathy and macular edema grading of Sahlsten et al. in view of the use of deep learning algorithms enable the automated analysis of ophthalmic images to generate predictions of comparable accuracy to clinical experts of Zhang et al. in order to set threshold ranges and assign the final severity result (see para [0084]). However, the combination of Sahlsten et al. and Zhang et al. as a whole does not teach wherein at least one DR severity classification denotes a moderate to moderately severe DR, a moderately severe to severe DR, or a moderate to severe DR.
In the same field of endeavor, Staurenghi et al. teaches wherein at least one DR severity classification denotes a moderate to moderately severe DR, a moderately severe to severe DR, or a moderate to severe DR (see page 955, Table 1
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Accordingly, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify a deep learning fundus image analysis for diabetic retinopathy and macular edema grading of Sahlsten et al. in view of the use of deep learning algorithms enable the automated analysis of ophthalmic images to generate predictions of comparable accuracy to clinical experts of Zhang et al. and impact of baseline diabetic retinopathy severity scale scores on visual outcomes in the VIVID-DME and VISTA-DME studies of Staurenghi et al. in order to demonstrate consistent treatment benefit across various baseline levels of retinopathy (see page 955 Table 1).
Regarding claim 2, the rejection of claim 1 is incorporated herein.
Staurenghi et al. in the combination further teach further comprising: determining the range or set of DR severity threshold scores, each DR severity threshold score indicating a minimum or maximum score corresponding to a DR severity classification of the plurality of DR severity classifications (see page 955, Table 1; “ Severity categories for characteristics graded in multiple fields are of the form ‘maximum severity/extent’, where maximum severity can be absent (A), questionable (Q), definitely present (D), moderate (M), severe (S), or very severe (VS), and extent is the number of fields at that severity level. For example, M/2–3 means that there are two or three fields from fields 3 to 7 with moderate severity, and none with higher severity”).
Regarding claim 4, the rejection of claim 1 is incorporated herein.
Staurenghi et al. in the combination further teach wherein the at least one range or set of DR severity threshold scores comprises a portion of a Diabetic Retinopathy Severity Scale (DRSS) between and including 43 and 47, between and including 47 and 53, or between and including 43 and 53 (see page 955, 1st para; “with a DRSS score ≤43, moderate (26.3%) in patients with a DRSS score of 47, and high (50.2%) in patients with a DRSS score ≥53”).
Regarding claim 5, the rejection of claim 1 is incorporated herein.
Zhang et al. in the combination further teach wherein the input data further comprises one or more of: baseline demographic characteristics associated with the subject and baseline clinical characteristics associated with the subject (see para [0198]; “Demographic characteristics of participants Parameter Value (n = 1,005) Age (Years) 61.33 ± 9.19 Sex (Male/Female) 461/544 Eye (Right/Left) 506/499 BCVA (logMAR) 0.24 ± 0.37 AL (mm) 29.46 ± 2.27 MS (dB) 22.51 ± 5.14 BCEA (deg.sup.2) 20.38 ± 24.64 Data are presented as mean ± standard deviation. BCVA = best corrected visual acuity; AL = Axial length; BCEA = Bivariate contour ellipse area. Relationship Among Post-Operative Visual Outcomes, Axial Length and Maculopathy Grading”); and wherein the generating the output further comprises generating the output using one or more of the baseline demographic characteristics and the baseline clinical characteristics (see para [0014]; “the ophthalmic disease or disorder is selected from the group consisting of: age-related macular degeneration (AMD), diabetic macular edema (DME), and choroidal neovascularization (CNV). In some non-limiting embodiments, the prediction comprises a best corrected visual acuity (BCVA)”)
Regarding claim 6, the rejection of claim 1 is incorporated herein.
Sahlsten et al. in the combination further teach wherein the generating the metric (see page 3 2nd para; “Also, we calculate the confusion matrices for the multi-class classification tasks. For each metric in the binary classification tasks”) comprises generating the metric using a neural network system (see page 2, 7th para; “Here the neural network can be constructed in such a way, that it receives an input which is used in calculating an output12, such as class or grade of diabetic retinopathy”).
Regarding claim 7, the rejection of claim 6 is incorporated herein.
Sahlsten et al. in the combination further teach further comprising: training the neural network system using a training dataset comprising at least graded color fundus imaging data associated with a plurality of training subjects (see page 2, 1st para; “provided a non-open, anonymized retinal image dataset of patients with diabetes, including 41122 graded retinal color images from 14624 patient” see also page 3 7th para; “In order to distinguish features related to diabetic retinopathy and macular edema in the color images of patients’ fundi we chose to use a deep convolutional neural network….Here the neural network can be constructed in such a way, that it receives an input which is used in calculating an output1”).
Regarding claim 8, the rejection of claim 7 is incorporated herein.
Sahlsten et al. in the combination further teach wherein the training the neural network system further comprises training the neural network using one or more of: baseline demographic characteristics associated with the plurality of training subjects and baseline clinical characteristics associated with the plurality of training subjects (see para [0014]; “the machine learning procedure comprises training a machine learning algorithm using outcome classified patient data comprising macular sensitivity, axial length, best corrected visual acuity (BCVA), bivariate contour ellipse area (BCEA), or any combination hereof. In some non-limiting embodiments, the patient data is classified according to at least four categories of myopic maculopathy”).
Regarding claim 9, the scope of claim 9 is fully encompassed by the scope of claim 1, accordingly, the rejection analysis of claim 1 is equally applicable (see also Zhang et al para [0009]; “the present disclosure relates to a non-transitory computer-readable medium comprising machine-executable code that, upon execution by one or more computer processors, implements a method for providing an ophthalmic diagnosis”).
Regarding claim 10, the rejection of claim 9 is incorporated herein.
Sahlsten et al. in the combination further teach wherein the operations further comprise: receiving a determination of the range or set of DR severity threshold scores, each DR severity threshold score indicating a minimum or maximum score corresponding to a DR severity classification of the plurality of DR severity classifications (see page 955, Table 1; “ Severity categories for characteristics graded in multiple fields are of the form ‘maximum severity/extent’, where maximum severity can be absent (A), questionable (Q), definitely present (D), moderate (M), severe (S), or very severe (VS), and extent is the number of fields at that severity level. For example, M/2–3 means that there are two or three fields from fields 3 to 7 with moderate severity, and none with higher severity”).
Regarding claim 11, the rejection of claim 9 is incorporated herein.
Sahlsten et al. in the combination further teach wherein the at least one DR severity classification denotes a moderate to moderately severe DR, a moderately severe to severe DR, or a moderate to severe DR (see page 2, 4th para; “The NRDR/ RDR system considers the cases with no diabetic retinopathy and mild diabetic retinopathy as nonreferable diabetic retinopathy, and the cases with moderate or worse diabetic retinopathy as referable diabetic retinopathy”).
Regarding claim 12, the rejection of claim 9 is incorporated herein.
Staurenghi et al. in the combination further teach wherein the at least one range or set of DR severity threshold scores comprises a portion of a Diabetic Retinopathy Severity Scale (DRSS) between and including 43 and 47, between and including 47 and 53, or between and including 43 and 53 (see page 955, 1st para; “with a DRSS score ≤43, moderate (26.3%) in patients with a DRSS score of 47, and high (50.2%) in patients with a DRSS score ≥53”).
Regarding claim 13, the rejection of claim 9 is incorporated herein.
Zhang et al. in the combination further teach wherein the input data further comprises one or more of: baseline demographic characteristics associated with the subject and baseline clinical characteristics associated with the subject; (see para [0198]; “Demographic characteristics of participants Parameter Value (n = 1,005) Age (Years) 61.33 ± 9.19 Sex (Male/Female) 461/544 Eye (Right/Left) 506/499 BCVA (logMAR) 0.24 ± 0.37 AL (mm) 29.46 ± 2.27 MS (dB) 22.51 ± 5.14 BCEA (deg.sup.2) 20.38 ± 24.64 Data are presented as mean ± standard deviation. BCVA = best corrected visual acuity; AL = Axial length; BCEA = Bivariate contour ellipse area…Relationship Among Post-Operative Visual Outcomes, Axial Length and Maculopathy Grading”); and wherein the generating the output further comprises generating the output using one or more of the baseline demographic characteristics and the baseline clinical characteristic (see para [0015]; “the ophthalmic disease or disorder is selected from the group consisting of: age-related macular degeneration (AMD), diabetic macular edema (DME), and choroidal neovascularization (CNV). In some non-limiting embodiments, the prediction comprises a best corrected visual acuity (BCVA)”).
Regarding claim 14, the rejection of claim 9 is incorporated herein.
Sahlsten et al. in the combination further teach wherein the generating the metric (see page 3 2nd para; “Also, we calculate the confusion matrices for the multi-class classification tasks. For each metric in the binary classification tasks”) comprises generating the metric using a neural network system (see page 2, 7th para; “Here the neural network can be constructed in such a way, that it receives an input which is used in calculating an output12, such as class or grade of diabetic retinopathy”).
Regarding claim 15, the scope of claim 15 is fully encompassed by the scope of claim 1, accordingly, the rejection analysis of claim 1 is equally applicable.
Regarding claim 16, the rejection of claim 15 is incorporated herein.
Staurenghi et al. in the combination further teach wherein the operations further comprise: receiving a determination of the range or set of DR severity threshold scores, each DR severity threshold score indicating a minimum or maximum score corresponding to a DR severity classification of the plurality of DR severity classifications (see page 955, Table 1; “ Severity categories for characteristics graded in multiple fields are of the form ‘maximum severity/extent’, where maximum severity can be absent (A), questionable (Q), definitely present (D), moderate (M), severe (S), or very severe (VS), and extent is the number of fields at that severity level. For example, M/2–3 means that there are two or three fields from fields 3 to 7 with moderate severity, and none with higher severity”).
Regarding claim 17, the rejection of claim 15 is incorporated herein.
Sahlsten et al. in the combination further teach wherein the at least one DR severity classification denotes a moderate to moderately severe DR, a moderately severe to severe DR, or a moderate to severe DR (see page 2, 4th para; “The NRDR/ RDR system considers the cases with no diabetic retinopathy and mild diabetic retinopathy as nonreferable diabetic retinopathy, and the cases with moderate or worse diabetic retinopathy as referable diabetic retinopathy”).
Regarding claim 18, the rejection of claim 15 is incorporated herein.
Staurenghi et al. in the combination further teach wherein the at least one range or set of DR severity threshold scores comprises a portion of a Diabetic Retinopathy Severity Scale (DRSS) between and including 43 and 47, between and including 47 and 53, or between and including 43 and 53 (see page 955, 1st para; “with a DRSS score ≤43, moderate (26.3%) in patients with a DRSS score of 47, and high (50.2%) in patients with a DRSS score ≥53”).
Regarding claim 19, the rejection of claim 15 is incorporated herein.
Zhang et al. in the combination further teach wherein the input data further comprises one or more of: baseline demographic characteristics associated with the subject and baseline clinical characteristics associated with the subject; (see para [0198]; “Demographic characteristics of participants Parameter Value (n = 1,005) Age (Years) 61.33 ± 9.19 Sex (Male/Female) 461/544 Eye (Right/Left) 506/499 BCVA (logMAR) 0.24 ± 0.37 AL (mm) 29.46 ± 2.27 MS (dB) 22.51 ± 5.14 BCEA (deg.sup.2) 20.38 ± 24.64 Data are presented as mean ± standard deviation. BCVA = best corrected visual acuity; AL = Axial length; BCEA = Bivariate contour ellipse area…Relationship Among Post-Operative Visual Outcomes, Axial Length and Maculopathy Grading”); and wherein the generating the output further comprises generating the output using one or more of the baseline demographic characteristics and the baseline clinical characteristic (see para [0015]; “the ophthalmic disease or disorder is selected from the group consisting of: age-related macular degeneration (AMD), diabetic macular edema (DME), and choroidal neovascularization (CNV). In some non-limiting embodiments, the prediction comprises a best corrected visual acuity (BCVA)”).
Regarding claim 20, the rejection of claim 15is incorporated herein.
Sahlsten et al. in the combination further teach wherein the generating the metric (see page 3 2nd para; “Also, we calculate the confusion matrices for the multi-class classification tasks. For each metric in the binary classification tasks”) comprises generating the metric using a neural network system (see page 2, 7th para; “Here the neural network can be constructed in such a way, that it receives an input which is used in calculating an output12, such as class or grade of diabetic retinopathy”).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/WINTA GEBRESLASSIE/ Examiner, Art Unit 2677
/ANDREW W BEE/ Supervisory Patent Examiner, Art Unit 2677