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 Amendment
Claims 1, 11, and 15-16 have been amended changing the scope and contents of the claim.
Applicant’s amendment filed December 16, 2025 overcomes the following objection/rejection(s) from the last Office Action of September 16, 2025:
Rejections to the claims under 35 USC § 112(b)
Rejections to the claims under 35 USC § 102
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 10 and 16 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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) 1-7, 10, 12-14 and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Schmidt-Erfurth, Ursula, et al. "Machine learning to analyze the prognostic value of current imaging biomarkers in neovascular age-related macular degeneration." Ophthalmology Retina 2.1 (2018): 24-30. (hereinafter Schmidt-Erfurth), and further in view of Chen, S.-C.; Chiu, H.-W.; Chen, C.-C.; Woung, L.-C.; Lo, C.-M. A Novel Machine Learning Algorithm to Automatically Predict Visual Outcomes in Intravitreal Ranibizumab-Treated Patients with Diabetic Macular Edema. J. Clin. Med. 2018, 7, 475. https://doi.org/10.3390/jcm7120475 (hereinafter Chen).
Regarding independent claim 1, Schmidt-Erfurth discloses A method for predicting a treatment outcome (abstract, “To evaluate the potential of machine learning to predict best-corrected visual acuity (BCVA) outcomes from structural and functional assessments during the initiation phase in patients receiving standardized ranibizumab therapy for neovascular age-related macular degeneration (AMD);” page 24, right column, “a more accurate functional prognosis in the management of neovascular AMD.5;” page 25, left column, “The aim of this study is to introduce machine learning methodology to, first, correlate morphologic OCT parameters at baseline to the corresponding visual function in active neovascular disease; and second, predict final BCVA levels after 1 year of standardized anti-VEGF therapy from functional and structural parameters acquired during the initiation phase in a large-scale randomized clinical trial setting”), the method comprising:
receiving three-dimensional imaging data for a retina of a subject (Figure 1, “SD-OCT volume scans”);
generating a first output using a deep learning system and the three-dimensional imaging data (Page 26, right column, “Subsequently, fully automated segmentation algorithms based on graph theory and convolutional neural networks were applied to delineate the retinal layers and the CNV-associated lesion components, IRF, subretinal fluid (SRF), and pigment epithelial detachment (PED) (Schlegl T, Waldstein SM, Bogunovic H, et al. Fully Automated Detection and Quantification of Macular Fluid in Optical Coherence Tomography using Deep Learning, submitted for publication).”);
wherein the first output is a predicted outcome (abstract, “To evaluate the potential of machine learning to predict best-corrected visual acuity (BCVA) outcomes from structural and functional assessments during the initiation phase in patients receiving standardized ranibizumab therapy for neovascular age-related macular degeneration (AMD);” Figure 1, the SD-OCT volume scans are the input);
Schmidt-Erfurth fails to explicitly disclose as further recited. However, Chen discloses receiving the first output and baseline data as input for a symbolic model (page 3, “The input variables to build a prediction model were sex, age, diabetes type, insulin use, glycated hemoglobin (HbA1c) level, hypertension under treatment, hypercholesterolemia under treatment, lens status, degree of diabetic severity, the baseline macular OCT value (central point, central, inner and outer superior/nasal/inferior/temporal part), the timetable of ranibizumab treatment and baseline visual acuity (Table 1). ”); and
predicting, via the symbolic model, a treatment outcome for the subject undergoing a treatment (page 2, “ We used ANNs to build decision-support models to predict visual acuity in patients with DME at 52, 78 and 104 weeks after ranibizumab treatment;” page 3, “The input variables to build a prediction model were sex, age, diabetes type, insulin use, glycated hemoglobin (HbA1c) level, hypertension under treatment, hypercholesterolemia under treatment, lens status, degree of diabetic severity, the baseline macular OCT value (central point, central, inner and outer superior/nasal/inferior/temporal part), the timetable of ranibizumab treatment and baseline visual acuity (Table 1). ”).
With regard to specifically predicting treatment outcoems for those undergoing nAMD treatment, Chen is directed more toward predicting macular edema treatment outcome. However, Chen clearly proves the base fact that baseline features and patient characteristics can be used to predict outcomes of eye disease treatment. One of ordinary skill in the art before the effective filing date of the invention would be aware the diseases are different, though the structure of the studies and treatment options could be the same. Said in laymans terms, treating one eye disease may lead to understandings of another eye disease. Additionally, Ranibizumab can be used to treat AMD, which one of ordinary skill in the art before the effective filing date would be aware of.
Schmidt-Erfurth is directed toward “the potential of machine learning to predict best-corrected visual acuity (BCVA) outcomes from structural and functional assessments during the initiation phase in patients receiving standardized ranibizumab therapy for neovascular age-related macular degeneration (AMD) (abstract).” Chen is directed toward “ANN-based machine learning algorithm to automatically predict visual outcomes after ranibizumab treatment in diabetic macular edema (abstract).” As can be easily seen by one of ordinary skill in the art before the effective filing date of the claimed invention, both Schmidt-Erfurth and Chen are directed toward similar methods of endeavor of utilizing neural networks to predict outcomes for eye disease patients. Further, Chen allows for incorporating of additional clinical features into the prediction, as opposed to only image based features. It is well known in the art that factors such as age, sex, diabetes, hypertension and other values can have impacts on eye diseases. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to incorporate the teaching of Chen to ensure all features that may contribute to a predicted disease outcome are captured and utilized to generate the most accurate prediction.
Regarding dependent claim 2, the rejection of claim 1 is incorporated herein. Additionally, Schmidt-Erfurth in the combination further discloses wherein the three-dimensional imaging data comprises optical coherence tomography (OCT) imaging data (Figure 1, “SD-OCT volume scans;” page 25, right column, “SD-OCT was performed by certified operators using the Cirrus HD-OCT III instrument”).
Regarding dependent claim 3, the rejection of claim 1 is incorporated herein. Additionally, Schmidt-Erfurth in the combination further discloses wherein the baseline data comprises at least one of demographic data, a baseline visual acuity measurement (abstract, “The extracted quantitative OCT biomarkers and BCVA measurements at baseline and months 1, 2, and 3 were used to predict BCVA at 12 months using random forest machine learning;” Figure 1, “clinical data; visual acuity”), a baseline central subfield thickness measurement, a baseline low-luminance deficit, or a treatment arm.
Regarding dependent claim 4, the rejection of claim 3 is incorporated herein. Additionally, Chen in the combination further discloses wherein the demographic data comprises at least one of age or gender (Table 1- sex and age are listed).
It is well known in the art that factors such as age, sex, diabetes, hypertension and other values can have impacts on eye diseases. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to incorporate the teaching of Chen to ensure all features that may contribute to a predicted disease outcome are captured and utilized to generate the most accurate prediction.
Regarding dependent claim 5, the rejection of claim 1 is incorporated herein. Additionally, Schmidt-Erfurth in the combination further discloses wherein the treatment outcome includes at least one of a predicted visual acuity measurement (abstract, “The extracted quantitative OCT biomarkers and BCVA measurements at baseline and months 1, 2, and 3 were used to predict BCVA at 12 months using random forest machine learning;”), a predicted change in visual acuity, a predicted central subfield thickness, or a predicted reduction in central subfield thickness.
Regarding dependent claim 6, the rejection of claim 1 is incorporated herein. Additionally, Schmidt-Erfurth in the combination further discloses wherein the baseline data includes a baseline visual acuity measurement and further comprising:
identifying the baseline visual acuity measurement using the first output (abstract, “The extracted quantitative OCT biomarkers and BCVA measurements at baseline and months 1, 2, and 3 were used to predict BCVA at 12 months using random forest machine learning;” page 26, right column, “Supervised machine learning regression using random forests was applied to predict BCVA at 12 months (target variable) from the input variables listed in Table 1. Separate models were constructed for the visits at baseline and at month 1 to month 3.”).
Regarding dependent claim 7, the rejection of claim 1 is incorporated herein. Additionally, Schmidt-Erfurth in the combination further discloses wherein the treatment outcome is predicted at an nth month after a baseline point in time and
wherein the nth month is selected as a month between three months and thirty months after the baseline point in time (abstract, “The extracted quantitative OCT biomarkers and BCVA measurements at baseline and months 1, 2, and 3 were used to predict BCVA at 12 months using random forest machine learning;” page 26, right column, “Supervised machine learning regression using random forests was applied to predict BCVA at 12 months (target variable) from the input variables listed in Table 1.”).
Regarding independent claim 10, the rejection of claim 1 applies directly. Additionally, Schmidt-Erfurth discloses A method for predicting a treatment outcome for a subject undergoing a treatment for neovascular age-related macular degeneration (nAMD) (abstract, “To evaluate the potential of machine learning to predict best-corrected visual acuity (BCVA) outcomes from structural and functional assessments during the initiation phase in patients receiving standardized ranibizumab therapy for neovascular age-related macular degeneration (AMD);” page 24, right column, “a more accurate functional prognosis in the management of neovascular AMD.5;” page 25, left column, “The aim of this study is to introduce machine learning methodology to, first, correlate morphologic OCT parameters at baseline to the corresponding visual function in active neovascular disease; and second, predict final BCVA levels after 1 year of standardized anti-VEGF therapy from functional and structural parameters acquired during the initiation phase in a large-scale randomized clinical trial setting”), the method comprising:
generating a first predicted outcome using a deep learning system and three- dimensional imaging data for a retina of the subject (Figure 1, “machine learning;” abstract, “This system performs spatially resolved 3-dimensional segmentation of retinal layers, intraretinal cystoid fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachments (PED). The extracted quantitative OCT biomarkers and BCVA measurements at baseline and months 1, 2, and 3 were used to predict BCVA at 12 months using random forest machine learning”);
Schmidt-Erfurth fails to explicitly disclose as further recited. However, Chen discloses generating a second predicted outcome using a symbolic model and baseline data for the subject (page 3, “The input variables to build a prediction model were sex, age, diabetes type, insulin use, glycated hemoglobin (HbA1c) level, hypertension under treatment, hypercholesterolemia under treatment, lens status, degree of diabetic severity, the baseline macular OCT value (central point, central, inner and outer superior/nasal/inferior/temporal part), the timetable of ranibizumab treatment and baseline visual acuity (Table 1). ”); and
predicting the treatment outcome for the subject undergoing the treatment for (page 2, “ We used ANNs to build decision-support models to predict visual acuity in patients with DME at 52, 78 and 104 weeks after ranibizumab treatment;” page 3, “The input variables to build a prediction model were sex, age, diabetes type, insulin use, glycated hemoglobin (HbA1c) level, hypertension under treatment, hypercholesterolemia under treatment, lens status, degree of diabetic severity, the baseline macular OCT value (central point, central, inner and outer superior/nasal/inferior/temporal part), the timetable of ranibizumab treatment and baseline visual acuity (Table 1). ”).
With regard to specifically predicting treatment outcoems for those undergoing nAMD treatment, Chen is directed more toward predicting macular edema treatment outcome. However, Chen clearly proves the base fact that baseline features and patient characteristics can be used to predict outcomes of eye disease treatment. One of ordinary skill in the art before the effective filing date of the invention would be aware the diseases are different, though the structure of the studies and treatment options could be the same. Said in laymans terms, treating one eye disease may lead to understandings of another eye disease. Additionally, Ranibizumab can be used to treat AMD, which one of ordinary skill in the art before the effective filing date would be aware of.
Schmidt-Erfurth is directed toward “the potential of machine learning to predict best-corrected visual acuity (BCVA) outcomes from structural and functional assessments during the initiation phase in patients receiving standardized ranibizumab therapy for neovascular age-related macular degeneration (AMD) (abstract).” Chen is directed toward “ANN-based machine learning algorithm to automatically predict visual outcomes after ranibizumab treatment in diabetic macular edema (abstract).” As can be easily seen by one of ordinary skill in the art before the effective filing date of the claimed invention, both Schmidt-Erfurth and Chen are directed toward similar methods of endeavor of utilizing neural networks to predict outcomes for eye disease patients. Further, Chen allows for incorporating of additional clinical features into the prediction, as opposed to only image based features. It is well known in the art that factors such as age, sex, diabetes, hypertension and other values can have impacts on eye diseases. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to incorporate the teaching of Chen to ensure all features that may contribute to a predicted disease outcome are captured and utilized to generate the most accurate prediction.
Regarding dependent claim 12, the rejection of claim 10 is incorporated herein. Additionally, Schmidt-Erfurth in the combination further discloses wherein the three-dimensional imaging data comprises optical coherence tomography (OCT) imaging data (Figure 1, “SD-OCT volume scans;” page 25, right column, “SD-OCT was performed by certified operators using the Cirrus HD-OCT III instrument”).
Regarding dependent claim 13, the rejection of claim 10 is incorporated herein. Additionally, Schmidt-Erfurth in the combination further discloses wherein the baseline data comprises at least one of demographic data, a baseline visual acuity measurement (abstract, “The extracted quantitative OCT biomarkers and BCVA measurements at baseline and months 1, 2, and 3 were used to predict BCVA at 12 months using random forest machine learning;” Figure 1, “clinical data; visual acuity”), a baseline central subfield thickness measurement, a baseline low-luminance deficit, or a treatment arm.
Regarding dependent claim 14, the rejection of claim 13 is incorporated herein. Additionally, Chen in the combination further discloses wherein the demographic data comprises at least one of age or gender (Table 1- sex and age are listed).
It is well known in the art that factors such as age, sex, diabetes, hypertension and other values can have impacts on eye diseases. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to incorporate the teaching of Chen to ensure all features that may contribute to a predicted disease outcome are captured and utilized to generate the most accurate prediction.
Regarding independent claim 16, the rejection of claim 1 applies directly. Additionally, Schmidt-Erfurth discloses A system for managing an anti-vascular endothelial growth factor (anti- VEGF) treatment for a subject diagnosed with neovascular age-related macular degeneration (nAMD) (abstract, “To evaluate the potential of machine learning to predict best-corrected visual acuity (BCVA) outcomes from structural and functional assessments during the initiation phase in patients receiving standardized ranibizumab therapy for neovascular age-related macular degeneration (AMD);” page 24, right column, “a more accurate functional prognosis in the management of neovascular AMD.5;” page 25, left column, “The aim of this study is to introduce machine learning methodology to, first, correlate morphologic OCT parameters at baseline to the corresponding visual function in active neovascular disease; and second, predict final BCVA levels after 1 year of standardized anti-VEGF therapy from functional and structural parameters acquired during the initiation phase in a large-scale randomized clinical trial setting”), the system comprising:
a memory containing machine readable medium comprising machine executable code (page 25, left column, “The aim of this study is to introduce machine learning methodology to, first, correlate morphologic OCT parameters at baseline to the corresponding visual function in active neovascular disease; and second, predict final BCVA levels after 1 year of standardized anti-VEGF therapy from functional and structural parameters acquired during the initiation phase in a large-scale randomized clinical trial setting;” in order to execute machine learning, a processor is required, which can call the parameters/code for the machine learning from memory and read them to execute the model itself);
and a processor coupled to the memory, the processor configured to execute the machine executable code to cause the processor to (page 25, left column, “The aim of this study is to introduce machine learning methodology to, first, correlate morphologic OCT parameters at baseline to the corresponding visual function in active neovascular disease; and second, predict final BCVA levels after 1 year of standardized anti-VEGF therapy from functional and structural parameters acquired during the initiation phase in a large-scale randomized clinical trial setting;” in order to execute machine learning, a processor is required, which can call the parameters/code for the machine learning from memory and read them to execute the model itself):
receive three-dimensional imaging data for a retina of a subject (Figure 1, “SD-OCT volume scans”);
generate a first output using a deep learning system and the three-dimensional imaging data (Page 26, right column, “Subsequently, fully automated segmentation algorithms based on graph theory and convolutional neural networks were applied to delineate the retinal layers and the CNV-associated lesion components, IRF, subretinal fluid (SRF), and pigment epithelial detachment (PED) (Schlegl T, Waldstein SM, Bogunovic H, et al. Fully Automated Detection and Quantification of Macular Fluid in Optical Coherence Tomography using Deep Learning, submitted for publication).”);
wherein the first output is a predicted outcome (abstract, “To evaluate the potential of machine learning to predict best-corrected visual acuity (BCVA) outcomes from structural and functional assessments during the initiation phase in patients receiving standardized ranibizumab therapy for neovascular age-related macular degeneration (AMD);” Figure 1, the SD-OCT volume scans are the input);
Schmidt-Erfurth fails to explicitly disclose as further recited. However, Chen discloses receive the first output and baseline data as input for a symbolic model (page 3, “The input variables to build a prediction model were sex, age, diabetes type, insulin use, glycated hemoglobin (HbA1c) level, hypertension under treatment, hypercholesterolemia under treatment, lens status, degree of diabetic severity, the baseline macular OCT value (central point, central, inner and outer superior/nasal/inferior/temporal part), the timetable of ranibizumab treatment and baseline visual acuity (Table 1). ”); and
predict, via the symbolic model, a treatment outcome for the subject undergoing a treatment (page 2, “ We used ANNs to build decision-support models to predict visual acuity in patients with DME at 52, 78 and 104 weeks after ranibizumab treatment;” page 3, “The input variables to build a prediction model were sex, age, diabetes type, insulin use, glycated hemoglobin (HbA1c) level, hypertension under treatment, hypercholesterolemia under treatment, lens status, degree of diabetic severity, the baseline macular OCT value (central point, central, inner and outer superior/nasal/inferior/temporal part), the timetable of ranibizumab treatment and baseline visual acuity (Table 1). ”).
With regard to specifically predicting treatment outcoems for those undergoing nAMD treatment, Chen is directed more toward predicting macular edema treatment outcome. However, Chen clearly proves the base fact that baseline features and patient characteristics can be used to predict outcomes of eye disease treatment. One of ordinary skill in the art before the effective filing date of the invention would be aware the diseases are different, though the structure of the studies and treatment options could be the same. Said in laymans terms, treating one eye disease may lead to understandings of another eye disease. Additionally, Ranibizumab can be used to treat AMD, which one of ordinary skill in the art before the effective filing date would be aware of.
Schmidt-Erfurth is directed toward “the potential of machine learning to predict best-corrected visual acuity (BCVA) outcomes from structural and functional assessments during the initiation phase in patients receiving standardized ranibizumab therapy for neovascular age-related macular degeneration (AMD) (abstract).” Chen is directed toward “ANN-based machine learning algorithm to automatically predict visual outcomes after ranibizumab treatment in diabetic macular edema (abstract).” As can be easily seen by one of ordinary skill in the art before the effective filing date of the claimed invention, both Schmidt-Erfurth and Chen are directed toward similar methods of endeavor of utilizing neural networks to predict outcomes for eye disease patients. Further, Chen allows for incorporating of additional clinical features into the prediction, as opposed to only image based features. It is well known in the art that factors such as age, sex, diabetes, hypertension and other values can have impacts on eye diseases. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to incorporate the teaching of Chen to ensure all features that may contribute to a predicted disease outcome are captured and utilized to generate the most accurate prediction.
Regarding dependent claim 17, the rejection of claim 16 is incorporated herein. Additionally, Schmidt-Erfurth in the combination further discloses wherein the three-dimensional imaging data comprises optical coherence tomography (OCT) imaging data (Figure 1, “SD-OCT volume scans;” page 25, right column, “SD-OCT was performed by certified operators using the Cirrus HD-OCT III instrument”).
Regarding dependent claim 18, the rejection of claim 16 is incorporated herein. Additionally, Schmidt-Erfurth in the combination further discloses wherein the baseline data comprises at least one of demographic data, a baseline visual acuity measurement (abstract, “The extracted quantitative OCT biomarkers and BCVA measurements at baseline and months 1, 2, and 3 were used to predict BCVA at 12 months using random forest machine learning;” Figure 1, “clinical data; visual acuity”), a baseline central subfield thickness measurement, a baseline low-luminance deficit, or a treatment arm.
Regarding dependent claim 19, the rejection of claim 16 is incorporated herein. Additionally, Schmidt-Erfurth in the combination further discloses wherein the treatment outcome includes at least one of a predicted visual acuity measurement (abstract, “The extracted quantitative OCT biomarkers and BCVA measurements at baseline and months 1, 2, and 3 were used to predict BCVA at 12 months using random forest machine learning;”), a predicted change in visual acuity, a predicted central subfield thickness, or a predicted reduction in central subfield thickness.
Claim(s) 8-9 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Schmidt-Erfurth further in view of Chen as applied to claims 1 and 16 respectively above, and further in view of WO 2021/023804 (hereinafter WO ‘804).
Regarding dependent claim 8, the rejection of claim 1 is incorporated herein. Additionally, Schmidt-Erfurth and Chen in the combination as a whole fails to explicitly disclose wherein the treatment comprises a monoclonal antibody that targets vascular endothelial growth factor, and angiopoietin 2 inhibitor.
However, WO ‘804 discloses wherein the treatment comprises a monoclonal antibody that targets vascular endothelial growth factor, and angiopoietin 2 inhibitor (page 4, line 13, “administering to the patient an effective amount of a bispecific antibody which binds to human vascular endothelial growth factor (VEGF) and to human angiopoietin-2 (ANG-2) ”).
As noted above, Schmidt-Erfurth and Chen are directed toward predicting outcomes of eye diseases. Further, Schmidt-Erfurth is directed toward “To evaluate the potential of machine learning to predict best-corrected visual acuity (BCVA) outcomes from structural and functional assessments during the initiation phase in patients receiving standardized ranibizumab therapy for neovascular age-related macular degeneration (AMD) (abstract).” WO ‘804 is directed toward “The current invention relates to antibodies which bind to VEGF and ANG2 for use in the treatment of ocular vascular diseases such as neovascular AMD (nAMD) (also known as choroidal neovascularization [CNV] secondary to age-related macular degeneration [AMD] or wet AMD), diabetic retinopathy in particular diabetic macular edema (DME) or macular edema secondary to retinal vein occlusion (RVO) (abstract).” As can be easily seen by one of ordinary skill in the art, Schmidt-Erfurth, Chen and WO ‘804 are directed toward similar methods of endeavor of eye disease treatment. Further, it is well known in the art at the time of filing the claimed invention there are a variety of treatment options for specific diseases; not all treatments work the same across patient populations. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention incorporate the teaching of WO ‘804 in order to ensure all relevant potential treatments could be analyzed in the method of Schmidt-Erfurth to allow results for a wider patient population.
Regarding dependent claim 9, the rejection of claim 1 is incorporated herein. Additionally, Schmidt-Erfurth and Chen in the combination as a whole fails to explicitly disclose wherein the treatment comprises faricimab.
However, WO ‘804 discloses wherein the treatment comprises faricimab (page 30, line 13, “In one preferred embodiment the bispecific, bivalent anti-VEGF/ANG2 antibody is faricimab;” page 74, line 29, “Faricimab will be administered at a concentration of about 120 mg/ml.”).
As noted above, Schmidt-Erfurth and Chen are directed toward predicting outcomes of eye diseases. Further, Schmidt-Erfurth is directed toward “To evaluate the potential of machine learning to predict best-corrected visual acuity (BCVA) outcomes from structural and functional assessments during the initiation phase in patients receiving standardized ranibizumab therapy for neovascular age-related macular degeneration (AMD) (abstract).” WO ‘804 is directed toward “The current invention relates to antibodies which bind to VEGF and ANG2 for use in the treatment of ocular vascular diseases such as neovascular AMD (nAMD) (also known as choroidal neovascularization [CNV] secondary to age-related macular degeneration [AMD] or wet AMD), diabetic retinopathy in particular diabetic macular edema (DME) or macular edema secondary to retinal vein occlusion (RVO) (abstract).” As can be easily seen by one of ordinary skill in the art, Schmidt-Erfurth, Chen and WO ‘804 are directed toward similar methods of endeavor of eye disease treatment. Further, it is well known in the art at the time of filing the claimed invention there are a variety of treatment options for specific diseases; not all treatments work the same across patient populations. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention incorporate the teaching of WO ‘804 in order to ensure all relevant potential treatments could be analyzed in the method of Schmidt-Erfurth to allow results for a wider patient population.
Regarding dependent claim 20, the rejection of claim 16 is incorporated herein. Additionally, Schmidt-Erfurth and Chen in the combination as a whole fails to explicitly disclose wherein the treatment comprises faricimab.
However, WO ‘804 discloses wherein the treatment comprises faricimab (page 30, line 13, “In one preferred embodiment the bispecific, bivalent anti-VEGF/ANG2 antibody is faricimab;” page 74, line 29, “Faricimab will be administered at a concentration of about 120 mg/ml.”).
As noted above, Schmidt-Erfurth and Chen are directed toward predicting outcomes of eye diseases. Further, Schmidt-Erfurth is directed toward “To evaluate the potential of machine learning to predict best-corrected visual acuity (BCVA) outcomes from structural and functional assessments during the initiation phase in patients receiving standardized ranibizumab therapy for neovascular age-related macular degeneration (AMD) (abstract).” WO ‘804 is directed toward “The current invention relates to antibodies which bind to VEGF and ANG2 for use in the treatment of ocular vascular diseases such as neovascular AMD (nAMD) (also known as choroidal neovascularization [CNV] secondary to age-related macular degeneration [AMD] or wet AMD), diabetic retinopathy in particular diabetic macular edema (DME) or macular edema secondary to retinal vein occlusion (RVO) (abstract).” As can be easily seen by one of ordinary skill in the art, Schmidt-Erfurth, Chen and WO ‘804 are directed toward similar methods of endeavor of eye disease treatment. Further, it is well known in the art at the time of filing the claimed invention there are a variety of treatment options for specific diseases; not all treatments work the same across patient populations. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention incorporate the teaching of WO ‘804 in order to ensure all relevant potential treatments could be analyzed in the method of Schmidt-Erfurth to allow results for a wider patient population.
Claim(s) 11 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Schmidt-Erfurth further in view of Chen as applied to claim 10 above, and further in view of U.S. Patent No. 10,468,142 to Abou Shousha et al. (hereinafter Abou Shousha).
Regarding dependent claim 11, the rejection of claim 10 is incorporated herein. Additionally, Schmidt-Erfurth and Chen in the combination fails to explicitly disclose wherein the predicting comprises:
predicting the treatment outcome as a weighted average of the first predicted treatment outcome and the second predicted treatment outcome.
However, Abou Shousha discloses wherein the predicting comprises:
predicting the treatment outcome as a weighted average of the first predicted treatment outcome and the second predicted treatment outcome (Figure 11A, two networks are used to combine two different values processed from different inputs to generate one output; paragraph 0131, “As shown in FIG. 11A, the demographic network is configured to receive input data comprising patient data or demographic data and to generate output weights w with respect to one, more, or all y outputs. The final output, which may be generated by a combined output layer for both networks or by an analysis subsystem, as described herein, may be determined by calculating an element-wise product to weight each prediction value y with a prediction value w determined from the patient network taking as input data the patient related data;” paragraph 0130, “FIGS. 11A-11C schematically illustrates embodiments of the system 10 that include augmentation of the AI model 12 with patient data according to various embodiments. Such an AI model 12 may be implemented with or may be included in AI model 12 described with respect to FIGS. 1H & 1J. The patient data may include any type of patient data, such as demographic data or medical data. The AI model 12 has been trained and tuned to process input data to generate a desired output prediction, such as a category likelihood (see, e.g., FIG. 4), a disease prediction (see, e.g., FIG. 5), a severity prediction (see, e.g., FIG. 6), a severity score (see, e.g., FIG. 7), a risk prediction (see, e.g., FIG. 8), an action prediction (see, e.g., FIG. 9), or a treatment prediction (see, e.g., FIG. 10), for example. The input data may include images such as B-scans, color images, thickness maps, heat maps, bullseye maps, structure maps, and/or other input data described herein.”).
As noted above, Schmidt-Erfurth and Chen are directed toward predicting outcomes of eye diseases. Further, Schmidt-Erfurth is directed toward “To evaluate the potential of machine learning to predict best-corrected visual acuity (BCVA) outcomes from structural and functional assessments during the initiation phase in patients receiving standardized ranibizumab therapy for neovascular age-related macular degeneration (AMD) (abstract).” Abou Shousha is directed toward “a likelihood that the cornea or anterior segment of the eye represented in the input data will respond favorably or unfavorably to treatment for the predicted corneal or anterior segment condition or disease (paragraph 20).” As can be easily seen by one of ordinary skill in the art, Schmidt-Erfurth, Chen and Abou Shousha are directed toward similar methods of endeavor of image analysis for treatment prediction. Further, it is well known in the art at the time of filing the claimed invention that treatment success for a specific treatment can be based on a plurality of features; for example, initial health state, gender, age, sex, etc. Each feature can contribute respectively to an overall treatment success or failure. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Abou Shousha in order to ensure each feature is able to contribute to the overall score, while allowing a user to understand the impact each feature has.
Regarding dependent claim 15, the rejection of claim 10 is incorporated herein. Additionally, Schmidt-Erfurth and Chen in the combination fails to explicitly disclose wherein each of the first predicted treatment outcome, the second predicted treatment outcome, and the treatment outcome includes at least one of a predicted visual acuity measurement, a predicted change in visual acuity, a predicted central subfield thickness, or a predicted reduction in central subfield thickness.
However, Abou Shousha discloses wherein each of the first predicted treatment outcome, the second predicted treatment outcome, and the treatment outcome includes at least one of a predicted visual acuity measurement, a predicted change in visual acuity, a predicted central subfield thickness, or a predicted reduction in central subfield thickness (Figure 11A, two networks are used to combine two different values processed from different inputs to generate one output; paragraph 0131, “As shown in FIG. 11A, the demographic network is configured to receive input data comprising patient data or demographic data and to generate output weights w with respect to one, more, or all y outputs. The final output, which may be generated by a combined output layer for both networks or by an analysis subsystem, as described herein, may be determined by calculating an element-wise product to weight each prediction value y with a prediction value w determined from the patient network taking as input data the patient related data;” paragraph 0130, “FIGS. 11A-11C schematically illustrates embodiments of the system 10 that include augmentation of the AI model 12 with patient data according to various embodiments. Such an AI model 12 may be implemented with or may be included in AI model 12 described with respect to FIGS. 1H & 1J. The patient data may include any type of patient data, such as demographic data or medical data. The AI model 12 has been trained and tuned to process input data to generate a desired output prediction, such as a category likelihood (see, e.g., FIG. 4), a disease prediction (see, e.g., FIG. 5), a severity prediction (see, e.g., FIG. 6), a severity score (see, e.g., FIG. 7), a risk prediction (see, e.g., FIG. 8), an action prediction (see, e.g., FIG. 9), or a treatment prediction (see, e.g., FIG. 10), for example. The input data may include images such as B-scans, color images, thickness maps, heat maps, bullseye maps, structure maps, and/or other input data described herein.”).
As noted above, Schmidt-Erfurth and Chen are directed toward predicting outcomes of eye diseases. Further, Schmidt-Erfurth is directed toward “To evaluate the potential of machine learning to predict best-corrected visual acuity (BCVA) outcomes from structural and functional assessments during the initiation phase in patients receiving standardized ranibizumab therapy for neovascular age-related macular degeneration (AMD) (abstract).” Abou Shousha is directed toward “a likelihood that the cornea or anterior segment of the eye represented in the input data will respond favorably or unfavorably to treatment for the predicted corneal or anterior segment condition or disease (paragraph 20).” As can be easily seen by one of ordinary skill in the art, Schmidt-Erfurth, Chen and Abou Shousha are directed toward similar methods of endeavor of image analysis for treatment prediction. Further, it is well known in the art at the time of filing the claimed invention that treatment success for a specific treatment can be based on a plurality of features; for example, initial health state, gender, age, sex, etc. Each feature can contribute respectively to an overall treatment success or failure. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Abou Shousha in order to ensure each feature is able to contribute to the overall score, while allowing a user to understand the impact each feature has.
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.
Contact
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/COURTNEY JOAN NELSON/Primary Examiner, Art Unit 2661