DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Rejection under 35 U.S.C. 101 abstract idea is hereby withdrawn in light of Applicant’s remarks. Applicant’s arguments with regard to prior art rejection have been considered but are moot in view of the new grounds of rejection.
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 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, 7-10, 12-15, 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over D11 and further in view of D2.2
With regard to claim 1, D1 teach receiving optical coherence tomography (OCT) image data for a retina of a subject with neovascular age-related macular degeneration (nAMD) (see abstract: OCT scan of retina to assess age related macular degeneration); processing the OCT image data using a model system comprising a machine learning model to generate a prediction output (see abstract, fig. 1, p. 26 ¶ 1: predicting models); wherein the machine learning model comprises a deep learning model and wherein the processing comprises: processing the OCT image data and clinical data using the deep learning model to generate the prediction output, wherein the clinical data comprises at least one of a baseline choroidal neovascularization (CNV) type, a baseline visual acuity measurement, or a baseline age (see fig. 1: image data and clinical data – visual acuity – input into machine learning) and generating a final output that indicates a risk of developing see figs. 1-3, p. 26 ¶¶ 1-2, p. 25 ¶¶ 1-2: predicting BCVA outcomes based on extracted quantitative biomarkers, biomarkers include subretinal fluid, intraretinal cystoid fluid and pigment epithelial detachment)
D1 teach extracting biomarkers such as subretinal fluid, intraretinal cystoid fluid and pigment epithelial detachment and predicting BCVA but fails to teach predicting risk of developing fibrosis. However, D2 teach the missing features (see D2 abstract: risk factors for development of fibrosis; HRM an important biomarker; see p. 3 col 1 ¶¶ 6-7: OCT scans were graded for the presence or absence of pigment epithelial detachment, subretinal fluid, and intraretinal fluid).
One skilled in the art before the effective filing date would have found it obvious to combine the teachings to arrive at the claimed invention. In particular, D1 teach extracting biomarkers such as subretinal fluid, intraretinal cystoid fluid and pigment epithelial detachment. Separately, D2 teach using these biomarkers to predict the risk of fibrosis. One skilled in the art would have found it obvious to incorporate known teachings of D2 into the configuration of D1 in order to use the extracted biomarkers to predict the risk of fibrosis, yielding predictable results.
With regard to claim 2, D1 teach wherein the machine learning model comprises a deep learning model and wherein the processing comprises: segmenting, via a segmentation model comprising at least one neural network, the OCT image data to form segmented image data (see fig. 1, p. 26 ¶ 1: segmentation); and processing the segmented image data using the deep learning model of the model system to generate the prediction output (see fig. 1, p. 26 ¶ 1: deep learning).
With regard to claim 3, D1 teach wherein the machine learning model comprises a regression model and wherein the processing further comprises: extracting, via a feature extraction model, retinal feature data from the segmented image data, wherein the retinal feature data comprises at least one of a first feature value related to at least one retinal layer element or a second feature value related to at least one retinal pathological element (see fig. 1: layer segmentation, see p. 26 ¶ 1: feature extraction); and processing the OCT image data using the regression model to generate the prediction output (see p. 26 col 2 ¶¶ 1-2: regression model).
With regard to claim 4, D1 teach wherein the machine learning model comprises at least one convolutional neural network (see p. 26 ¶ 1: convolution neural network).
With regard to claim 7, D1 and D2 fail to explicitly teach wherein the final output comprises at least one of: a binary classification indicating whether fibrosis development is predicted; a clinical trial recommendation to either include or exclude the subject from a clinical trial based on either the prediction output or the binary classification; or a treatment recommendation to at least one of change a type of treatment or adjust a treatment regimen for the subject based on either the prediction output or the binary classification. D2 teach predicting the risk of fibrosis but fails to explicitly teach generating a binary classification indicating whether fibrosis development is predicted or recommending a treatment based on the prediction. However, it would have been obvious for one skilled in the art based on the teachings and the extracted biomarkers to use the extracted quantitative measurements to determine a risk or probability for developing fibrosis and employ a threshold for binary classification through routine experimentation. Additionally, D1 suggests treatment for retinal conditions (see p. 28 col 2 ¶ 3). It would have been obvious for one skilled in the art to prescribe treatment or change of treatment based on the analysis of biomarkers.
With regard to claim 8, D1 teach receiving optical coherence tomography (OCT) image data for a retina of a subject with neovascular age-related macular degeneration (nAMD) (see abstract: OCT scan of retina to assess age related macular degeneration); segmenting the OCT image data using a segmentation model to generate segmented image data (see fig. 1, p. 26 ¶ 1: segmentation); processing the segmented image data using a deep learning model to generate a prediction output (see fig. 1, p. 26 ¶ 1: deep learning); and generating a final output that indicates a risk of developing fibrosis in the retina based on the prediction output (see figs. 1-3, p. 26 ¶¶ 1-2, p. 25 ¶¶ 1-2: predicting outcomes based on extracted quantitative biomarkers, biomarkers include subretinal fluid and pigment epithelial detachment which are indicative of fibrosis). Note that D1 does not explicitly teach predicting the risk for developing fibrosis, however the biomarkers such as subretinal fluid and pigment epithelial detachment are inherently predictive of fibrosis, and therefore D1 anticipates determining a risk for developing fibrosis. Alternatively, it would have been obvious based on the teachings and the extracted biomarkers to use the extracted quantitative measurements to determine a risk or probability for developing fibrosis.
With regard to claim 9, D1 teach wherein at least one of the segmentation model or the deep learning model comprises at least one convolutional neural network (see p. 26 ¶ 1: convolution neural network).
With regard to claim 10, D1 teach wherein the processing comprises: processing the segmented image data and clinical data using the deep learning model to generate the prediction output, wherein the clinical data comprises at least one of a baseline choroidal neovascularization (CNV) type, a baseline visual acuity measurement, or a baseline age (see fig. 1: image data and clinical data – visual acuity – input into machine learning).
With regard to claim 12, see discussion of claim 7.
With regard to claim 13, D1 teach receiving at least one of clinical data or retinal feature data for a retina of a subject with neovascular age-related macular degeneration (nAMD) (see abstract: OCT scan of retina to assess age related macular degeneration); processing the at least one of the clinical data or the retinal feature data using a regression model to generate a prediction output (see abstract, fig. 1, p. 26 ¶ 1: predicting models; see p. 26 col 2 ¶¶ 1-2: regression model); wherein the clinical data comprises at least one of a baseline choroidal neovascularization (CNV) type, a baseline visual acuity measurement, or a baseline age (see fig. 1: image data and clinical data – visual acuity – input into machine learning) and wherein the retinal feature data comprises at least one of a first feature value related to at least one retinal layer element or a second feature value related to at least one retinal pathological element (see fig. 1: layer segmentation, see p. 26 ¶ 1: feature extraction); and generating a final output that indicates a risk of developing see figs. 1-3, p. 26 ¶¶ 1-2, p. 25 ¶¶ 1-2: predicting BCVA outcomes based on extracted quantitative biomarkers, biomarkers include subretinal fluid and pigment epithelial detachment).
D1 teach extracting biomarkers such as subretinal fluid, intraretinal cystoid fluid and pigment epithelial detachment and predicting BCVA but fails to teach predicting risk of developing fibrosis. However, D2 teach the missing features (see D2 abstract: risk factors for development of fibrosis; HRM an important biomarker; see p. 3 col 1 ¶¶ 6-7: OCT scans were graded for the presence or absence of pigment epithelial detachment, subretinal fluid, and intraretinal fluid).
One skilled in the art before the effective filing date would have found it obvious to combine the teachings to arrive at the claimed invention. In particular, D1 teach extracting biomarkers such as subretinal fluid, intraretinal cystoid fluid and pigment epithelial detachment. Separately, D2 teach using these biomarkers to predict the risk of fibrosis. One skilled in the art would have found it obvious to incorporate known teachings of D2 into the configuration of D1 in order to use the extracted biomarkers to predict the risk of fibrosis, yielding predictable results.
With regard to claim 14, D1 teach extracting, via a feature extraction model, the retinal feature data from segmented image data (see fig. 1: layer segmentation, see p. 26 ¶ 1: feature extraction).
With regard to claim 15, D1 teach segmenting, via a segmentation model comprising at least one neural network, OCT image data to form the segmented image data (see p. 26 ¶ 1: convolution neural network).
With regard to claim 17, D1 fails to explicitly teach wherein the regression model is trained using at least one of Ridge regularization, Lasso regularization, or Elastic Net regularization, however Examiner takes Official Notice to the fact that these regularization techniques are extremely well known in the art before the effective filing date and one skilled in the art would have found it obvious to incorporate known teachings into the configuration of D1 yielding predictable and enhanced results. The motivation is that regularization techniques prevent overfitting enhancing model generalizability.
With regard to claim 18, D1 fails to explicitly teach wherein the prediction output comprises a score that indicates a probability that fibrosis is likely to develop, however the biomarkers such as subretinal fluid and pigment epithelial detachment are inherently predictive of fibrosis, and therefore D1 anticipates determining a risk for developing fibrosis. Alternatively, it would have been obvious based on the teachings and the extracted biomarkers to use the extracted quantitative measurements to determine a risk or probability for developing fibrosis.
With regard to claim 19, see discussion of claim 7.
With regard to claim 20, D1 teach wherein the retinal feature data comprises at least one of a grade for subretinal hyperreflective material (SRHM), a grade for pigment epithelial detachment (PED), a maximal height of subretinal fluid (SRF), a maximal thickness between an interface of outer plexiform layer (OPL) and Henle's fiber layer (HFL) and a retinal pigment epithelial (RPE) layer, or a thickness of between an inner limiting membrane (ILM) layer to the RPE layer (see figs. 1-3, p. 26 ¶¶ 1-2, p. 25 ¶¶ 1-2: predicting outcomes based on extracted quantitative biomarkers, biomarkers include subretinal fluid and pigment epithelial detachment which are indicative of fibrosis).
Claims 6 and 11 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AVINASH YENTRAPATI whose telephone number is (571)270-7982. The examiner can normally be reached on 8AM-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sumati Lefkowitz can be reached on (571) 272-3638. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AVINASH YENTRAPATI/Primary Examiner, Art Unit 2672
1 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.
2 Casalino, Giuseppe, et al. "Tomographic biomarkers predicting progression to fibrosis in treated neovascular age-related macular degeneration: a multimodal imaging study." Ophthalmology Retina 2.5 (2018): 451-461.
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