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
The amendments filed 07/07/2026 have been entered.
Response to Arguments
Applicant’s arguments and amendments, filed 07/07/2026 with respect to the rejection(s) of the claims have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Hamrah et al. (US 11,998,270). Accordingly the office action is made non-final because it introduces new grounds of rejection that were not presented in the prior office action.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Differences of the claims are bolded and italicized.
Instant Application
Claim 1, A system for determining of the presence of at least one neuroma on an ocular surface of a subject, the system comprising: a) an in vivo confocal microscope configured to produce an image of at least a portion of the ocular surface by confocal microscopy; and b) a computer programmed to determine the presence of at least one neuroma on the ocular surface from input data corresponding to the image produced by the in vivo confocal microscope, wherein the computer is programmed with a neural network configured to determine the presence of at least one neuroma.
US 11,998,270 B1
Claim 1, A method of (i) identifying the presence of a neuroma on an ocular surface of a subject, or (ii) differentially diagnosing neuropathic corneal pain from another ocular indication in a subject, wherein the method of part (i) comprises: a) directing light from an in vivo confocal microscope onto the ocular surface of the subject to produce an image of at least a portion of the ocular surface; b) sending the image to a computer programmed with a neural network to determine the presence of a neuroma; and c) storing or providing the result of part b) to a user, and the method of part (ii) comprises: a) acquiring an image of at least a portion of an ocular surface of the subject; b) sending the image to a computer programmed to provide an analysis of the ocular surface; and c) storing or displaying the image and/or analysis of the ocular surface to a user, wherein the resulting image and/or analysis of the ocular surface indicates the presence or absence of at least one parameter associated with neuropathic corneal pain.
Claim 2, The system of claim 1, wherein the neuroma is a micro-neuroma.
Claim 2, The method of claim 1, wherein the neuroma is a micro-neuroma
Claim 3, The system of claim 1, wherein the ocular surface is the corneal surface.
Claim 3, The method of claim 1, wherein the ocular surface is the corneal surface.
Claim 5, The system of claim 1, wherein the neural network is a multilayer perceptron.
Claim 4, The method of claim 1, wherein the neural network is trained using a plurality of images of an ocular surface from a population of subjects.
Claim 6, The system of claim 5, wherein the multilayer perceptron comprises: a) a plurality of input nodes, wherein each input node is configured to contain at least one data point; b) a plurality of hidden nodes grouped in at least one layer, wherein each of the plurality of hidden nodes receives as input all of the at least one data points from the plurality of input nodes; and c) a plurality of output nodes, wherein the plurality of hidden nodes and plurality of output nodes are trained with a plurality of images of an ocular surface.
Claim 10, A non-transitory computer readable medium having instructions stored thereon, wherein (I) the instructions, when executed by a processor, perform a method for automatically determining the presence of at least one neuroma on at least one image of an ocular surface of a subject, the method comprising: a) acquiring at least one image of an ocular surface of a subject; and b) determining the presence of a neuroma on the at least one image of an ocular surface of a subject by analyzing the at least one image of an ocular surface of a subject using a trained neural network, wherein the trained neural network comprises: i) a residual learning architecture; and ii) a backpropagation algorithm comprising a gradient descent optimizer, wherein the neural network is trained using a plurality of images of an ocular surface from a population of subjects, wherein the plurality of images of an ocular surface from a population of subjects are augmented using data blending augmentation such as mix-up or data interpolating augmentation prior to training the neural network; or (II) the non-transitory computer readable medium comprises: a) a neural network comprising: i) a pre-trained residual learning architecture comprising about 50 layers, wherein the residual learning architecture was pre-trained with at least one of images from an image database or a plurality of images of an ocular surface from a population of subjects; ii) batch normalization; iii) Dropout regularization; and iv) data augmentation prior to analysis by the residual learning architecture, wherein the data augmentation comprises data blending augmentation or data interpolating augmentation, which optionally is selected from at least one of mixup, random image flipping, random image rotation, or random image crops; and b) a backpropagation algorithm comprising: i) stochastic gradient descent, wherein the stochastic gradient descent further comprises momentum gradient acceleration with a value of 0.9; and ii) a learning rate of 0.00001.
Claim 7, The system of claim 6, wherein the plurality of hidden nodes further comprises a transfer function to determine the presence of at least one micro-neuroma in an eye of a subject.
Claim 11, The non-transitory computer readable medium of claim 10, wherein the at least one image of an ocular surface of a subject are acquired using an in vivo confocal microscope.
Claim 8, The system of claim 7, wherein the derivative of the transfer function is used to update the statistical weights of each of the plurality of hidden nodes.
Claim 12, The non-transitory computer readable medium of claim 10, wherein the plurality of images of an ocular surface from a population of subjects used to train the neural network are pre-processed by normalizing each image against parameters from an image database or by conversion of a grayscale single channel pixel intensity to a three channel RGB color pixel intensity.
Claim 9, The system of claim 7, wherein the transfer function is a sigmoid function.
Claim 10, The system of claim 6, wherein the plurality of output nodes further comprises a sigmoid transfer function.
Claim 11, The system of claim 1, wherein the neural network is trained using a plurality of images of an ocular surface from a population of subjects.
Claim 12, The system of claim 11, wherein the number of images of the ocular surface used to train the neural network is at least 1,000.
Claim 13, The system of claim 11, wherein the number of images of the ocular surface used to train the neural network is at least 10,000.
Claim 14, The system of claim 11, wherein a portion of the plurality of images of the ocular surface comprises images of micro-neuromas.
Claim 15, The system of claim 1, wherein the input data for the neural network is a function of the response of the in vivo confocal microscope.
Claim 16, The system of claim 15, wherein the input data for the neural network is normalized to values between 0-1.
Claim 17, The system of claim 6, wherein the plurality of output nodes return a value representative of the presence of a micro-neuroma on an ocular surface of a subject.
Claim 18, The system of claim 1, wherein the computer communicates wirelessly with the in vivo confocal microscope.
Claim 19, The system of claim 1, wherein the computer is directly connected to the in vivo confocal microscope.
Claim 20, The system of claim 1, wherein the computer is part of the in vivo confocal microscope.
Claim 21, The system of claim 1, wherein the computer communicates remotely with the in vivo confocal microscope.
Claim 92, The system of claim 1, which utilizes a non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by a processor, perform a method for automatically determining the presence of at least one neuroma on at least one image of an ocular surface of a subject, the method comprising :a) acquiring at least one image of an ocular surface of a subject; and b) determining the presence of a neuroma on the at least one image of an ocular surface of a subject by analyzing the at least one image of an ocular surface of a subject using the neural network, wherein the neural network comprises: i) a residual learning architecture; and ii) a backpropagation algorithm comprising a gradient descent optimizer, wherein the neural network is trained using a plurality of images of an ocular surface from a population of subjects, wherein the plurality of images of an ocular surface from a population of subjects are augmented using data blending augmentation such as mix-up or data interpolating augmentation prior to training the neural network
Claim 1 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 11,998,270 B1. Although the claims at issue are not identical, they are not patentably distinct from each other because both require an in vivo confocal microscope to produce an ocular-surface image and a computer programmed with a neural network to determine the presence of a neuroma. Merely claiming the components used to perform the patented method as a system does not render claim 1 patentably distinct.
Claims 2 and 3 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2 and 3 of U.S. Patent No. 11,998,270 B1. Although the claims at issue are not identical, they are not patentably distinct from each other because the patent claims expressly require identifying a neuroma on an ocular surface, wherein the neuroma is a micro-neuroma and the ocular surface is a corneal surface, as recited in instant claims 2 and 3
Claims 5-10 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 2 of U.S. Patent No. 11,998,270 B1 in view of Odaibo et al. (US 2017/0357879). Odaibo teaches the claimed multilayer-perceptron architecture, activation functions, sigmoid functions, and backpropagation weight updating ([0020-0022], [0043-0045]). It would have been obvious to employ Odaibo’s multilayer perceptron architecture as the neural network of the patented method to classify ophthalmic images.
Claim 11 is rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 4 of U.S. Patent No. 11,998,270 B1. Although the claims at issue are not identical, they are not patentably distinct from each other because claim 1 of U.S. 11,998,270 B1 requires sending an image to a computer programmed with a neural network, and claim 4 requires that the neural network be trained using a plurality of ocular-surface images from a population of subjects as recited in instant claim 11.
Claims 12-14 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 2 of U.S. Patent No. 11,998,270 B1 in view of Zhang et al. (US 2019/0110753). Zhang teaches training ophthalmic-image classifiers using at least 1,000 and 10,000 labeled medical images ([0065], [0144]). It would have been obvious to use these dataset sizes and include micro-neuroma images as positive training examples, thereby improving classification accuracy.
Claim 15 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 11,998,270 B1. Although the claims at issue are not identical, they are not patentably distinct from each other because US 11,998,270 B1, requires producing an image using an vivo confocal microscope and sending that image to a computer programmed with a neural network, such that the input data supplied to the neural network is necessarily a function of the response of the in vivo confocal microscope, as recited in claim 15.
Claims 16-17 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 2 of U.S. Patent No. 11,998,270 B1 in view Odaibo et al. (US 2017/0357879). Odaibo teaches normalizing ophthalmic-image data and returning probabilistic classification values between 0 and 1([0017], [0061]). Selecting a 0-1 normalization range would have been an obvious conventional normalization, and the resulting output represents the detected image class.
Claims 18-21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 2 of U.S. Patent No. 11,998,270 B1 in view Odaibo et al. (US 2017/0357879). Odaibo teaches wired, wireless, directly attached, and remote computing arrangements for ophthalmic imaging systems ([0067], [0070-0071]). Integrating the computer into the microscope would have been an obvious packaging alternative that reduces external components and communication cabling.
Claim 92 is rejected on the ground of nonstatutory double patenting as being unpatentable over claims 10 and 11 of U.S. Patent No. 11,998,270 B1. Although the claims at issue are not identical, they are not patentably distinct from each other because the patented claims recited the same computer-readable medium, neural network architecture, training, and in vivo confocal microscopy limitations. Reciting a system that utilizes the patented medium does not render claim 92 patentable distinct.
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John Sipes
Examiner
Art Unit 2872
/J.C.S./ Examiner, Art Unit 2872
/BUMSUK WON/ Supervisory Patent Examiner, Art Unit 2872
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