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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claimed invention is directed to non-statutory subject matter because the claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. As summarized in the 2019 Revised Patent Subject Matter Eligibility Guidance, examiners must perform a Two-Part Analysis for Judicial Exceptions.
Step 1
In Step 1, it must be determined whether the claimed invention is directed to a process, machine, manufacture or composition of matter. The instant invention encompasses a computer implemented method in claims 1-8 (i.e., a process). All claims are directed to one of the four statutory categories and meet the requirements of step 1.
Step 2A
Prong One
The claimed invention is directed to an abstract idea without significantly more. The instant invention is broadly directed to A digital holographic wrapped phase aberration compensation method. Claim 1 recites the following (with emphasis added):
Claim 1: A digital holographic wrapped phase aberration compensation method based on deep learning, characterized in that, the method comprises two stages comprising a network training stage and a holographic measurement stage, and the method is divided into the following steps:
a. a step in the network training stage is:
automatically generating, by a computer, simulated wrapped phase map data to train a neural network model, and obtaining a trained neural network model; and
b. in the holographic measurement stage,
processing, by the trained neural network model, a sample to be measured, and obtaining a three-dimensional profile distribution of the sample to be measured.
Claim 1 encompass the abstract idea, which is also encompassed by the dependent claims 2-8.
Claim 1 recites the steps for processing data and generating a 3D data, which is directed to the mathematical relationships and calculations, a mathematical concept.
Prong Two
This judicial exception is not integrated into a practical application because mere instruction to implement on a computer or a computer model, or merely using a computer or computer model as a tool to perform the abstract idea, adding insignificant extra solution activity, and/or generally linking the use of the abstract idea to a technological environment or field of use is not considered integration into a practical application. Claim 1 recites using training data to train a neural network model. Using training data to train a neural network model is a generic feature of neural network, which does not represent a technological improvement. The using of the computer and the neural network model does not add improvement to the functioning of a computer or to any other technology field, which failed to enable the abstract idea to integrate into a practical application. Claims 2-3, 7-8 are about mathematical relationships and calculations, which are abstract idea. Claim 4 recites a specific network model. Claim 5-6 recites the tools to acquire data and specific data, which is data obtaining. The claims do not include additional elements that are sufficient to enable the abstract idea to integrate into a practical application. The conventional computers over generic network as presented are directed to the components of a system amount to merely field of use type limitations and/or extra solution activity to implement the mental processes using collected data to predict a result.
Step 2B
Step 2B in the analysis requires us to determine whether the claims do significantly more than simply describe that abstract method. Mayo, 132 S. Ct. at 1297. We must examine the limitations of the claims to determine whether the claims contain an "inventive concept" to "transform" the claimed abstract idea into patent-eligible subject matter. Alice, 134 S. Ct. at 2357 (quoting Mayo, 132 S. Ct. at 1294, 1298). The transformation of an abstract idea into patent-eligible subject matter "requires 'more than simply stat[ing] the [abstract idea] while adding the words 'apply it."' Id. (quoting Mayo, 132 S. Ct. at 1294) (alterations in original). "A claim that recites an abstract idea must include 'additional features' to ensure 'that the [claim] is more than a drafting effort designed to monopolize the [abstract idea].'" Id. (quoting Mayo, 132 S. Ct. at 1297) (alterations in original). Those "additional features" must be more than "well-understood, routine, conventional activity." Mayo, 132 S. Ct. at 1298.
The present claims include the additional elements other than the abstract idea which include a computer. These additional elements are merely conventional computer. Any potentially technical aspects of the claims are well-known generic computer components performing conventional functions (e.g., a processor performing generic data handling using mathematical concepts). The present claims have been analyzed both individually and in combination and, the instant claims do not provide any improvement of the functioning of the computer or improvement to computer technology or any other technical field. There do not appear to be any meaningful limitations other than those that are well-understood, routine and conventional in the field. Thus the present claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claims are generally linked to implement an abstract idea on a computer. When looked at individually and as a whole, the claim limitations are determined to be an abstract idea without "significantly more," and thus not patent eligible.
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.
Claim(s) 1, 4 and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable by Niu et al. (“Interferometric Wavefront Sensing System Based on Deep Learning” from IDS) in view of Nguyen et al. (US 2018/0292784 A1).
Regarding claim 1, Niu teaches:
A wrapped phase aberration compensation method based on deep learning, characterized in that, (Abstract: “Here, we apply deep learning algorithms to the interferometric system to detect wavefront under general conditions. This method can accurately extract the wavefront phase distribution and analyze aberrations, and it is verified by experiments that this method not only has higher measurement accuracy and faster calculation speed but also has good performance in the noisy environments.”) the method comprises two stages comprising a network training stage and a holographic measurement stage, and the method is divided into the following steps:
a. a step in the network training stage is:
automatically generating, by a computer, simulated wrapped phase map data to train a neural network model, and obtaining a trained neural network model; (Section 2.2.1:
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” The output of the net1 is used to train the net2 as shown in FIG. 2. And detailed in section 2.2.2 and 2.2.3) and
b. in the measurement stage,
processing, by the trained neural network model, a sample to be measured, and obtaining a three-dimensional profile distribution of the sample to be measured. (The Net2 processing the input of the wavefront and generate the output coefficients as shown in FIG. 2 (c ), which are then used to generate the 3D wavefront distribution using Equation 6. Page 5: “
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”)
However, Niu does not explicitly teach that the interferometric wavefront sensing system can be applied in a holographic scenario. On the other hand, Ngugen teaches that using an interferometric sensing technique to mitigate aberration in a digital holographic scenario. ([0005, “The present disclosure relates to a digital holography microscope. The digital holography microscope comprising two microscope objectives configured in a bi-telecentric configuration; a sample holder configured to receive a sample; a couple charged device configured to capture one or more images; a display; and a processor configured to retrieve a Convolutional Neural Network (CNN) model associated with a type of the sample, mitigate aberrations in the one or more images using at least the CNN model”)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to have applied the interferometric wavefront sensing method of Niu to the digital holographic scenario of Nguyen to mitigate aberration in digital holographic images.
Regarding claim 4, Niu in view of Nguyen teaches:
The digital holographic wrapped phase aberration compensation method based on deep learning according to claim 1, wherein the neural network model is resnet50.(Niu, page 6: “
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Niu teaches to use residual network (resnet) as the neural network model. Niu also teaches to add 5 resnet to accelerate the learning process. It would have been obvious for a person ordinary skill in the art to have chosen a number of layers, e. g. 50 to be used in the neural network to further accelerate the learning process.)
Regarding claim 6, Niu in view of Nguyen teaches:
The digital holographic wrapped phase aberration compensation method based on deep learning according to claim 1, wherein the sample to be measured is any microstructure used for holographic imaging and is a transmission type sample or a reflection type sample. (Niu, page 1, “When light is transmitted over long distances in space, it is often interfered by numerous factors (atmospheric turbulence, humidity, etc.) to distort the wavefront. The wavefront aberration of light has traditionally been an important factor affecting the imaging quality of optical systems”)
Allowable Subject Matter
Claims 2-3, 5, 7-8 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: none of the references along or in combination teaches the limitations of “step one: generating, by the computer, a large number of random Zernike polynomial coefficients A, fitting, by using the Zernike polynomial coefficients A, several continuous two-dimensional surfaces as phase aberrations through Zernike polynomials, and superimposing the phase aberrations on a same type of microstructure phase as the sample to be measured to construct a microstructure phase aberration distribution φ; step two: converting the microstructure phase aberration distribution φ into a complex exponent and calculating a phase angle of the complex exponent to obtain a simulated wrapped phase map ϕ with a value in a range of [-π, π]; step three: establishing the neural network model, training the neural network model by treating the simulated wrapped phase map ϕ as an input of the neural network model and treating the corresponding Zernike polynomial coefficient A as a label of the neural network model, and obtaining the trained neural network model.” Recited in claim 2.
“S1: building a digital holographic optical setup to measure the sample to be measured and record a hologram of the sample to be measured, performing numerical reconstruction based on the hologram to obtain a complex amplitude U of the sample to be measured, calculating a wrapped phase map of the complex amplitude U and inputting the wrapped phase map into the trained neural network model, and outputting a Zernike polynomial coefficient Ac; S2: fitting, by using the Zernike polynomial coefficient Ac, phase aberration φac and multiplying a conjugate complex exponent exp(-jφa) of the fitted phase aberration φac by the complex amplitude U to obtain a pre-compensated wrapped phase map with most of the phase aberration being compensated; S3: performing phase filtering and phase unwrapping on the pre-compensated wrapped phase map to obtain a continuous phase distribution φc containing only a small part of phase aberration, and performing edge enhancement and local adaptive threshold segmentation on the continuous phase distribution φc to obtain a binary mask that only represents a background region; S4: extracting, by using the binary mask, phase data of the background region in continuous phase distribution φc, constructing a Zernike polynomial equation set based on the phase data of the background region, solving a Zernike polynomial coefficient Ar of residual aberration, and performing, by using the Zernike polynomial coefficient Ar of the residual aberration, Zernike polynomial fitting to obtain a residual aberration phase distribution φr; and S5: subtracting the phase distribution φr from the continuous phase distribution φc to recover and obtain a true phase of the sample to be measured, and performing wavelength conversion on the true phase to output and obtaining the three-dimensional profile distribution of the sample to be measured.” Recited in claim 3.
Conclusion
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/YANNA WU/Primary Examiner, Art Unit 2615