DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to communications: Application filed on 6/19/2024.
Claims 1-11 are pending. Claims 1 and 9-11 are independent.
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.
Claim 10 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because invention is directed to non-statutory subject matter. The claims fails to positively recite a hardware element such as “one or more processors” as part of the claimed invention, and the specification does not explicitly define “computer-readable storage medium” as non-transitory. Thus the broadest reasonable interpretation of “a computer program stored in a computer-readable storage medium” includes transitory signals and thus is non-statutory subject matter.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “network unit configured to acquire” in claim 11.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 102
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 4-7 and 10-11 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Liang et al. (US11,669,943).
In regards to claim 1, Liang et al. discloses a method of improving quality of medical data based on deep learning, the method being performed by a computing device including at least one processor, the method comprising:
acquiring at least one of a noise ratio of label data having a correlation with noise of input data for training of a neural network model, a resolution of the input data, and a resolution of the label data (Liang et al. col4 ln39-51, acquires predetermined size of resized original image and low resolution guidance image); and
training the neural network model based on at least one of the input data, the label data, the noise ratio, the resolution of the input data, and the resolution of the label data (Liang et al. col9 ln17 to 29, trains training instance on original image and guidance image).
In regards to claim 4, Liang et al. discloses the method of claim 1, wherein training the neural network model based on at least one of the input data, the label data, the noise ratio, the resolution of the input data, and the resolution of the label data comprises training the neural network model so that the neural network model improves at least one of the noise and resolution of the input data by using at least one of the noise ratio, the resolution of the input data, and the resolution of the label data (Liang et al. col6 ln1-8, the image quality can be expected to improve along the pipeline of encoder-decoder neural networks).
In regards to claim 5, Liang et al. discloses the method of claim 4, wherein the neural network model comprises:
a first neural network for extracting features from the input data and generating output data of the neural network model (Liang et al. fig. 2 col7 ln7-24, generates output value in an output feature map); and
a second neural network for adjusting at least one of noise and resolution of output of the first neural network based on at least one of the noise ratio, the resolution of the input data, and the resolution of the label data (Liang et al. fig. 2 col6 ln1-8, the image quality can be expected to improve along the pipeline of encoder-decoder neural networks.).
In regards to claim 6, Liang et al. discloses the method of claim 5, wherein the second neural network performs an operation of applying at least one of the noise ratio, the resolution of the input data, and the resolution of the label data to the output of the first neural network in accordance with a number of channels of the first neural network (Liang et al. col6 ln62 to col7 ln6, the depth d of each block represents a number of slices or channels in the block).
In regards to claim 7, Liang et al. discloses the method of claim 4, wherein the neural network model comprises a third neural network for extracting features from the input data and generating output data of the neural network model in which at least one of noise and resolution thereof has been adjusted (Liang et al. col6 ln24-31, guidance system reduces the number of features that it generates by processing a reduced-size version of the original image. The term “third” in this claim is being interpreted as a label to distinguish this specific neural network from other embodiments and not that the claim requires three neural networks).
Claim 10 recites substantially similar limitations to claim 1. Thus claim 10 is rejected along the same rationale as claim 1.
Claim 11 recites substantially similar limitations to claim 1. Thus claim 11 is rejected along the same rationale as claim 1. Liang et al. also discloses a processor including at least one core (Liang et al. fig. 9 col13 ln15-22), memory including program code executable on the processor (Liang et al. col13 ln23-37), and a network unit configured to acquire (Liang et al. col14 ln44-51, communications conduit(s) can be implemented as a local area computer network as defined in specification as filed pg20 ln5-22 “the network unit 130 may perform data transmission and reception using a wired/wireless communication system such as a local area network).
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) 2-3 and 8-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liang et al. in view of Cai et al. (US2022/0367039).
In regards to claim 2, Liang et al. discloses the method of claim 1. Liang does not explicitly disclose wherein:
a signal intensity of the label data corresponds to a signal intensity of the input data; and
noise of the label data is different from noise of the input data.
However Cai et al. discloses wherein:
a signal intensity of the label data corresponds to a signal intensity of the input data (Cai et al. para[0086], generate B0mode data in which the signal intensity at each sampling point is expressed by a degree of brightness); and
noise of the label data is different from noise of the input data (Cai et al. para[0060], N can be one or more and indicates the noise characteristic information for corresponding noisy and clean images can vary during training).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the enhancement system of Liang et al. with the denoising method of Cai et al. in order to avoid images being degraded due to noise (Cai et al. para[0002]).
In regards to claim 3, Liang et al. as modified by Cai et al. discloses the method of claim 2, wherein the noise of the label data is determined through a linear combination of dependent noise having a correlation with the noise of the input data and independent noise having no correlation with the noise of the input data (Cai et al. para[0056], the noise characteristic information of to-be-processed medical image data is used to determine whether to use trained neural network 1 or trained neural network 2).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the enhancement system of Liang et al. with the denoising method of Cai et al. in order to avoid images being degraded due to noise (Cai et al. para[0002]).
In regards to claim 8, Liang et al. discloses the method of claim 7. Liang et al. does not explicitly disclose wherein the third neural network performs an operation of generating output data including a first channel representing a signal without noise and a second channel representing noise adjusted according to the noise ratio with no signal present.
However Cai et al. discloses wherein the third neural network performs an operation of generating output data including a first channel representing a signal without noise and a second channel representing noise adjusted according to the noise ratio with no signal present (Cai et al. para[0060], trained to produce images based on noisy images and their corresponding clean images).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the enhancement system of Liang et al. with the denoising method of Cai et al. in order to avoid images being degraded due to noise (Cai et al. para[0002]).
In regards to claim 9, Liang et al. discloses a method of improving quality of medical data based on deep learning, the method being performed by a computing device including at least one processor, the method comprising:
alleviating or improving at least one of the noise and resolution of the medical data based on the adjustment ratio by using a pre-trained neural network model (Liang et al. fig. 1 col4 ln59-67, the predicted image 122 represents a detail-corrected, color-corrected, and lighting-corrected version of the original image 104);
wherein the neural network model is pre-trained based on at least one of input data for training of the neural network model, label data, a noise ratio of the label data having a correlation with noise of the input data, the noise ratio, a resolution of the input data, and a resolution of the label data (Liang et al. col9 ln17 to 29, trains training instance on original image and guidance image).
Liang et al. does not explicitly disclose acquiring medical data and an adjustment ratio for at least one of noise and resolution of the medical data.
However Cai et al. substantially discloses acquiring medical data and an adjustment ratio for at least one of noise and resolution of the medical data (Cai et al. fig 2 201 and 203 para[0043] acquires medical image data and predetermined correction of a noise ratio at the same depth value).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the enhancement system of Liang et al. with the denoising method of Cai et al. in order to avoid images being degraded due to noise (Cai et al. para[0002]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhao et al. (US2023/0021926) teaches using noise reduction in images.
Huang et al. (US11,540,798) teaches generating normalized image data corresponding to noisy images.
Sun et al. (US2021/0397895) teaches providing machine learning with label data.
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/N.H/Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
