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 .
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.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Objections
The numbering of claims is not in accordance with 37 CFR 1.126 which requires the original numbering of the claims to be preserved throughout the prosecution. When claims are canceled, the remaining claims must not be renumbered. When new claims are presented, they must be numbered consecutively beginning with the number next following the highest numbered claims previously presented (whether entered or not).
Misnumbered claim 10 has been renumbered as claim 9.
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 use the word “means” or “step” but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) recite(s) sufficient structure, materials, or acts to entirely perform the recited function. Such claim limitation(s) is/are: “a dose-aware network unit that provides,” “a noise variance calibration unit that performs,” and “an adaptive noise reduction unit that controls” in claims 1-8.
One of ordinary skill in the art would understand that said “units” provide sufficient structure, materials, or acts to entirely perform the recited function because the units are comprised in a neural network algorithm performed on a computer.
Because this/these claim limitation(s) is/are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are not being interpreted to cover only the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof.
If applicant intends 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 remove the structure, materials, or acts that performs the claimed function; or (2) present a sufficient showing that the claim limitation(s) does/do not recite sufficient structure, materials, or acts to perform the claimed function.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-8 and 10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 10 recite the limitation “the oversmoothing issue.” There is no antecedent basis for this limitation in the claims. Furthermore, the limitation renders the claims indefinite because it is not unclear what is considered to be the “issue.” For the purpose of further examination, the limitation has been interpreted as “a noise variance calibration unit that performs noise-variance calibration on the input image fed to the CNN .”
Claims 2-8 depend from claim 1 and therefore inherit all of the deficiencies of claim 1 discussed above.
Claim 3 further recites the limitation “an alpha parameter.” The limitation renders the claim indefinite because the claim does not define what alpha corresponds to. It appears that the alpha parameter is related to the noise variance levels based on pg. 10 of the specification. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). For the purpose of further examination, the claim has been interpreted as using noise variations.
Claim 5 further recites the limitation “the detailed structure.” There is no antecedent basis for this limitation in the claim. In addition, the term “detailed” is a relative and/or subjective term which renders the claim indefinite. The term “detailed” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. What is considered to be detailed may be blurred/unclear in different applications/scenarios, and the standards for determining whether another invention may infringe on this limitation is not clearly defined in the claims or the specification.
A claim that requires the exercise of subjective judgment without restriction renders the claim indefinite. In re Musgrave, 431 F.2d 882, 893, 167 USPQ 280, 289 (CCPA 1970). Claim scope cannot depend solely on the unrestrained, subjective opinion of a particular individual purported to be practicing the invention. Datamize LLC v. Plumtree Software, Inc., 417 F.3d 1342, 1350, 75 USPQ2d 1801, 1807 (Fed. Cir. 2005)); see also Interval Licensing LLC v. AOL, Inc., 766 F.3d 1364, 1373, 112 USPQ2d 1188 (Fed. Cir. 2014).
For the purpose of further examination, the claim has been interpreted as preserving the anatomical structures of the input image.
Claim 6 depends from claim 5 and therefore inherit all of the deficiencies of claim 5 discussed above.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tang et al. (“CCN-CL: A content-noise complementary network with contrastive learning for low-dose computed tomography denoising,” Computers in Biology and Medicine 147 (2022) 105759), in view of Kim et al. (“CNN-based CT denoising with an accurate image domain noise insertion technique,” Medical Imaging 2021: Physics of Medical Imaging, edited by Hilde Bosmans, Wei Zhao, Lifeng Yu, Proceedings of SPIE Vol. 11595, 1159545), hereinafter referred to as Tang and Kim, respectively.
Regarding claims 1 and 10, Tang teaches a low-dose computed tomography (LDCT) denoising neural network apparatus and method, the method performed in an LDCT denoising neural network apparatus, comprising:
a dose-aware network unit/step that provides an LDCT image as an input image to a convolutional neural network (CNN) and outputs a simulated normal-dose computed tomography (NDCT) image as an output image (Tang pg. 2 right column: “Suppose X denotes an LDCT image and Y represents the normal-dose CT (NDCT) image corresponding to X, then the relationship between X and Y can be described as X = Y + N where the N denotes the noise between the LDCT image and its corresponding NDCT image”; Tang pg. 3 left column: “our network takes an LDCT image as the input … and respectively output a predicted content and a predicted noise”; Tang Fig. 1: NDCT, Denoised, & LDCT); and
an adaptive noise reduction unit/step that controls the CNN to progressively remove the noise according to the dose level of a given LDCT image (Tang pg. 4 right column: “The Adam method [64] is selected to optimize the performance of our proposed network … the number of training epochs is set to 60 for all the networks” – the Adam method (Adaptive Moment Estimation) is an adaptive learning algorithm; Tang Fig. 1: the CNN performs denoising in each iteration).
However, Tang does not appear to explicitly teach using a noise variance calibration unit that performs noise-variance calibration on the input image fed to the CNN to address the oversmoothing issue of the CNN.
Pertaining to the same field of endeavor, Kim teaches using a noise variance calibration unit/step that performs noise-variance calibration on the input image fed to the CNN to address the oversmoothing issue of the CNN (Note that no patentable distinction is made by an intended use or result limitations unless some structural difference is imposed by the use or result on the structure or material recited in the claim. Kim Fig. 1: Variance map; Kim pg. 2: “Assuming CT noise arises from the Poisson noise in projection data, we calculated the projection variance by the inverse of the detected number of photons Ndet at every detector cell”; Kim pg. 3: “We assume that the generated noise is uncorrelated with the inherent noise in a NDCT image. Since the noise variation of a QDCT image is four times larger than that of a NDCT image, the generated noise is scaled by √3 and added to the NDCT image to produce a synthesized QDCT image” – QDCT corresponds to quarter-dose i.e., low-dose).
Tang and Kim are considered to be analogous art because they are directed to neural network methods and systems for CT denoising. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the content-noise complementary network with contrastive learning for LDCT denoising (as taught by Tang) to calibrate noise variations (as taught by Kim) because the combination provides more consistent results by normalizing data (Kim pg. 3).
Regarding claim 2, Tang, in view of Kim, teaches the apparatus of claim 1, wherein the dose-aware network unit pre-generates the LDCT image configured to have a varying dose levels by adding noise to an NDCT image (Tang pg. 2 right column discussed above, Tang Eq. (6) & pg. 2-3: “The other type aims at finding a mapping to predict the noise and then gain the content indirectly … we utilize content-noise complementary learning strategy”; also see Kim Fig. 1: Clean image [Wingdings font/0xE0] … variance map [Wingdings font/0xE0] Filtering [Wingdings font/0xE0] CT noise [Wingdings font/0xE0] Noisy image).
Regarding claim 3 , Tang, in view of Kim, teaches the apparatus of claim 2, wherein the dose-aware network generates the varying dose levels by adjusting the intensity of the noise by controlling an alpha parameter of the NDCT image (Tang pg. 2-3, Eq. (6) & Kim Fig. 1 discussed above).
Regarding claim 4, Tang, in view of Kim, teaches the apparatus of claim 2, wherein the dose-aware network unit trains the CNN to minimize the discrepancy between the NDCT image and the simulated NDCT image (Tang pg. 4 left column: “We use the L1 loss to calculate the loss between the output and the corresponding NDCT image … The contrastive regularization loss of our network minimizes the gap between the output of network Xdenoised and the clear image Y while maximizing the gap from the input image X”).
Regarding claim 5, Tang, in view of Kim, teaches the apparatus of claim 1, wherein the noise variance calibration unit adjusts the noise so that the detailed structure of the input image is preserved, based on the discrepancy between the output image and the input image (Tang Fig. 5 & pg. 5 left column: “the proposed CCN-CL network model performs better in tissue details preservation”).
Regarding claim 6, Tang, in view of Kim, teaches the apparatus of claim 5, wherein the noise variance calibration unit adjust the noise so that the output image has a noise variance higher than or equal to that of the NDCT image by a threshold (Kim pg. 3 discussed above).
Regarding claim 7, Tang, in view of Kim, teaches the apparatus of claim 1, wherein the adaptive noise reduction unit recognizes the dose level of the given LDCT image through the CNN and progressively remove the noise by performing repeated input/output operations through the CNN (Tang Fig. 1 & pg. 4 right column discussed above; also see Kim pg. 3: “using the ADAM optimizer”).
Regarding claim 8, Tang, in view of Kim, teaches the apparatus of claim 7, wherein the adaptive noise reduction unit inputs an N-th (where N is a natural number) intermediate image to the CNN to generate an (N+1)-th intermediate image and again inputs the (N+1)-th intermediate image to the CNN to generate an (N+2)-th intermediate image (Tang Fig. 1, pg. 4 right column & Kim pg. 3 discussed above).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SOO J SHIN whose telephone number is (571)272-9753. The examiner can normally be reached M-F; 10-6.
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/Soo Shin/
Primary Examiner, Art Unit 2667 571-272-9753
soo.shin@uspto.gov
Prosecution Insights