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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 10 November 2024 & 10 March 2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
The listing of references in the specification is not a proper information disclosure statement. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
The following title is suggested: SYSTEM, METHOD AND COMPUTER-ACCESSIBLE MEDIUM FOR CNN FGATIR MRI WITH POWER SPECTRUM REGULARIZATION FOR DIRECT VISUALIZATION OF SUBCORTICAL ANATOMY
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.
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.
Claims 1, 4-7, 12, 14, & 27 are rejected under 35 U.S.C. 103 as being unpatentable over Meyer et al. (US PGPUB 20230342886; hereinafter "Meyer").
With regards to Claim 1, a non-transitory computer-accessible medium having stored thereon computer-executable instructions for creating or providing a visualization of an anatomical structure, wherein, when a computer processor executes the instructions (computer implemented method stored on non-transitory computer readable medium for denoising MR image; see Meyer Claim 16), the computer processor is configured to perform the procedures comprising:
receiving a raw magnetic resonance image (MRI) (acquiring complex magnetic resonance (MR) image data of an area of interest of a subject, wherein the image data comprises complex noisy input image, i.e. raw MR image; see Meyer Claim 1); and
applying a (applying a complex de-noising convolutional neural network (C-DnCNN) to the noisy input image; see Meyer Claim 1; the DnCNN is used in this work, i.e. Meyer discloses that the Zhang1 DnCNN {as incorporated in ¶ [0047]}; see Meyer ¶ [0094 & 0148]).
While Meyer discloses that incorporated Zhang1’s “DnCNN is used in this work” (see Meyer ¶ [0094 & 0148]), Meyer does not explicitly disclose the Zhang1’s power regularization CNN. In particular, Zhang1 teaches of a regularization parameter λ and an adjustable penalty function ρk, i.e. power regularization (see [3] Zhang1 pg. 3146, ¶ 5 also cited in the instant specification as exemplary reference 19).
Meyer also teaches of incorporated reference [20b] Zhang2 which teaches of a tunable noise level map based on noise level σ and regularization parameter λ which is tuned based on a “compromise between noise reduction and detail preservation. When it is too small, much noise will remain; on the opposite, details will be smoothed out along with suppressing noise” (see [20b] Zhang2 pg. 4611, ¶ 3-5).
Meyer and Zhang are both considered to be analogous to the claimed invention because they are in the same field of neural networks for denoising images. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Meyer to incorporate the above teachings of Zhang to provide at least a power regularization CNN. Doing so would amount to combining prior art elements according to known methods to yield predictable results as Meyer already admits to utilizing the Zhang DnCNN as cited above.
Claims 14 & 27 recite similar limitations and are rejected under the same rationale as Claim 1.
With regards to Claim 41, wherein the power regularization CNN is tuned to provide an amount of regularization (the C-DnCNN for MRI denoising utilizes a tunable complex-valued noise level map; see Meyer ¶ [0124 & 0132] as also taught by incorporated reference Zhang2 as a tunable noise level map based on noise level σ and regularization parameter λ which is tuned based on a “compromise between noise reduction and detail preservation. When it is too small, much noise will remain; on the opposite, details will be smoothed out along with suppressing noise” (see [20b] Zhang2 pg. 4611, ¶ 3-5); it should be appreciated that Applicant has also cited Zhang2 in the instant specification as exemplary reference 20).
With regards to Claim 54, wherein the power regularization CNN is tuned to provide an amount of regularization that is related to an amount of denoising performed on the MRI (the C-DnCNN for MRI denoising utilizes a tunable complex-valued noise level map; see Meyer ¶ [0124 & 0132] as also taught by incorporated reference Zhang2 as a tunable noise level map based on noise level σ and regularization parameter λ which is tuned based on a “compromise between noise reduction and detail preservation. When it is too small, much noise will remain; on the opposite, details will be smoothed out along with suppressing noise” (see [20b] Zhang2 pg. 4611, ¶ 3-5); it should be appreciated that Applicant has also cited Zhang2 in the instant specification as exemplary reference 20).
Claims 6, recite similar limitations and are rejected under the same rationale as Claim 5.
With regards to Claim 74, wherein the amount of regularization is selected to at least one of (claimed in the alternative), or (b) not prevent any denoising by the power regularization CNN (a tunable noise level map based on noise level σ and regularization parameter λ which is tuned based on a “compromise between noise reduction and detail preservation. When it is too small, much noise will remain; on the opposite, details will be smoothed out along with suppressing noise” (see [20b] Zhang2 pg. 4611, ¶ 3-5).
With regards to Claim 121, wherein the applying the power regularization CNN to the MRI provides a sharp and denoised MRI (the output of the complex de-noising convolutional neural network (C-DnCNN) is sharper structures and less noise; see Meyer ¶ [0144] ).
Allowable Subject Matter
Claims 40-41, 44, & 46-47 are allowed.
Claims 2-3, 8-11, & 13 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.
The following is a statement of reasons for the indication of allowable subject matter:
With regards to Claim 40-41, 44, 46-47 & claims 2-3, neither the cited nor searched prior art teaches of using FGATIR MRI data with a power regularization convolutional neural network.
With regards to Claims 8-10, while Meyer only teaches of using MSE for evaluating the evaluate the performance of the proposed denoising method and not for adjusting the regularization parameter (see Meyer ¶ [0135]). Zhang1 teaches of a penalty function, as detailed above; however, Zhang1 does not explicitly teach of it being applied to a residual power spectrum to minimize over smoothing.
With regards to Claims 11 & 13, neither the cited nor search prior art teaches of a power regularization CNN which targets a power spectrum energy level of 1 for all frequencies in a range between about 0Hz to about 150kHz
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASHISH S. JASANI whose telephone number is (571) 272-6402. The examiner can normally be reached M-F 9:00 am - 5:00 pm (CST).
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/ASHISH S. JASANI/Examiner, Art Unit 3798
/KEITH M RAYMOND/Supervisory Patent Examiner, Art Unit 3798
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