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 .
Election/Restrictions
Applicant’s election without traverse of Group I, Claims 1-8 for continued examination in the reply filed on January 22, 2026 is acknowledged.
Claims 9-20 withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected groups, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on January 22, 2026.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on January 17, 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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, 2, and 5 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Qiang et. al. (Chinese Patent CN108171742A).
Regarding claim 1, Qiang et. al. discloses a noise filtering method comprising: converting a scanning electron microscope (SEM) image into a converted design image using a conversion model; converting the converted design image into a gray level co-occurrence matrix (GLCM) (Qiang et. al. Abstract); extracting statistical characteristics of the GLCM; and determining whether the converted design image includes noise or not, based on the statistical characteristics (Qiang et. al. page 2, paragraph 3, Abstract, where the LoG operator is the Laplacian of Gaussian filter that is a popular edge detection technique in image processing that combines Gaussian smoothing with the Laplacian operator to identify edges while reducing noise).
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Regarding claim 2, Qiang et. al. discloses the noise filtering method of claim 1, further comprising: quantizing the converted design image (Qiang et. al. page 6, Figure 1 the converted design image is normalized as a grey matrix and gradient matrix).
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Regarding claim 5, Qian et. al. discloses the noise filtering method of claim 1, wherein the statistical characteristics include at least one of a contrast, a homogeneity, an entropy, an energy, a correlation, a dissimilarity, a standard deviation, a mean, and a variance (Qiang et. al. page 4, the gray gradient co-occurrence matrix texture feature value parameters include a specific formula of 15-dimension feature vector).
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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.
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) 3, 4, 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Qiang et. al. (Chinese Patent CN108171742A) in view of Tian et. al. (Chinese Patent CN115546062 A).
Regarding claim 3, Qiang et. al. discloses the noise filtering method of claim 2. However, Qiang et. al. fails to disclose wherein each pixel of the converted design image is converted into a number corresponding to one of a red, green, and blue (RGB) value and luminosity.
Tian et. al. teaches wherein each pixel of the converted design image is converted into a number corresponding to one of a red, green, and blue (RGB) value and luminosity (Tian et. al. page 8, Figure 1 where each pixel in the image is divided into R, G, B three primary color components). Quantifying the pixel values is an important step in the image conversion process. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Qiang et. al. and Tian et. al. to specify the tricolor pixel values to the gray scale value.
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Regarding claim 4, Tian et. al. further discloses the noise filtering method of claim 3, wherein each pixel of the converted design image is converted into the number based on at least any one of an average value, an intermediate value, a root-mean-square (RMS) value, a minimal value, and a maximal value of one of the RGB value and the luminosity (Tian et. al. page 8, Figure 1 where the average value of the grey value of each of the three primary color components can be obtained).
Regarding claim 6, Qiang et. al. discloses the noise filtering method of claim 1. However, Qiang et. al. fails to disclose wherein the determining whether the converted design image includes noise or not includes when the statistical characteristics are out of a preset reference range, determining the converted design image includes noise.
Tian et. al. teaches wherein the determining whether the converted design image includes noise or not includes when the statistical characteristics are out of a preset reference range, determining the converted design image includes noise (Tian et. al. page 15, where image noise is viewed as a multi-dimensional random process, the method for describing the noise is using the probability distribution function and the probability density distribution function). Noise can seriously affect the quality of image analysis, and can be defined theoretically as non-predictability and to know the random error. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to combine the teachings of Qiang et. al. and Tian et. al. to include to source of noise with the noise filtering method to have a cleaner result.
Regarding claim 7, Qiang et. al. discloses the noise filtering method of claim 1. However, Qiang et. al. fails to disclose wherein when the converted design image is determined as including noise, the SEM image corresponding to the converted design image is determined as the noise.
Tian et. al. teaches wherein when the converted design image is determined as including noise, the SEM image corresponding to the converted design image is determined as the noise (Tian et. al. page 16, Figure 5 where global noise affects the whole grey value and whole visual processing of the selected area). Understanding the source of the noise is important to the claimed invention to build an appropriate noise filtering method. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to combine the teachings of the Qiang et. al. and Tian et. al. so that the noise source is clearly defined.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Qiang et. al. (Chinese Patent CN 108171742 A) in view of Wallingford et. al. (United States Patent Publication US 2022/0044391 A1).
Regarding claim 8, Qiang et. al. discloses the noise filtering method of claim 1. However, Qiang et. al. fails to disclose wherein the conversion model uses a generative adversarial network (GAN) algorithm.
Wallingford et. al. teaches wherein the conversion model uses a generative adversarial network (GAN) algorithm (Wallingford et. al. [0074]-[0075] where GANS have been generalized to allow pixel-to-pixel mapping). GANs play a crucial role in image analysis by enabling the creating of high-quality synthetic images and enhancing existing images, especially from random noise. This is an important feature to the claimed invention. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Qiang et. al. and Wallingford et. al. to introduce GANs in the noise filtering method for SEM imaging.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESSICA YIFANG LIN whose telephone number is (571)272-6435. The examiner can normally be reached M-F 7:00am-6:15pm, with optional day off.
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/JESSICA YIFANG LIN/Examiner, Art Unit 2668 February 2, 2026
/VU LE/Supervisory Patent Examiner, Art Unit 2668