DETAILED ACTION
This action is in response to the remarks and amendments filed on June 23rd, 2026. Claims 1-3 and 5-9 are pending and have been examined.
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 .
Response to Arguments
Applicant's arguments filed June 23rd, 2026 have been fully considered but they are not persuasive.
On page 6 of “Remarks”, applicant alleges that “Gupta taught a different modality than in the method of claim 1. Gupta adapts wavelets in medical imaging like MRI/EGG, whereas amended claim 1 rests on a broader system application – handling multi-format semiconductor CDSEM inputs, applying the baseline wavelet denoising matrix, and routing the output to a metrology-focused image evaluation step.” Examiner respectfully disagrees. Gupta is cited to teach the wavelet denoising of images, where the denoising parameters are automatically selected (Gupta Abstract “Wavelet based denoising methods have been successful in reducing the speckle noise in past but they require a manual selection of few parameters for optimum denoising. We propose a computerized method to select these parameters by using an evolutionary technique called Differential Evolution (DE).”). Sugihara is then cited to teach that wavelet denoising can be applied to multi-format semiconductor CDSEM inputs. The combination of these references suggests that one of ordinary skill in the art could have applied the wavelet denoising method of Gupta to the multi-format CDSEM inputs of Sugihara. In response to applicant's argument that the Gupta fails to show applying a baseline wavelet denoising matrix and routing the output to a metrology-focused image evaluation step, it is noted that these features are not recited in the rejected claim. 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).
Furthermore, on page 6 of “Remarks”, applicant alleges that “Claim 1 explicitly includes a format conversion step in step 1 to handle diverse incoming image formats. As correctly admitted on page 5 of the Action, Gupta did not teach the format conversion of step 1: reading the CDSEM images of different formats and performing format conversion on the CDSEM images of different formats.”, “Gupta also did not disclose "selecting wavelet basis functions ... wherein the wavelet basis functions comprise Haar wavelet, db4 (Daubechies 4) wavelet, and coif3 (Coiflet 3) wavelet", as now recited in step 2 of amended claim 1.”, and “Sugihara fails to cure the deficiencies of Gupta. Sugihara focuses primarily on the hardware-software interface, automation, and recipes of the CDSEM. Sugihara generally addresses how to optimize electron beam parameters (e.g., probe current, acceleration voltage, scan speed, frame integration count) or recipe execution paths to acquire high-quality images while minimizing wafer damage (charging/shrinking). Sugihara treats the downstream image data processing as a system component. However, Applicant's amended claim 1 focuses on a transform-domain CDSEM digital image processing pipeline. The method of claim 1 assumes the raw CDSEM digital images have already been captured, and the claimed method establishes a multi-level discrete wavelet transform denoising routine to mathematically separate high-frequency electron noise from the underlying semiconductor geometry. Thus, Sugihara does not disclose step 1, step 2, to step 5 of claim 1. Oyeleke and Wong fail to make up for the deficiencies of Gupta and Sugihara.” Examiner respectfully disagrees. Gupta was not cited to teach the format conversion of step 1. Instead, Sugihara is cited to teach the format conversion of step 1, specifically reading CDSEM images of different formats and performing format conversion on the CDSEM images of different formats. Furthermore, Gupta was not cited to teach wavelet basis functions including the Haar wavelet, db4 wavelet, and coif3 wavelet. Instead, Wong is cited to teach these wavelets, as previously taught in now canceled dependent claim 4. Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
For at least the reasons stated above, the rejections are maintained.
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.
Claims 1 and 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over “COMPUTERIZED AUTOMATION OF WAVELET BASED DENOISING METHOD TO REDUCE SPECKLE NOISE IN OCT IMAGES” (herein after referred to by its primary author, Gupta) in view of US20210142457 (herein after referred to by its primary author, Sugihara) and “Exploring the potential of wavelets in the field of image processing” (herein after referred to by its primary author, Wong).
In regards to claim 1, Gupta teaches a method for improving quality of Gupta Section 3.1); step 2: setting wavelet parameters, comprising: selecting wavelet basis functions; setting a number N for wavelet decomposition levels ; and setting a threshold function, and setting thresholds corresponding to the number N of wavelet decomposition levels respectively (Gupta Section 3.2 “It can be inferred from the section 2 that threshold value, decomposition level and wavelet type are three parameters that play a significant role in wavelet based denoising. Hence, we considered these parameters as the elements of a parameter vector.”; Section 3.2 “Soft thresholding is then applied to detail coefficients using the threshold value from optimum vector which is given by 1st element of optimum vector.”); step 3: performing wavelet decomposition, threshold processing, and multidimensional wavelet reconstruction (Gupta Section 3.2), wherein the wavelet decomposition is performed to obtain scaling function coefficients of different levels and wavelet coefficients of different levels, wherein the threshold processing comprises performing threshold processing on the wavelet coefficients of different levels to obtain processed wavelet coefficients of each of the different levels (Gupta Section 3.2 “The acquired images, using the method mentioned in section 3.1, were de-noised using the proposed method. The optimum parameter is found out for each input image using the steps discussed in section 2.2. Decomposition level and wavelet type, which are 2nd and 3rd elements of optimum vector, serve as inputs to find the wavelet coefficients of input image. We have used six levels of decomposition and Daubechies wavelets [22] from order 1 to 10 as mother wavelets. Soft thresholding is then applied to detail coefficients using the threshold value from optimum vector which is given by 1st element of optimum vector.” Examiner note: This reference teaches performing wavelet decomposition which results in “approximation coefficients” and “detail coefficients” (as described in section 2) which are analogous to the scaling and wavelet coefficients of the current disclosure, respectively. Soft thresholding is then applied to the coefficients according to the calculated threshold.), and wherein the multidimensional wavelet reconstruction comprises performing multidimensional wavelet reconstruction by using the scaling function coefficients and the processed wavelet coefficients of each said level to obtain denoised images (Gupta Section 3.2 “Image is reconstructed after the thresholding with the decomposition level and wavelet type remaining the same as in image decomposition.”); and step 4: performing image quality evaluation on the denoised images (Gupta Figure 2; Section 4.1 “After the subjective comparison, a quantitative analysis using percentage improvement in SNRI was performed to compare the effect of denoising using different methods.”).
Gupta does not teach a method for improving quality of critical dimension scanning electron microscope (CDSEM) images, at least comprising: step 1: reading the CDSEM images, comprising: reading the CDSEM images of different formats and performing format conversion on the CDSEM images of different formats.; and wherein the wavelet basis functions comprise Haar wavelet, db4 (Daubechies 4) wavelet, and coif3 (Coiflet 3) wavelet.
However, Sugihara teaches a method for improving quality of critical dimension scanning electron microscope (CDSEM) images, at least comprising: step 1: reading the CDSEM images, comprising: reading the CDSEM images of different formats and performing format conversion on the CDSEM images of different formats (Sugihara Paragraph [0073] “When inspecting a detected inspection image, the contour line of each figure pattern is extracted (obtained). However, as described above, since the template and edge filter for use in obtaining the contour line (pattern edges) are conventionally fixed, and layout data is needed to extract the pattern edges, if a profile change occurs due to an image change and the like resulting from noise, charging, focus deviation, and so on, a problem arises in that an error (deviation) is generated at the edge position. Then, a wavelet transform which can reduce the influence of the error due to the image change, and the like is used according to the first embodiment.”; Paragraph [0076] “In the scanning step (S102), using the substrate 101 on which a figure pattern has been formed, the image acquisition mechanism 150 acquires an image of the substrate 101. Specifically, the image acquisition mechanism 150 irradiates the substrate 101, on which a figure pattern has been formed, with the multiple primary electron beams 20 to acquire a secondary electron image of the substrate 101 by detecting the multiple secondary electron beams 300 emitted from the substrate 101 due to the irradiation with the multiple primary electron beams 20. As described above, reflected electrons and secondary electrons may be projected on the multi-detector 222, or alternatively, after reflected electrons having been emitted along the way, only remaining secondary electrons (the multiple secondary electron beams 300) may be projected thereon.”; Paragraph [0080] “In the gradient calculation step (S104), the gradient calculation unit 56 (differential intensity calculation unit) calculates, for each pixel of the frame image 31, the gradient (differential intensity) of the gray scale value of the pixel concerned.” Examiner note: The first paragraphs shows that wavelet transform is used for the purpose of denoising, the second paragraph shows that the images used in this reference are scanning electron microscope images, and the final paragraph shows that format conversion is applied to the images and the gray scale value of the pixels is used).
Sugihara is considered to be analogous to the claimed invention because they are both in the same field of scanning electron microscope image denoising. Therefore, 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 system of Gupta to include the teachings of Sugihara, to provide the advantage of reducing error caused by noise and other uncontrollable factors (Sugihara Paragraph [0073] “… if a profile change occurs due to an image change and the like resulting from noise, charging, focus deviation, and so on, a problem arises in that an error (deviation) is generated at the edge position. Then, a wavelet transform which can reduce the influence of the error due to the image change, and the like is used according to the first embodiment.”)
Furthermore, Wong teaches wherein the wavelet basis functions comprise Haar wavelet, db4 (Daubechies 4) wavelet, and coif3 (Coiflet 3) wavelet. (Wong Section 1 “In 1909, Alfred Haar proposed the Haar wavelet transform. The Haar transform is the simplest form of Discrete Wavelet Transform (DWT) and intends to preserve the features of the original image while reducing the size of the image significantly”; Section 4.3 “The best results are given by using the db4, coif3 and bior2.8 wavelets.”).
Wong is considered to be analogous to the claimed invention because they are both in the same field of wavelet decomposition for image denoising. Therefore, 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 system of Gupta in view of Sugihara to include the teachings of Wong, to provide the advantage of selecting a mother/basis wavelet which is suitable for the corresponding threshold function (Wong Section 4.3.2 and 4.3.3)
In regards to claim 3, Gupta in view of Sugihara and Wong teaches the method for improving the quality of the CDSEM images according to claim 1, wherein in step 1, the performing the format conversion on the CDSEM images of different formats comprises converting color images into grayscale images and converting between different grayscale formats (Wong Section 5 “The images used in this research for wavelet transform are retrieved from MATLAB with a size of 512x512 pixels. In contrast to using the general RGB values of the images, this research has limited to using the 256 grey scales of them.”).
In regards to claim 5, Gupta in view of Sugihara and Wong teaches the method for improving the quality of the CDSEM images according to claim 1, wherein in step 2, the number N of wavelet decomposition levels is a positive integer less than or equal to 3 (Wong Section 4.2 “Decomposition: Choose a wavelet and decompose it to level N, where N=1...5.”).
In regards to claim 6, Gupta in view of Sugihara and Wong teaches the method for improving the quality of the CDSEM images according to claim 1, wherein in step 2, the threshold function is a soft threshold function or a hard threshold function (Gupta Section 3.2 “Soft thresholding is then applied to detail coefficients using the threshold value from optimum vector which is given by 1st element of optimum vector.”).
In regards to claim 7, Gupta in view of Sugihara and Wong teaches the method for improving the quality of the CDSEM images according to claim 1, wherein in step 3, a sub-method for obtaining the scaling function coefficients and wavelet coefficients of different levels comprises: performing N-level wavelet decomposition on the CDSEM images read in step 1 according to the selected wavelet basis functions, the number N of wavelet decomposition levels, the threshold function, and the thresholds to obtain the scaling function coefficients and wavelet coefficients of the different levels (Gupta Section 3.2 “The optimum parameter is found out for each input image using the steps discussed in section 2.2. Decomposition level and wavelet type, which are 2nd and 3rd elements of optimum vector, serve as inputs to find the wavelet coefficients of input image. We have used six levels of decomposition and Daubechies wavelets [22] from order 1 to 10 as mother wavelets. Soft thresholding is then applied to detail coefficients using the threshold value from optimum vector which is given by 1st element of optimum vector.”).
In regards to claim 8, Gupta in view of Sugihara and Wong teaches the method for improving the quality of the CDSEM images according to claim 1, wherein in step 3, a sub-method for performing the threshold processing comprises filtering out high-frequency noise from the CDSEM images according to the set threshold function and thresholds (Wong Section 4.2.2 “The optimal threshold is dependent on the noise’s variance. As explained above, the noise is added to the original image and the higher the noise variance, the higher the probability of adding a large noise term to the image. Therefore, the larger the noise variance, the larger the threshold coefficient has to be in order to remove the noise.” Examiner note: Thresholding is performed for the purpose of removing small coefficients which represent the noise in the image. Wong teaches that thresholding is performed, and although it does not explicitly state that it is for the purpose of removing noise, it can be seen from Wong that the thresholding is done for the purpose of removing noise).
In regards to claim 9, Gupta in view of Sugihara and Wong teaches the method for improving the quality of the CDSEM images according to claim 1, wherein in step 4, the image quality evaluation is performed on the denoised CDSEM images by adopting signal-to-noise-ratio (SNR) (Gupta Figure 2 Description “Comparison of SNR Improvements using different methods”) and peak signal-to-noise-ratio (PSNR) evaluation criteria (Wong Section 4.3.1 “The threshold is plotted against the PSNR in Figure 4.7. This gives the same result as above. Initially, the higher the threshold, the higher the PSNR, which means that the denoised image is better.”).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Gupta in view of Sugihara and Wong, and further in view of “On the Performance of Lossless Wavelet Compression Scheme on Digital Medical Images in JPEG, PNG, BMP and TIFF Formats” (herein after referred to by its primary author, Oyeleke).
In regards to claim 2, Gupta in view of Sugihara and Wong teaches the method for improving the quality of the CDSEM images according to claim 1, but fails to teach wherein in step 1, the CDSEM images of different formats comprise CDSEM images of JPEG, TIFF, PNG, and BMP formats.
However, Oyeleke teaches wherein in step 1, the CDSEM images of different formats comprise CDSEM images of JPEG, TIFF, PNG, and BMP formats (Oyeleke Section 3 “Digital images can exist in different formats, but more commonly used formats are the: joint photographic expert group (JPEG /JPG), portable network graphics (PNG), bitmap (BMP) and TIFF formats.”).
Oyeleke is considered to be analogous to the claimed invention because they are both in the same field of wavelet decomposition. Therefore, 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 system of Gupta in view of Sugihara and Wong to include the teachings of Oyeleke, to provide the advantage of choosing a correct image format for conversion using wavelet decomposition. (Oyeleke Table 1)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/CALEB L ESQUINO/Examiner, Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677
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