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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) filed on September 3, 2024, has been considered by the examiner.
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:
an ‘imaging system’ in Claims 1, 3, 22, 23, 26 and Claim 27.
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.
The Examiner has interpreted the ‘imaging system’ to be a system comprising a focused-ion beam (FIB) source, an atomic force microscope (AFM) or a scanning electron microscope (SEM) (see pg. 8, lines 10-15).
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.
Claims 24 and 25 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 Claims 24 and 25 are directed towards software per se and signals per se.
Under the broadest reasonable interpretation, this limitation includes products that do not have a physical or tangible form (see MPEP §2106.03, “Non-limiting examples of claims that are not directed to any of the statutory categories include: Products that do not have a physical or tangible form, such as information (often referred to as “data per se”) or a computer program per se (often referred to as “software per se”) when claimed as a product without any structural recitation... For example, the BRI of machine readable media can encompass non-statutory transitory forms of signal transmission, such as a propagating electrical or electromagnetic signal per se. See In re Nuijten, 500 F.3d 1346, 84 USPQ2d 1495 (Fed. Cir. 2007). When the BRI encompasses transitory forms of signal transmission, a rejection under 35 U.S.C. 101 as failing to claim statutory subject matter would be appropriate. Thus, a claim to a computer readable medium that can be a compact disc or a carrier wave covers a non-statutory embodiment and therefore should be rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See, e.g., Mentor Graphics v. EVE-USA, Inc., 851 F.3d at 1294-95, 112 USPQ2d at 1134 (claims to a "machine-readable medium" were non-statutory, because their scope encompassed both statutory random-access memory and non-statutory carrier waves)”).
The examiner suggests rewriting Claim 24 to read “A non-transitory computer readable medium, storing a computer program, which when the program is executed by a computer, cause the computer to carry out the method of claim 1” to ensure that Claim 24 falls under the four statutory categories of invention. The examiner also suggests rewriting Claim 25 to read “A non-transitory computer-readable medium, on which a computer program executable by a computing device is stored, the computer program comprising code for executing the method of claim 1” to ensure that Claim 25 falls under the four statutory categories of invention.
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) 1-6, 9-13, 15-17, 20-22, and 24-27 rejected under 35 U.S.C. 103 as being unpatentable over Liang et al. (US Pub No 2024033111), hereinafter Liang, in view of Fukuda et al. (US Pub No 20220414833), hereinafter Fukuda, and further in view of Taguchi et al. (WO Pub No 2024014020), hereinafter Taguchi.
As to Claim 1, Liang teaches a method for image enhancement comprising (see abstract, “An improved systems and methods for correcting distortion of an inspection image are disclosed”):
acquiring an imaging dataset of an object comprising integrated circuit patterns using an imaging system (see paragraph [0061], “Inspection images such as SEM images can be used to identify or classify a defect(s) of the manufactured ICs”, where the SEM (scanning electron microscopy) is interpreted as the imaging system, and IC stands for integrated circuit);
obtaining a reference dataset corresponding to the acquired imaging dataset (see paragraph [0061], “For example, a generated image capturing a test device region of a wafer may be compared with a reference image capturing the same test device region. The reference image may be predetermined (e.g., by simulation) and include no known defect”); and
generating an enhanced imaging dataset (see paragraph [0013], “correcting a distortion of the inspection image based on the selected alignment model”)
Liang fails to teach that the enhanced dataset is obtained by filtering the dataset with one or more learned filters. However, in ana analogous art, Fukuda teaches a method for image enhancement comprising (see paragraphs [0007], “Therefore, the present invention provides an inspection apparatus and a measurement apparatus that are capable of improving the accuracy of evaluation and measurement by a distortion correction technique”):
acquiring an imaging dataset of an object comprising circuit patterns using an imaging system (see paragraph [0027], “a semiconductor circuit captured by a scanning electron microscope (SEM) is exemplified as an inspection target sample”, where the SEM is interpreted as the imaging system);
obtaining a reference dataset corresponding to the acquired imaging dataset (see paragraph [0029], “As illustrated in FIG. 1 , a reference image 101 is an image that coincides with an inspection image 102 except for noise when there is no defect or image distortion”); and
generating an enhanced imaging dataset (see paragraph [0035], “With the estimated distortion amount 304 output by the image distortion estimation unit 303 and the inspection image 102 as input, the image distortion correction unit 305 corrects the inspection image 102 by the estimated distortion amount 304 and outputs the corrected inspection image 306”),
by filtering the acquired imaging dataset (see paragraph [0039], “For example, a smoothing filter such as a Gaussian filter and a moving average filter may be applied to the inspection image as pre-processing, and a smoothing filter, down sample, and upsample may be used together for pre-processing and post-processing”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute the alignment model taught by Liang with the image distortion correction technique taught by Fukuda. The motivation for doing would be to use the filter parameters to reduce noise while still maintaining defects in images, so that they can be detected. Fukuda teaches in paragraph [0056], “By adjusting the filter size and parameters of the smoothing filter that smooths the estimated distortion amount and suppressing the difference in the predicted distortion amount in the adjacent region as a constraint condition on the evaluation criteria in step S603, it is possible to reduce the variation of the distortion amount in the region near the estimated distortion amount and suppress the high-frequency correction amount that causes the defect to be undetected.” Thus, it would have been obvious to combine the filters taught by Fukuda with the integrated circuit inspection method taught by Liang in order to prevent overcorrection.
Both Liang and Fukuda fails to teach one or more learned filters, wherein the one or more learned filters are obtained by solving an optimization problem comprising the deviation of the enhanced imaging dataset from the reference dataset.
However, in an analogous art, Taguchi teaches a method of image inspection which comprises obtaining an inspection image of an object (see paragraph [0010], “an imaging means for imaging ultraviolet light reflected from the packaging material”),
and generating an enhanced imaging dataset (see paragraph [0024], “Therefore, the reconstructed image data generated when the original image data relating to the defective package is inputted to the identification means is substantially identical to the original image data from which the noise portion...has been removed”, where the ‘reconfiguration image data’ is interpreted as the enhanced imaging dataset),
by filtering the acquired imaging dataset with one or more learned filters and see paragraph [0059], “The AI model 100 in the embodiment is a generation model constructed by deep learning (deep learning) a neural network 90 using only image data” and see paragraph [0061], “ In each convolutional layer (93), the result of convolutional calculation using a plurality of filters (kernels) (94) is outputted as the input data of the next layer.”),
wherein the one or more learned filters are obtained by solving an optimization problem comprising the deviation of the enhanced imaging dataset from the reference dataset (see paragraph [0073], “In the network update process in step S104, a known learning algorithm such as error reverse propagation is used to update the weights (parameters) of the filters (94, 96) in the neural network (90) to more appropriate ones so that the loss function representing the difference between the learning data and the reconstructed image data is reduced as much as possible”, where the examiner has interpreted ‘reducing a loss function’ to be solving an optimization problem).
Thus, it would have been obvious to one of ordinary skill in art before the effective filing date of the claimed invention to combine the learned filters taught by Taguchi with the image enhancement method taught by Liang in view of Fukuda. The motivation for doing so would be to obtain more accurate enhanced images. Taguchi teaches in paragraph [0074], “By repeating the processing of the steps (S102 to 104) many times, the error between the learning data and the reconstructed image data is minimized in the neural network (90), and more accurate reconstructed image data is outputted”). Thus, it would have been obvious to combine the learned filters taught by Taguchi with the teachings of Fukuda in order to obtain the invention as claimed in Claim 1.
As to Claim 2, Liang in view of Fukuda and Taguchi teaches comprising detecting defects in the acquired imaging dataset by comparing the enhanced imaging dataset to the reference dataset (see Fukuda, paragraph [0036], “By comparing the corrected inspection image 306 with the reference image 101, the inspection unit 307 inspects an abnormal portion such as a defect”).
As to Claim 3, Liang teaches a method for image enhancement comprising (see abstract):
acquiring an imaging dataset of an object comprising integrated circuit patterns using an imaging system dataset (see paragraph [0061]);
obtaining a reference dataset corresponding to the acquired imaging dataset (see paragraph [0029]);
and generating an enhanced imaging dataset (see paragraph [0013]).
Liang fails to teach generating the enhanced imaging dataset by filtering the reference dataset.
However, Fukuda teaches an enhanced imaging dataset can be obtained by filtering the reference dataset (see paragraph [0039], “For example, a smoothing filter such as a Gaussian filter and a moving average filter may be applied to the inspection image as pre-processing, and a smoothing filter”, and see paragraph [0043] , “In step S504, the image distortion correction unit 305 constituting the inspection apparatus 301 corrects the inspection image 102 or the reference image 101 using the estimated distortion amount output by the image distortion estimation unit 303, and outputs the corrected inspection image 306 or the corrected reference image”). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute the alignment model taught by Liang with the image distortion correction technique taught by Fukuda. The motivation for doing would be to use the filter parameters to reduce noise while still maintaining defects in images, so that they can be detected (see paragraph [0056]).
Fukuda fails to teach one or more learned filters, wherein the one or more learned filters are obtained by solving an optimization problem comprising the deviation of the enhanced imaging dataset from the reference dataset.
However, in an analogous art, Taguchi teaches generating an enhanced imaging dataset (see paragraph [0024]),
by filtering an imaging dataset with one or more learned filters (see paragraph [0061])
wherein the one or more learned filters are obtained by solving an optimization problem comprising the deviation of the enhanced imaging dataset from the reference dataset (see paragraph [0073]).
Thus, it would have been obvious to one of ordinary skill in art before the effective filing date of the claimed invention to combine the learned filters taught by Taguchi with the image enhancement method taught by Fukuda. The motivation for doing so would be to obtain more accurate enhanced images (see Taguchi, paragraph [0074]). Thus, it would have been obvious to combine the learned filters taught by Taguchi with the teachings of Liang and Fukuda in order to obtain the invention as claimed in Claim 3.
As to Claim 4, Liang in Fukuda and Taguchi teaches detecting defects in the acquired imaging dataset by comparing the enhanced imaging dataset to the imaging dataset (see Fukuda, paragraph [0044], “In step S505, the inspection unit 307 constituting the inspection apparatus 301 compares the corrected inspection image 306 with the reference image 101 or the inspection image 102 with the corrected reference image (not illustrated) to calculate the presence or absence and the coordinates of an abnormal portion such as a defect”).
As to Claim 5, Liang fails to explicitly teach a linear filter. However, Fukuda teaches applying a linear filter to an inspection image to create an enhanced image (see Fukuda, paragraph [0039], “For example, a smoothing filter such as a Gaussian filter and a moving average filter may be applied to the inspection image as pre-processing, and a smoothing filter, downsample, and upsample may be used together for pre-processing and post-processing”, where it is well known to those of ordinary skill in the art that a Gaussian filter, smoothing filters, and lowpass and high pass filters are all linear filters). It would have been obvious to combine the filters taught by Fukuda with the teachings of Liang in order to remove noise in inspection data while still preserving potential defects (see Fukuda, paragraph [0056]).
Fukuda fails to explicitly teach the filters are learned. Instead, Fukuda teaches that filter parameters are defined (see paragraph [0038], “As in a correction condition 402, in addition to the downsample magnification of the pre-processing and the upsample magnification for restoring the estimated distortion amount to the original image size, the filter size and the standard deviation of the Gaussian filter for smoothing the estimated distortion amount are defined,” ). wherein at least one of the one or more learned filters is a linear filter.
However, Taguchi teaches that filter parameters may be learned (see paragraph [0073], “In the network update process in step S104, a known learning algorithm such as error reverse propagation is used to update the weights (parameters) of the filters (94, 96) in the neural network (90) to more appropriate ones so that the loss function representing the difference between the learning data and the reconstructed image data is reduced as much as possible”). Thus, it would have been obvious to one of ordinary skill in art before the effective filing date of the claimed invention to combine the learned filters taught by Taguchi with the linear filters taught by Liang in view of Fukuda. The motivation for doing so would be to obtain more appropriate filter parameters (see paragraph [0073] of Taguchi). Thus, it would have been obvious to combine the learned filter parameters taught by Taguchi with the linear filter taught by Liang in view of Fukuda in order to obtain the invention as claimed in Claim 5.
As to Claim 6, Liang fails to explicitly teach a linear translation invariant filter. However, Fukuda teaches applying a linear translation invariant filter to an inspection image to create an enhanced image (see Fukuda, paragraph [0039], “For example, a smoothing filter such as a Gaussian filter and a moving average filter may be applied to the inspection image as pre-processing, and a smoothing filter, downsample, and upsample may be used together for pre-processing and post-processing”, where it is well known to those of ordinary skill in the art that a Gaussian filter, smoothing filters, and lowpass and highpass filters are all linear translation invariant filters). It would have been obvious to combine the filters taught by Fukuda with the teachings of Liang in order to remove noise in inspection data while still preserving potential defects (see Fukuda, paragraph [0056]).
Fukuda fails to explicitly teach the filters are learned. However, Taguchi teaches that filter parameters may be learned (see paragraph [0073]). Thus, it would have been obvious to one of ordinary skill in art before the effective filing date of the claimed invention to combine the learned filter parameters taught by Taguchi with the translation invariant filters taught by Fukuda. The motivation for doing so would be to obtain more appropriate filter parameters (see paragraph [0073] of Taguchi). Thus, it would have been obvious to combine the learned filter parameters taught by Taguchi with the linear filter taught by Fukuda in order to obtain the invention as claimed in Claim 6.
As to Claim 9, Liang in view of Fukuda teaches filters may be applied (see Fukuda paragraph [0039]), but fails to explicitly teach that the filters are applied through convolution. However, Taguchi teaches the at least one learned filter is applied by use of convolution (see Taguchi, paragraph [0061], “In each convolutional layer (93), the result of convolutional calculation using a plurality of filters (kernels) (94) is outputted as the input data of the next layer.”). It would have been obvious to one of ordinary skill in art before the effective filing date of the claimed invention to combine the learned filter parameters and convolution taught by Taguchi with the filters taught by Fukuda. The motivation for doing so would be to obtain more appropriate filter parameters (see paragraph [0073] of Taguchi).
As to Claim 10, Liang in view of Fukuda teaches a filter may be applied (see Fukuda paragraph [0039]), but fails to teach a learned filter. However, Taguchi teaches a single learned filter is applied (see Taguchi, paragraph [0071], “In each convolutional layer (93), the result of convolutional calculation using a plurality of filters (kernels) (94) is outputted as the input data of the next layer.”). It would have been obvious to one of ordinary skill in art before the effective filing date of the claimed invention to combine the learned filter parameters and convolution taught by Taguchi with the translation invariant filters taught by Fukuda. The motivation for doing so would be to obtain more appropriate filter parameters (see paragraph [0073] of Taguchi).
As to Claim 11, Liang fails to explicitly teach a filter with one or more assumptions are imposed on it. However, Fukuda teaches wherein one or more assumptions are imposed on one or more of filters (see Fukuda, paragraph [0038], “As in a correction condition 402, in addition to the downsample magnification of the pre-processing and the upsample magnification for restoring the estimated distortion amount to the original image size, the filter size and the standard deviation of the Gaussian filter for smoothing the estimated distortion amount are defined”, where the ‘assumption’ is the restriction of size and deviation on the filter) ,
and wherein the reference dataset is identical to the imaging dataset (see paragraph [0029], “illustrated in FIG. 1 , a reference image 101 is an image that coincides with an inspection image”). It would have been obvious to combine the filters taught by Fukuda with the teachings of Liang in order to remove noise in inspection data while still preserving potential defects (see Fukuda, paragraph [0056]).
Fukuda fails to explicitly teach the filters are learned through solving an optimization problem. However, Taguchi teaches that filters are learned (see paragraph [0073]). Thus, it would have been obvious to one of ordinary skill in art before the effective filing date of the claimed invention to combine the learned filter parameters taught by Taguchi with the translation invariant filters taught by Liang in view of Fukuda in order to obtain more appropriate filter parameters (see paragraph [0073] of Taguchi). Thus, it would have been obvious to combine the learned filter parameters taught by Taguchi with the linear filter taught by Liang in view of Fukuda in order to obtain the invention as claimed in Claim 11.
As to Claim 12, Liang in view of Fukuda and Taguchi teaches the one or more learned filters are obtained by training a machine learning model (see Taguchi, paragraph [0058], “ A learning part 25 uses learning data to make a deep neural network”, and see paragraph [0073], “In the network update process in step S104, a known learning algorithm such as error reverse propagation is used to update the weights (parameters) of the filters (94, 96) in the neural network (90) to more appropriate ones so that the loss function representing the difference between the learning data and the reconstructed image data is reduced as much as possible,”). Thus, it would have been obvious to one of ordinary skill in art before the effective filing date of the claimed invention to combine the machine learning model taught by Taguchi with the filters taught by Liang in view of Fukuda in order to obtain more appropriate filter parameters (see paragraph [0073] of Taguchi).
As to Claim 13, Fukuda in view of Taguchi fails to teach that the machine learning model operates on patches.
However, Liang teaches a model for correcting image distortion that operates on patches (see abstract, “evaluating, by a machine learning model, alignments between each patch of the plurality of patches and a corresponding patch of the reference image”).
Thus, it would have been obvious to combine the patches taught by Liang with the teachings of Fukuda and Taguchi. The motivation for doing so would be to use further image patches to improve defect detection. Liang teaches in paragraph [0039], “To improve defect detection performance, obtaining an accurate SEM image without distortion nor misalignment is desired. While various distortion correction techniques for SEM images have been introduced, many of them rely on local alignments of smaller patches of the SEM image.” Thus, it would have been obvious to combine the patches taught by Liang of Liang with the teachings of Fukuda and Taguchi in order to obtain the invention as claimed in Claim 13.
As to Claim 15, Liang in view of Fukuda and Taguchi teaches wherein the machine learning model comprises a neural network (see Taguchi, paragraph [0023], “The ‘neural network’ includes, for example, a convolutional neural network having a plurality of convolutional layers”),
and uses the weights and/or activation functions of one or more layers of the neural network as one or more learned filters (see Taguchi, paragraph [0073], “In the network update process in step S104, a known learning algorithm such as error reverse propagation is used to update the weights (parameters) of the filters (94, 96) in the neural network (90) to more appropriate ones so that the loss function representing the difference between the learning data and the reconstructed image data is reduced as much as possible.”). It would have been obvious to one of ordinary skill in art before the effective filing date of the claimed invention to combine the neural network taught by Taguchi with the filters taught by Liang in view of Fukuda in order to obtain more appropriate filter parameters (see paragraph [0073] of Taguchi).
As to Claim 16, Fukuda in view of Taguchi teaches generating enhanced imaging datasets by applying the one or more learned filters to imaging datasets. However, Fukuda in view of Taguchi fails to explicitly to teach the optimization problem comprises the deviation of further enhanced imaging datasets from corresponding reference datasets, and wherein the further enhanced imaging datasets are generated by applying the one or more learned filters to further imaging datasets.
However, Liang teaches a model for image enhancement comprising solving an optimization problem comprising the deviation of two datasets (see paragraph [0098], “According to some embodiments, an alignment model that can minimize the L0 norm that counts the total number of a nonzero distance between local alignment results and the alignment model can be determined as an alignment model for a corresponding inspection image”).
wherein the optimization problem further comprises the deviation of further enhanced imaging datasets from corresponding reference datasets (see paragraph [0010], “the method comprises: acquiring an inspection image; determining local alignment results for a plurality of patches of the inspection image based on a reference image corresponding to the inspection image; estimating a first alignment model based on a first subset of the local alignment results and estimating a second alignment model based on a second subset of the local alignment results”, wherein the second subset of the plurality of patches is the further enhanced imaging data).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the further datasets taught by Liang with the reference image enhancement taught by Fukuda in view of Taguchi. The motivation for doing so would be to use further image patches to improve defect detection (see Liang, paragraph [0039]). Thus, it would have been obvious to combine the teachings of Liang with the teachings of Fukuda and Taguchi in order to obtain the invention as claimed in Claim 16.
As to Claim 17, Fukuda in view of Taguchi teaches generating enhanced reference datasets (see Fukuda, paragraph [0043], “In step S504, the image distortion correction unit 305 constituting the inspection apparatus 301 corrects the inspection image 102 or the reference image 101 using the estimated distortion amount output by the image distortion estimation unit 303, and outputs the corrected inspection image 306 or the corrected reference image”)
by applying the one or more filters to further reference datasets (see paragraph [0039], “ For example, a smoothing filter such as a Gaussian filter and a moving average filter may be applied”)
Fukuda fails to teach the filters are learned through an optimization problem. However, Taguchi teaches generating an enhanced imaging dataset by filtering the acquired imaging dataset with one or more learned filters and see paragraphs [0059]-[0061]),
wherein the one or more learned filters are obtained by solving an optimization problem comprising the deviation of the enhanced imaging dataset from the reference dataset (see paragraph [0073], “In the network update process in step S104, a known learning algorithm such as error reverse propagation is used to update the weights (parameters) of the filters (94, 96) in the neural network (90) to more appropriate ones so that the loss function representing the difference between the learning data and the reconstructed image data is reduced as much as possible”).
Thus, it would have been obvious to one of ordinary skill in art before the effective filing date of the claimed invention to combine the learned filters taught by Taguchi with the image enhancement method taught by Fukuda. The motivation for doing so would be to obtain more accurate enhanced image (see Taguchi, paragraph [0074]).
Both Fukuda and Taguchi fails to teach further filtered reference datasets are generated by applying the one or more learned filters to further reference datasets.
However, in an analogous art Liang teaches a model for image enhancement comprising solving an optimization problem comprising the deviation of the enhanced imaging dataset from the reference dataset (see paragraph [0098], “According so some embodiments, an alignment model that can minimize the L0 norm that counts the total number of a nonzero distance between local alignment results and the alignment model can be determined as an alignment model for a corresponding inspection image”).
wherein the optimization problem can further comprises the deviation of further enhanced imaging datasets from corresponding reference datasets (see paragraph [0010], “The method comprises: acquiring an inspection image; determining local alignment results for a plurality of patches of the inspection image based on a reference image corresponding to the inspection image; estimating a first alignment model based on a first subset of the local alignment results and estimating a second alignment model based on a second subset of the local alignment results”, wherein the second subset of the plurality of patches is the further enhanced imaging data).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the further datasets taught by Liang with the reference image enhancement taught by Fukuda in view of Taguchi. The motivation for doing so would be to use further image patches to improve defect detection (see Liang, paragraph [0039]). Thus, it would have been obvious to combine the teachings of Liang with the teachings of Fukuda and Taguchi in order to obtain the invention as claimed in Claim 17.
As to Claim 20, Liang in view of Fukuda teaches the one or more filters reduce deviations of the imaging dataset from the reference dataset caused by imaging artifacts due to systematic deviations caused by the imaging system (see Fukuda, paragraph [0031], “The inspection image 102 of FIG. 1 illustrates an example of radial distortion as image distortion, but the present invention is not limited to this”, where the radial distortion is due to systematic deviations caused by the imaging system, and see corresponding Fig. 1, and see Fukuda, paragraph [0038], “in a correction condition 402, in addition to the downsample magnification of the pre-processing and the upsample magnification for restoring the estimated distortion amount to the original image size, the filter size and the standard deviation of the Gaussian filter for smoothing the estimated distortion amount are defined”).
Fukuda fails to explicitly teach the filters are learned. However, Taguchi teaches that filter parameters may be learned (see paragraph [0073]). Thus, it would have been obvious to one of ordinary skill in art before the effective filing date of the claimed invention to combine the learned filter parameters taught by Taguchi with the filters taught by Liang in view of Fukuda. The motivation for doing so would be to obtain more appropriate filter parameters (see paragraph [0073] of Taguchi). Thus, it would have been obvious to combine the learned filter parameters taught by Taguchi with the filters taught by Fukuda in order to obtain the invention as claimed in Claim 20.
As to Claim 21, Liang in view of Fukuda and Taguchi teaches the systematic deviations comprise at least one of shifts, defocus, aberrations, time-delayed integration blur, wave front errors, or thermal drift (see Fukuda, Fig. 1, inspection image 102, a positional shift is caused by image distortion, and see paragraph [0031], “In that case, the inspection apparatus of the present embodiment estimates positional displacement due to image distortion between the reference image 101 and the inspection image 102. The inspection image 102 of FIG. 1 illustrates an example of radial distortion as image distortion”.
As to Claim 22, Liang, in view of Fukuda and Taguchi a method for image enhancement comprising:
acquiring an imaging dataset of an object comprising integrated circuit patterns using an imaging system (see Liang, paragraph [0027], “(see Liang, paragraph [0061], “Inspection images such as SEM images can be used to identify or classify a defect(s) of the manufactured ICs”, where the SEM (scanning electron microscopy) is interpreted as the imaging system, and IC stands for integrated circuit);
one or more iterations comprising the following steps: obtaining a reference dataset corresponding to the imaging dataset (see Liang, paragraph [0061], “For example, a generated image capturing a test device region of a wafer may be compared with a reference image capturing the same test device region. The reference image may be predetermined (e.g., by simulation) and include no known defect”);
obtaining a subset of the imaging dataset and a corresponding subset of the corresponding reference dataset (see paragraph [066 -0067], “According to some embodiments of the present disclosure, image aligner 430 can segment an inspection image into a plurality of smaller patches… some embodiments, image aligner 430 can determine, for each of a plurality of patches …a corresponding portion or patch in a reference image”, where each patch is a subset of each imaging dataset),
enhancing subsets of data (see paragraph [0012], “aligning a plurality of patches of the inspection image based on a reference image corresponding to the inspection image”)
and combining the enhanced subsets of the imaging dataset to obtain an enhanced imaging dataset. (see paragraph [0069], “For example, patches 511_1 to 511_n can be put together based on the alignment”).
Liang fails to teach applying learned filters to create enhanced subsets of data. Fukuda teaches applying filters to inspection datasets and reference datasets (see paragraph [0039] and [0043]) but fails to teach that the filters are learned. However, Taguchi teaches in at least one iteration one or more learned filters obtained in any of the previous steps are used as initial value for the one or more learned filters to be optimized in the optimization problem (see Taguchi, paragraph [0073], “In the network update process in step S104, a known learning algorithm such as error reverse propagation is used to update the weights (parameters) of the filters (94, 96) in the neural network (90) to more appropriate ones so that the loss function representing the difference between the learning data and the reconstructed image data is reduced as much as possible”, and see Taguchi paragraph [0072], “When the error is not sufficiently small in a step S103, network update processing (learning of a neural network 90) is performed in a step S104, and then the process is returned again to a step S102 to repeat the series of processing”, where it is understood that the previous filter is used in the repeated step).
Thus, it would have been obvious to combine the many iterations of learned filters taught by Taguchi with the teachings of Liang and Fukuda. The motivation for doing so would be to obtain more accurate enhanced images. Taguchi teaches in paragraph [0074], “By repeating the processing of the steps (S102 to 104) many times, the error between the learning data and the reconstructed image data is minimized in the neural network (90), and more accurate reconstructed image data is outputted”). Thus, it would have been obvious to combine the learned filters taught by Taguchi with the teachings of Fukuda in order to obtain the invention as claimed in Claim 22.
As to Claim 24, Liang in view of Fukuda and Taguchi teaches a computer program comprising instructions which, when the program is executed by a computer (see Liang, paragraph [0104], “A non-transitory computer readable medium may be provided that stores instructions for a processor of a controller”), cause the computer to carry out the method of claim 1.
As to Claim 25, Liang in view of Fukuda and Taguchi teaches a computer-readable medium (see Liang, paragraph [0104], “A non-transitory computer readable medium may be provided that stores instructions for a processor of a controller”), on which a computer program executable by a computing device is stored, the computer program comprising code for executing the method of claim 1.
As to Claim 26, Liang in view of Fukuda and Taguchi teaches a system (see Liang, abstract, “An improved systems and methods for correcting distortion of an inspection image are disclosed”) for image enhancement comprising
an imaging system configured to provide an imaging dataset of an object comprising integrated circuit patterns (see Liang, paragraph [0038], “Inspection can be carried out using a scanning charged-particle microscope (SCPM). For example, an SCPM may be a scanning electron microscope (SEM)” and see paragraph [0061], “Inspection images such as SEM images can be used to identify or classify a defect(s) of the manufactured ICs”, where the SEM (scanning electron microscopy) is interpreted as the imaging system);
one or more processing devices and one or more machine-readable hardware storage devices comprising instructions that are executable by the one or more processing devices (see Liang, paragraph [0006], “The apparatus comprises a memory storing a set of instructions; and at least one processor configured to execute the set of instructions to cause the apparatus to perform”) for executing the method of claim 1.
As to Claim 27, Liang in view of Fukuda and Taguchi teaches a system for defect detection (see Liang, abstract, “An improved systems and methods for correcting distortion of an inspection image are disclosed”, and see paragraph [0059], “The reconstructed images can be used to reveal various features of the internal or external structures of wafer 230, and thereby can be used to reveal any defects that may exist in the wafer”) comprising
an imaging system configured to provide an imaging dataset of an object comprising integrated circuit patterns (see Liang, paragraph [0038], “Inspection can be carried out using a scanning charged-particle microscope (SCPM). For example, an SCPM may be a scanning electron microscope (SEM)” and see paragraph [0061], “Inspection images such as SEM images can be used to identify or classify a defect(s) of the manufactured ICs”, where the SEM (scanning electron microscopy) is interpreted as the imaging system);
one or more processing devices; and- one or more machine-readable hardware storage devices comprising instructions that are executable by the one or more processing devices (see Liang, paragraph [0006], “The apparatus comprises a memory storing a set of instructions; and at least one processor configured to execute the set of instructions to cause the apparatus to perform”) for executing the method of claim 2.
Claims 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Liang et al. (US Pub No 2024033111), hereinafter Liang, in view of Fukuda et al. (US Pub No 20220414833), hereinafter Fukuda, further in view of Taguchi et al. (WO Pub No 2024014020), hereinafter Taguchi, and further in view of Ferro (US Pub No 20240070968), hereinafter Ferro.
As to Claim 7, Liang in view of Fukuda teaches applying a linear translation invariant filter to an inspection image to create an enhanced image (see Fukuda, paragraph [0039]). Taguchi teaches that filter parameters may be learned (see paragraph [0073]).
Liang, Fukuda, and Taguchi fail to explicitly teach a finite impulse response filter. However, in an analogous art, Ferro teaches an inspection method (see paragraph [0017], “VCT scans may be performed, e.g., on a composite aircraft part under inspection”),
which comprises applying a fine impulse response filter to image data (see paragraph [0046], “Moreover, in particular embodiments, as shown in FIG. 4E, the directional filter 408 may be a low pass filter 410, such as a Gaussian filter, which generally refers to a filter whose finite impulse response approximates a Gaussian function”).
Thus, it would have been obvious to one of ordinary skill in art before the effective filing date of the claimed invention to substitute the finite impulse response filter taught by Ferro with the learned filters taught by Fukuda in view of Taguchi. The motivation for doing so would be to better remove artifacts found in inspection data. Ferro teaches in paragraph [0045-0046], “In one example, as shown in FIG. 4D, an example representation of CT nonuniformity due to scatter 405 and/or other artifacts in the component part 400, in which a background model as described herein can be computed and then removed from the acquired volume for enhanced detection of the ply-level information. Referring back to FIG. 2 , the method 200 further includes at (222) applying a directional filter 408,”. Thus, it would have been obvious to combine the finite impulse response filter taught by Ferro with the learned filter parameters taught by Fukuda in view of Taguchi in order to obtain the invention as claimed in Claim 7.
As to Claim 8, Liang in view of Fukuda teaches applying a linear translation invariant filter to an inspection image to create an enhanced image (see Fukuda, paragraph [0039]). Taguchi teaches that filter parameters may be learned (see paragraph [0073]).
Liang, Fukuda, and Taguchi fail to explicitly teach a linear, translation invariant finite impulse response filter. However, in an analogous art, Ferro teaches an inspection method (see paragraph [0017], “VCT scans may be performed, e.g., on a composite aircraft part under inspection”),
which comprises applying a fine impulse response filter to image data (see paragraph [0046], “Moreover, in particular embodiments, as shown in FIG. 4E, the directional filter 408 may be a low pass filter 410, such as a Gaussian filter, which generally refers to a filter whose finite impulse response approximates a Gaussian function”, where it is well known to those of ordinary skill in the art that a Gaussian filter is a linear translation invariant filter).
Thus, it would have been obvious to one of ordinary skill in art before the effective filing date of the claimed invention to substitute the finite impulse response filter taught by Ferro with the learned filters taught by Liang, Fukuda and Taguchi. The motivation for doing so would be to better remove artifacts found in inspection data (see Ferro, paragraph [0045-0046]). Thus, it would have been obvious to combine the linear, translation invariant and finite impulse response filter taught by Ferro with the learned filter parameters taught by Fukuda in view of Taguchi in order to obtain the invention as claimed in Claim 8.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over unpatentable over Liang et al. (US Pub No 2024033111), hereinafter Liang, in view of Fukuda et al. (US Pub No 20220414833), hereinafter Fukuda, further in view of Taguchi et al. (WO Pub No 2024014020), hereinafter Taguchi, and further in view of Kobori et al. (US Pub No 20230316798 ), hereinafter Kobori.
As to Claim 14, Liang in view of Fukuda and Taguchi teaches the reference dataset is identical to the imaging dataset, but fails to teach that the machine learning model (see Liang, [0061], “For example, a generated image capturing a test device region of a wafer may be compared with a reference image capturing the same test device region”),
and that the model operates on patches (see abstract, “evaluating, by a machine learning model, alignments between each patch of the plurality of patches and a corresponding patch of the reference image”),
but fails to teach that the model learns a lower dimensional subspace of the patches and wherein the one or more learned filters represent the projection operation into the subspace and back to a patch space.
However, in the analogous art of image analysis, Kobori teaches (see paragraph [0024], “In the feature extraction unit 130, each of a plurality of patches 14 clipped from the target human image 10 is processed to be flattened, i.e., converted into one-dimensional sequence data by a linear embedding function 134. Further, linear projection using a learned filter is performed on the one-dimensional sequence data converted from the plurality of patches 14 by the linear embedding function 134”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the projection taught by Liang with the inspection method and filters taught by Liang in view of Fukuda and Taguchi. The motivation for doing so would be to convert data dimensions into sizes suitable for processing. Kobori teaches in paragraph [0024], “The input of ViT needs to be one-dimensional sequence data. For this reason, the target human image 10 itself…is processed to be flattened, i.e., converted into one-dimensional sequence data by a linear embedding function 134.” Thus, it would have been obvious to combine the projection and lower dimensional subspace taught by Kobori with the teachings of Liang, Fukuda, and Taguchi, in order to obtain the invention as claimed in Claim 14.
Claims 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Liang et al. (US Pub No 2024033111), hereinafter Liang, in view of Fukuda et al. (US Pub No 20220414833), hereinafter Fukuda, further in view of Taguchi et al. (WO Pub No 2024014020), hereinafter Taguchi, and further in view of Sangappa et al. (WO Pub No 2023034548), hereinafter Sangappa.
As to Claim 18, Fukuda in view of Taguchi fails to teach that the deviation in the optimization problem is measured by the Huber loss function.
However, in the analogous field of image analysis, Sangappa teaches a machine learning model that determines a difference between two images (see paragraph [0003], “The Engine determines a difference between the first pose of the reference image and the variation of the first pose of the source image.”
that uses Huber loss function can be used to measure deviation in an optimization problem (see paragraph [0058], “For a predicted segmented mask image output during the training phase resulting from an input training reference image, the feedback loss function provides an indication of a measure of a classification error”, where the error is the deviation, and see paragraph [0067], “It is understood that various embodiments of the Engine described herein may use any suitable machine learning training techniques to train the machine learning network 130 for each sensor, including, but not limited to a neural net based algorithm, such as Artificial Neural Network, Deep Learning; a robust linear regression algorithm, such as Random Sample Consensus, Huber Regression”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Huber loss function taught by Sangappa with the inspection method taught by Fukuda in view of Taguchi. The motivation for doing so would be to use the Huber loss function to train the neural network to improve the detection of differences between images (see paragraph [0002], “Various embodiments of a Pose Correction Engine (“Engine”) provide for significant improvements and advantages over conventional systems by providing the preprocessing of user source images for use in determining whether objects portrayed in user source images are fraudulent or counterfeit”, and see paragraph [0067], ““It is understood that various embodiments of the Engine described herein may use any suitable machine learning training techniques to train the machine learning network 130”). Thus, it would have been obvious to combine the Huber loss function taught by Sangappa with the inspection method taught by Fukuda in view of Taguchi in order to obtain the invention as claimed Claim 18.
As to Claim 19, Fukuda in view of Taguchi fails to teach wherein the optimization problem is solved using robust regression.
However, Sangappa teaches that an optimization problem can be solved using robust regression (see paragraph [0067], “It is understood that various embodiments of the Engine described herein may use any suitable machine learning training techniques to train the machine learning network 130 for each sensor, including, but not limited to a neural net based algorithm, such as Artificial Neural Network, Deep Learning; a robust linear regression algorithm, such as Random Sample Consensus, Huber Regression”, where the Huber loss function is a function used for robust regression).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the robust regression Huber loss function taught by Sangappa with the inspection method taught by Fukuda in view of Taguchi. The motivation for doing so would be to use robust regression to train the neural network to improve the detection of differences between images (see paragraph [0002] and [0067]). Thus, it would have been obvious to combine the robust regression taught by Mendez with the inspection method taught by Fukuda in view of Taguchi in order to obtain the invention as claimed Claim 19.
Claims 23 is rejected under 35 U.S.C. 103 as being unpatentable over Liang et al. (US Pub No 2024033111), hereinafter Liang, in view of Fukuda et al. (US Pub No 20220414833), hereinafter Fukuda, further in view of Taguchi et al. (WO Pub No 2024014020), hereinafter Taguchi, and further in view of Perry et al. (US Pub No 20200033783), hereinafter Perry.
As to Claim 23, Liang, in view of Fukuda and Taguchi a method for image enhancement comprising:
acquiring an imaging dataset of an object comprising integrated circuit patterns using an imaging system (see Liang, paragraph [0027], “(see Liang, paragraph [0061], “Inspection images such as SEM images can be used to identify or classify a defect(s) of the manufactured ICs”, where the SEM (scanning electron microscopy) is interpreted as the imaging system, and IC stands for integrated circuit);
one or more iterations comprising the following steps: obtaining a reference dataset corresponding to the imaging dataset (see Liang, paragraph [0061], “For example, a generated image capturing a test device region of a wafer may be compared with a reference image capturing the same test device region. The reference image may be predetermined (e.g., by simulation) and include no known defect”);
obtaining a subset of the imaging dataset and a corresponding subset of the corresponding reference dataset (see paragraph [066 -0067], “According to some embodiments of the present disclosure, image aligner 430 can segment an inspection image into a plurality of smaller patches… some embodiments, image aligner 430 can determine, for each of a plurality of patches …a corresponding portion or patch in a reference image”, where each patch is a subset of each imaging dataset),
enhancing subsets of data (see paragraph [0012], “aligning a plurality of patches of the inspection image based on a reference image corresponding to the inspection image”)
and combining the enhanced subsets of the imaging dataset to obtain an enhanced imaging dataset. (see paragraph [0069], “For example, patches 511_1 to 511_n can be put together based on the alignment”).
Liang fails to teach applying learned filters to create enhanced subsets of data. Fukuda teaches applying filters to inspection datasets and reference datasets (see paragraph [0039] and [0043]), and detecting defects in enhanced image data (see Fukuda, paragraph [0036], “By comparing the corrected inspection image 306 with the reference image 101, the inspection unit 307 inspects an abnormal portion such as a defect”), but fails to teach that the filters are learned. However, Taguchi teaches in at least one iteration one or more learned filters obtained in any of the previous steps are used as initial value for the one or more learned filters to be optimized in the optimization problem (see Taguchi, paragraph [0073], “In the network update process in step S104, a known learning algorithm such as error reverse propagation is used to update the weights (parameters) of the filters (94, 96) in the neural network (90) to more appropriate ones so that the loss function representing the difference between the learning data and the reconstructed image data is reduced as much as possible”, and see Taguchi paragraph [0072], “When the error is not sufficiently small in a step S103, network update processing (learning of a neural network 90) is performed in a step S104, and then the process is returned again to a step S102 to repeat the series of processing”, where it is understood that the previous filter is used in the repeated step).
Thus, it would have been obvious to combine the many iterations of learned filters taught by Taguchi with the teachings of Liang and Fukuda. The motivation for doing so would be to obtain more accurate enhanced images. Taguchi teaches in paragraph [0074], “By repeating the processing of the steps (S102 to 104) many times, the error between the learning data and the reconstructed image data is minimized in the neural network (90), and more accurate reconstructed image data is outputted.”).
Liang in view of Fukuda in view of Taguchi fails and applying the method of claim 2 and combining the detected defects to obtain a defect detection in the imaging dataset.
However, in an analogous art, Perry teaches obtaining subset of the imaging dataset and a corresponding subset of the corresponding reference dataset (see paragraph [0011], “A set of defect maps is created, with each defect map being created by comparing one of the scanned images to digital reference data for the subject image. The comparisons may be performed patch versus patch ”);
comparing the enhanced imaging dataset to the reference dataset to obtain detect defects (see paragraph [0011], “The comparisons may be performed patch versus patch where each patch received a score that represents its similarity to the reference patch which result in the defect map image.”),
and combining the detected defects to obtain a defect detection in the imaging dataset (see paragraph [0012], “The set of defect maps are combined to form an integrated defect map”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the defect patches taught by Perry with the . The motivation for doing so would be to accurately automatically identify defects. Perry teaches in paragraph [0013], “In this manner the disclosed apparatus and method should significantly save time and resources for customers and printing device providers alike as identification of PIP scratch defect errors will occur accurately and automatically”. Thus, it would have been obvious to combine the defect patches taught by Perry with the teachings of Fukuda and Taguchi in order to obtain the invention as claimed in Claim 23.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chiang et al. (US Pub No 20230022057) teaches a method of retrieving images of integrated circuits using a machine learning model that operates on patches.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SOUMYA THOMAS whose telephone number is (571)272-8639. The examiner can normally be reached M-F 8:30-5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Mehmood can be reached at (571) 272-2976. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/S.T./ Examiner, Art Unit 2664
/JENNIFER MEHMOOD/ Supervisory Patent Examiner, Art Unit 2664
Prosecution Insights