DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/11/25 has been entered.
Response to Arguments
Applicant’s arguments filed on 11/11/25 have been considered but are moot because the arguments do not apply to any of the references being used in the current rejection. The amendment necessitates the new ground(s) of rejection presented due to the added language in the independent claim.
Status of the Application
Claim(s) 1-4, 6-20 is/are pending.
Claim(s) 1-4, 6-20 is/are rejected.
Claim Rejections – 35 U.S.C. § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
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Claim(s) 1, 8-10, 13-14, 17-20 is/are rejected under 35 U.S.C. § 103 as being unpatentable over Brogden et al. (US 20170256380 A1) [hereinafter Brogden] in view of Uemoto et al. (US 20170122852 A1) [hereinafter Uemoto] and Tanaka et al. (US 20080315097 A1) [hereinafter Tanaka]
Regarding claim 1, Brogden teaches a method comprising:
imaging (see e.g. ion beam imaging, [0097-98]), using a charged particle microscope (see e.g. [0097-98], fig 16: 1604, fig 24: 2402, 2404, [0050,189]), a grid (see e.g. TEM grid, fig 24: 2412, [0082]) positioned on a stage of the charged particle microscope to obtain an image (see fig 24), the grid including a support portion (e.g. top part of 2412) and a plurality of posts (see in fig 24, fig 27: 610) extending from the support portion,
determining, with a processing device (required for intended operation of system), based on the image, a designated weld location for each post of the plurality of posts (see target attachment point, e.g. [0095]);
Brogden may fail to explicitly disclose determining, with the processing device, based on the image, whether the designated weld location of each post of the plurality of posts is defective.
However, Uemoto teaches a system to determine whether a post is deformed or damaged, and to designate unusable post locations to skip for future use (see Uemoto, [0084]), said system comprising determining, with the processing device, based on the image, whether the designated weld location of each post of the plurality of posts is defective (see [0084], weld locations on that post would be deemed defective). It would have been obvious to a person having ordinary skill in the art at the time the application was effectively filed to combine the teachings of Uemoto in the system of the combined prior art, because a skilled artisan would have been motivated to look for ways to designate unusable post locations, in the manner taught by Uemoto.
Brogden may fail to explicitly disclose storing a stage location associated with each weld location that is not defective.
However, the use of storing observation conditions for a specimen to facilitate future observation, rather than trying to find the lamella from scratch each time, was well known in the art at the time the application was effectively filed. For example, Tanaka teaches a system for storing a stage location associated with each valid sample location (see Tanaka, e.g. [0049]) which enables the ability to automatically and efficiently acquire information from multiple samples and/or different sample holders (see [0005], fig. 14). It would have been obvious to a person having ordinary skill in the art at the time the application was effectively filed to combine the teachings of Tanaka in the system of the prior art because a skilled artisan would have been motivated to store more information about each sample, including information about known effective imaging parameters including stage position, corresponding to multiple samples and thereby associated with each weld positions, in order to try to improve efficiency of operation with multiple samples.
Regarding claim 8, the combined teaching of Brogden, Uemoto, and Tanaka teaches defective includes bent, tilted or rotated (determination of damaged/unusable position will naturally occur when the defective weld location is bent, tilted, or rotated, see e.g. Uemoto, [0084]).
Regarding claim 9, the combined teaching of Brogden, Uemoto, and Tanaka teaches wherein defective includes missing material (determination of damaged/unusable position will naturally occur when the defective weld location is missing material, see e.g. Uemoto, [0084]).
Regarding claim 10, the combined teaching of Brogden, Uemoto, and Tanaka may fail to explicitly disclose determining, based on the image, whether a lamella is already present at the weld location of each post. However, given the alignment and imaging of the attachment position (see Brogden, e.g. figs 24, 27), it would have been obvious to a person having ordinary skill in the art at the time the application was effectively filed to not attach a lamella on top of an existing lamella on the same spot during imaging, thereby determining weld invalidity for the purposes of the system.
Regarding claim 13, the combined teaching of Brogden, Uemoto, and Tanaka teaches determining, based on the image, whether the grid is valid (see Uemoto, [0084], determines validity/damage to the holder).
Regarding claim 14, the combined teaching of Brogden, Uemoto, and Tanaka teaches determining, based on the image, whether the grid is located in a designated location (determining attachment position of TEM grid, moving both until relative location is in a designated position relative to the lamella, see Brogden, [0095]).
Regarding claim 17, the combined teaching of Brogden, Uemoto, and Tanaka teaches determining, based on the image, whether the grid is tilted (required for rotationally aligning sample with TEM grid, see e.g. Brogden, claim 53).
Regarding claim 18, the combined teaching of Brogden, Uemoto, and Tanaka teaches determining, based on the image, whether the grid is rotated (required for rotationally aligning sample with TEM grid, see e.g. Brogden, claim 53).
Regarding claim 19, the combined teaching of Brogden, Uemoto, and Tanaka teaches determining, based on the image, whether the grid is flipped (required for rotationally aligning sample with TEM grid, see e.g. Brogden, claim 53).
Regarding claim 20, the combined teaching of Brogden, Uemoto, and Tanaka teaches automatically navigating to each weld location for welding of a lamella (see e.g. Tanaka, [0005]; Brogden, claim 26)
Claim(s) 2 is/are rejected under 35 U.S.C. § 103 as being unpatentable over Brogden, Uemoto, and Tanaka, as applied to claim 1 above, in further view of Brogden (US 20150348751 A1) [hereinafter Brogden II].
Regarding claim 2, the combined teaching of Brogden, Uemoto, and Tanaka may fail to explicitly disclose determining, based on the image, whether there is contamination present on or around the weld location. However, Brogden II teaches it was well known in the art to identify contamination defects during observation of thinly sliced samples (see Brogden II, [0002]), and teaches a system to use machine vision on multiple images (see e.g. [0047]) to compensate for topographical variations in the lamella (see [0005]). It would have been obvious to a person having ordinary skill in the art at the time the application was effectively filed to combine the teachings of Brogden II in the system of the prior art because a skilled artisan would have been motivated to look for ways to identify contamination type defects, while better compensating for topographical variations, in the manner taught by Brogden II.
Claim(s) 3-4 is/are rejected under 35 U.S.C. § 103 as being unpatentable over Brogden, Uemoto, Tanaka, and Brogden II, as applied to claim 2 above, in further view of https://en.wikipedia.org/w/index.php?title=Convolutional_neural_network [hereinafter Wikipedia].
Regarding claim 3, the combined teaching of Brogden, Uemoto, Tanaka, and Brogden II may fail to explicitly disclose determining, based on the image, whether there is contamination present on or around the weld location is performed using an artificial neural network trained to identify contamination. However, the use of convolutional neural networks for image recognition was well known in the art at the time the application was effectively filed. For example, Wikipedia teaches using CNNs to analyze visual imagery and for image recognition and classification (see e.g. Wikipedia, p1). It would have been obvious to a person having ordinary skill in the art at the time the application was effectively filed to try to use CNNs for automating the image recognition of the features taught by the prior art. It is noted that broadly providing an automatic or mechanical means to replace a manual activity which accomplishes the same result does not differentiate the claimed apparatus from a prior art apparatus. See In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958).
Regarding claim 4, the combined teaching of Brogden, Uemoto, Tanaka, Brogden II, and Wikipedia teaches the artificial neural network is a convolutional neural network (see Wikipedia).
Claim(s) 6-7, 11-12, 15-16 is/are rejected under 35 U.S.C. § 103 as being unpatentable over Brogden, Uemoto, and Tanaka, as applied to claim 1 above, in further view of https://en.wikipedia.org/w/index.php?title=Convolutional_neural_network [hereinafter Wikipedia].
Regarding claim 6, the combined teaching of Brogden, Uemoto, and Tanaka may fail to explicitly disclose determining, based on the image, whether each post is defective is performed using an artificial neural network trained to identify defects associated with posts configured to receive lamella. The prior art teaches comparing images of each post (see e.g. using template matching, Uemoto, [0213]). However, the use of convolutional neural networks for image recognition was well known in the art at the time the application was effectively filed. For example, Wikipedia teaches using CNNs trained to analyze visual imagery and for image recognition and classification (see e.g. Wikipedia, p1). It would have been obvious to a person having ordinary skill in the art at the time the application was effectively filed to try to use CNNs for automating and enhancing the image recognition of the same features taught by the prior art. It is noted that broadly providing an automatic or mechanical means to replace a manual activity which accomplishes the same result does not differentiate the claimed apparatus from a prior art apparatus. See In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958).
Regarding claim 7, the combined teaching of Brogden, Uemoto, Tanaka, and Wikipedia teaches the artificial neural network is a convolutional neural network (see Wikipedia).
Regarding claim 11, the combined teaching of Brogden, Uemoto, and Tanaka may fail to explicitly disclose determining, based on the image, whether each post is defective is performed using an artificial neural network trained to identify whether the lamella is already present at the weld location of each post. However, the use of convolutional neural networks for image recognition was well known in the art at the time the application was effectively filed. For example, Wikipedia teaches using CNNs trained to analyze visual imagery and for image recognition and classification (see e.g. Wikipedia, p1). It would have been obvious to a person having ordinary skill in the art at the time the application was effectively filed to try to use CNNs for automating and enhancing the image recognition of the same features taught by the prior art. It is noted that broadly providing an automatic or mechanical means to replace a manual activity which accomplishes the same result does not differentiate the claimed apparatus from a prior art apparatus. See In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958).
Regarding claim 12, the combined teaching of Brogden, Uemoto, Tanaka, and Wikipedia teaches the artificial neural network is a convolutional neural network (see Wikipedia).
Regarding claim 15, the combined teaching of Brogden, Uemoto, and Tanaka may fail to explicitly disclose determining, based on the image, whether the grid is located in the designated location is performed using an artificial neural network trained to identify whether the grid is located in the designated location. The prior art teaches comparing images of each grid (see e.g. using template matching, Uemoto, [0213]; see also correcting for rotations, Brogden, claim 53). However, the use of convolutional neural networks for image recognition was well known in the art at the time the application was effectively filed. For example, Wikipedia teaches using CNNs trained to analyze visual imagery and for image recognition and classification (see e.g. Wikipedia, p1). It would have been obvious to a person having ordinary skill in the art at the time the application was effectively filed to try to use CNNs for automating and enhancing the image recognition of the same features taught by the prior art. It is noted that broadly providing an automatic or mechanical means to replace a manual activity which accomplishes the same result does not differentiate the claimed apparatus from a prior art apparatus. See In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958).
Regarding claim 16, the combined teaching of Brogden, Uemoto, Tanaka, and Wikipedia teaches the artificial neural network is a convolutional neural network (see Wikipedia).
Conclusion
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/JAMES CHOI/Examiner, Art Unit 2881