DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/5/2022 and 1/13/2023 were filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 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 1-2 and 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Shimase (US5952658A) in view of Steele (US20040241890A1) in further view of Mollica (US20040256573A1).
Regarding claim 1, Shimase teaches A method of automatic detection of a required peak for sample machining by a focused ion beam by means of a system comprising
an ion column with an ion source (1) arranged for irradiating a sample by the focused ion beam (2),
a working chamber (100), to which the ion column is connected (1),
a detector of secondary particles, which is located in the working chamber or in the ion column,
a sample holder (10) located in the working chamber (100) and arranged for accommodating a sample (9), a sample (9) located in the sample holder (10),
and an evaluation unit (80) comprising a memory (84b) which stores at least information on the required number of peaks (Col. 13 lines 10-20),
comprising a first group of steps comprising a steps of:
a) irradiating individual machined spots in a machined area of the sample by the focused ion beam (Col. 10 lines 15-20 ion beam 2 applied to an LSI 9 as a target which is placed on a stage 10,) and
b) detecting a quantity of secondary particles emitted from the machined area (Col. 10 lines 15-20 secondary ions 8 out of secondary particles which have been generated at this time are detected), and
c) storing discrete values obtained by averaging the detected quantity of secondary particles from the whole machined area to the memory(Col. 8 lines 20-45 secondary ions stored in the form of database).
Shimase is silent on a sampling frequency in a range of 1 to 3 Hz,
and a second group of steps performed simultaneously with the first group of steps, wherein the second group of steps is performed by the evaluation unit, and wherein the second group of steps comprises a sequence of steps of:
d) transforming the stored discrete values according to frequencies at least to a part with high frequencies and to a part with remaining frequencies by performing at least one-level discrete wavelet transformation of the stored discrete values based on decomposition filters of a mother wavelet,
e) resetting part of transformed discrete values with high frequencies,
f) creating a filtered signal by performing an inverse discrete wavelet transformation of transformed discrete values based on reconstruction filters of the mother wavelet,
g) detecting the number of filtered signal peaks, and
h) issuing a command to stop sample machining by the focused ion beam after reaching a given number of peaks based on the information on the required number of peaks.
Steele teaches and a second group of steps performed simultaneously with the first group of steps, wherein the second group of steps is performed by the evaluation unit, and wherein the second group of steps comprises a sequence of steps of:
d) transforming the stored discrete values according to frequencies at least to a part with high frequencies and to a part with remaining frequencies by performing at least one-level discrete wavelet transformation of the stored discrete values based on decomposition filters of a mother wavelet ([0013,0113] separate the sharp peak signal from the other two components of the signal (low frequency signal and induced noise signals); nvCPD signal/image data are decomposed into the wavelet domain) ,
e) resetting part of transformed discrete values with high frequencies ([0013, 0114] eject noise from the nvCPD signal/image, taken to be high frequencies),
f) creating a filtered signal by performing an inverse discrete wavelet transformation of transformed discrete values based on reconstruction filters of the mother wavelet ([0114] By adjusting the coefficients and performing reconstruction, the three components (peak, low frequency, and noise) of the nvCPD signal/image can be selectively filtered out),
g) detecting the number of filtered signal peaks ([0115, 0120-0121] filtered signal, where the peaks of the signal are counted as positive or negative peak)
h) issuing a command to stop sample machining by the focused ion beam after reaching a given number of peaks based on the information on the required number of peaks ([0120-0121] detects positive and negative peak, ends the sequence).
Shimase and Steele are considered to be analogous to the claimed invention because they are in the same field of ion beam machining. It would have been obvious to have modified Shimase to incorporate the teachings of Steele to transform stored discrete values, reset the discrete values, create a filtered signal, detect the number of peaks, and stop sample machining at the number of peaks are reached in order to be able to detect, locate, and classify relatively small quantities of chemical content and physical features on machined objects (Steele [0015]).
Shimase and Steele are silent on a sampling frequency in a range of 1 to 3 Hz.
Mollica teaches a sampling frequency in a range of 1 to 3 Hz ([0034] 1.6 Hz sampling frequency).
Shimase, Steele, and Mollica are considered to be analogous to the claimed invention because they are in the same field of ion beam machining. It would have been obvious to have modified Shimase and Steele to incorporate the teachings of Mollica to have a sampling frequnecy in a range of 1 to 3Hz in order to be able to compensate adequately for the spatially distributed non-uniformities typically encountered (Mollica [0028]).
Regarding claim 2, Shimase, Steele, and Mollica teach the method of automatic detection of the required peak for sample machining by the focused ion beam according to claim 1, but Shimase and Mollica are silent on wherein in the step of transforming stored discrete values, the stored discrete values are separated at least to the part with the high frequencies, to a part with medium high frequencies, to a part with medium low frequencies, and to a part with low frequencies, forming the remaining frequencies, by performing four-level discrete wavelet transformation of the stored discrete values based on decomposition filters of the mother wavelet.
Steele teaches wherein in the step of transforming stored discrete values, the stored discrete values are separated at least to the part with the high frequencies, to a part with medium high frequencies, to a part with medium low frequencies, and to a part with low frequencies, forming the remaining frequencies, by performing four-level discrete wavelet transformation of the stored discrete values based on decomposition filters of the mother wavelet ([0113] Daubechies, where Daubechies is understood to separate frequencies).
It would have been obvious to have modified Shimase and Mollica to incorporate the teachings of Steele to transform stored discrete values using a wavelet transformation in order to take signal data into a wavelet domain to denoise and reconstruct the signal (Steele [0113]).]
Regarding claim 9, Shimase, Steele, and Mollica teach the method of automatic detection of the required peak for sample machining by the focused ion beam according to claim 1, and Shimase teaches wherein peaks (Col. 13 lines 5-40 bottom B1 and B2) are a local minima of the filtered signal (Fig. 8 where B1 and B2 are shown to be local minima).
Regarding claim 10, Shimase, Steele, and Mollica teach the method of automatic detection of the required peak for sample machining by the focused ion beam according to claim 1, but Shimase and Mollica are silent on wherein the mother wavelet is Daubechies-4.
Steele teaches wherein the mother wavelet is Daubechies-4 ([0113] Daubechies).
It would have been obvious to have modified Shimase and Mollica to incorporate the teachings of Steele to use a Daubechies 4 wavelet transformation in order to take signal data into a wavelet domain to denoise and reconstruct the signal (Steele [0113]).
Allowable Subject Matter
Claims 3-8 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 3, the closest prior arts are Shimase (US5952658A), Steele (US20040241890A1), and Mollica (US20040256573A1).
Shimase, Steele, and Mollica teach the method of automatic detection of the required peak for sample machining by the focused ion beam according to claim 2. However, Shimase, Steele, and Mollica do not teach wherein in the step of resetting part of transformed discrete values, the parts of transformed discrete values with high frequencies, with medium high frequencies, and with medium low frequencies are reset.
Regarding claim 4, the closest prior arts are Shimase (US5952658A), Steele (US20040241890A1), and Mollica (US20040256573A1).
Shimase, Steele, and Mollica teach the method of automatic detection of the required peak for sample machining by the focused ion beam according to claim 1. However, Shimase, Steele, and Mollica do not teach wherein after the step of creating a filtered signal and before the step of detecting the number of peaks, a step of averaging the filtered signal with the use of a floating window and averaging a magnitude of values of the filtered signal located in this floating window is further performed by the evaluating unit.
Regarding claim 5, the closest prior arts are Shimase (US5952658A), Steele (US20040241890A1), and Mollica (US20040256573A1).
However, Shimase, Steele, and Mollica do not teach the method of automatic detection of the required peak for sample machining by the focused ion beam according to claim 4,
wherein a floating window length corresponds to a number of detected discrete values up to a maximum floating window length corresponding to 3 to 15% of a current number of detected discrete values, however, up to the maximum floating window length corresponding to the maximum number of 100 discrete values.
Regarding claim 6, the closest prior arts are Shimase (US5952658A), Steele (US20040241890A1), and Mollica (US20040256573A1).
Shimase, Steele, and Mollica teach the method of automatic detection of the required peak for sample machining by the focused ion beam according to claim 1. However, Shimase, Steele, and Mollica do not teach wherein after the step of detecting the number of peaks and before the step of issuing a command to stop sample machining, a step of skipping close peaks is further performed by the evaluating unit, wherein the close peaks are peaks with a distance from a closest peak smaller than 50% of an average distance value between individual consecutive peaks.
Regarding claim 7, the closest prior arts are Shimase (US5952658A), Steele (US20040241890A1), and Mollica (US20040256573A1).
Shimase, Steele, and Mollica teach the method of automatic detection of the required peak for sample machining by the focused ion beam according to claim 1. However, Shimase, Steele, and Mollica do not teach wherein after the step of detecting the number of peaks of the filtered signal and before the step of issuing a command to stop sample machining by the focused ion beam, a step of skipping a last peak is performed by the evaluating unit.
Regarding claim 8, the closest prior arts are Shimase (US5952658A), Steele (US20040241890A1), and Mollica (US20040256573A1).
However, Shimase, Steele, and Mollica do not teach the method of automatic detection of the required peak for sample machining by the focused ion beam according to claim 7,
wherein peaks are local a maxima of the filtered signal.
Conclusion
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/ABIGAIL H RHUE/Examiner, Art Unit 3761 1/6/2026
/VY T NGUYEN/Examiner, Art Unit 3761