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 .
Claim Rejections - 35 USC § 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:
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.
Claim(s) 1, 9, and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Maekawa et al. (JP-2021156641-A), and further in view of Hayashi (US-20110280470-A1).
Regarding claim 1, Maekawa teaches:
An inspection device (“The present invention relates to a nozzle inspection device, a nozzle inspection method, and a nozzle inspection program,” Para [0001]) comprising:
a data converter configured to:
receive an inspection image of an inspection object (“acquisition unit that acquires an image of a portion of a manufacturing apparatus for manufacturing fibers by melt spinning that includes a nozzle hole,” Para [0009]);
a neural network processor configured to generate reference data the nozzle hole and multiple bad images including a defective portion of the nozzle hole as training data to inspect the quality of the nozzle hole included in the image acquired by the acquisition unit,” Para [0009], where the learning model uses a “convolutional neural network” (Para [0010]), which is an ANN); and
a detector configured to determine whether the inspection object is defective based on a comparison of the inspection data with the reference data (“the inspection unit 16 inputs the extracted image of the inspection target area 58 into the learning model 20 and obtains an output indicating whether the nozzle holes included in the inspection target area 58 are in good condition or in a defective condition,” Para [0056]).
Maekawa is not relied upon to teach the following limitations. Hayashi, however, further teaches:
convert the inspection image into grayscale data (“each of the light receiving parts 2 is used for capturing an image of a half of the wafer W,” Para [0045], where “The light receiving part 2 is provided with an image line sensor having the sensitivity to an infrared light,” Para [0045]); and
use the grayscale data to generate inspection data corresponding to an average brightness value of the inspection image (the control processing part 6a receives the image data of the wafer W that has been imaged by the light receiving part 2 (the gray level data of each of the picture elements),” Para [0049]); and
generate reference data corresponding to average brightness values of reference images of reference objects (“the control processing part 6a judges whether or not an average gray level of an image that has been obtained is in a permissible gray level range (a permissible range: a defect detectable range),” Para [0064]).
Hayashi is considered to be analogous to the claimed invention because they are both in the field of defect detection. 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 incorporated the teachings of Hayashi into Maekawa for the benefit of very accurate defect detection.
Regarding claim 9, the rejection of claim 1 is incorporated herein. Maekawa in view of Hayashi teaches the device of claim 1, and Maekawa further teaches:
an imaging device configured to non-destructively image a cross-section (“There are no particular restrictions on the cross-sectional shape of the nozzle hole in the nozzle being inspected,” Para [0022]) of the inspection object to generate the inspection image (“an acquisition unit that acquires an image of a portion of a manufacturing apparatus for manufacturing fibers by melt spinning that includes a nozzle hole,” Para [0009]
and to transmit the inspection image to the data converter (“the acquisition unit 12 acquires the image to be inspected when the image to be inspected is input to the nozzle inspection device 10,” Para [0030].
Regarding claim 12, the rejection of claim 1 is applied, mutatis mutandis, to claim 12.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Maekawa in view of Hayashi as applied to claim 1 above, and further in view of Yokouchi (WO-2024202460-A1).
Regarding claim 11, the rejection of claim 1 is incorporated herein. Maekawa in view of Hayashi teach the device of claim 1, but are not relied upon to teach the following limitations. Yokouchi, however, further teaches:
wherein the artificial neural network is an unsupervised artificial neural network (“After the training data is prepared, unsupervised learning is performed using an autoencoder (AE) (step S20),” Para [0085]).
Yokouchi is considered to be analogous to the claimed invention because they are both in the field of inkjet printer related defect detection. 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 incorporated the teachings of Yokouchi into Maekawa and Hayashi for the benefit of training the learning model in a more efficient manner; in other words, the training is not time-consuming.
Allowable Subject Matter
Claims 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lin et al. (US-20180164792-A1) teaches a system for wafer defect detection using adaptive machine learning.
Gurundath et al. (US-20210183036-A1) teaches a method for printer nozzle defect detection.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RACHEL A OMETZ whose telephone number is (571)272-2535. The examiner can normally be reached 8:30am-5:30pm ET Monday-Thursday, 7:30am-3:30pm ET every other Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vu Le can be reached at 571-272-7332. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Rachel Anne Ometz/Examiner, Art Unit 2668 8/10/26
Rachel.ometz@uspto.gov
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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