DETAILED ACTION
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3, 9, and 11 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Aaron (“conditional Image Generation with PixelCNN Decoders”).
Regarding Claims 1 and 9, Aaron (“conditional Image Generation with PixelCNN Decoders”) teaches an image processing method for acquiring data of an estimated captured image obtained from reference data of a sample by using a system including an input acceptance unit, an estimation unit, and an output unit, the data being used when comparing the estimated captured image and an actual captured image of the sample, [Abstract] the image processing method comprising:
an input step of accepting, by the input acceptance unit, input of the reference data, process information of the sample, and trained model data (it’s clear that trained PixelCNN has weights) [Abstract “The model can be conditioned on any vector, including descriptive labels or tags, or latent embeddings created by other networks [trained model data with weights]”; Section 2 paragraph 1 and 2]; an estimation step of calculating, by the estimation unit, captured image statistics which represent a probabilistic distribution of values that are attainable by data of the captured image by using the reference data, the process information, and the model data [Section 2 para. 1-3; 2.3, and 3.3]; and an output step of outputting, by the output unit, the captured image statistics, [Section 2 para. 1-3] wherein the estimated captured image is able to be generated from the captured image statistics [section “We show that a single Conditional PixelCNN model can be used to generate images from diverse classes such as dogs, lawn mowers and coral reefs, by simply conditioning on a one-hot encoding of the class”; Section 2 para. 4 “During sampling the predictions are sequential: every time a pixel is predicted, it is 2 fed back into the network to predict the next pixel”. Abstract “When conditioned on an embedding produced by a convolutional network given a single image of an unseen face, it generates a variety of new portraits of the same person with different facial expressions, poses and lighting conditions”].
Regarding claims 3 and 11, Aaron teaches wherein the process information includes a manufacturing condition for the sample or an image capturing condition for the captured image [Section 1 paragraph 1, 4, section 3.3 paragraph 4].
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.
Claims 2 and 10, are rejected under 35 U.S.C. 103 as being unpatentable over Aaron (“conditional Image Generation with PixelCNN Decoders”) in view of Grove et al. (Patent No. US 10,163,061).
Regarding claims 2 and 10, Aaron doesn’t explicitly teach the claim limitation.
However, Grove wherein the system further includes a machine learning unit and a storage unit, the image processing method further comprises a learning necessity (retraining) determination step of determining, by the machine learning unit, necessity of learning for the model data [Abstract Col. 2, lines 48-51, Col. 6 lines 1-10, 27-35], in a case where it is determined in the learning necessity determination step that the learning is necessary, input of a training dataset including the reference data, the process information, and the captured image for the learning is accepted, the captured image statistics and the data of the captured image of the training dataset are compared with each other, and the model data is updated based on a result of the comparison, [Col. 6 lines 5-17] in a case where it is determined in the learning necessity determination step that the learning is unnecessary, the storage unit stores, as the model data, a parameter used when the estimation unit calculates the captured image statistics [Abstract Col. 2, lines 48-51, Col. 6 lines 1-10, 27-35].
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify to Aaron to teach the claim limitation, feature as taught by Grove; because the modification enables the system improve the performance quality of the machine learning.
Claims 4, 6, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Aaron (“conditional Image Generation with PixelCNN Decoders”) in view of Mitsui (Pub. No. US 2006/0261268).
Regarding claim 4, Aaron doesn’t explicitly teach the claim limitation.
Mitsui teaches a step of evaluating an influence of the process information on the sample by using the captured image statistics [Para. 11, 18, and 22].
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify to Aaron to teach the claim limitation, feature as taught by Mitsui; because the modification enables the system to determine the location in the process where manufacturing defect happens.
Regarding claims 6 and 14, Aaron doesn’t explicitly teach the claim limitation.
However, Mitsui teaches wherein the sample is a semiconductor circuit [Para. 11, 18, and 22].
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify to Aaron to teach the claim limitation, feature as taught by Mitsui; because the modification enables the system to determine the location in the process where manufacturing defect happens.
Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Aaron (“conditional Image Generation with PixelCNN Decoders”) in view of WINDMARK et al. (Pub. No. US 2018/0293460).
Regarding claims 5 and 13, Aaron doesn’t explicitly teach the claim limitation.
However, WINDMARK teaches wherein the captured image statistics include a mean image and a standard deviation image [Para. 44 and 82].
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify to Aaron to teach the claim limitation, feature as taught by WINDMARK; because the modification enables the system to determine the location in the process where manufacturing defect happens.
Claims 6-7 and 15are rejected under 35 U.S.C. 103 as being unpatentable over Aaron (“conditional Image Generation with PixelCNN Decoders”) in view of Companion et al. (Patent No. US 6,330,354).
Regarding claim 6, Aaron doesn’t explicitly teach the claim limitation.
However, Companion teaches the image processing system according to claim 10, wherein the pattern inspection system further includes a template image creation unit and a pattern matching processing unit, the input acceptance unit accepts input of the data of the captured image, the template image creation unit creates a template image from the captured image statistics, the pattern matching processing unit performs pattern matching between the template image and the captured image, and the output unit outputs a result of the pattern matching [fig. 1, 3 and related description].
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify to Aaron to teach the claim limitation, feature as taught by Mitsui; because the modification enables the system to determine the location in the process where manufacturing defect happens.
Regarding claims 7 and 15, Aaron doesn’t explicitly teach the claim limitation.
However, Companion teaches the system further including a template image creation unit and a pattern matching processing unit, and the pattern inspection method comprising: accepting, by the input acceptance unit, input of the data of the captured image [Abstract, brief summary]; creating, by the template image creation unit, a template image from the captured image statistics [Abstract, Summary]; performing, by the pattern matching processing unit, pattern matching between the template image and the captured image (difference image) [Abstract, Col. 5 lines 1-49]; and outputting, by the output unit, a result of the pattern matching [Col. 8 lines 55-64, Col. 9 lines 40-45].
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify to Aaron to teach the claim limitation, feature as taught by Mitsui; because the modification enables the system to determine the location in the process where manufacturing defect happens.
Claims 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Aaron (“conditional Image Generation with PixelCNN Decoders”) in view of Grove et al. (Patent No. US 10,163,061).
Regarding claims 8 and 16, Aaron in view of Grove doesn’t explicitly teach the claim limitation.
However, Companion teaches the system further including a template image creation unit and a pattern matching processing unit, and the pattern inspection method comprising: accepting, by the input acceptance unit, input of the data of the captured image ) [Abstract, Col. 5 lines 1-49]; creating, by the template image creation unit, a template image from the captured image statistics; performing, by the pattern matching processing unit, pattern matching between the template image and the captured image [Abstract, brief summary] ; and outputting, by the output unit, a result of the pattern matching [Col. 8 lines 55-64, Col. 9 lines 40-45].
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify to Aaron to teach the claim limitation, feature as taught by Mitsui; because the modification enables the system to determine the location in the process where manufacturing defect happens.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Aaron (“conditional Image Generation with PixelCNN Decoders”) in view of Black et al. (Patent No. US 9,785,752).
Regarding claim 12, Aaron doesn’t explicitly teach the claim limitation.
However, Black teaches wherein an influence of the process information on the sample is evaluated by using the captured image statistics [fig. 9, 10 and related description].
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify to Aaron to teach the claim limitation, feature as taught by Black; because the modification provides a method for evaluating tissue image analysis feature distribution functions with the intent to stratify patient cohorts into two or more distinct categories of interest.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SOLOMON G BEZUAYEHU whose telephone number is (571)270-7452. The examiner can normally be reached on Monday-Friday 10 AM-7 PM..
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/SOLOMON G BEZUAYEHU/ Primary Examiner, Art Unit 2666