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 § 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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.
Claim(s) 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US PgPub. No. 2025/0200741 by Kaminsky et al. (hereinafter ‘Kaminsky’).
In regards to claims 1, Kaminsky teaches a method for detecting an anomaly in an input image, the method performed by one or more processors and comprising: (See Kaminsky Abstract, Kaminsky teaches detecting defects).
generating a first anomaly map of pixel-level features of an input image and a second anomaly map of structural features of the input image, the generating based on the input image and based on a first non-defect image corresponding to the input image; (See Kaminsky Figure 4 and 5, paragraphs [0126]-[0127] and [0133]-[0135], Kaminsky teaches creating two anomaly maps based on reference and input images.)
generating a multi-anomaly map by merging the first anomaly map and the second anomaly map; and detecting the anomaly in the input image based on the multi-anomaly map. (See Kaminsky paragraphs [0136]-[0139], Kaminsky teaches combining two defect maps and detecting defects.)
In regards to claim 2, Kaminsky teaches further comprising: generating the first non-defect image from the input image by using a generative artificial intelligence (AI) model. (See Kaminsky paragraph [0083]).
In regards to claim 3, Kaminsky teaches performing a preprocessing on the input image to generate a preprocessed input image; and generating a second non-defect image similar to the preprocessed input image by using a generative artificial intelligence (AI) model. (See Kaminsky paragraph [0082]-[0083]).
In regards to claim 4, Kaminsky teaches wherein; the generating the first anomaly map of pixel-level features of the input image and the second anomaly map of structural features of the input image based on the input image and based on the non-defect image corresponding to the input image comprises generating the first anomaly map and the second anomaly map based on the preprocessed input image and the second non-defect image corresponding to the preprocessed input image. (See Kaminsky Figure 4 and 5, paragraphs [0126]-[0127] and [0133]-[0135]).
In regards to claim 5, Kaminsky teaches wherein: the generating the first anomaly map of pixel-level features of the input image and the second anomaly map of structural features of the input image based on the input image and the non-defect image corresponding to the input image comprises generating the first anomaly map based on a difference between a first pixel of the input image and a second pixel of the non-defect image, wherein the second pixel corresponds to the first pixel. (See Kaminsky paragraph [0043]).
In regards to claim 6, Kaminsky teaches wherein: the generating the first anomaly map of the pixel-level features of the input image and the second anomaly map of the structural features of the input image based on the input image and the non-defect image corresponding to the input image comprises calculating a patch similarity between a patch of a predetermined size in the input image and a corresponding patch in the non-defect image; and generating the second anomaly map based on the patch similarity. (See Kaminsky paragraph [0101]).
In regards to claim 7, Kaminsky teaches wherein the patch similarity is calculated by performing padding on the input image and the non-defect image and moving the patch by a stride of a predetermined spacing. (See Kaminsky paragraph [0101]).
In regards to claim 8, Kaminsky teaches wherein: the merging the first anomaly map and the second anomaly map comprises multiplying pixel values of pixels of the first anomaly map with pixel values of respectively corresponding pixels in the second anomaly map. (See Kaminsky paragraph [0135]).
In regards to claim 9, Kaminsky teaches wherein: the detecting the anomaly in the input image based on the multi-anomaly map comprises detecting a non-defect region and/or abnormal region in the input image based on a condition related to pixel values of the multi-anomaly map. (See Kaminsky paragraph [0135]).
In regards to claim 10, Kaminsky teaches further comprising: determining a defective patch within the input image by using the multi-anomaly map in response to the anomaly being detected in the input image; and transmitting information about the defective patch to a defect classification system. (See Kaminsky paragraph [0133]-[0135]).
Claims 11-16 recite limitations that are similar to that of claims 1, 2 and 5-8, respectively. Therefore, claims 11-16 are rejected similarly as claims 1, 2 and 5-8, respectively.
In regards to claim 17, Kaminsky teaches a system for detecting a defect in a semiconductor manufacturing process, the system comprising: an anomaly detection device configured to generate a multi-anomaly map based on an unlabeled input image transmitted from inspection equipment of the semiconductor manufacturing process and a non-defect image similar to the input image, and detect an anomaly in the input image based on the multi-anomaly map; and a defect classification device configured to determine a type of the defect corresponding to the anomaly of the input image. (See Kaminsky [0133]-[0135], Kaminsky teaches creating two anomaly maps based on reference and input images. Kaminsky also teaches in paragraphs [0136]-[0139], Kaminsky teaches combining two defect maps and detecting defects.).
In regards to claim 18, Kaminsky teaches wherein: when detecting the anomaly in the input image based on the multi-anomaly map, the anomaly detection device further configured to use the multi-anomaly map to determine a patch including the anomaly and transmit information about the patch to the defect classification device. (See Kaminsky paragraphs [0136]-[0139]).
In regards to claim 19, Kaminsky teaches wherein: when generating the multi-anomaly map based on the input image and the non-defect image, the anomaly detection device further configured to merge a first anomaly map related to pixel-level features of the input image and a second anomaly map related to structural features of the input image to generate the multi-anomaly map. (See Kaminsky paragraph [0136] and [0143]).
In regards to claim 20, Kaminsky teaches wherein: the anomaly detection device further configured to generate the first anomaly map based on differences between first pixels of the input image and second pixels of the non-defect image and generate the second anomaly map based on similarity of patches between the input image and the non-defect image. (See Kaminsky paragraphs [0043] and [0101]).
Conclusion
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/UTPAL D SHAH/Primary Examiner, Art Unit 2668
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