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 Interpretation – 112(f)
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“measurement device configured to…” and “image inspection device configured to…” in claim 17.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
Regarding the “measurement device”, the corresponding structure described in the specification (paragraph 47) is a critical dimension (CD)-scanning electron microscope (SEM).
Regarding an “image inspection device”, the corresponding structure described in the specification is a computer/processor implementing the function as a program/algorithm (paragraphs 45 and 94-105). Therefore, according to MPEP 2181(II)(B), the entire algorithm associated with the function being performed (i.e. detecting an anomaly in an input image by using an image feature and a marker feature of the input image by using an artificial intelligence (AI) model) as disclosed, for example, figures 3-9 and paragraphs 58-89, is also considered to be included in the structure covered by the interpretation and thus incorporated into the claim.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Objections
Claim 3 is objected to because of the following informalities:
In line 5, the term “market patch” appears to be a typo and should say “marker patch”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 5, 6, 9 and 14-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 5 recites the limitation "the marker patch of the measurement image" in lines 3-4. There is insufficient antecedent basis for this limitation in the claim. In parent claim 3, recites “obtaining patch information of a marker patch of a plurality of patches in the input image”, not the measurement image.
Claim 6 is rejected by the virtue of it’s dependency upon rejected claim 5.
Claim 9 recites “generating a measurement image by removing the marker from the input image” in line 2 and “for the determining of the comparison result of the marker feature, determining a patch similarity between a marker patch of a plurality of patches in the measurement image…” in lines 6-8. These limitations appear to contradict each other in that the markers are removed in the measurement image but the marker patches are still in the measurement image. According to the specification paragraph 62, “the marker is located in the marker patch”. The limitations are generally vague and indefinite
Claim 14 recites the limitation "the marker patch of the measurement image" in lines 4-5. There is insufficient antecedent basis for this limitation in the claim. In parent claim 12, recites “obtain patch information of a marker patch of a plurality of patches in the input image”, not the measurement image.
Claim 15 is rejected by the virtue of it’s dependency upon rejected claim 14.
Claim 16 recites the limitation "for the generating of the reference image feature of the one or more reference images and the reference marker feature of the reference marker of the one or more reference images" in lines 1-2. There is insufficient antecedent basis for this limitation in the claim. Parent claim 10 discloses generating an image feature and marker feature on of an input image, but not a for a “reference image” as referred to in claim 10.
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.
Claims 1-4, 7, 8, 10-13 and 16 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2004/0264759 to Hattori et al. (“Hattori”).
Regarding claim 1, Hattori discloses a processor-implemented method comprising:
generating an image feature of an input image and a marker feature of a marker marked on the input image (Fig. 1, element S103 and S107; Fig. 3B, elements “p” and “Tp”; paragraphs 49-53 and 91, wherein an image (i.e. input image) of the pattern “p” to be measured is analyzed to generate a binarized “image feature” of the pattern and detects alignment marks “Tp” in the image and generates a position of the marks (i.e. “marker feature”));
determining a comparison result of the image feature by comparing the image feature of the input image with a reference image feature of one or more reference images (Fig. 1, element S108; Fig. 3A, element “M”, paragraphs 44 and 92-101, wherein the binarized pattern (i.e. image feature) is compared with a master pattern “M” (i.e. reference image feature of one or more reference images) to determine an inspection result (i.e. comparison result));
determining a comparison result of the marker feature by comparing the marker feature with a reference marker feature of a reference marker on the one or more reference images (Fig. 1, element S104; paragraphs 49-88, wherein the positions of alignment marks detected in the input image (i.e. marker features) are compared with those of the master pattern to determine a final alignment between the two (i.e. comparison result)); and
detecting whether an anomaly is in the input image based on the comparison result of the image feature and the comparison result of the marker feature (Figs. 8A, 8B, 9A, 9B; paragraphs 92-101, wherein “based on” the alignment of the marks and inspection of the pattern a defects or deficits (i.e. anomaly) are detected).
Regarding claim 2, Hattori discloses the method of claim 1, wherein the generating of the image feature of the input image and the marker feature of the marker marked on the input image comprises:
generating a measurement image by removing the marker from the input image (Fig. 5C; paragraphs 56-57, wherein a measurement image “Ep” is generated by extracting the pattern without the markers (i.e. removing the markers)); and
generating a feature map of the measurement image by using an artificial intelligence (AI) model (paragraphs 89-91, wherein the host computer (i.e. AI model) determines the base position of the pattern to be measured and binarizes the pattern (i.e. generating a feature map)).
Regarding claim 3, Hattori discloses the method of claim 2, wherein the generating of the image feature of the input image and the marker feature of the marker marked on the input image further comprises obtaining patch information of a marker patch of a plurality of patches in the input image, and the marker patch includes the marker (paragraph 50, wherein regions/patches of the input image corresponding to markers are searched for and obtained).
Regarding claim 4, Hattori discloses the method of claim 3, wherein the comparing of the image feature of the input image with the reference image feature of the one or more reference images comprises:
determining one or more map similarities between the feature map of the measurement image and a reference feature map of the one or more reference images (paragraphs 95-99, wherein the comparison between binarized pattern (i.e. feature map) and the master pattern (i.e. reference feature map) is used to determine a map of similarities); and
determining a greatest map similarity among the one or more map similarity as a normal image score of the input image (paragraphs 95-99, wherein when the similarity is greatest then the input image is considered normal (i.e. having a normal image score) as opposed to having an anomaly).
Regarding claim 7, Hattori discloses the method of claim 1, further comprising generating the reference image feature of the one or more reference images (Fig. 3A, element “M”, paragraphs 44 and 92-101, wherein a master pattern “M” (i.e. reference image feature of one or more reference images) is generated) and the reference marker feature of the reference marker of the one or more reference images (paragraphs 49-88, wherein the positions of alignment marks (i.e. reference marker feature) in a master pattern are generated from the master pattern).
Regarding claim 8, Hattori discloses the method of claim 7, wherein the generating of the reference image feature of the one or more reference images and the reference marker feature of the reference marker of the one or more reference images comprises:
generating a feature map of the one or more reference images from which the reference marker is removed by using an artificial intelligence (AI) model (Fig. 5C; paragraphs 46-48 and 56-57, wherein a feature map “Em” is generated by extracting the master pattern without the markers (i.e. removing the markers) via a computer (i.e. AI model)); and
obtaining patch information of a marker patch including the reference marker in the one or more reference image (paragraphs 46-48 and 50, wherein regions/patches of the reference image master pattern corresponding to markers are obtained).
Regarding claim 10, Hattori discloses an apparatus comprising:
one or more processors (Fig. 2, elements 4 and 5; paragraphs 44-46) configured to:
generate an image feature of an input image and a marker feature of a marker marked on the input image (Fig. 1, element S103 and S107; Fig. 3B, elements “p” and “Tp”; paragraphs 49-53 and 91, wherein an image (i.e. input image) of the pattern “p” to be measured is analyzed to generate a binarized “image feature” of the pattern and detects alignment marks “Tp” in the image and generates a position of the marks (i.e. “marker feature”));
determine a comparison result of the image feature by comparing the image feature of the input image with a reference image feature of one or more reference images (Fig. 1, element S108; Fig. 3A, element “M”, paragraphs 44 and 92-101, wherein the binarized pattern (i.e. image feature) is compared with a master pattern “M” (i.e. reference image feature of one or more reference images) to determine an inspection result (i.e. comparison result));
determine a comparison result of the marker feature by comparing the marker feature with a reference marker feature of a reference marker on the one or more reference images (Fig. 1, element S104; paragraphs 49-88, wherein the positions of alignment marks detected in the input image (i.e. marker features) are compared with those of the master pattern to determine a final alignment between the two (i.e. comparison result)); and
detect whether an anomaly is in the input image based on the comparison result of the image feature and the comparison result of the marker feature (Figs. 8A, 8B, 9A, 9B; paragraphs 92-101, wherein “based on” the alignment of the marks and inspection of the pattern a defects or deficits (i.e. anomaly) are detected).
Regarding claim 11, Hattori discloses the apparatus of claim 10, wherein, for the generating of the image feature of the input image and the marker feature of the marker marked on the input image, the one or more processors are configured to:
generate a measurement image by removing the marker from the input image (Fig. 5C; paragraphs 56-57, wherein a measurement image “Ep” is generated by extracting the pattern without the markers (i.e. removing the markers)); and
generate a feature map of the measurement image by using an artificial intelligence (AI) model (paragraphs 89-91, wherein the host computer (i.e. AI model) determines the base position of the pattern to be measured and binarizes the pattern (i.e. generating a feature map)).
Regarding claim 12, Hattori discloses the apparatus of claim 11, wherein for the generating of the image feature of the input image and the marker feature of the marker marked on the input image further, the one or more processors are configured to obtain patch information of a marker patch of a plurality of patches in the input image, and the marker patch includes the marker (paragraph 50, wherein regions/patches of the input image corresponding to markers are searched for and obtained).
Regarding claim 13, Hattori discloses the apparatus of claim 12, wherein, for the comparing of the image feature of the input image with the reference image feature of the one or more reference images, the one or more processors are configured to:
determine one or more map similarity between the feature map of the measurement image and a reference feature map of the one or more reference images (paragraphs 95-99, wherein the comparison between binarized pattern (i.e. feature map) and the master pattern (i.e. reference feature map) is used to determine a map of similarities); and
determine a greatest map similarity among the one or more map similarities as a normal image score of the input image (paragraphs 95-99, wherein when the similarity is greatest then the input image is considered normal (i.e. having a normal image score) as opposed to having an anomaly).
Regarding claim 16, Hattori discloses the apparatus of claim 10, wherein, for the generating of the reference image feature of the one or more reference images (Fig. 3A, element “M”, paragraphs 44 and 92-101, wherein a master pattern “M” (i.e. reference image feature of one or more reference images) is generated) and the reference marker feature of the reference marker of the one or more reference images (paragraphs 49-88, wherein the positions of alignment marks (i.e. reference marker feature) in a master pattern are generated from the master pattern), the one or more processors are configured to:
generate a feature map of the one or more reference images from which the reference marker is removed by using an artificial intelligence (AI) model (Fig. 5C; paragraphs 46-48 and 56-57, wherein a feature map “Em” is generated by extracting the master pattern without the markers (i.e. removing the markers) via a computer (i.e. AI model)); and
obtain patch information of a marker patch including the reference marker in the one or more reference images (paragraphs 46-48 and 50, wherein regions/patches of the reference image master pattern corresponding to markers are obtained).
Allowable Subject Matter
Claims 17-20 are allowed.
The following is an examiner’s statement of reasons for allowance:
Regarding claim 17, as noted above, an “image inspection device” invokes interpretation under 112(f) and the corresponding structure described in the specification is a computer/processor implementing the functions as a program/algorithm (paragraphs 45 and 94-105). Therefore, according to MPEP 2181(II)(B), the entire algorithm associated with the function being performed as disclosed, for example, figures 3-9 and paragraphs 58-89, is also considered to be included in the structure covered by the interpretation and thus incorporated into the claim. As such, none of the prior art teach or fairly suggests determining a greatest patch similarity among the one or more patch similarities as a normal marker score of the marker, wherein the detecting of whether the anomaly is in an input image based on the comparison result of the image feature and the comparison result of the marker feature comprises of determining the input image as being normal in response to the normal image score satisfying a first condition based on the one or more map similarities AND the normal marker score satisfying a second condition based on the one or more patch similarities, and determining the input image as being abnormal in response to the normal image score not satisfying the first condition based on the one or more map similarities OR the normal marker score not satisfying the second condition based on the one or more patch similarities, in combination with the other limitations associated with the algorithm disclosed in the specification. As noted in the rejections above, the prior art of Hattori discloses a similar process for detecting anomalies, however neither Hattori nor any other prior art found discloses the above limitation.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See attached PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AARON W CARTER whose telephone number is (571)272-7445. The examiner can normally be reached 8am - 5pm (Mon - Fri).
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/AARON W CARTER/Primary Examiner, Art Unit 2661
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