DETAILED ACTION
Claim Rejections - 35 USC § 112
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 1, 2, 3, 7, 10 and 11 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.
Claims 1, 10 and 11 recite the phrase “converted inspection target data similar to the first inspection target data”, which fails to provide objective boundaries for determining the scope of the claim. The claims recite converting inspection target data into converted inspection target data that is “similar” to the first inspection target data, but do not specify the characteristics by which similarity is measured, the degree of similarity required, or any objective criterion for determining whether the converted inspection target data satisfies the recited similarity. As a result, one of ordinary skill in the art would not be able to determine with reasonable certainty when the limitation is met.
Claim 2 recites the phrase “optimum combination”, which fails to provide objective boundaries for determining the scope of the claim. The claim recites selecting “an optimum combination” among combinations of inspection model data and conversion model data based on inspection results but does not specify the criterion by which a combination is determined to be optimum. The claim fails to identify whether optimum refers to accuracy, precision, recall, processing speed, resource utilization, or any other measurable performance characteristic. Accordingly, one of ordinary skilled art would not be reasonably apprised of the scope of the claimed subject matter.
Claim 1 recites the limitation "the converted inspection target data obtained by conversion by the conversion processor from the inspection target data is inspected". There is insufficient antecedent basis for “the conversion processor” in the claim.
Claims 3 and 7 recites the limitation "based on a result of the learning by the inspection learning means". There is insufficient antecedent basis for “the inspection learning means” in the claims.
Allowable Subject Matter
Claims 1-3 and 7-11 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 1, and similarly regarding claims 10 and 11, the prior art of record, alone or in combination, fails to teach at least “converting inspection target data that is data based on the inspection target product into converted inspection target data similar to the first inspection target data based on conversion model data for conversion of second inspection target data into the converted inspection target data, the conversion model data being generated by learning second inspection target data and the first inspection target data, the second inspection target data being data based on a second product, wherein in the inspecting of the quality when a determination is made in accordance with external information that the inspection target product is a first product, the inspection target data that is data based on the inspection target product is directly inspected, and when a determination is made in accordance with the external information that the inspection target product is a second product, the converted inspection target data obtained by conversion by the conversion processor from the inspection target data is inspected.”
At best, Ikeda et al (US 20190147586) teaches in ¶122 “Next, the defect inspection apparatus 100 executes preprocessing on the acquired input image (step S302). Furthermore, by using a CNN engine including an identifier having a CNN that has undergone pre-learning, the defect inspection apparatus 100 executes processing that extracts, from the input image after preprocessing, a feature for each of one or a plurality of classifications (classes). By this feature extraction processing, the defect inspection apparatus 100 generates a feature detection image for each of the one or plurality of classes (step S303).”.
Conclusion
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/KEVIN KY/Primary Examiner, Art Unit 2671
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