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 .
Election/Restrictions
Applicant’s election without traverse of invention I, claims 1-6 and 13-14 in the reply filed on 12/15/2025 is acknowledged.
Claims 7-12 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention II, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 12/15/2025.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
As to claim 14, the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because it is drawn to a computer program per se. It could therefore be considered transitory. The examiner notes that the claim does state that it is executed off of a non-transitory storage medium. However, the statutory category and broadest reasonable interpretation of the claim itself in being claimed as a “computer program” could simply be a transitory signal. As such the claim does not fall within one of the four statutory categories and is rejected under 101.
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.
Claim(s) 1-3 and 13-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Seo (KR 102117697 B1, where the examiner has provided a machine translation hereinwith for citations).
As to claim 1, Seo, discloses and shows in figures 2 and 4, a surface defect detection method, the surface defect detection method being performed by a surface defect detection apparatus (20 and 30) for detecting a surface defect of an inspection target object having a glossy surface, the surface defect detection method comprising ([0020]; [0023]):
acquiring a photographed image of an inspection target object (via defect recognition device 30s camera 31) ([0036]); and
detecting a defect by using the acquired photographed image ([0036]);
wherein acquiring the photographed image comprises:
radiating pattern light of a stripe pattern having a predetermined interval (shown in for example figure 2) onto a surface of the inspection target object ([0029]; [0036]); and
acquiring a photographed image by photographing reflected light reflected from the surface of the inspection target object (explicitly shown in figure 4 as reflected light) ([0036]).
As to claim 2, Seo discloses a surface defect detection method, wherein acquiring the photographed image further comprises adjusting one or more characteristics of the pattern light including at least one of a color temperature of the pattern light, an interval and width of the stripe pattern, and a direction in which the stripe pattern is arranged (Fig. 3; [0032], as explicitly disclosed the width W1 and W2 can be adjusted wider or more narrow).
As to claim 3, Seo discloses a surface defect detection method, wherein: acquiring the photographed image is repeatedly performed a plurality of times while adjusting the characteristics of the pattern light differently according to preset conditions; and detecting the defect comprises detecting a surface defect of the inspection target object by using a plurality of photographed images acquired by repeating acquiring the photographed image ([0037]; [0038]).
As to claim 13, Seo discloses a non-transitory computer-readable storage medium having stored thereon a program that, when executed by a processor, causes the processor to execute the surface defect detection method of claim 1 ([0036], where implicitly if not inherently a defect detection unit that is implemented as a software program as disclosed requires at the very least a processor to execute the software).
As to claim 14, Seo disclose a computer program that is executed by a computing apparatus and stored in a non-transitory computer-readable storage medium in order to perform the surface defect detection method of claim 1 (([0036], where implicitly if not inherently a defect detection unit that is implemented as a software program as disclosed requires at the very least some sort of non-transitory computer-readable medium to be stored on or it would have to be re-written every time the machine were turned on and off).
Claim Rejections - 35 USC § 102
(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) 1-4, and 13-14 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Cho et al. (U.S. PGPub No. 2021/0318252 A1).
As to claim 1, Cho discloses and shows in figures 1 and 18, a surface defect detection method, the surface defect detection method being performed by a surface defect detection apparatus (100) for detecting a surface defect of an inspection target object having a glossy surface, the surface defect detection method comprising ([0046]):
acquiring a photographed image of an inspection target object (via photographing unit 140) ([0106], ll. 1-5); and
detecting a defect by using the acquired photographed image ([0102], ll. 1-3);
wherein acquiring the photographed image comprises:
radiating pattern light of a stripe pattern having a predetermined interval onto a surface of the inspection target object ([0033]; [0106], ll. 1-3; where figure 18 shows at least two strip patterns); and
acquiring a photographed image by photographing reflected light reflected from the surface of the inspection target object ([0086]; [0118]).
As to claim 2, Cho disclose a surface defect detection method, wherein acquiring the photographed image further comprises adjusting one or more characteristics of the pattern light including at least one of a color temperature of the pattern light, an interval and width of the stripe pattern, and a direction in which the stripe pattern is arranged ([0013], [0116]; as explicitly disclosed and shown in figure 18 the direction of the pattern can be modulated).
As to claim 3, Cho discloses a surface defect detection method, wherein: acquiring the photographed image is repeatedly performed a plurality of times while adjusting the characteristics of the pattern light differently according to preset conditions; and detecting the defect comprises detecting a surface defect of the inspection target object by using a plurality of photographed images acquired by repeating acquiring the photographed image ([0014]; [0120]).
As to claim 4, Cho disclose a surface defect detection method, wherein acquiring the photographed image further comprises adjusting one or more settings of a camera for acquisition of the photographed image when the characteristics of the pattern light are changed ([0129], [0130]; where the setting adjusted is extracting only pixels from the edge field data (area 13 in figure 2), as opposed to the entire image as is more commonly done in Cho).
As to claim 13, Cho discloses a non-transitory computer-readable storage medium having stored thereon a program that, when executed by a processor, causes the processor to execute the surface defect detection method of claim 1 ([0017]).
As to claim 14, Seo disclose a computer program that is executed by a computing apparatus and stored in a non-transitory computer-readable storage medium in order to perform the surface defect detection method of claim 1 (([0017]).
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) 5 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Cho.
As to claims 5 and 6, Cho does not explicitly disclose a surface defect detection method, further comprising: acquiring a plurality of training images, each including a reflected light pattern distorted due to a corresponding surface defect, by photographing a surface of a product being of a same type as the inspection target product and having a corresponding surface defect; and training a defect detection model by using each of the acquired training images; wherein detecting the defect comprises determining that the surface of the inspection target object has a defect when the defect is detected in at least one of the plurality of photographed images by inputting each of the plurality of photographed images to the defect detection model or a surface defect detection method, further comprising: acquiring a training image set including a plurality of training images acquired by photographing a surface of a product being of a same type as the inspection target product and having a corresponding surface defect while changing the characteristics of the pattern light; and training the defect detection model using the acquired training image set; wherein detecting the defect comprises detecting a defect of the inspection target object by inputting the plurality of photographed images as one set to the defect detection model.
However, Cho does disclose in ([0048], ll. 9-16; [0100]; [0148]; [0149]) explicitly the use of neural network learning models, and specifically deep learning based neural network models that the images are input into in order to review and classify defects in the images measured. Further Cho discloses the basic concept of supervised or reinforcement based learning. Doing so with a series of training images (i.e. learning data in Cho). One having ordinary skill in the art would recognize that this method of learning for neural networks involves using a known image as claimed with a defect present to teach the model that if such pattern/feature is found it should be classified as some particular type of defect. If this learning is not done then the model would obviously be incapable of performing any type of meaningful classification as disclosed. Lastly, since the model of Cho is explicitly intended to function under a variety of lighting patterns, it is obvious if not essential the model is likewise trained with images under these variations, or again the model would not function for its intended purpose in the system of Cho.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Cho with a surface defect detection method, further comprising: acquiring a plurality of training images, each including a reflected light pattern distorted due to a corresponding surface defect, by photographing a surface of a product being of a same type as the inspection target product and having a corresponding surface defect; and training a defect detection model by using each of the acquired training images; wherein detecting the defect comprises determining that the surface of the inspection target object has a defect when the defect is detected in at least one of the plurality of photographed images by inputting each of the plurality of photographed images to the defect detection model or a surface defect detection method, further comprising: acquiring a training image set including a plurality of training images acquired by photographing a surface of a product being of a same type as the inspection target product and having a corresponding surface defect while changing the characteristics of the pattern light; and training the defect detection model using the acquired training image set; wherein detecting the defect comprises detecting a defect of the inspection target object by inputting the plurality of photographed images as one set to the defect detection model in order to provide the advantage of increased accuracy, as explicitly disclosed in Cho using a deep learning model as taught by Cho yields an increase in defect recognition rate, further obviously using a neural network vs human defect pattern recognition reduces human error while simultaneously decreasing processing time ([0136], ll. 20-26).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL P LAPAGE whose telephone number is (571)270-3833. The examiner can normally be reached Monday-Friday 8-5:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tarifur Chowdhury can be reached at 571-272-2287. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael P LaPage/Primary Examiner, Art Unit 2877