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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-14 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-13 of U.S. Patent No. 12,087,421. Although the claims at issue are not identical, they are not patentably distinct from each other because claims of the instant patent fully discloses the features of the application.
Regarding claims 1 and 10 patent ‘421 discloses
A sensor unit which photographs a product to generate image data and measures at least one of a color, a saturation, a brightness, a transparency, and a reflectance of the product (note claim 1, col. 8 lines 1-4);
A detection unit which detects a defect on the product by inputting the image data to a convolutional neural network (CNN) trained to detect a defect on a product surface (note claim 1, col. 8 lines 5-8); and
a preprocessor which converts the image data based on at least one of the color, the saturation, the brightness, the transparency, and the reflectance of the product to input the converted data to the detection unit (note claim 8, col. 9 lines 10-13),
wherein a number of convolution layers of the convolutional neural network is determined based on a defect detection difficulty determined according to at least one of the color, the saturation, the brightness, the transparency, and the reflectance of the product and a defect type (note claim 1, col. 8 lines 9-14),
wherein a preprocessor wherein the preprocessor determines a frequency of an image sharpening filter based on the transparency of the product and converts the image data by applying the image sharpening filter to the image data (note claim 11, col. 9 lines 26-29).
Regarding claims 2 and 11 patent ‘421 discloses,
Wherein the convolutional neural network performs a learning by receiving a predetermined size of training image data for a first defect type to divide the training image data into grids having a predetermined size (note claim 2, col. 8 lines 21-25).
Regarding claims 3 and 12 patent ‘421 discloses,
Wherein the training image data is configured by a three-channel image obtained by dividing an image for the same product with respect to RGB and a size of the training data is 448 × 448, and a size of the grid is 7 × 7 (note claim 3, col. 8 lines 26-30).
Regarding claims 4 and 13 patent ‘421 discloses,
Wherein the convolutional neural network performs the learning as many as the number obtained by dividing a size of one training image data by a size of the grid (note claim 4, col. 8 lines 31-35).
Regarding claims 5 and 14 patent ‘421 discloses,
Wherein the convolutional neural network learns N defect types (note claim 5, col. 8 lines 36-38).
Regarding claim 6 patent ‘421 discloses,
Wherein the number of convolution layers of the convolutional neural network is determined further based on a similarity between at least one of the color, the saturation, the brightness, the transparency, and the reflectance of the product used as the training data of the convolutional neural network and at least one of the color, the saturation, the brightness, the transparency, and the reflectance of the product (note claim 6, col. 8 lines 39-48).
Regarding claim 7 patent ‘421 discloses,
Wherein the number of convolution layers of the convolutional neural network is increased in inverse proportion to the similarity (note claim 7 col. 9 lines 5-7)
Regarding claim 8 patent ‘421 discloses,
Wherein the detection unit detects a position of the defect, a size of the defect, and a type of the defect on the product and if a predetermined number or more of defects of the same position, same size, and same type occur in a predetermined consistency level, it is determined that the defect is not a defect (note claim 1, col. 8 lines 15-20)
Regarding claim 9 patent ‘421 discloses,
Wherein the detecting unit displays a bounding box on the image data based on the position of the defect and the size of the defect present on the product and outputs the image data displayed with the bounding box and the type of the defect (note claim 12, col. 10 lines 2-6)
Related Prior Art
Dal Mutto et al (10,579,875) A detection unit which detects a defect on the product by inputting the image data to a convolutional neural network (CNN) trained to detect a defect on a product surface (note fig. 12, shows CNN and described col. 22 lines 1-5 and 45-55).
Fang et al (10,964,004) convolution layers of the convolutional neural network is determined based on a defect detection difficulty determined (note fig. 3, block 201 convolution layer and col. 4 lines 51-55).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GREGORY M DESIRE whose telephone number is (571)272-7449. The examiner can normally be reached Monday-Friday 6:30am- 3:00pm.
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G.D.
July 19, 2026
/GREGORY M DESIRE/Primary Examiner, Art Unit 2676
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