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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: reference character 233 shown in Figure 2. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Interpretation
Claims 1 and 11 each recite “…at least one of a color, a saturation, a brightness, a transparency and a reflectance of the product.”
Superguide Corp. v. DirecTV Enterprises, Inc., 69 USPQ2d 1865 (Fed. Cir. 2004) stated that:
A common treatise on grammar teaches that "an article of a preposition applying to all the members of the series must either be used only before the first term or else be repeated before each term." William Strunk, Jr. & E.B.White, The Elements of Style 27 (4th ed. 2000). Thus, "[i]n spring, summer, or winter" means "in spring, in summer, or in winter." Id. Applying this grammatical principle here, the phrase "at least one of" modifies each member of the list, i.e., each category in the list.
Thus, the plain meaning of the phrase “at least one of x, y, and z”, is “at least one of x, and at least one of y, and at least one of z”. The claimed limitations mentioned above will be examined under this interpretation.
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:
“a sensor unit which photographs…” in claim 1 (Page 4, lines 25-27 of the specification, for example, says the sensor unit is a camera.); and
“a detection unit which detects…” in claim 1 (Page 5, lines 28-30 of the specification, for example, says the detection unit is a CNN with a plurality of convolution layers and a plurality of fully connected layers.).
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.
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.
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.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
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 the present claims are merely obvious variations of the patented claims.
Below is a comparison between present claim 1 and patented claims 2 and 7:
Present claim 1
Patented claims 2 and 7
An AI-based product surface inspecting apparatus for adjusting a number of convolution layers, comprising:
An AI-based product surface inspecting apparatus, comprising:
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; and
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; and
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,
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,
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, and
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, and
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,
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, and
wherein the number of convolution layers of the convolutional neural network is increased in inverse proportion to the similarity, and
wherein the number of convolution layers of the convolutional neural network is increased in inverse proportion to the similarity.
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.
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.
As shown above, besides wording, the only difference between the claims is that patented claims 2 and 7 are not claimed together in the same claim.
Hence the patent includes each element claimed although not necessarily in a single claim, with the only difference between the present claims and the patented claims being the lack of the actual combination of the elements in a single claim.
Therefore, one of ordinary skill in the art before the effective filing date of the claimed invention could have combined patented claims 2 and 7 by known methods, and that in combination, each element merely performed the same function as it does separately. The results of the combination would have been predictable.
Therefore, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claims 2-14 are similarly rejected as above over claims 1-13 of U.S. Patent No. 12,087,421.
Claims 1-14 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-10 of U.S. Patent No. 12,670,576. Although the claims at issue are not identical, they are not patentably distinct from each other because the present claims are merely broader versions of the patented claims.
Below is a comparison between present claim 1 and patented claim 4:
Present claim 1
Patented claim 4
An AI-based product surface inspecting apparatus for adjusting a number of convolution layers, comprising:
An AI-based apparatus for detecting a defect of a product, comprising:
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; and
a sensor unit; a detection unit; a preprocessor configured to:
photograph a product to generate image data and measure at least one of a color, a saturation, a brightness, a transparency, and a reflectance of the product; and
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,
detect 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; convert at least one of the color, the saturation, and the brightness of the image data with respect to at least one of the color, the saturation, and the brightness of the product used as training data of the convolutional neural network;
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, and
wherein a number of convolution layers of the convolutional neural network is adjusted 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,
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,
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
wherein the number of convolution layers of the convolutional neural network is increased in inverse proportion to the similarity, and
wherein the number of convolution layers of the convolutional neural network is increased in inverse proportion to the similarity
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.
and 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, 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 image data is 448×448, and a size of the grid is 7×7; and 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.
As shown above, besides wording, the main difference between the claims is that present claim 1 is merely a broader version of patented claim 4. Thus, present claim 1 is anticipated by patented claim 4.
Claims 2-14 are similarly rejected as above over claims 1-10 of U.S. Patent No. 12,670,576.
Claims 1-14 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-14 of copending Application No. 18/972,881 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the present claims are merely broader versions of the copending claims.
Below is a comparison between present claim 1 and copending claim 2:
Present claim 1
Copending claim 2
An AI-based product surface inspecting apparatus for adjusting a number of convolution layers, comprising:
An AI-based product surface inspecting apparatus for adjusting a number of convolution layers, comprising:
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; and
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; and
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,
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,
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, and
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,
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,
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,
wherein the number of convolution layers of the convolutional neural network is increased in inverse proportion to the similarity, and
wherein the number of convolution layers of the convolutional neural network is increased in inverse proportion to the similarity, and
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.
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.
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, and 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.
As shown above, besides wording, the main difference between the claims is that present claim 1 is merely a broader version of copending claim 2. Thus, present claim 1 is anticipated by copending claim 2.
Claims 2-14 are similarly rejected as above over claims 1-14 of copending Application No. 18/972,881 (reference application).
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 1-14 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-14 of copending Application No. 18/972,879 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the present claims are merely obvious variations of the copending claims.
Below is a comparison between present claim 1 and copending claims 1-2 and 6-7:
Present claim 1
Copending claims 1-2 and 6-7
An AI-based product surface inspecting apparatus for adjusting a number of convolution layers, comprising:
An AI-based product surface inspecting apparatus, comprising:
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; and
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;
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,
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; 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,
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, and
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, and
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.
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,
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.
wherein the number of convolution layers of the convolutional neural network is increased in inverse proportion to the similarity, and
wherein the number of convolution layers of the convolutional neural network is increased in inverse proportion to the similarity.
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.
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.
As shown above, besides wording, present claim 1 is merely a broader version of copending claims 1-2 and 6-7. The only other difference being that copending claims 1-2 and 6-7 are not claimed together in the same claim.
Hence the patent includes each element claimed although not necessarily in a single claim, with the only difference between the present claims and the patented claims being the lack of the actual combination of the elements in a single claim.
Therefore, one of ordinary skill in the art before the effective filing date of the claimed invention could have combined copending claims 1-2 and 6-7 by known methods, and that in combination, each element merely performed the same function as it does separately. The results of the combination would have been predictable.
Therefore, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claims 2-14 are similarly rejected as above over claims 1-14 of copending Application No. 18/972,879 (reference application).
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 1-14 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-15 of copending Application No. 18/972,878 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the present claims are merely obvious variations of the copending claims.
Below is a comparison between present claim 1 and patented claims 1-2 and 6-7:
Present claim 1
Copending claims 1-2 and 6-7
An AI-based product surface inspecting apparatus for adjusting a number of convolution layers, comprising:
An AI-based product surface inspecting apparatus, comprising:
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; and
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;
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,
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; 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,
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, and
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, and,
wherein the preprocessor converts at least one of the color, the saturation, and the brightness of the image data with respect to at least one of the color, the saturation, and the brightness of the product used as training data of the convolutional neural network.
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,
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.
wherein the number of convolution layers of the convolutional neural network is increased in inverse proportion to the similarity, and
wherein the number of convolution layers of the convolutional neural network is increased in inverse proportion to the similarity.
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.
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.
As shown above, besides wording, present claim 1 is merely a broader version of copending claims 1-2 and 6-7. The only other difference being that copending claims 1-2 and 6-7 are not claimed together in the same claim.
Hence the patent includes each element claimed although not necessarily in a single claim, with the only difference between the present claims and the patented claims being the lack of the actual combination of the elements in a single claim.
Therefore, one of ordinary skill in the art before the effective filing date of the claimed invention could have combined copending claims 1-2 and 6-7 by known methods, and that in combination, each element merely performed the same function as it does separately. The results of the combination would have been predictable.
Therefore, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claims 2-14 are similarly rejected as above over claims 1-15 of copending Application No. 18/972,878 (reference application).
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 1-14 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-16 of copending Application No. 18/972,877 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the present claims are merely obvious variations of the copending claims.
Below is a comparison between present claim 1 and copending claims 1-2 and 6-7:
Present claim 1
Copending claims 1-2 and 6-7
An AI-based product surface inspecting apparatus for adjusting a number of convolution layers, comprising:
An AI-based product surface inspecting apparatus, comprising:
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; and
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;
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,
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; 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,
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, and
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, and,
wherein the preprocessor performs auto cropping to extract a shape of the product from the image data to extract at least one feature of a brightness and a shadow of the product based on a shape of the automatically cropped product.
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,
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.
wherein the number of convolution layers of the convolutional neural network is increased in inverse proportion to the similarity, and
wherein the number of convolution layers of the convolutional neural network is increased in inverse proportion to the similarity.
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.
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.
As shown above, besides wording, present claim 1 is merely a broader version of copending claims 1-2 and 6-7. The only other difference being that copending claims 1-2 and 6-7 are not claimed together in the same claim.
Hence the patent includes each element claimed although not necessarily in a single claim, with the only difference between the present claims and the patented claims being the lack of the actual combination of the elements in a single claim.
Therefore, one of ordinary skill in the art before the effective filing date of the claimed invention could have combined copending claims 1-2 and 6-7 by known methods, and that in combination, each element merely performed the same function as it does separately. The results of the combination would have been predictable.
Therefore, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claims 2-14 are similarly rejected as above over claims 1-16 of copending Application No. 18/972,877 (reference application).
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 1-14 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-12 of copending Application No. 18/972,876 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the present claims are merely broader versions of the copending claims.
Below is a comparison between present claim 1 and copending claim 4:
Present claim 1
Copending claim 4
An AI-based product surface inspecting apparatus for adjusting a number of convolution layers, comprising:
An AI-based apparatus for detecting a defect of a product, comprising:
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; and
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; and
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,
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,
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, and
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,
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,
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.
wherein the number of convolution layers of the convolutional neural network is increased in inverse proportion to the similarity, and
wherein the number of convolution layers of the convolutional neural network is increased in inverse proportion to the similarity.
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.
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, and 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.
As shown above, besides wording, the main difference between the claims is that present claim 1 is merely a broader version of copending claim 4. Thus, present claim 1 is anticipated by copending claim 4.
Claims 2-14 are similarly rejected as above over claims 1-12 of copending Application No. 18/972,876 (reference application).
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 1-14 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 7-20 of copending Application No. 18/149,762 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the present claims are merely obvious variations of the copending claims.
Below is a comparison between present claim 1 and copending claims 7-8 and 12-13:
Present claim 1
Copending claims 7-8 and 12-13
An AI-based product surface inspecting apparatus for adjusting a number of convolution layers, comprising:
An AI-based product surface inspecting apparatus, comprising:
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; and
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; and
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,
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,
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, and
wherein the 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.
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,
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.
wherein the number of convolution layers of the convolutional neural network is increased in inverse proportion to the similarity, and
wherein the number of convolution layers of the convolutional neural network is increased in inverse proportion to the similarity.
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.
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.
As shown above, besides wording, present claim 1 is merely a broader version of copending claims 7-8 and 12-13. The only other difference being that copending claims 7-8 and 12-13 are not claimed together in the same claim.
Hence the patent includes each element claimed although not necessarily in a single claim, with the only difference between the present claims and the patented claims being the lack of the actual combination of the elements in a single claim.
Therefore, one of ordinary skill in the art before the effective filing date of the claimed invention could have combined copending claims 7-8 and 12-13 by known methods, and that in combination, each element merely performed the same function as it does separately. The results of the combination would have been predictable.
Therefore, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claims 2-14 are similarly rejected as above over claims 7-20 of copending Application No. 18/149,762 (reference application).
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Allowable Subject Matter
Claims 1-14 would be allowable if rewritten or amended, or by filing Terminal Disclaimers to overcome the Double Patenting rejections set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter:
In the closest prior art:
Fam et al. (US 2022/0196571) disclose of abnormal surface pattern detection for production line defect remediation (Figure 1 illustrates an AI-trained defect inspection system that analyzes images of products on an assembly line to identify and classify abnormal surface patterns.).
Wada (US 2021/0341394) discloses a visual inspections device that uses AI (Figure 1 and paragraph [0031].).
Wang et al. (US 2020/0019938) disclose systems and methods for artificial-intelligence-based automated surface inspection (Figure 9 and paragraph [0092].).
However, the closest prior art fails to teach and/or suggest “…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, and 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, wherein the number of convolution layers of the convolutional neural network is increased in inverse proportion to the similarity…”
Conclusion
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/STEPHEN G SHERMAN/Primary Examiner, Art Unit 2621
8 September 2026
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