DETAILED ACTION
[1] Remarks
I. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
II. Claims 1-14 are pending and have been examined, where claims 1-14 are rejected. Explanations will be provided below.
III. Inventor and/or assignee search were performed and determined no double patenting rejection(s) is/are necessary. The following the applications and issued patents which belong to the same assignee as the current application.
Issued Patent 12087421 claim 1 does not 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,
wherein the number of convolution layers of the convolutional neural network is increased in inverse proportion to the similarity.”
Co-pending Applications 18/972876, 18/972877, 18/972878 and 18/972879 and issued patent 12670576 claim 1 lacks enough of the same limitations found in the current claim 1. Therefore, a double patenting rejection is improper.
IV. Patent eligibility (updated in 2019) shown by the following: Claims 1-15 pass patent eligibility test because there is/are no limitation or a combination of limitations amounting to an abstract idea. Also, the following limitation or the combinations of the limitations: “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” effects a transformation or a reduction of a particular article to a different state or thing / adds a specific limitation(s) other than what is well-understood, routine and conventional in the field, or adding unconventional steps that confine the claim to a particular useful application and providing improvements to the technical field of deep learning, which recite additional elements that integrate the judicial exception into a practical application and amounting significant more.
V. There are no PCT associated with the current application.
[2] Claim 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.
Use of the word “means” (or “step for”) in a claim with functional language creates a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is invoked is rebutted when the function is recited with sufficient structure, material, or acts within the claim itself to entirely perform the recited function. Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim elements in this application that use the word “means” (or “step for”) are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word “means” (or “step for”) are presumed not to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
Claim(s) 1-9 are interpreted under 35 U.S.C. 112(f) or pre-AIA U.S.C. 112 6th paragraph because of the following reason(s): the claim limitations uses the term “means” or a term used as a substitute for “means” that is a generic placeholder; the term “means” or the generic placeholder is modified by functional language, typically linked by the transition word “for” or another linking word or phrase, such as “configured to” or “so that”; the term “means” or the generic placeholder is not modified by sufficient structure or material for performing the claimed function;
Claim(s) 10-15 do not require 35 U.S.C. 112(f) or pre-AIA U.S.C. 112 6th paragraph interpretation because they are method claims and / or they are CRM claims.
Upon examination of the specification and claims, the examiner has determined, under the best understanding of the scope of the claim(s), rejection(s) under 35 U.S.C. 112(a)/(b) is not necessitated because of the following reasons: sufficient support are provided in the written description / drawings of the invention.
[3] Grounds of Rejection
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. See 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); and 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) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent is shown to be commonly owned with this application. See 37 CFR 1.130(b).
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claim 1 is provisionally rejected under the judicially created doctrine of obviousness-type double patenting as being unpatentable over claim 1 of copending Application No. 18/972,880 in view of Brauer (US 20180157933).
Claim 1 of copending application 18/972,880 discloses 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 (1st limitation); 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 (2nd limitation),
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 (3rd limitation), 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 (4th limitation),
wherein the number of convolution layers of the convolutional neural network is increased in inverse proportion to the similarity (5th limitation).
Claim 1 of copending application 18/972,880 is silent in disclosing 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.
Brauer 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 (see paragraph 45, a wafer may include a plurality of dies, each having repeatable patterned features, also see figure 2 illustration below).
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It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include the detection unit detects a position of the defect, a size of the defect, and a type of the defect on the product in order to tracks the exact position, size, and type of defect over time and learns that these marks are standard and stops flagging them as errors.
The following are mapping of the copending application 18/972,880 mapped to the claims in the current application.
Copending App: 18/972,880
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Current application
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[4] Allowable Subject Matter
Claims 1-15 are allowable / patentable. The following is an examiner’s statement of reasons for allowance by comparing claims to closest found references. The references are divided into primary and secondary, where primary would have been utilized in a USC 102 or main USC 103 reference and secondary would had been utilized a secondary USC 103 reference, but these references do not cover enough of the claim’s scope to warrant a rejection.
Primary reference, Alekseevich (US 20180349742) discloses 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 document
wherein a number of convolution layers of the convolutional neural network is determined based on document based upon the size or contents of its assigned set of confused graphemes).
Alekseevich is silent in disclosing
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 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.
Also primary reference, Neshatpour (K. Neshatpour, F. Behnia, H. Homayoun and A. Sasan, "ICNN: An iterative implementation of convolutional neural networks to enable energy and computational complexity aware dynamic approximation," 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE), Dresden, Germany, 2018, pp. 551-556) discloses
wherein the number of convolution layers of the convolutional neural network is determined further based on a accuracy between at least one of the color, the saturation, the brightness, the transparency, and the reflectance of the image image (see figure 3 illustration below, the algorithm is iterate based on the accuracy where this accuracy is calculated using the similarity between the input testing image and training image, see descriptions of figure 3, the images include brightness feature)
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wherein the number of convolution layers of the convolutional neural network is increased in image object (see figure 3 illustration above, more convolutional layers are added when condition is met, the addition is adding N number of convolutional layers at every iteration)
Secondary reference, Brecher (US 5544256) discloses
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Primary reference, Fluegge (US 20140210990) 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 (see figure 1, 120s are the products being inspected, 110 is the sensor unit); and
a detection unit which detects a defect on the product by inputting the image data to a machine vision algorithm to detect a defect on a product surface (see figure 1, 112 and 142),
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Secondary reference, Brauer (US 20180157933) 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 (see paragraph 45, a wafer may include a plurality of dies, each having repeatable patterned features, also see figure 2 illustration below).
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Alekseevich, Neshatpour, Brecher, Fluegge, and Brauer taken alone or in combination with each other, are silent in disclosing all the limitations of claims 1 and 10. For reasons above all claims are allowable.
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.”
CONTACT INFORMATION
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEX LIEW (duty station is located in New York City) whose telephone number is (571)272-8623 (FAX 571-273-8623), cell (917)763-1192 or email alexa.liew@uspto.gov. Please note the examiner cannot reply through email unless an internet communication authorization is provided by the applicant. The examiner can be reached anytime.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, MISTRY ONEAL R, can be reached on (313)446-4912. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ALEX KOK S LIEW/Primary Examiner, Art Unit 2674 Telephone: 571-272-8623
Date: 7/16/26
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