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 .
DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07/16/2026 has been entered.
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) 16-35 is/are rejected under 35 U.S.C. 103 as being unpatentable over de Bonfim Gripp et al. (2019/0287237 A1, previously cited reference) in view of Dubois et al. (2010/029069 A1, previously cited reference) or Schneider et al. (2019/0257714 A1, previously cited reference).
Regarding claims 16, 30 and 35, de Bonfim Gripp discloses of a method for quality control of transparent materials such as lens/lenses made of such material as in particular plastics, or polymers or glass/glasses and/or a piece of glass or other transparent substance (i.e. with curved sides for concentrating or dispersing light rays and/or a transparent object) is included in a method for automatic inspection of materials (i.e. a piece of glass or other transparent substance) [pars. 0002, 0003, 0015, 0020, 0034 and 0068], wherein at least one (i.e. a piece of glass or other transparent substance) is subjected to at least one image generation process by image capturing and at least one basic image is generated therefrom that is captured image by camera/capturing device 120 and the image processed by (i.e. device 200) [pars. 0021-22 and 0087-90], the method (figs. 1-9B) comprising the further method steps:
a) class-specific examination, by a class-specific artificial intelligence or a class-specific neural network [pars. 0042, 0043], of at least substantially all pixels of the at least one basic image at least within the lens materials (i.e. a piece of glass or other transparent substance) waviness the state of having a curved, undulating, or uneven surface with periodic irregularities, or material imperfections or surface defects or materials shape (i.e. curved, flat or regular) [pars. 0006-7, 0011, 0034] and class-specific categorization of each examined pixel according to potential membership in at least one predefined defect class included in "the processed image is transformed into a binary image having 2 pixel values, and the features extracted therefrom feed a defect classification algorithm..." and [pars. 0080, 0085] " Thus, each dimensional scale would have an independent and tuned set of spatial filtering 307, calculation of curvatures 308, extraction of features 309, and classifier 310. The classifier 310 in each dimensional scale may be implemented, for example, by single-layer perceptrons, multilayer perceptrons, decision trees, convolutional neural networks...") (figs. 3A-3B and 4] [pars. 0022 and 0074-0078] of a plurality of predefined (a system that analyzes a multiple set of dimensional scales independently, i.e., a multi-scale classifier [par. 0084] optical requirements of the system is decided/set ahead of time [par. 0096]) defect classes as can be seen in depicted drawing (fig. 3B) [par. 0057], wherein for each of the plurality of predefined defect classes (i.e. surface defects having different sizes) [par. 0084] there is an independent class-specific artificial intelligence or an independent class-specific neural network (is a system that analyzes a multiple set of dimensional scales independently) [pars. 0084-85], as can be seen depicted in drawing (figs. 3B and 6; artificial neural network 600) [pars. 0042, 0043, 0080, 0092];
b) assigning at least one value pixel values, for example white/black to each pixel is (value to each pixel), or pixel area for which categorization was possible in step a) is image binarization [pars. 0022, 0074-78];
c) class-specific examination of each pixel pixel values (i.e. between translucent regions and black matte regions) and/or pixel area from step b) on the basis of the assigned at least one value and class-specific categorization according to membership in a predefined defect class is included in classifying defects and/or included in the steps of identify and classify defects of multiple sizes, wherein each dimensional scale (= defect class), classification of defects 311 by classifier 310 the classifier 310 in each dimensional scale …. implemented, for example, by single-layer perceptrons, multilayer perceptrons, decision trees, convolutional neural networks...') [pars. 0034, 0048, 0022, 0074-0078 and 0084-85] (figs. 3A-3B and 4);
d) class-specific quantification of at least one or each pixel and/or pixel area assigned to a defect class in step c) according to each pixel and/or or pixel area’s intensity magnitude of light reflected is included in classifying defects and/or included in the steps of identify and classify defects of multiple sizes, wherein each dimensional scale (= defect class), classification of defects 311 by classifier 310, the classifier 310 in each dimensional scale …. implemented, for example, by single-layer perceptrons, multilayer perceptrons, decision trees, convolutional neural networks...') [pars. 0034, 0048, 0022, 0074-0078 and 0084-85] (figs. 3A-3B and 4);
e) judging (i.e. evaluating quality report) each pixel and/or pixel area quantified in step d) as acceptable or unacceptable on the basis of at least one predefined quality criterion is included in classifying defects and/or included in the steps of identify and classify defects of multiple sizes, wherein each dimensional scale (= defect class), classification of defects 311 by classifier 310 the classifier 310 in each dimensional scale …. implemented, for example, by single-layer perceptrons, multilayer perceptrons, decision trees, convolutional neural networks...') [pars. 0034, 0048, 0022, 0074-0078 and 0084-85] (figs. 3A-3B and 4); and
f) rejecting the lens(es) with at least one pixel and/or pixel area judged to be unacceptable, so that an automated and objectified quality control results quality information/report [pars. 0003, 0082, 0086, 0096] (fig. 3A; 312).
de Bonfim Gripp fail to teaches the constructional/structural change of the transparent materials such as lens/lenses that is made of such material as plastics, or polymers or glass/glasses is of the type ophthalmic lenses, and that the defect(s) is of the type wherein the basic image is at least within the lens contour or lens shape and class-specific categorization of each examined pixel according to potential membership in at least one predefined defect class.
However, even though, de Bonfim Gripp fail to teaches the constructional/structural change of the type of lens(es) and defect(s), the constructional/structural change(s) is/are considered obvious design variation of lens(es) that is made of the same material(s) as de Bonfim Gripp lens(es) and is/are considered obvious in view of de Bonfim Gripp teaches of method of examination and/or inspection of the at least one basic image that is at least within the lens(es) defects such as unwanted surface irregularities, waviness, dents, and surface roughness, and variation of curvature of the material [pars. 0037, 0069 and 0097]. It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify de Bonfim Gripp lens(es) with ophthalmic lenses and examine and/or inspect of the at least one basic image that is at least within the lens(es) defects such as contour or shape in order to identify, locate and/or classify defects within the ophthalmic lenses accurately and determine the quality of the ophthalmic lenses based on the information obtained from the features extracted from the images the automatic inspection of the lens(es) when substituted with ophthalmic lenses, since the propose modification of the prior art would not change the principle of operation of the prior art invention being modified.
Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify de Bonfim Gripp lens(es) with ophthalmic lenses and examine and/or inspect of the at least one basic image that is at least within the lens(es) defects such as contour or shape in order to identify, locate and/or classify defects within the ophthalmic lenses accurately and determine the quality of the ophthalmic lenses based on the information obtained from the features extracted from the images the automatic inspection of the lens(es) when substituted with ophthalmic lenses, since the propose modification of the prior art would not change the principle of operation of the prior art invention being modified. In this case, since it has been held that the provision of adjustability, where needed, involves only routine skill in the art, In re Stevens, 101 USPQ 284 (CC1954).
Further, Dubois or Schneider from the same field of endeavor teaches of a method and apparatus for detecting defects in optical components such as lenses, particularly cosmetic defects consisting of surface flaws and occlusions. Wherein the optical components of transparent material, for example, optical ophthalmic lenses, must be tested for defects such as scratches, smears, cracks, chips, stains (Dubois, [pars. 0001-2, 0021, 0056]), and detecting defects in lens contour (Dubois, [pars. 0092, 0095 and 0178]) in order to accurately detects surface flaws and occlusions. Also, Schneider from the same field of endeavor teaches of a device and method/evaluation system for surface defects in the lens, defects such as contour, or its edge contour (Schneider, [pars. 0006, 0028, 0054, 0056]). It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify de Bonfim Gripp lens(es) as desired appropriate such as with constructional/structural change(s) of the lens(es) being ophthalmic lenses, and examine and/or inspect the ophthalmic lens(es) surface defects, such as contour or shape defect(s) in order to identify, locate and/or classify defects within the ophthalmic lenses accurately and determine the quality of the ophthalmic lenses based on the information obtained from the features extracted from the images the automatic inspection of the lens(es) when substituted with ophthalmic lenses, since the propose modification of the prior art would not change the principle of operation of the prior art invention being modified and since it has been held that the provision of adjustability, where needed, involves only routine skill in the art, In re Stevens, 101 USPQ 284 (CC1954)
Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify de Bonfim Gripp lens(es) with ophthalmic lenses and examine and/or inspect the ophthalmic lens(es) defects such as contour or shape defect(s) in order to identify, locate and/or classify defects within the ophthalmic lenses accurately and determine the quality of the ophthalmic lenses based on the information obtained from the features extracted from the images the automatic inspection of the lens(es) when substituted with ophthalmic lenses, since the propose modification of the prior art would not change the principle of operation of the prior art invention being modified and since it has been held that the provision of adjustability, where needed, involves only routine skill in the art, In re Stevens, 101 USPQ 284 (CC1954).
For the purposes of clarity, the method step’s structure recited in claims 30 and 35 is/are symmetrical to the method step’s structure recited in claim 16, as such, the method claim 16 provide the method steps for the method claims 30 and 35 as rejected above as being unpatentable over de Bonfim Gripp in view of Dubois or Schneider.
Additionally, binarization is the process of converting an image to black and white (binary) by separating pixels into two categories, usually based on a threshold value. It is also used in data processing to convert continuous variables into two categories, and in computer science to simplify structures like trees or grammars. The most common type in image processing is thresholding, where pixels above a certain value become white (or 1) and those below it become black (or 0) (i.e. black-and-white image by assigning a binary value (0 or 1, black or white) to each pixel).
In addition, de Bonfim Gripp teaches [par. 0086] classifiers 310, classifiers is plurality in combination with plurality neural networks predefined defect classes, wherein for each of the plurality of predefined defect classes there is an independent class-specific artificial intelligence or an independent class-specific neural, as can be seen depicted in drawing (figs. 3B and 6). Therefore, first and/or second classifying by a first/second artificial intelligence or neural network is include in the combination of the plurality of classifiers and with plurality neural networks as claimed in claims 30 and 35.
As to claims 17-18, de Bonfim Gripp when modified with Dubois or Schneider, de Bonfim Gripp also discloses a method (figs. 1-9B) that is implemented using limitations such as, wherein the basic image is stored or saved in a database for statistical analysis of the process [pars. 0095] (claim 17); and wherein the basic image captured image is stored or saved in the database with at least one imaged lens captured materials (i.e. a piece of glass or other transparent substance) contour (i.e. curvatures and ripples) or desired lens shape for statistical analysis of the process [pars. 0095, 0098] (claim 18)
As to claims 19-21, de Bonfim Gripp when modified with Dubois or Schneider, de Bonfim Gripp also discloses a method (figs. 1-9B) that is implemented using limitations such as, wherein class-specific categorization (i.e. skewness, kurtosis or combinations) according to membership in exactly one predefined defect class is performed in step c) (claim 19); wherein the at least one predefined quality criterion in step e) is involves intensity magnitude of light reflected and/or location (see abstract) [pars. 0007, 0013-14, 0017-19, 0043, 0074-0080 and 0091-92] (de Bonfim Gripp, claims 1 and 4) (claim 20); and wherein at least two defect classes (i.e. skewness, kurtosis or combinations) are predefined as independent main defect classes using artificial intelligence techniques (figs. 2-3B) (claim 21).
As to claims 22-25, de Bonfim Gripp when modified with Dubois or Schneider, de Bonfim Gripp also discloses a method (figs. 1-9B) that is implemented using limitations such as, wherein three independent main defect classes "flaw", "contamination", "engraving" (i.e. skewness, kurtosis or combinations and/or distortion, dirt, scratches, bubbles and surface roughness) are predefined (claim 22); wherein at least one of the steps a), c) or d) are carried out by least one respective class-specific AI system or class-specific neural networks (claim 23); wherein the at least one class- specific AI system or the neural networks is/are trained before the method is carried out in such a way that in step e) a possible defects/erroneous judging (i.e. evaluating quality report) as acceptable or unacceptable converges towards zero when the method is carried out (claim 24); wherein the at least one class- specific AI system (artificial intelligence techniques) or the neural networks (i.e. artificial neural network is/are trained in advance before the method is carried out and is/are further trained during a repeated execution of the method in such a way that in step e) a possible defects/erroneous judging as acceptable or unacceptable converges towards zero in the course of the execution of the method [pars. 0007, 0013-14, 0017-19, 0043, 0074-0080, 0091-0097] using artificial intelligence techniques (figs. 2-3B) (claim 25)
As to claims 26-29, de Bonfim Gripp when modified with Dubois or Schneider, de Bonfim Gripp also discloses a method (figs. 1-9B) that is implemented using limitations such as, wherein in step e) at least one predefined customer-specific quality criterion/principle or standard by which quality may be judged or decided by (i.e. pixel values, binarization of the image, a defect classification algorithm) is used in such a way that additionally a customer-specific quality control of each lens results (claim 26); wherein in step e) at least one predefined quality category/principle or standard by which quality may be judged or decided by (i.e. pixel values, binarization of the image, a defect classification algorithm) and/or for image processing software, as well as software for identifying and classifying defects is used as quality criterion [pars. 0007, 0013-14, 0017-19, 0043, 0050, 0074-0080, 0091-0097] using artificial intelligence techniques (figs. 2-3B) (claim 27); wherein the quality control is a cosmetic quality control improving material quality materials such as glasses [pars. 0015, 0024] (claim 28); and wherein an optical pattern is generated on a screen flat material/screen illuminated and at least one raw image is captured by a camera (i.e. image capture device 120 or 420), from which at least one basic image is generated, as can be seen in depicted drawing (fig. 4) [pars. 0026, 0048, 0087] (claim 29)
As to claims 31-34, de Bonfim Gripp when modified with Dubois or Schneider, de Bonfim Gripp also discloses a method (figs. 1-9B) that is implemented using limitations such as, wherein the image generation process is or comprises transmissive deflectometry an optical measurement technique that uses a camera (i.e. image capture device 120 or 420) to analyze how light from light source 100 which generates a light pattern is deflected as is/are reflected or transmitted through a transparent object, like a lens/glasses, to determine its optical properties [pars. 0067-78], as can be seen I depicted drawing (figs. 1-9B) (claim 31); wherein an optical pattern light pattern is generated from light source 100 on a screen flat material/screen illuminated and at least one raw image is captured by a camera (i.e. image capture device 120 or 420), from which at least one basic image is generated, as can be seen in depicted drawing (fig. 4) [pars. 0026, 0048, 0087] (claim 32); wherein the raw image captured by (i.e. image capture device 120 or 420) is based on the pattern imaged by the lens, wherein the pattern varies in brightness is wherein included in each pixel with different colors and brightness in an extension direction [par. 0075] as can be seen in depicted drawing (fig. 4) (claim 33); and wherein patterns light pattern phase-shifted by (i.e. 900 ) are generated and corresponding raw images are captured by (i.e. image capture device 120 or 420) (claim 34).
Response to Arguments
Applicant’s arguments/remarks, see pages 7-9, filed on 07/16/2026, with respect to the rejection(s) of claim(s) have been fully considered but are not persuasive.
Applicant’s arguments:
a) Applicant argues that “While claims 16-35 are rejected in view of Bonfim Gripp and Dubois, this rejection is respectfully traversed. Specifically, and without concession as to the rejection, claim 16 has been amended to recite:
a) class-specific examination, by a first class-specific artificial intelligence or a class-specific neural network, of at least substantially all pixels of the at least one basic image at least within the lens contour or lens shape and class-specific categorization of each examined pixel according to potential membership in at least one predefined defect class of a plurality of predefined defect classes, wherein for each of the plurality of predefined defect classes there is an independent class-specific artificial intelligence or an independent class-specific neural network…
Neither Bonfim Gripp nor Dubois teach this combination of features. More specifically, neither Bonfim Gripp nor Dubois teach that for each of the plurality of predefined defect classes there is an independent class-specific artificial intelligence or an independent class-specific neural network. While the Office Action may generally identify teachings of AI in the reference, this is not sufficient to anticipate or render obvious the specifically claimed features.
Examiner's response:
With respect to argument (a), it is respectfully pointed out to applicant that this argument is not persuasive because the teachings or suggestions of the prior art that have been used as evidence within a rejection of the claimed invention in view of the prior art under 35 U.S.C. 102 or 35 U.S.C. 103, as set forth by the Court, are to be evaluated and determined not just from one or more specifically identified quotes to individual sections of the text of the prior art document but are in fact to be evaluated and determined from all that the prior art document teaches or suggests, In re BODE et al, 193 USPQ 12 at 17 (CCPA, 1977), with some reliance on the knowledge of one of ordinary skill at the time the invention was made in order to provide an enabling disclosure, In re BODE et al, 193 USPQ 12 at 16 (CCPA, 1977). It is respectfully pointed out to applicant that the newly amended claim after this argument is not persuasive because it is noted that the features/amendment such as, “for each of the plurality of predefined defect classes there is an independent class-specific artificial intelligence or an independent class-specific neural network.”, is clearly taught by de Bonfim Gripp in [pars. 0084, 0096, 0057, 008485] and as can be seen depicted in drawing (figs. 3B and 6; artificial neural network 600) [pars. 0042, 0043, 0080, 0092]. de Bonfim Gripp teaches in [par. 0086] of classifiers 310, classifiers is plurality, and the classifiers are in combination with plurality neural networks, obviously for each of the ones in the plurality is each implicitly predefined (because a system that analyzes a multiple set of dimensional scales independently, i.e., a multi-scale classifier [par. 0084], further, the optical requirements of the system is decided/set ahead of time [par. 0096]) and there is an independent class-specific artificial intelligence or an independent class-specific neural, as can be seen depicted in drawing (figs. 3B and 6). It is respectfully pointed out to applicant that this argument(s) (a) is/are not persuasive because first and/or second classifying by a first/second artificial intelligence or neural network is include in the combination of the plurality of classifiers with plurality neural networks as claimed in claims, 16, 30 and 35.
b) Applicant argues that Again, neither Bonfim Gripp nor Dubois teach, suggest nor disclose use of different artificial intelligence or neural networks, let alone that the first artificial intelligence or neural network and the second artificial intelligence or neural network are independent of each other. Claim 35 recites that the classification is performed by different class-specific neural networks which operate independently of one another and classify only into different defect classes and are or have been trained independently of each other. Again, the references are silent on this combination of features. At least on this basis, all independent claims are distinguishable from the references and withdrawal of the rejection is earnestly solicited. The dependent claims are further distinguishable at least based on the above and the additional feature(s) recited therein.
Examiner's response:
With respect to argument (b), it is respectfully pointed out to applicant that for the same reasons as discussed above in relation to arguments (a), applicant arguments regarding arguments (b) is/are not persuasive.
In addition, it is respectfully pointed out to applicant that these arguments are not persuasive because each of the defect classification 604 different class-specific neural networks which operate independently of one another and classify only into different defect classes and are or have been trained independently of each other, as can be seen depicted in drawing (figs. 3B and 6; artificial neural network 600) [pars. 0042, 0043, 0080, 0092].
In conclusion, it is respectfully pointed out to applicant that upon further consideration, the claims are still rejected as shown in the detail above and the argument(s) is/are not persuasive because one of ordinary skill in the art at the time the invention was made would have fairly and reasonably recognized that the prior art does properly support a rejection of the claimed invention under 35 U.S.C. 103.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Isiaka Akanbi whose telephone number is (571) 272-8658. The examiner can normally be reached on 8:00 a.m. - 4:30 p.m.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tarifur R. Chowdhury can be reached on (571) 272-2287. The fax phone number for the organization where this application or proceeding is assigned is 703-872-9306.
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/ISIAKA O AKANBI/Primary Examiner, Art Unit 2877
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