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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/26/2026 has been entered.
Response to Arguments
Rejections under 35 U.S.C. §§ 102 and 103
Applicant’s first argument is that the claimed index indicating a degree of smoothness does not describe the inspection for localized defects taught by Yamada and points out that words such as “defect” and “recess” do not appear in the present disclosure and are not shown in the figures, however, this argument is not persuasive. Smoothness can reasonably be interpreted in a variety of length scales, so a defect in part of a surface may reasonable be said to mar the smoothness of the surface as a whole, and patent examination relies on the broadest reasonable interpretation of the claims as written, without importing extraneous limitations from the specification or Applicant’s remarks. It may further be noted that the figures of the present disclosure also do not depict a lack of smoothness (FIG. 4B is the closest, but shows the effect of curvature, not smoothness or lack thereof), and the specification also does not appear to use words such as “gloss” that might point in a different direction from defect detection.
Applicant’s second argument is that Yamada’s process of rearranging pixels does not affect the brightness or intensity values of individual pixels, however, this argument is not persuasive. Changing the brightness attributed to a particular position in an image, changing a first pixel from a first value to a second value, changes the brightness or intensity of that individual pixel, even if there exists a second pixel that is now set to that first value and a third pixel that previously held that second value (as might occur when redistributing intensity in an intensity distribution).
Applicant’s third argument is that rearranging pixels does not serve to calculate a third intensity distribution by eliminating a scattering component from a second intensity distribution as now recited in claim 1, however, this argument is not persuasive. An image in which the pixels (representing intensity) have been redistributed represents a different intensity distribution than the original image. It is also unclear what scattering component is to be eliminated from the second intensity distribution (i.e., what physical mechanism). If Applicant is referring to the distortion present in the light reflected from a curved surface (as seen in FIG. 4B of the present disclosure), Yamada teaches eliminating that component by undistorting the light (via rearrangement of pixels). An alternative interpretation of a scattering component (diffuse reflection due to microscopic roughness of the surface) is not consistent with the claims, as it is not produced by the macroscopic curvature of the surface (deviation from a flat shape in claim 1) and would not vanish in a macroscopically flat shape of surface (claim 2).
Applicant’s third argument is that Hirose and Luo do not teach certain features of claims 1, 14, and 15, however, this argument is moot. This action does not rely on Hirose or Luo to teach any of the limitations of the independent claims.
Claim Objections
Claims 1, 7, and 14-15 are objected to because of the following informalities:
Claims 1 and 14-15 have status markers listed as “(Previously Presented)”, despite being currently amended.
Claim 7 is labeled as “(Currently Amended)”, but is identical to the previously presented version of claim 7, including markups for the amendments made to claim 7 found in that previous version. Unless new amendments are intended, the claim should appear as a clean version labeled “(Previously Presented)” or equivalent.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-2, 4, and 6-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Where applicant acts as his or her own lexicographer to specifically define a term of a claim contrary to its ordinary meaning, the written description must clearly redefine the claim term and set forth the uncommon definition so as to put one reasonably skilled in the art on notice that the applicant intended to so redefine that claim term. Process Control Corp. v. HydReclaim Corp., 190 F.3d 1350, 1357, 52 USPQ2d 1029, 1033 (Fed. Cir. 1999).
The term “a scattering component of light” in claims 1, 14, and 15 is used by the claims to mean something “produced by a deviation of the curved shape from a flat shape,” while the accepted meaning is “light sent in other directions, usually but not always in random directions” such as “at a rough surface, having a microscopically irregular structure” (see Paschotta (Non-Patent Literature “Scattering”)). The term is indefinite because the specification does not clearly redefine the term, and it is unclear how a scattering component of light would be produced by a deviation of the curved shape from a flat shape (independent claims) or decrease commensurately with a decrease in the deviation (claim 2). The term is interpreted to encompass distortions in specularly reflected light caused by macroscopic curvature of a reflective surface.
Claims 2, 4, and 6-13 are indefinite for depending on at least one indefinite claim.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-2, 9-11, and 14-15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yamada (Foreign Patent Document JP H06307835 A).
Regarding claim 1, Yamada teaches a coating evaluation device comprising:
a light source unit including at least one light source (FIG. 2, light source unit 26, with light-emitting surface 26a), the light source unit being configured to irradiate a coating surface with incident light having a first intensity distribution (paragraphs 27-28, light emitted has a luminance gradient in direction A1);
an intensity acquisition unit including a camera (FIG. 2, CCD camera 28), the intensity acquisition unit being configured to acquire a second intensity distribution of light reflected from the coating surface (FIG. 6(b), light-emitting surface reflected image PS, appearing curved due to the curvature of the surface under test); and
a controller configured to calculate (FIG. 2, image processor 20) a third intensity distribution associated with the second intensity distribution based on shape information (FIG. 6(c) or 6(d), light-emitting surface reflected image PS, appearing straight due to image processing, the third intensity distribution being the one captured in the image after processing) representing a curved shape of the coating surface, the shape information being obtained by measuring the coating surface or acquiring design data pertaining to the coating surface (paragraph 12, the shape information is used to correct the captured image), the third intensity distribution being calculated by using a simulation technology that uses computer graphics (FIG. 6(d) shows a result of such a simulation, which after it has been processed by image processing processor 20 as a simulation of how light might be reflected from a flat surface otherwise similar to coating surface 4) to eliminate a scattering component of light on the coating surface that is included in the second intensity distribution, the scattering component being produced by a deviation of the curved shape from a flat shape (FIG. 6 shows correcting curved light-emitting surface reflected image PS in FIG. 6(b) to straighten the light-emitting surface reflected image PS as shown in 6(c). Note that deviation of a curved surface from a flat shape produces a distortion in the distribution of scattered light, which can be eliminated by rearranging pixels in an image that captures the distorted light), and
estimate a brilliance evaluation value that pertains to the coating surface based on the third intensity distribution by using an evaluation model, the evaluation model being configured to output the brilliance evaluation value in response to an input including the third intensity distribution (paragraph 10, differential processing after image processing to correct for curvature),
the brilliance evaluation value being an index indicating at least one of a degree of smoothness of the coating surface, a proportion of the light reflected by the coated surface via a diffuse reflection, and a degree of resolution of an image appearing on the coating surface (FIG. 2 shows detecting a recess 6. The presence of a defect, such as recess 6, causes the surface coating 4, taken as a whole, to be less smooth, even if certain parts of the surface (such as those away from the defect) are still smooth).
Regarding claim 2, Yamada teaches the coating evaluation device according to claim 1 (as described above), wherein a difference between the second intensity distribution and the third intensity distribution decreases commensurately with a decrease in the deviation (note that smaller deviations would produce less distortion in the second intensity distribution, so would require less severe rearrangement of pixels), and the second intensity distribution and the third intensity distribution match each other when the curved shape is the flat shape (FIG. 6(a) shows a case of a flat surface under test, which already has a rectangular light-emitting surface reflected image PS which does not need correction).
Regarding claim 9, Yamada teaches the coating evaluation device according to claim 1 (as described above), wherein the first intensity distribution has a first region in which an intensity of the incident light is equal to or greater than a first threshold value (FIG. 2, the region of light-emitting surface 26a toward the lower-left-hand edge, shown with longer lines m to indicate more intense illumination), and a second region in which the intensity of the incident light is less than the first threshold value and is equal to or less than a second threshold value (FIG. 2, the region of light-emitting surface 26a toward the upper-right-hand edge, shown with shorter lines m to indicate less intense illumination).
Regarding claim 10, Yamada teaches the coating evaluation device according to claim 1 (as described above), wherein the shape acquisition unit is configured to acquire position information pertaining to a region of the coating surface that has been irradiated with the incident light. and is configured to acquire the shape information based on the position information (FIG. 6, the image detected is spatially resolved (containing position information) and is used to correct the light-emitting surface reflected image PS into a rectangle, with the shape determining the degree of correction required).
Regarding claim 11, Yamada teaches the coating evaluation device according to claim 1 (as described above), wherein the light source unit is configured to emit the incident light, which includes a first marking pattern (FIG. 2, light source 26, with a pattern indicated by relative lengths of lines m);
the intensity acquisition unit is configured to acquire an intensity distribution of light reflected from a region of the coating surface that has been irradiated with the first marking pattern as a second marking pattern (FIG. 6(b)); and
the controller is configured to acquire reference shape information pertaining to the region that has been irradiated with the first marking pattern based on a difference between the first marking pattern and the second marking pattern, and is configured to acquire position information pertaining to a region on the coating surface that has the shape information matching the reference shape information (FIG. 6, by straightening the raw data (6(b)) into a rectangle to match the emitted light, the position of defects can be determined).
Regarding claim 14, Yamada teaches a coating evaluation method comprising: acquiring shape information representing a curved shape of a coating surface based on measuring the coating surface or based on data pertaining to the coating surface (paragraph 12, the shape information used to correct the captured image);
irradiating the coating surface with incident light having a first intensity distribution (paragraphs 27-28, light emitted has a luminance gradient in direction A1);
acquiring a second intensity distribution of light reflected from the coating surface (FIG. 6(b), light-emitting surface reflected image PS, appearing curved due to the curvature of the surface under test);
calculating (FIG. 2, using image processor 20) a third intensity distribution associated with the second intensity distribution based on the shape information (FIG. 6(c) or 6(d), light-emitting surface reflected image PS, appearing straight due to image processing, the third intensity distribution being the one captured in the image after processing) by using a simulation technology that uses computer graphics (FIG. 6(d) shows a result of such a simulation, which after it has been processed by image processing processor 20 as a simulation of how light might be reflected from a flat surface otherwise similar to coating surface 4), the third intensity distribution being calculated to eliminate a scattering component of light on the coating surface that is included in the second intensity distribution, the scattering component being produced by a deviation of the curved shape from a flat shape (FIG. 6 shows correcting curved light-emitting surface reflected image PS in FIG. 6(b) to straighten the light-emitting surface reflected image PS as shown in 6(c). Note that deviation of a curved surface from a flat shape produces a distortion in the distribution of scattered light, which can be eliminated by rearranging pixels in an image that captures the distorted light); and
estimating a brilliance evaluation value that pertains to the coating surface based on the third intensity distribution by using an evaluation model, the evaluation model being configured to output the brilliance evaluation value in response to an input including the third intensity distribution (paragraph 10, differential processing after image processing to correct for curvature),
the brilliance evaluation value being an index indicating at least one of a degree of smoothness of the coating surface, a proportion of the light reflected by the coated surface via a diffuse reflection, and a degree of resolution of an image appearing on the coating surface (FIG. 2 shows detecting a recess 6. The presence of a defect, such as recess 6, causes the surface coating 4, taken as a whole, to be less smooth, even if certain parts of the surface (such as those away from the defect) are still smooth).
Regarding claim 15, Yamada teaches a non-transitory computer-readable storage medium having a coating evaluation program stored thereon, the program being executable by a computer to control (FIG. 1, host computer 22)
a light source unit including at least one light source (FIG. 2, light source unit 26, with light-emitting surface 26a), the light source unit being configured to irradiate a coating surface with incident light having a first intensity distribution (paragraphs 27-28, light emitted has a luminance gradient in direction A1); and
an intensity acquisition unit including a camera (FIG. 2, CCD camera 28), the intensity acquisition unit being configured to acquire a second intensity distribution of light reflected from the coating surface (FIG. 6(b), light-emitting surface reflected image PS, appearing curved due to the curvature of the surface under test),
to execute a step for acquiring shape information representing a curved shape of the coating surface (necessary to turn raw data represented in FIG. 6(b) into corrected images like that of 6(c)), the shape information being obtained by measuring the coating surface or acquiring design data pertaining to the coating surface (paragraph 12, the shape information is used to correct the captured image),
a step for calculating a third intensity distribution associated with the second intensity distribution based on the shape information (FIG. 6(c) or 6(d), light-emitting surface reflected image PS, appearing straight due to image processing, the third intensity distribution being the one captured in the image after processing) by using a simulation technology that uses computer graphics (FIG. 6(d) shows a result of such a simulation, which after it has been processed by image processing processor 20 as a simulation of how light might be reflected from a flat surface otherwise similar to coating surface 4), the third intensity distribution being calculated to eliminate a scattering component of light on the coating surface that is included in the second intensity distribution, the scattering component being produced by a deviation of the curved shape from a flat shape (FIG. 6 shows correcting curved light-emitting surface reflected image PS in FIG. 6(b) to straighten the light-emitting surface reflected image PS as shown in 6(c). Note that deviation of a curved surface from a flat shape produces a distortion in the distribution of scattered light, which can be eliminated by rearranging pixels in an image that captures the distorted light), and
a step for estimating a brilliance evaluation value that pertains to the coating surface based on the third intensity distribution by using an evaluation model, the evaluation model being configured to output the brilliance evaluation value in response to an input including the third intensity distribution (paragraph 10, differential processing after image processing to correct for curvature),
the brilliance evaluation value being an index indicating at least one of a degree of smoothness of the coating surface, a proportion of the light reflected by the coated surface via a diffuse reflection, and a degree of resolution of an image appearing on the coating surface (FIG. 2 shows detecting a recess 6. The presence of a defect, such as recess 6, causes the surface coating 4, taken as a whole, to be less smooth, even if certain parts of the surface (such as those away from the defect) are still smooth).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 4 and 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yamada (Foreign Patent Document JP H06307835 A) in view of Luo (Non-Patent Literature “Automated Visual Defect Detection for Flat Steel Surface: A Survey”).
Regarding claim 4, Yamada teaches the coating evaluation device according to claim 1 (as described above).
Yamada does not explicitly teach that the evaluation model is a trained model generated through machine learning that is based on teaching data in which an intensity distribution of reflected light obtained by irradiating a flat evaluated coating surface with the incident light and the brilliance evaluation value pertaining to the evaluated coating surface are taken as a set.
In the same field of endeavor of optical surface inspection, Luo does teach that an evaluation model is a trained model generated through machine learning (section D, Machine Learning) that is based on teaching data in which an intensity distribution of reflected light obtained by irradiating a flat evaluated coating surface with the incident light and the brilliance evaluation value pertaining to the evaluated coating surface are taken as a set (FIG. 8, left-hand side shows reflected light inputs based on flat workpieces to be evaluated). By training a model, the machine learning methods surveyed by Luo are able to do defect detection and defect classification tasks.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the surface inspection device of Yamada with the machine learning techniques taught by Luo in order to improve the analysis of the images after correcting for the curvature of the surface via image processing, taking advantage of techniques known in the art of automated optical inspection of flat surfaces to solve the same problem of defect detection and classification.
Regarding claim 12, Yamada teaches the coating evaluation device according to claim 1 (as described above).
Yamada does not explicitly teach a material acquisition unit configured to acquire material information pertaining to the coating surface, and the controller being configured to estimate the evaluation value that corresponds to a combination of the material information and the third intensity distribution by using the evaluation model that outputs a brilliance evaluation value pertaining to the coating surface in response to an input including the material information and the third intensity distribution.
In the same field of endeavor of optical surface inspection, Luo does teach acquiring material information pertaining to the coating surface, and the controller being configured to estimate the evaluation value that corresponds to a combination of the material information and the third intensity distribution by using the evaluation model that outputs a brilliance evaluation value pertaining to the coating surface in response to an input including the material information and the third intensity distribution (the top of the right-hand column of page 2 mentions water droplets and mill scales, materials distinct from the material of the surface under test, as examples of pseudo defects. Luo evaluates inspection methods, among other ways, on their ability to distinguish pseudo defects from actual defects, which requires awareness of the material under test). By using awareness of material properties surface inspection methods can be better able to distinguish true defects from false defects.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the surface inspection method of Yamada with consideration of the materials under inspection in the manner of Luo in order to improve the specificity of defect detection.
Regarding claim 13, Yamada, as modified by Luo, teaches or renders obvious the coating evaluation device according to claim 12 (as described above).
Yamada does not explicitly teach that the evaluation model is a trained model generated through machine learning that is based on teaching data in which the material information pertaining to a flat evaluated coating surface, an intensity distribution of reflected light obtained by irradiating the evaluated coating surface with the incident light, and a brilliance evaluation value pertaining to the evaluated coating surface are taken as a set.
In the same field of endeavor of optical surface inspection, Luo does teach that the evaluation model is a trained model generated through machine learning (section D, Machine Learning) that is based on teaching data in which the material information pertaining to a flat evaluated coating surface (Luo is inspecting flat surfaces, similar to the surfaces Yamada is inspecting after image processing techniques flatten the surface in the images. The material information is contained in training data of images of the kinds of surfaces to be inspected, which are made of the material to be inspected), an intensity distribution of reflected light obtained by irradiating the evaluated coating surface with the incident light (FIG. 8, left-hand image inputs), and a brilliance evaluation value pertaining to the evaluated coating surface are taken as a set (section D, labeled images of defective or non-defective samples). By using machine learning on data that includes information on the kind of material the models will be used to inspect, the machine learning techniques taught by Luo increase in accuracy.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the surface inspection device of Yamada with the machine learning techniques of Luo that include material information in order to optimize the resulting model to identify defects in that material more accurately.
Claim(s) 6-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yamada (Foreign Patent Document JP H06307835 A) in view of Hirose (Foreign Patent Document JP 2002148195 A).
Regarding claim 6, Yamada teaches the coating evaluation device according to claim 1 (as described above).
Yamada does not explicitly teach that the first intensity distribution has a periodic structure in a first direction.
In the same field of endeavor of optical surface inspection, Hirose does teach that the first intensity distribution has a periodic structure in a first direction (FIG. 1(b), which is periodic in the x direction). By using a periodic pattern in the x direction, Hirose can measure the sharpness of the transitions (the derivative) at transitions along the periodic pattern, allowing multiple gradients to measure along in the measurement area.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the surface inspection device of Yamada with a periodic pattern in the manner of Hirose to perform differential analysis across multiple iterations of a pattern emitted by the light source.
Regarding claim 7, Yamada teaches the coating evaluation device according to claim 1 (as described above).
Yamada does not explicitly teach that the first intensity distribution has a periodic structure in a second direction that is different from the first direction.
In the same field of endeavor of optical surface inspection, Hirose does teach that the first intensity distribution has a periodic structure in a second direction (FIG. 1(b), the y direction) that is different from the first direction (FIG. 1(b), the x direction, which also has a periodic structure). By using a periodic pattern in the y direction, Hirose can measure the sharpness of the transitions (the derivative) at transitions along the periodic pattern, allowing multiple gradients to measure along in the measurement area.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the surface inspection device of Yamada with a periodic pattern in the manner of Hirose to perform differential analysis across multiple iterations of a pattern emitted by the light source, including in a second direction.
Regarding claim 8, Yamada teaches the coating evaluation device according to claim 1 (as described above), wherein the controller is configured to calculate a main-direction vector at a prescribed position on the coating surface based on the shape information (FIG. 2, the direction along which arrow A1 is projected onto the surface under test), and is configured to set an intensity distribution that has a structure in a direction of the main-direction vector as the first intensity distribution (FIG. 2, illumination intensity is structured corresponding to the lengths of lines m along the A1 direction).
Yamada does not explicitly teach that the structure along the main-direction vector is periodic.
In the same field of endeavor of optical surface inspection, Hirose does teach that the structure along the main-direction vector is periodic (FIG. 1(b), the pattern is periodic in the x direction). By using a periodic pattern in the x direction, Hirose can measure the sharpness of the transitions (the derivative) at transitions along the periodic pattern, allowing multiple gradients to measure along in the measurement area.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the surface inspection device of Yamada with a periodic pattern in the manner of Hirose to perform differential analysis across multiple iterations of a pattern emitted by the light source along the main direction.
Conclusion
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/PAUL SCHNASE/Examiner, Art Unit 2877
/TARIFUR R CHOWDHURY/Supervisory Patent Examiner, Art Unit 2877