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 .
This action is responsive to the amendment of 5/12/2026.
Response to Arguments
Objections
The objections to the claims are overcome by amendment.
Rejections under 35 U.S.C. § 112
The existing rejections under 35 U.S.C. § 112(b) are overcome by amendment.
Prior Art Rejections
Applicant’s argument is that Yamada does not teach either a brilliance evaluation value that is an index indicating a proportion of the light reflected by the coated surface via a diffuse reflection or a degree of resolution of an image appearing on the coating surface, however, this argument is moot. Yamada is not relied on in the present action to teach either of the cited claim elements.
As the independent claims are not allowed, the dependent claims are not automatically allowable.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 18 and 19 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 18, while the specification does allow for the use of design information as the shape information, including computer-aided design (CAD) data (paragraph 11), and the use of 3D scanners (paragraph 13), no mention is made of obtaining shape information from a 3D printer in any part of the original disclosure, including the specification, drawings, and claims, nor is the underlying concept of 3D printing disclosed using other terms, whether explicitly, implicitly, or inherently. Further, while Applicant’s remarks assert that no new matter is added in the new claims, no particular passage of the disclosure is cited as supporting the newly added limitations, including those regarding the use of a 3D printer.
Regarding claim 19, the coating evaluation device of the original disclosure (including specification, drawings, and claims) does not appear to include a 3D printer (see FIG. 1, for example, which includes units to acquire various information (11, 13, and 15), illuminate the object (19), perform calculations (110, 120, and 130), and output (400), but does not include means to coat a surface, let alone means to produce a surface to be coated), nor disclose the underlying concept of 3D printing using other terms. Further, the disclosure does not appear to contemplate the means by which the surface is produced or the coating is applied to the surface, so it is unclear whether the inventors possessed a coating evaluation device as claimed that further incorporated a 3D printer or other manufacturing means on or before the effective filing date of the claimed invention.
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) 1-2, 9-11, and 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yamada (foreign patent document JP H06307835 A) in view of Asae (foreign patent document JP-H07311030-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 (FIG. 2, image processor 20) configured to acquire shape information representing a curved shape of the coating surface (paragraph 13, the extent of curvature in the image is information regarding the degree of curvature of the surface), the shape information being different from the second intensity distribution (a particular aspect measurable from an image (such as the degree of curvature of a bright reflection visible in the image) is different from both the image itself and the distribution of light that was captured in forming the image) and obtained by measuring the coating surface or acquiring design data pertaining to the coating surface (paragraph 13, the degree of curvature that needs to be corrected in the image is found from the image itself, which is obtained by measuring the coating surface),
calculate 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), the third intensity distribution being calculated by using a simulation technology that includes at least one of rendering and shading (FIG. 6(d) shows a rendering of such a simulation, which after it has been rendered by image processing processor 20 as a simulation of how light might be reflected from a flat surface otherwise similar to coating surface 4), 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).
Yamada does not explicitly teach the brilliance evaluation value being an index indicating at least one of 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.
In the same field of endeavor of optical coating evaluation, Asae does teach the brilliance evaluation value being an index indicating at least one of 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. 10, which shows images due to a difference in properties of a coated surface (page 14, brief description of FIG. 10). Note that FIG. 10C shows both a poor degree of resolution in the image reflected from the coating surface and considerable muddying of the stripes, as may be caused by an excess of diffuse reflection). By measuring diffuse scattering and resolution of an image, Asae is able to measure three different types of textures on different length scales (section [0008], second paragraph).
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 coating evaluation device of Yamada with the resolution and diffuse light measurement of Asae to gain the predictable benefit of measuring multiple types of textures with a reasonable expectation of success.
Regarding claim 2, Yamada, as modified by Asae, teaches or renders obvious the coating evaluation device according to claim 1 (as described above).
Yamada further teaches that 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, as modified by Asae, teaches or renders obvious the coating evaluation device according to claim 1 (as described above).
Yamada further teaches that 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, as modified by Asae, teaches or renders obvious the coating evaluation device according to claim 1 (as described above).
Yamada further teaches that 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, as modified by Asae, teaches or renders obvious the coating evaluation device according to claim 1 (as described above).
Yamada further teaches that 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), the second intensity distribution being different from the shape information (note that a particular aspect measurable from an image (such as the degree of curvature of a bright reflection visible in the image);
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 includes at least one of rendering and shading (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); 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),
Yamada does not explicitly teach the brilliance evaluation value being an index indicating at least one of 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.
In the same field of endeavor of optical coating evaluation, Asae does teach that the brilliance evaluation value being an index indicating at least one of 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. 10, which shows images due to a difference in properties of a coated surface (page 14, brief description of FIG. 10). Note that FIG. 10C shows both a poor degree of resolution in the image reflected from the coating surface and considerable muddying of the stripes, as may be caused by an excess of diffuse reflection). By measuring diffuse scattering and resolution of an image, Asae is able to measure three different types of textures on different length scales (section [0008], second paragraph).
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 coating evaluation device of Yamada with the resolution and diffuse light measurement of Asae to gain the predictable benefit of measuring multiple types of textures with a reasonable expectation of success.
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 different from the second intensity distribution (a particular aspect measurable from an image (such as the degree of curvature of a bright reflection visible in the image) is different from both the image itself and the distribution of light that was captured in forming the image) and 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 includes at least one of rendering and shading (FIG. 6(d) shows a rendering of such a simulation, which after it has been rendered by image processing processor 20 as a simulation of how light might be reflected from a flat surface otherwise similar to coating surface 4), 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).
Yamada does not explicitly teach the brilliance evaluation value being an index indicating at least one of 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.
In the same field of endeavor of optical coating evaluation, Asae does teach the brilliance evaluation value being an index indicating at least one of 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. 10, which shows images due to a difference in properties of a coated surface (page 14, brief description of FIG. 10). Note that FIG. 10C shows both a poor degree of resolution in the image reflected from the coating surface and considerable muddying of the stripes, as may be caused by an excess of diffuse reflection). By measuring diffuse scattering and resolution of an image, Asae is able to measure three different types of textures on different length scales (section [0008], second paragraph).
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 coating evaluation device of Yamada with the resolution and diffuse light measurement of Asae to gain the predictable benefit of measuring multiple types of textures with a reasonable expectation of success.
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 Asae (foreign patent document JP-H07311030-A), further in view of Luo (Non-Patent Literature “Automated Visual Defect Detection for Flat Steel Surface: A Survey”).
Regarding claim 4, Yamada, as modified by Asae, teaches or renders obvious 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, as modified by Asae, 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, as modified by Asae, teaches or renders obvious the coating evaluation device according to claim 1 (as described above).
Yamada further teaches that 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, as modified by Asae, 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 Asae, 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, as modified by Asae, 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 Asae (foreign patent document JP-H07311030-A), further in view of Hirose (foreign patent document JP 2002148195 A).
Regarding claim 6, Yamada, as modified by Asae, teaches or renders obvious 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, as modified by Asae, 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, as modified by Asae and Hirose, teaches or renders obvious the coating evaluation device according to claim 6 (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, as modified by Asae and Hirose, 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, as modified by Asae, teaches or renders obvious the coating evaluation device according to claim 1 (as described above).
Yamada further teaches that 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, as modified by Asae, 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.
Claim(s) 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yamada (foreign patent document JP H06307835 A) in view of Asae (foreign patent document JP-H07311030-A), further in view of Penny (US patent document 20190118300).
Regarding claim 18, Yamada, as modified by Asae, teaches or renders obvious the coating evaluation device according to claim 1 (as described above).
Yamada does not explicitly teach that the shape information is acquired as computer-aided design data or as measured data obtained from a 3D printer.
In the same field of endeavor of optical evaluation of manufacturing processes, Penny does teach that the shape information is acquired as computer-aided design data (paragraph 25, last sentence, cad data can be used to generate control signals) or as measured data obtained from a 3D printer (FIG. 1, hypercube 44, measured from the 3D printer 20). By using CAD data and taking measurements from a 3D printer, Penny is able to keep track of both the intended shape of an object and how the shape of the object develops during manufacturing.
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, as modified by Asae, by getting shape information from CAD code and monitoring manufacturing of the object in the manner of Penny to gain a better of understanding the surface shape prior to evaluating the coating and gaining the predicable benefit of having an estimate of the extent to which curvature will need to be corrected for before taking the measurements.
Regarding claim 19, Yamada, as modified by Asae, teaches or renders obvious the coating evaluation device according to claim 18 (as described above).
Yamada does not explicitly teach that the coating evaluation device includes a 3D printer and the shape information is acquired from the 3D printer.
In the same field of endeavor of optical evaluation of manufacturing processes, Penny does teach that the coating evaluation device includes a 3D printer (FIG. 1, 3D printer 20) and the shape information is acquired from the 3D printer (FIG. 1, hypercube 44). By measuring how the component 36 is shaping up, Penny is able to control more precisely the remainder of the manufacturing process (paragraph 6).
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, as modified by Asae, by getting shape information by monitoring the additive manufacturing of the object in the manner of Penny to gain the predictable benefit of knowing how the curvature of the object will need to be corrected for when evaluating the coating and whether any superficial defects will need to be concealed by the coating.
Regarding claim 20, Yamada, as modified by Asae, teaches or renders obvious the coating evaluation device according to claim 1 (as described above).
Yamada does not explicitly teach that the shape information is acquired in advance before acquiring the second intensity distribution.
In the same field of endeavor of optical evaluation of manufacturing processes, Penny does teach that the shape information is acquired in advance before acquiring the second intensity distribution (paragraph 19, CAD code, a type of shape information, is used in manufacturing the object, which necessarily comes before imaging a coating on the object). By acquiring shape information about the object in advance of applying and evaluating a coating, Penny is able to correct defects in the underlying structure (paragraph 19) that may remain visible after the coating is applied.
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, as modified by Asae, by getting shape information in advance of evaluating the coating in the manner of Penny to gain the predictable benefit of preventing or fixing defects before the coating is even applied or to know how the curvature of the object will need to be corrected for when evaluating the coating.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAUL D SCHNASE whose telephone number is (703)756-1691. The examiner can normally be reached Monday - Friday 8:30 AM - 5:00 PM ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tarifur Chowdhury can be reached at (571) 272-2287. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PAUL SCHNASE/Examiner, Art Unit 2877
/TARIFUR R CHOWDHURY/Supervisory Patent Examiner, Art Unit 2877
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