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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 April 02, 2026 has been entered.
Claim Status
Applicant’s amendment filed on April 02, 2026 is acknowledged. Currently claims 1-2 and 4-11 are pending. Claims 1, 10, and 11 have been amended. Claim 3 is cancelled.
Response to Arguments and Amendments
Applicant’s arguments with respect to the rejections of claims 1-11 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection of claims 1-2 and 4-11 is made in view of Toyoda in view of Umehara and Minato.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 1-2 and 4-11 are rejected under 35 U.S.C. 103 as being unpatentable of Toyoda et al., US 20130279793 A1, (hereinafter “Toyoda”) in view of Umehara et al., US 20180047152 A1, (hereinafter “Umehara”) in further view of Minato et al., US 20150117750 A1, (hereinafter “Minato”).
Regarding claim 1, Toyoda teaches an image processing apparatus comprising:
at least one memory storing instructions ([0071] “Moreover, the digital signals are inputted into the control device 214, then being stored into a memory 208.”); and
at least one processor that, upon execution of the stored instructions ([0071] “In the control device 214, the digital signals are subjected to purpose-dependent image processing by a CPU 209 and image processing hardware 210 such as ASIC or FPGA.”), are configured to:
acquire image data obtained by imaging an object having a surface on which an illumination image is generated, ([0108] “Namely, a SEM-image acquisition unit 1701 for acquiring image data from the circuit pattern 401 or the like”) ([0061] “Also, in the case of inspecting a defect such as a scum by making the comparison between the design data and an inspection-pattern image, the essential difference therebetween (i.e., the design data is line-segment data; whereas the inspection-pattern image is luminance information) exerts an influence upon the comparison.” wherein an illumination image is inspection-pattern image);
calculate a variation amount of an edge position of the illumination image and a variation amount of an edge luminance of the illumination image based on the image data ([0109] “Moreover, the calculation units are so configured as to become accessible to the memory unit, depending on the requirements.”) ([0145] “This condition makes it possible to identify the pattern's groove portion, thereby allowing the calculation of a variation in the image signal of this identified region by using the variance, standard deviation, or the like.” wherein an edge position is the pattern’s groove portion) ([0146] “In the scum detection where the luminance distribution is utilized, FIG. 28 illustrates upper-portion contour data 2801 and lower-portion contour data 2802 about the pattern edge of a scum-existing line pattern.” wherein a variation amount of an edge luminance is the luminance distribution about the pattern edge) ([0061] “Also, in the case of inspecting a defect such as a scum by making the comparison between the design data and an inspection-pattern image, the essential difference therebetween (i.e., the design data is line-segment data; whereas the inspection-pattern image is luminance information) exerts an influence upon the comparison.” wherein an illumination image is inspection-pattern image); and
evaluate by calculatingthe variation amount of the edge position and the variation amount of the edge luminance as an evaluation value indicating a state of the surface of the object, ([0145] “This condition makes it possible to identify the pattern's groove portion, thereby allowing the calculation of a variation in the image signal of this identified region by using the variance, standard deviation, or the like.” wherein an edge position is the pattern’s groove portion) ([0146] “In the scum detection where the luminance distribution is utilized, FIG. 28 illustrates upper-portion contour data 2801 and lower-portion contour data 2802 about the pattern edge of a scum-existing line pattern.” wherein a variation amount of an edge luminance is the luminance distribution about the pattern edge) ([0151] “Next, the plurality of intervals (i.e., error values) 2703 between the upper-portion contour data 2701 and the lower-portion contour data 2702 are measured all-inclusively for a plurality of regions within the scum-detection target region 2704 along the pattern's edge (step 3703). Moreover, the average value or standard deviation of the error values measured for each of the regions is calculated (step 3704). The value determined here may be either of the average value and the standard deviation. Otherwise, both of them may be calculated to be used as evaluation targets at the following steps.” wherein an evaluation value is the average value and the variation amount of the edge position and edge luminance are plurality of intervals between the contour data).
Toyoda does not specifically disclose wherein the surface of the object is illuminated by a fluorescent tube.
However, Umehara teaches wherein the surface of the object is illuminated by a fluorescent tube ([0021] “FIG. 1 is a schematic front view of the appearance inspection apparatus 100 according to this embodiment. Referring to FIG. 1, the appearance inspection apparatus 100 includes a light source 10A such as a light emitting diode (LED). The appearance inspection apparatus 100 includes an illumination device 10 and a camera 20. The illumination device 10 radiates light from the light source 10A to an inspection surface.” wherein a fluorescent tube is a light source).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include a fluorescent tube of Umehara in the image processing apparatus of Toyoda to better identify any surface irregularities without damaging the objects.
Toyoda in view of Umehara does not specifically disclose a calculated weighted sum, wherein a weight in the weighted sum is based on a characteristic of an object.
However, Minato teaches a calculated weighted sum, wherein a weight in the weighted sum is based on a characteristic of an object ([0065] “However, the evaluation function of the degree of pixel separation is not limited to the summation of the foreground likelihood and the background likelihood. For example, a product, a weighted sum, a weighted product, a nonlinear function sum, a nonlinear function product of the likelihood of the foreground and the likelihood of the background may be used.”) ([0064] “When the user increases the priority for the color information, the value of parameter .lamda. is decreased to increase the weight of the data term. When the user increases the priority for the edge information, the value of parameter .lamda. is increased to increase the weight of the smoothing term…This is because the data term can be estimated to have high reliability in the case that there is a clear color different between the foreground and the background. On the other hand, the value of parameter .lamda. is increased to increase the weight of the smoothing term when the difference between the foreground representative color and the background representative color is small. This is because the better result tends to be obtained in the region segmentation based on the edge information rather than the color information in the case that there is an unclear color different between the foreground and the background.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include a weighted sum wherein a weight of the variation amount is based on an object’s surface characteristic of Minato in the object surface image processing apparatus of Toyoda in view of Umehara to control the weight of surface characteristics of the object according to their ability to enhance edge detection, thereby allowing for improved accuracy during edge detection.
Regarding claim 2, Toyoda in view of Umehara and Minato teaches the image processing apparatus according to claim 1, wherein the variation amount of the edge position and the variation amount of the edge luminance are calculated (Toyoda - [0109] “Moreover, the calculation units are so configured as to become accessible to the memory unit, depending on the requirements.”) (Toyoda - [0145] “This condition makes it possible to identify the pattern's groove portion, thereby allowing the calculation of a variation in the image signal of this identified region by using the variance, standard deviation, or the like.” wherein an edge position is the pattern’s groove portion) (Toyoda - [0146] “In the scum detection where the luminance distribution is utilized, FIG. 28 illustrates upper-portion contour data 2801 and lower-portion contour data 2802 about the pattern edge of a scum-existing line pattern.” wherein a variation amount of an edge luminance is the luminance distribution about the pattern edge) based on a first profile indicating the edge position of the illumination image and a second profile indicating the edge luminance of the illumination image (Toyoda - [0127] “Namely, each line pattern in which a scum 2500 exists at the lower portion 2503 (which, hereinafter, will be referred to as "the pattern-edge lower portion 2503") of the pattern edge, the cross-section shape 2504 (hereinafter, referred to as "the cross-section profile 2504") of each line pattern, the luminance profile 2507 (hereinafter, referred to as "the image profile 2507") of each line pattern's image data corresponding to the cross-section, and design data 2501 based on the pattern-matching result.” wherein a first profile is the cross-section profile and a second profile is the luminance profile) (Toyoda - [0061] “Also, in the case of inspecting a defect such as a scum by making the comparison between the design data and an inspection-pattern image, the essential difference therebetween (i.e., the design data is line-segment data; whereas the inspection-pattern image is luminance information) exerts an influence upon the comparison.” wherein an illumination image is inspection-pattern image).
The motivation for combining Toyoda, Umehara and Minato is the same motivation as used for claim 1.
Regarding claim 4, Toyoda in view of Umehara and Minato teaches the image processing apparatus according to claim 1, wherein a degree of contribution of the variation amount of the edge position to an evaluation value is made smaller as gloss image clarity of the object is lower (Toyoda - [0108] “a detection unit 1704 for detecting edge branch points that are identified by the pattern matching, an identification unit 1705 for identifying the detected edge branch points on the basis of a predetermined condition,” ) (Toyoda -[0151] “Next, the plurality of intervals (i.e., error values) 2703 between the upper-portion contour data 2701 and the lower-portion contour data 2702 are measured all-inclusively for a plurality of regions within the scum-detection target region 2704 along the pattern's edge (step 3703). Moreover, the average value or standard deviation of the error values measured for each of the regions is calculated (step 3704). The value determined here may be either of the average value and the standard deviation. Otherwise, both of them may be calculated to be used as evaluation targets at the following steps.” wherein an evaluation value is the average value and the variation amount of the edge position and edge luminance are plurality of intervals between the contour data) (Umehara - [0029] “When the inspection surface K is a glossy surface, the ratio of the intensity of the light that is specularly reflected to enter the camera 20A is significantly higher than the ratio of the intensity of the light that is diffusely reflected to enter the camera 20A.” wherein gloss image clarity is a glossy surface and a degree of contribution is the ratio of the intensity; It is obvious to one of ordinary skill in the art that if the surface is glossy and the ratio of the intensity of the light that is specularly reflected is higher, then when the surface is not glossy, the ratio of the intensity of the light that is specularly reflected is lower).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the image processing apparatus of Toyoda in view of Minato, to evaluate edge reflectivity of Umehara because high edge reflectivity can be indicative of surface irregularities or roughness.
Regarding claim 5, Toyoda in view of Umehara and Minato teaches the image processing apparatus according to claim 1, wherein a degree of contribution of the variation amount of the edge position to an evaluation value is made smaller as a specular reflectivity of the object is lower (Toyoda - [0108] “a detection unit 1704 for detecting edge branch points that are identified by the pattern matching, an identification unit 1705 for identifying the detected edge branch points on the basis of a predetermined condition,” ) (Toyoda - [0151] “Next, the plurality of intervals (i.e., error values) 2703 between the upper-portion contour data 2701 and the lower-portion contour data 2702 are measured all-inclusively for a plurality of regions within the scum-detection target region 2704 along the pattern's edge (step 3703). Moreover, the average value or standard deviation of the error values measured for each of the regions is calculated (step 3704). The value determined here may be either of the average value and the standard deviation. Otherwise, both of them may be calculated to be used as evaluation targets at the following steps.” wherein an evaluation value is the average value and the variation amount of the edge position and edge luminance are plurality of intervals between the contour data) (Umehara - [0035] “In a range S4 with irregularities such as a flaw on the inspection surface K, the degree of specular reflection is lower and the degree of diffuse reflection is higher than those in a range S3 without irregularities such as a flaw. Therefore, the intensity of light that enters the camera 20 from the range S4 is lower than the intensity of light that enters the camera 20 from the range S3.” wherein a degree of contribution is the intensity of light).
The motivation for combining Toyoda, Umehara and Minato is the same motivation as used for claim 4.
Regarding claim 6, Toyoda in view of Umehara and Minato teaches the image processing apparatus according to claim 1, wherein a degree of contribution of the variation amount of the edge position to an evaluation value is made smaller as a diffuse reflectivity of the object is higher (Toyoda - [0108] “a detection unit 1704 for detecting edge branch points that are identified by the pattern matching, an identification unit 1705 for identifying the detected edge branch points on the basis of a predetermined condition,”) (Toyoda - [0151] “Next, the plurality of intervals (i.e., error values) 2703 between the upper-portion contour data 2701 and the lower-portion contour data 2702 are measured all-inclusively for a plurality of regions within the scum-detection target region 2704 along the pattern's edge (step 3703). Moreover, the average value or standard deviation of the error values measured for each of the regions is calculated (step 3704). The value determined here may be either of the average value and the standard deviation. Otherwise, both of them may be calculated to be used as evaluation targets at the following steps.” wherein an evaluation value is the average value and the variation amount of the edge position and edge luminance are plurality of intervals between the contour data) (Umehara - [0035] “In a range S4 with irregularities such as a flaw on the inspection surface K, the degree of specular reflection is lower and the degree of diffuse reflection is higher than those in a range S3 without irregularities such as a flaw. Therefore, the intensity of light that enters the camera 20 from the range S4 is lower than the intensity of light that enters the camera 20 from the range S3.” wherein a degree of contribution is the intensity of light).
The motivation for combining Toyoda, Umehara and Minato is the same motivation as used for claim 4.
Regarding claim 7, Toyoda in view of Umehara and Minato teaches the image processing apparatus according to claim 1, wherein a degree of contribution of the variation amount of the edge position to an evaluation value is made smaller as a gradient of the edge is smaller (Toyoda - [0108] “a detection unit 1704 for detecting edge branch points that are identified by the pattern matching, an identification unit 1705 for identifying the detected edge branch points on the basis of a predetermined condition,” ) (Toyoda -[0155] “First, the SEM image of a pattern, i.e., the evaluation target, is acquired using the SEM 201 (step 4401). After that, the line profile is created based on the luminance information on the image (step 4402). Here, as illustrated in FIG. 44B, the line profile exhibits a tendency that the signal intensity becomes higher and sharper in its location where the inclination of the pattern 4405 is larger.” wherein a gradient is the signal intensity; It is obvious to one of ordinary skill in the art to make a degree of contribution to an evaluation value smaller as a gradient of the edge is smaller because when the line profile exhibits a higher signal intensity where the inclination of the edge pattern is larger) (Toyoda - [0151] “Next, the plurality of intervals (i.e., error values) 2703 between the upper-portion contour data 2701 and the lower-portion contour data 2702 are measured all-inclusively for a plurality of regions within the scum-detection target region 2704 along the pattern's edge (step 3703). Moreover, the average value or standard deviation of the error values measured for each of the regions is calculated (step 3704). The value determined here may be either of the average value and the standard deviation. Otherwise, both of them may be calculated to be used as evaluation targets at the following steps.” wherein an evaluation value is the average value and the variation amount of the edge position and edge luminance are plurality of intervals between the contour data).
The motivation for combining Toyoda, Umehara and Minato is the same motivation as used for claim 1.
Regarding claim 8, Toyoda in view of Umehara and Minato teaches the image processing apparatus according to claim 1, wherein a degree of contribution of the variation amount of the edge position to an evaluation value is made smaller as a contrast of the edge is smaller (Toyoda - [0108] “a detection unit 1704 for detecting edge branch points that are identified by the pattern matching, an identification unit 1705 for identifying the detected edge branch points on the basis of a predetermined condition,”) (Toyoda - [0173] “At this time, it is desirable not to perform an operation that will exert an influence on the form of the profile, such as an automatic adjustment of the image's contrast/brightness. This not-to-perform-the-operation is desirable in order to evaluate the height of the profile with a constant criterion always used under whatever conditions.” wherein a degree of contribution is an influence on the form of the profile and a contrast is the image’s contrast/brightness; It is obvious to one of ordinary skill in the art to make a degree of contribution to an evaluation value smaller as a contrast of the edge is smaller because it is not desirable to adjust the image’s contrast while evaluating the profile) (Toyoda - [0151] “Next, the plurality of intervals (i.e., error values) 2703 between the upper-portion contour data 2701 and the lower-portion contour data 2702 are measured all-inclusively for a plurality of regions within the scum-detection target region 2704 along the pattern's edge (step 3703). Moreover, the average value or standard deviation of the error values measured for each of the regions is calculated (step 3704). The value determined here may be either of the average value and the standard deviation. Otherwise, both of them may be calculated to be used as evaluation targets at the following steps.” wherein an evaluation value is the average value and the variation amount of the edge position and edge luminance are plurality of intervals between the contour data).
The motivation for combining Toyoda, Umehara and Minato is the same motivation as used for claim 1.
Regarding claim 9, Toyoda in view of Umehara and Minato teaches the image processing apparatus according to claim 1, wherein a degree of contribution of the variation amount of the edge position to an evaluation value is made smaller as a width of the illumination image is larger (Toyoda - [0108] “a detection unit 1704 for detecting edge branch points that are identified by the pattern matching, an identification unit 1705 for identifying the detected edge branch points on the basis of a predetermined condition,”) (Toyoda - [0061] “Also, in the case of inspecting a defect such as a scum by making the comparison between the design data and an inspection-pattern image, the essential difference therebetween (i.e., the design data is line-segment data; whereas the inspection-pattern image is luminance information) exerts an influence upon the comparison.” wherein an illumination image is inspection-pattern image) (Toyoda - [0162] “Furthermore, if the occurrence level becomes even higher, a scum is formed which is equipped with its constant width or height (: degree-of-risk level 4). FIG. 38 indicates the relationship between the extent of the width and height of a scum that has occurred, and the degree-of-risk level of this scum. What is referred to as "the degree-of-risk level" here means the easiness with which the short-circuit of the patterns will take place, the extent of the difficulty with which this scum can be removed, and the like. Namely, a microscopic scum whose occurrence level is low is removable in the fabrication process. However, the higher the scum's occurrence level becomes, the more difficult the scum's removal becomes.” wherein a degree of contribution is the degree of risk level and a width of the illumination image is the width of a scum; wherein the extent of the width and height of a scum is indicated by an occurrence level) (Toyoda - [0151] “Next, the plurality of intervals (i.e., error values) 2703 between the upper-portion contour data 2701 and the lower-portion contour data 2702 are measured all-inclusively for a plurality of regions within the scum-detection target region 2704 along the pattern's edge (step 3703). Moreover, the average value or standard deviation of the error values measured for each of the regions is calculated (step 3704). The value determined here may be either of the average value and the standard deviation. Otherwise, both of them may be calculated to be used as evaluation targets at the following steps.” wherein an evaluation value is the average value and the variation amount of the edge position and edge luminance are plurality of intervals between the contour data).
The motivation for combining Toyoda, Umehara and Minato is the same motivation as used for claim 1.
Regarding claim 10, the claim recites similar limitations to claim 1 but in the form of a method. Therefore, claim 10 recites similar limitations to claim 1 and is rejected for similar rationale and reasoning (see the analysis for claim 1 above).
Regarding claim 11, the claim recites similar limitations to claim 1 but in the form of a non-transitory computer-readable storage medium (Minato - [0022] “Additionally, the present invention can also be considered as an image examination method or an examination region setting method, in which at least one of the steps is performed, a program configured to cause a computer to perform the image examination method or the examination region setting method, and a storage medium in which the program is stored.”). Therefore, claim 11 recites similar limitations to claim 1 and is rejected for similar rationale and reasoning (see the analysis for claim 1 above).
Conclusion
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/AMANDA H PEARSON/Examiner, Art Unit 2666 /MING Y HON/Primary Examiner, Art Unit 2666
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