DETAILED ACTION
Claims 1-8, 10-18 and 20 are pending in this application and have been examined under the effective filing date of 08/10/2023 in accordance with the applicant’s claim for foreign priority. Claims 1, 3, 5-8 and 10-16 are amended and claims 9 and 19 are canceled in this application.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/13/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Response to Arguments
35 U.S.C. 112(f)
Applicant’s arguments (see Remarks, filed 02/27/2026) regarding the claim interpretations under 35 U.S.C. 112(f) have been fully considered by the examiner and are persuasive. Therefore, in view of the amendments to the claims, the examiner agrees to withdraw the interpretations made under 35 U.S.C. 112(f).
35 U.S.C. 102 and 103
Applicant’s arguments (see Remarks, filed 02/27/2026) regarding the rejections to claims 1 and 16 have been fully considered by the examiner and are not persuasive. Applicant argues that Kishimoto fails to teach each and every limitation of claim because the corner pattern detecting of Kishimoto differs from the disclosed invention. Applicant argues that Kishimoto does not teach a determination that gradients of pixel pairs are equal or identical to one another as part of the corner detection method. The examiner disagrees, Kishimoto teaches in column 12 lines 15-25 that inclination vectors, which are analogous to a gradient, are compared to determine whether they are equal to or smaller than a selected constant, indicating that gradients are compared and determined to be equal to the same value or not, therefore they would be equal to one another. This comparison is used to determine the edge direction for edges which make up the corner pattern, further, to determine the corner itself, Kishimoto uses the detected gradients which have been verified as described above, therefore the detection of the corner is based on the comparison of the gradients/inclination vectors (see Kishimoto, column 9 lines 20-50). Therefore, for at least the reasons above, the examiner maintains the rejections made in view of Kishimoto as fully discussed above.
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(Kishimoto, column 12 lines 15-25, emphasis added)
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(Kishimoto, column 9)
Claim Rejections - 35 USC § 102
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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, 6-8 and 15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kishimoto (US 6339479 B1).
Regarding claim 1 Kishimoto discloses; An image signal processor comprising:
a first determination processor configured to determine whether a target kernel including a target pixel corresponds to a corner pattern (Kishimoto, column 9 lines 36-51, figure 4 shows a system with right-angle corner pattern detecting means (determiner) for detecting the corner pattern in an image, column 10 lines 57 – column 11 line 2 groups of pixels are interpolated based upon the edge and corner pattern detection, the examiner is interpreting a target kernel to be a group of pixels in the image per [0035] and [0036] of applicant’s specification, additionally, [00186]-[00194] of the applicant’s specification, a processor or other hardware may perform the functions of the determiners, therefore, as per column 2 lines 23-40 of Kishimoto, the system may perform the method using hardware and computing components, making the system’s means for executing the process using hardware functionally equivalent to the determiners of the present claim);
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(Kishimoto, column 9 emphasis added)
a second determination processor configured to determine a corner pattern group corresponding to the target kernel when the target kernel corresponds to the corner pattern (Kishimoto, Figure 6, shows a corner pattern as determined by the corner pattern determination means, column 10 lines 46- column 11 line 2 details that as per figure 6 pixels in the group belonging to the corner pattern are interpolated, which means it would contain a target pixel to be interpolated within the corner pattern, additionally, [00186]-[00194] of the applicant’s specification, a processor or other hardware may perform the functions of the determiners, therefore, as per column 2 lines 23-40 of Kishimoto, the system may perform the method using hardware and computing components, making the system’s means for executing the process using hardware functionally equivalent to the determiners of the present claim);
a third determination processor configured to determine a target corner pattern corresponding to the target kernel from among a plurality of corner patterns of a corner pattern group corresponding to the target kernel (Kishimoto, column 9 lines 36-51, figure 4 shows a system with right-angle corner pattern detecting means (determiner) for detecting the corner pattern, column 10 lines 57 – column 11 line 2 pixels are interpolated based upon the edge and corner pattern directions, the examiner is interpreting a target kernel to be a group of pixels in the image per [0035] and [0036] of applicant’s specification, further figures 6 and 7 show multiple blocks in differing shades to show multiple corner patterns being distinguished, additionally, [00186]-[00194] of the applicant’s specification, a processor or other hardware may perform the functions of the determiners, therefore, as per column 2 lines 23-40 of Kishimoto, the system may perform the method using hardware and computing components, making the system’s means for executing the process using hardware functionally equivalent to the determiners of the present claim);
and a pixel interpolation processor configured to interpolate the target pixel using pixel data of a pixel corresponding to the target corner pattern (Kishimoto , column 10 lines 57 – column 11 line 2 pixels are interpolated based upon the edge and corner pattern directions, pixels have interpolation vectors generated for them corresponding to the corner patterns, additionally, [00186]-[00194] of the applicant’s specification, a processor or other hardware may perform the functions of the determiners and the interpolator, therefore, as per column 2 lines 23-40 of Kishimoto, the system may perform the method using hardware and computing components, making the system’s means for executing the process using hardware functionally equivalent to the determiners of the present claim);
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(Kishimoto, Figures 6 and 7)
wherein the second determination processor is configured to:
determine whether gradient directions of pixel pairs located to face each other in an edge region of the target kernel cross each other (Kishimoto, Figure 6, each box is a pixel, the lines are gradients of the pixels in the area, and the vectors are determined as being perpendicular or crossing each other)
and determine that the target corner pattern corresponds to a corner pattern of a first group when the gradient directions cross each other (Kishimoto, column 10 lines 15-25, the maximum inclination/gradient in each direction is determined, column 10 lines 45- 67 a perpendicular vector is computed based on these maximum gradients in each direction and their positions, as well as the maximum pixel values, column 11 lines 40-50 the perpendicular vector is used to determine the corner pattern accordingly, Column 12 line 58 – column 13 line 10 the corner is detected based on the perpendicular vector (generated using the gradient sum) which includes the pixel to be interpolated (target pixel), therefore the determination of the corner region includes the target pixel);
and determine whether the gradient directions of a plurality of pixel pairs arranged in a specific region within the target kernel are equal to each other (Kishimoto, column 12 lines 15-40, the inclination vectors (gradients) for the plurality of pixels are used to determine which group/pattern they belong to by determining whether the inclination/gradient is equal to or within a specific range of values, therefore pixel gradients/inclination vectors in these areas would be equal to each other or fall within the same range of values)
and determine that the target corner pattern corresponds to a corner pattern of a second group when the gradient directions of the plurality of pixel pairs in the specific region within the target kernel are equal to each other (Kishimoto, column 12 lines 15-40, the inclination vectors (gradients) for the plurality of pixels are used to determine which group/pattern they belong to by determining whether the inclination/gradient is equal to or within a specific range of values, therefore pixel gradients/inclination vectors in these areas would be equal to each other or fall within the same range of values).
Regarding claim 2, Kishimoto discloses; The image signal processor according to claim 1, wherein the corner pattern is a pattern filled with a texture region and a non-texture region (Kishimoto, column 2 lines 4-22, the corner region may have a jaggy edge (texture), interpolation of target pixels are used to correct this jaggy edge, further, column 11 lines 3-33 details that the shaded corner regions in figures 6 and 7 have similar pixel values, meaning their colors/attributes are similar, further, some of the pixels have a value of 0, indicating no texture, therefore this is functionally equivalent to a texture and non-texture region in the corner pattern per [0053] of the applicant’s spec defines texture regions as groups of pixels having similarity in color or other attributes.),
wherein the texture region and the non-texture region are distinguished from each other through boundary lines (Kishimoto, Figure 4 and column 9 lines 38-50, the edge detection unit detects a vertical, horizontal and diagonal inclination/boundary based on the pixel values to determine areas in the image, because this is based on the edges and pixel values of the image, pixels with similar characteristics/textures would be grouped and separated by boundary lines),
and wherein the boundary lines include: a horizontal line passing through the target kernel, contacting one side of the target pixel (Kishimoto, column 11 line 3- 35, the regions are grouped by pixel values, figure 7 shows the pixels (each box is one pixel in the figure), the 6 pixels in the lower left corner have a value of 0, the pixels in the 3 shaded regions with the bold lines have a mean pixel value (texture and non-texture regions), and the grid lines in the i and j directions (vertical and horizontal lines in the target region of pixels/kernel) delineate each region, further, the vectors shown in figure 7 are generated based upon the directions of the interpolated pixel vectors in the i and j directions);
and a vertical line passing through the target kernel, contacting another side of the target pixel (Kishimoto, column 11 line 3- 35, the regions are grouped by pixel values, figure 7 shows the pixels (each box is one pixel in the figure), the 6 pixels in the lower left corner have a value of 0, the pixels in the 3 shaded regions with the bold lines have a mean pixel value (texture and non-texture regions), and the grid lines in the i and j directions (vertical and horizontal lines in the target region of pixels/kernel) delineate each region, further, the vectors shown in figure 7 are generated based upon the directions of the interpolated pixel vectors in the i and j directions).
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(Kishimoto, Figure 7)
Regarding claim 3, Kishimoto discloses; The image signal processor according to claim 1, wherein the first determination processor is configured to:
calculate the gradient sum in a specific direction within the target kernel (Kishimoto, column 11 line 3- 35, the regions are grouped by pixel values, figure 7 shows the pixels (each box is one pixel in the figure), the 6 pixels in the lower left corner have a value of 0, the pixels in the 3 shaded regions with the bold lines have a mean pixel value (texture and non-texture regions), and the grid lines in the i and j directions (vertical and horizontal lines in the target region of pixels/kernel) delineate each region, further, the vectors shown in figure 7 are generated based upon the directions of the interpolated pixel vectors in the i and j directions, the vectors further determined based upon the pixel interpolation values are generated in vector form (gradients) in a specific direction);
and determine whether the target kernel corresponds to the corner pattern based on the gradient sum (Kishimoto, column 11 lines 40-51 the detection and direction of the detected gradients dictates whether a right angle corner pattern is detected in the area, further column 12 line 67 through column 13 line 25, the corner is determined/detected using the equation (9) with values from the determined vectors shown in figure 7, since the absolute value of equation 9 is used, the functions of equations 9 would be a gradient sum using the vector values in order to determine the corner pattern).
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(Kishimoto, Figure 7, emphasis added)
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(Kishimoto, column 11, emphasis added)
Regarding claim 4 Kishimoto discloses; The image signal processor according to claim 3, wherein the gradient sum is a sum of differences between pixel data values of pixel pairs arranged in each direction of the target kernel (Kishimoto, column 11 lines 40-51 the detection and direction of the detected gradients dictates whether a right angle corner pattern is detected in the area, further column 12 line 67 through column 13 line 25, the corner is determined/detected using the equation (9) with values from the determined vectors shown in figure 7, since the absolute value of equation 9 is used, the functions of equations 9 would be a gradient sum using the vector values in order to determine the corner pattern, further the equations of equation (9) are differences of pixel interpolation values which then have the absolute values taken).
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(Kishimoto, Column 13, emphasis added)
Regarding claim 6 Kishimoto discloses; The image signal processor according to claim 3, wherein the first determination processor is configured to:
determine a position at which the largest gradient sum for each direction of the target kernel is obtained to be a boundary position at which the target corner pattern exists (Kishimoto, column 10 lines 15-25, the maximum inclination/gradient in each direction is determined, column 10 lines 45- 67 a perpendicular vector is computed based on these maximum gradients in each direction and their positions, as well as the maximum pixel values, column 11 lines 40-50 the perpendicular vector is used to determine the corner pattern accordingly).
Regarding claim 7 Kishimoto discloses; The image signal processor according to claim 1, wherein the first determination processor is configured to:
calculate the gradient sum in a specific direction within the target kernel (Kishimoto, column 10 lines 15-25, the maximum inclination/gradient in each direction is determined);
and determine which region of the target corner pattern includes the target pixel based on the gradient sum (Kishimoto, Column 12 line 58 – column 13 line 10 the corner is detected based on the perpendicular vector (generated using the gradient sum) which includes the pixel to be interpolated (target pixel), therefore the determination of the corner region includes the target pixel).
Regarding claim 8 Kishimoto discloses; The image signal processor according to claim 7, wherein the first determination processor is configured to:
calculate a maximum gradient sum in a horizontal direction of a plurality of pixel pairs located in a specific region within the target kernel (Kishimoto, column 10 lines 15-25, the maximum inclination/gradient in each direction is determined for the pixels in an area)
and a maximum gradient sum in a vertical direction of the plurality of pixel pairs located in the specific region within the target kernel (Kishimoto, column 10 lines 15-25, the maximum inclination/gradient in each direction is determined for the pixels in an area);
and determine a position of the target pixel based on the maximum gradient sum in the horizontal direction and the maximum gradient sum in the vertical direction (Kishimoto, column 10 lines 15-25, the maximum inclination/gradient in each direction is determined, column 10 lines 45- 67 a perpendicular vector is computed based on these maximum gradients in each direction and their positions, as well as the maximum pixel values, column 11 lines 40-50 the perpendicular vector is used to determine the corner pattern accordingly, Column 12 line 58 – column 13 line 10 the corner is detected based on the perpendicular vector (generated using the gradient sum) which includes the pixel to be interpolated (target pixel), therefore the determination of the corner region includes the target pixel).
Regarding claim 15 Kishimoto discloses; The image signal processor according to claim 1, wherein the pixel iinterpolation processor is configured to:
interpolate the target pixel by applying a weighted average to the target corner pattern based on the determination results of the first determination processor, the second determination processor, and the third determination processor (Kishimoto, column 1, line 55 through column 2 lines 5, the system uses a mean pixel interpolation method on the target pixel using a weighted average mean of the neighboring pixels (pixels in the corner pattern)).
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.
2. Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Kishimoto (US 6339479 B1) in view of Xu (US 12254638 B2).
Regarding claim 5 Kishimoto fails to disclose; The image signal processor according to claim 3, wherein the first determination processor is configured to:
determine that the target kernel does not correspond to the corner pattern when the gradient sum is less than a first value;
and determine that the target kernel corresponds to the corner patterns when the gradient sum is greater than the first value.
However, Xu teaches; determine that the target kernel does not correspond to the corner pattern when the gradient sum is less than a first value (Xu, Column 2 lines 12-20 the corner candidate points are determined when their feature values are greater than a threshold, further, those values which are less than this value are not corner candidates);
and determine that the target kernel corresponds to the corner patterns when the gradient sum is greater than the first value (Xu, Column 2 lines 12-20 the corner candidate points are determined when their feature values are greater than a threshold, further, those values which are less than this value are not corner candidates).
The combination of Kishimoto and Xu would be obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The system of Kishimoto teaches an image processing method where corner patterns are detected and used to smooth images during processing, but it does not teach the comparison of the gradient values as a means to detect the corner points. Xu teaches this deficiency, the addition of this feature to the system of Kishimoto would have reasonably improved the system to help improve the object and edge detection methods of the system as taught in Xu columns 1 and 2 respectively.
3. Claims 10-14, 16-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kishimoto (US 6339479 B1) in view of Yu (US 20220375097 A1).
Regarding claim 10, Kishimoto fails to teach; The image signal processor according to claim 1, wherein the corner pattern of the first group includes two corners that come in contact with each other at one vertex of the target pixel.
However, in the same field of endeavor, Yu teaches;
The image signal processor according to claim 9, wherein the corner pattern of the first group includes two corners that come in contact with each other at one vertex of the target pixel (Yu, [0056] figure 3B shows multiple corners of adjacent objects, which touch in the center, further, the edges of objects/boxes 3000A-3000D shown below in figure 3B are flush with one another and therefore the center point is a corner vertex which is shared, [0062]-[0065] edge detect to detect adjacent pixel regions/corners may determine select targets).
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(Yu, figure 3B)
The combination of Kishimoto and Yu would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. Kishimoto teaches a corner detection and pixel interpolation method, but it does not teach this method in regards to multiple shared corners. Yu teaches this limitation, and the addition of this method of Yu would have reasonably improved the system of Kishimoto by allowing multiple objects or the corner patterns of multiple objects to be better identified when such overlap occurs. (Yu [0010]-[0017])
Regarding claim 11 The combination of Kishimoto and Yu teaches; The image signal processor according to claim 1, wherein the corner pattern of the second group is configured to share one vertex of the target pixel as a vertex of a corner (Yu, [0056] figure 3B shows multiple corners of adjacent objects, which touch in the center, further, the edges of objects/boxes 3000A-3000D shown below in figure 3B are flush with one another and therefore the center point is a corner vertex which is shared, further, there are 4 sets of corner patterns which intersect, meaning there is at least a first and second group [0062]-[0065] edge detect to detect adjacent pixel regions/corners may determine select targets).
The combination of Kishimoto and Yu would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. Kishimoto teaches a corner detection and pixel interpolation method, but it does not teach this method in regards to multiple shared corners. Yu teaches this limitation, and the addition of this method of Yu would have reasonably improved the system of Kishimoto by allowing multiple objects or the corner patterns of multiple objects to be better identified when such overlap occurs. (Yu [0010]-[0017])
Regarding claim 12 The combination of Kishimoto and Yu teaches; The image signal processor according to claim 1, wherein the third determination processor is configured to:
compare gradient directions of the plurality of corner patterns with each other (Yu, [0061] changes in the gradient of pixels in the corner or edge patterns may be assessed for changes, particularly in the perpendicular direction to the edges or corners [0098] corners may be compared to other corners of objects in the repository of corners (plurality of corner patterns));
and determine whether there is a pattern having the same gradient direction in a corner direction from among the plurality of corner patterns (Yu, [0061] changes in the gradient of pixels in the corner or edge patterns may be assessed for changes, particularly in the perpendicular direction to the edges or corners [0096] Viable regions (which include corners which may or may not overlap per [0089]-[0092] of Yu) may have their dimensions in x and y directions compared , [0097] this comparison will identify a match to the dimensions of other viable corner regions in the repository, [0098] corners may be compared to other corners of objects in the repository of corners (plurality of corner patterns), because the gradient determination is part of determining the viable corner regions, the comparison of corner regions disclosed in the aforementioned paragraphs would functionally include a gradient comparison).
The combination of Kishimoto and Yu would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. Kishimoto teaches a corner detection and pixel interpolation method, but it does not teach this method in regards to multiple shared corners. Yu teaches this limitation, and the addition of this method of Yu would have reasonably improved the system of Kishimoto by allowing multiple objects or the corner patterns of multiple objects to be better identified when such overlap occurs. Further, Yu teaches comparing corner regions to corner regions stored in a repository to match the dimensions, and it teaches a method of computing these dimensions directionally using gradients, which allows for minimizing errors in determining if regions overlap or share vertices. (Yu, [0010]-[0017], [0080]-[0098] and [0062])
Regarding claim 13 The combination of Kishimoto and Yu teaches; The image signal processor according to claim 12, wherein the third determination processor is configured to:
determine whether the gradient directions are directed toward a lower-right end, a lower-left end, an upper-right end, or an upper-left end with respect to a vertical boundary and a horizontal boundary within the target kernel (Kishimoto, column 10 lines 45 through column 11 line 35, inclination vectors (gradients) are determined based upon the pixel values, which indicate the direction of the pixel intensity changes in the corner pattern, figure 6 and 7 show these vectors are determined with respect to an i and j direction);
and determine two corner patterns having the same gradient direction (Yu, [0061] changes in the gradient of pixels in the corner or edge patterns may be assessed for changes, particularly in the perpendicular direction to the edges or corners [0096] Viable regions (which include corners which may or may not overlap per [0089]-[0092] of Yu) may have their dimensions in x and y directions compared , [0097] this comparison will identify a match to the dimensions of other viable corner regions in the repository, [0098] corners may be compared to other corners of objects in the repository of corners (plurality of corner patterns), because the gradient determination is part of determining the viable corner regions, the comparison of corner regions disclosed in the aforementioned paragraphs would functionally include a gradient comparison).
The combination of Kishimoto and Yu would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. Kishimoto teaches a corner detection and pixel interpolation method, but it does not teach this method in regards to multiple shared corners. Yu teaches this limitation, and the addition of this method of Yu would have reasonably improved the system of Kishimoto by allowing multiple objects or the corner patterns of multiple objects to be better identified when such overlap occurs. Further, Yu teaches comparing corner regions to corner regions stored in a repository to match the dimensions, and it teaches a method of computing these dimensions directionally using gradients, which allows for minimizing errors in determining if regions overlap or share vertices. (Yu, [0010]-[0017], [0080]-[0098] and [0062])
Regarding claim 14 The combination of Kishimoto and Yu teaches; The image signal processor according to claim 13, wherein the third determination processor is configured to:
when the corner pattern group corresponding to the target kernel is a corner pattern of the first group, determine a corner pattern corresponding to the corner pattern of the first group from among the two corner patterns to be the target corner pattern (Yu, [0065] a corner may be identified as being an open corner from a plurality of corners and determined to be target corner);
and when the corner pattern group corresponding to the target kernel is a corner pattern of the second group, determine a corner pattern corresponding to the corner pattern of the second group from among the two corner patterns to be the target corner pattern (Yu, [0111] minimum viable regions and other characteristics may be used in determining the target corner (second group and method of target corner).
The combination of Kishimoto and Yu would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. Kishimoto teaches a corner detection and pixel interpolation method, but it does not teach this method in regards to multiple shared corners. Yu teaches this limitation, and the addition of this method of Yu would have reasonably improved the system of Kishimoto by allowing multiple objects or the corner patterns of multiple objects to be better identified when such overlap occurs. Further, Yu teaches comparing corner regions to other corners in the image in order to find open corners and overlap, this method allows for more accurate correction of images and object detection by verifying overlap of objects. (Yu, [0004]-[0006], [0010]-[0017], [0080]-[0098], [0111] and [0062])
Regarding claim 16 The combination of Kishimoto and Yu teaches; An image signal processing method comprising:
distinguishing a plurality of corner patterns from each other, each having a different type, by using horizontal and vertical lines crossing a target kernel and forming a boundary for a target pixel included in the target kernel (Yu, [0004] a plurality of corners are detected, where one is a target corner which is an open corner, the other corners being either open of closed corners, where each is formed by edges detected (boundary lines), [0039] and figure 2E, regions with corners are detected, where each region is a pixel region and the lines are in the horizontal and vertical direction, [0061] pixel discontinuities and pixels values are used to determine the regions, which is analogous to a pixel kernel, further pixel intensity can indicate corner, and per [0004] the system gets a target corner for the region, which indicates the target corner and target pixel can be analogous);
classifying the plurality of corner patterns into corner patterns of a first group and corner patterns of a second group (Yu, [0065] a corner may be identified as being an open corner from a plurality of corners and determined to be target corner, [0111] minimum viable regions and other characteristics may be used in determining the target corner (second group and method of target corner);
determining a target corner pattern from among corner patterns corresponding to one of the first-group corner pattern and the second-group corner pattern (Yu, [0065] a corner may be identified as being an open corner from a plurality of corners and determined to be target corner, [0111] minimum viable regions and other characteristics may be used in determining the target corner (second group and method of target corner, the system acquires a target corner multiple ways from multiple types of corners, therefore there may be a first and second type of corner, where a target pixel may be determined from);
and interpolating the target pixel using pixel data of a pixel corresponding to the target corner pattern (Kishimoto, column 10 lines 57 – column 11 line 2 pixels are interpolated based upon the edge and corner pattern directions, pixels have interpolation vectors generated for them corresponding to the corner patterns);
wherein the classifying the plurality of corner patterns includes: determining whether gradient directions of pixel pairs located to face each other in an edge region of the target kernel cross each other (Kishimoto, Figure 6, each box is a pixel, the lines are gradients of the pixels in the area, and the vectors are determined as being perpendicular or crossing each other)
and determining that the target corner pattern corresponds to a corner pattern of a first group when the gradient directions cross each other (Kishimoto, column 10 lines 15-25, the maximum inclination/gradient in each direction is determined, column 10 lines 45- 67 a perpendicular vector is computed based on these maximum gradients in each direction and their positions, as well as the maximum pixel values, column 11 lines 40-50 the perpendicular vector is used to determine the corner pattern accordingly, Column 12 line 58 – column 13 line 10 the corner is detected based on the perpendicular vector (generated using the gradient sum) which includes the pixel to be interpolated (target pixel), therefore the determination of the corner region includes the target pixel);
and determining whether the gradient directions of a plurality of pixel pairs arranged in a specific region within the target kernel are equal to each other (Kishimoto, column 12 lines 15-40, the inclination vectors (gradients) for the plurality of pixels are used to determine which group/pattern they belong to by determining whether the inclination/gradient is equal to or within a specific range of values, therefore pixel gradients/inclination vectors in these areas would be equal to each other or fall within the same range of values)
and determining that the target corner pattern corresponds to a corner pattern of a second group when the gradient directions of the plurality of pixel pairs in the specific region within the target kernel are equal to each other (Kishimoto, column 12 lines 15-40, the inclination vectors (gradients) for the plurality of pixels are used to determine which group/pattern they belong to by determining whether the inclination/gradient is equal to or within a specific range of values, therefore pixel gradients/inclination vectors in these areas would be equal to each other or fall within the same range of values).
The combination of Kishimoto and Yu would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. Kishimoto teaches a corner detection and pixel interpolation method, but it does not teach this method in regards to multiple shared corners. Yu teaches this limitation, and the addition of this method of Yu would have reasonably improved the system of Kishimoto by allowing multiple objects or the corner patterns of multiple objects to be better identified when such overlap occurs. Further, Yu teaches comparing corner regions to corner regions stored in a repository to match the dimensions, and it teaches a method of computing these dimensions directionally using gradients, which allows for minimizing errors in determining if regions overlap or share vertices. (Yu, [0010]-[0017], [0080]-[0098] and [0062])
Regarding claim 17 The combination of Kishimoto and Yu teaches; The image signal processing method according to claim 16, wherein the classifying the plurality of corner patterns includes:
calculating a gradient sum in a specific direction within the target kernel (Kishimoto, column 11 line 3- 35, the regions are grouped by pixel values, figure 7 shows the pixels (each box is one pixel in the figure), the 6 pixels in the lower left corner have a value of 0, the pixels in the 3 shaded regions with the bold lines have a mean pixel value (texture and non-texture regions), and the grid lines in the i and j directions (vertical and horizontal lines in the target region of pixels/kernel) delineate each region, further, the vectors shown in figure 7 are generated based upon the directions of the interpolated pixel vectors in the i and j directions, the vectors further determined based upon the pixel interpolation values are generated in vector form (gradients) in a specific direction);
determining whether the target kernel corresponds to the plurality of corner patterns based on the gradient sum (Yu, [0061] gradients of pixels can be used to determine boundaries and edges which may correspond to the corners in an image, this is done for the boundaries and corners of multiple objects, therefore it is done for multiple corners/corner patterns);
and determining which region of a corresponding corner pattern includes the target pixel based on the gradient sum (Kishimoto, Column 12 line 58 – column 13 line 10 the corner is detected based on the perpendicular vector (generated using the gradient sum) which includes the pixel to be interpolated (target pixel), therefore the determination of the corner region includes the target pixel).
Regarding claim 18 The combination of Kishimoto and Yu teaches; The image signal processing method according to claim 16, wherein the classifying the plurality of corner patterns includes:
determining corner patterns including two corners that come in contact with each other at one vertex of the target pixel to be the corner patterns of the first group (Yu, [0056] figure 3B shows multiple corners of adjacent objects, which touch in the center, further, the edges of objects/boxes 3000A-3000D shown below in figure 3B are flush with one another and therefore the center point is a corner vertex which is shared, [0062]-[0065] edge detect to detect adjacent pixel regions/corners may determine select targets);
and determining corner patterns that share one vertex of the target pixel as a vertex of a corner to be the corner patterns of the second group (Yu, [0056] figure 3B shows multiple corners of adjacent objects, which touch in the center, further, the edges of objects/boxes 3000A-3000D shown below in figure 3B are flush with one another and therefore the center point is a corner vertex which is shared, further, there are 4 sets of corner patterns which intersect, meaning there is at least a first and second group [0062]-[0065] edge detect to detect adjacent pixel regions/corners may determine select targets).
The combination of Kishimoto and Yu would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. Kishimoto teaches a corner detection and pixel interpolation method, but it does not teach this method in regards to multiple shared corners. Yu teaches this limitation, and the addition of this method of Yu would have reasonably improved the system of Kishimoto by allowing multiple objects or the corner patterns of multiple objects to be better identified when such overlap occurs. (Yu [0010]-[0017])
Regarding claim 20 The combination of Kishimoto and Yu teaches; The image signal processing method according to claim 16, wherein the determining the target corner pattern includes:
determining whether there are two corner patterns having the same gradient direction by comparing gradient directions of the plurality of corner patterns with each other (Yu, [0061] changes in the gradient of pixels in the corner or edge patterns may be assessed for changes, particularly in the perpendicular direction to the edges or corners [0096] Viable regions (which include corners which may or may not overlap per [0089]-[0092] of Yu) may have their dimensions in x and y directions compared , [0097] this comparison will identify a match to the dimensions of other viable corner regions in the repository, [0098] corners may be compared to other corners of objects in the repository of corners (plurality of corner patterns), because the gradient determination is part of determining the viable corner regions, the comparison of corner regions disclosed in the aforementioned paragraphs would functionally include a gradient comparison);
when the corner pattern group corresponding to the target kernel is a corner pattern of a first group, determine a corner pattern corresponding to the corner pattern of the first group from among the two corner patterns to be the target corner pattern (Yu, [0065] a corner may be identified as being an open corner from a plurality of corners and determined to be target corner);
and when the corner pattern group corresponding to the target kernel is a corner pattern of a second group, determine a corner pattern corresponding to the corner pattern of the second group from among the two corner patterns to be the target corner pattern (Yu, [0111] minimum viable regions and other characteristics may be used in determining the target corner (second group and method of target corner).
The combination of Kishimoto and Yu would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. Kishimoto teaches a corner detection and pixel interpolation method, but it does not teach this method in regards to multiple shared corners. Yu teaches this limitation, and the addition of this method of Yu would have reasonably improved the system of Kishimoto by allowing multiple objects or the corner patterns of multiple objects to be better identified when such overlap occurs. Further, Yu teaches comparing corner regions to other corners in the image in order to find open corners and overlap, this method allows for more accurate correction of images and object detection by verifying overlap of objects. (Yu, [0004]-[0006], [0010]-[0017], [0080]-[0098], [0111] and [0062])
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. For a listing of analogous art provided by the examiner, please see the attached PTO-892 Notice of References Cited form.
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/J.M.E./Examiner, Art Unit 2666
/Molly Wilburn/Primary Examiner, Art Unit 2666
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