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 .
Drawings
The drawings are objected to because in Fig. 1A, 106a should be labeled as 106.1, 122a should be labeled as 122.1; in Fig. 1B, 106b should be labeled as 106.2; in Fig. 1C, 106c should be labeled as 106.3, in Fig. 1D, 106d should be labeled as 106.4; in Fig. 2, 202a should be labeled as 202.1, 202b should be labeled as 202.2, 202c should be labeled as 202.3, 202d should be labeled as 202.4, 206a should be labeled as 206.1, 206b should be labeled as 206.2, 206c should be labeled as 206.3, 206d should be labeled as 206.4, 208a should be labeled as 208.1, 208b should be labeled as 208.2, 208c should be labeled as 208.3, 208d should be labeled as 208.4 to be consistent with the specification. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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 1-3 and 10 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Witte (US Patent Pub. No.: US 2018/0253873 A1).
Regarding claim 1, Witte teaches a method for processing an image of a subterranean formation (The purpose of the vectorization system and method is to assist geologists or other technicians to rapidly and accurately vectorize well log curves that are visually evident in a raster well log image. [0023]), the method comprising: performing edge detection on the image to produce a first processed image (A further advantage of the system and method may be that the image-guided shortest path algorithm can also be used to improve the pre-processing step of image rectification, by automatically tracing the grid lines and track edges of the raster log image. [0027]); performing gridlines localizer on the first processed image to produce a second processed image (A further advantage of the system and method may be that the image-guided shortest path algorithm can also be used to improve the pre-processing step of image rectification, by automatically tracing the grid lines and track edges of the raster log image. [0027]); performing noise filtering on the second processed image to produce a third processed image ( 2) optionally removing isolated noise by detecting and removing connected components below some number of connected pixels. [0034]); and performing pattern recognition on the third processed image to determine that the image that corresponds to the third processed image is linear scale type or logarithmic scale type (In other embodiments, the system and method may alternatively use other interpolation processes such as the image-guided shortest path, linear or some other common form of curve interpolation, thus degrading to manual vectorization when images are sufficiently noisy to preclude automatic curve extraction. [0028]).
Regarding claim 2, Witte teaches the method of claim 1, wherein the image comprises a 2D plot segment image of the subterranean formation (FIGS. 2 and 3 illustrate two examples of a raster well log image containing two tracks and multiple well log curves. [0009]
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Regarding claim 3, Witte teaches the method of claim 1, wherein performing edge detection comprises detecting edge pixels in the image, and wherein the edge pixels correspond to gridlines, curves, or both in the image (A further advantage of the system and method may be that the image-guided shortest path algorithm can also be used to improve the pre-processing step of image rectification, by automatically tracing the grid lines and track edges of the raster log image. [0027]).
Regarding claim 10, Witte teaches the method of claim 1, further comprising performing a wellsite action (A system and method for well log vectorization are described that assist geologists or other technicians to rapidly and accurately vectorize well log curves that are visually evident in a raster well log image. The resulting digital well log curves are instrumental for qualitative interpretation and quantitative analysis of geologic information measured along well bores. Abstract) in response to the image being determined to be linear scale type or logarithmic scale type (In other embodiments, the system and method may alternatively use other interpolation processes such as the image-guided shortest path, linear or some other common form of curve interpolation, thus degrading to manual vectorization when images are sufficiently noisy to preclude automatic curve extraction. [0028]).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Witte (US Patent Pub. No.: US 2018/0253873 A1) hereinafter Witte, in view of Dong (Detection of Performance of Hybrid Rice Pot-Tray Sowing Utilizing Machine Vision and Machine Learning Approach, Sensors 2019, 19, 5332), hereinafter Dong.
Regarding claim 4, Witte teaches all of the elements of the claimed invention as stated in claim 1 except for the following limitations as further recited. However, Dong teaches wherein performing the gridlines localizer comprises: determining a sum of edge pixels in each column of the first processed image (Step 2. Get the pixel sum of every row and every column of the binary image. Page 9 4th paragraph); identifying the columns having sums that exceed a threshold (It is common knowledge that a threshold can be selected in order to identify peaks.); and determining that the identified columns comprise peaks (Figure 8b shows a histogram of row numbers and their pixel sums. In the histogram of the row pixel sum, a regulation is found that the pixel sums of the rows in which there are horizontal gridlines are much bigger than those of rows in which there is no horizontal gridline. Page 9 4th paragraph.
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It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Witte to incorporate the teachings of Dong to determine a sum of edge pixels in each column of the first processed image, identify the columns having sums that exceed a threshold and determine that the identified columns comprise peaks in order to obtain pixel coordinates of gridlines.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Witte (US Patent Pub. No.: US 2018/0253873 A1) hereinafter Witte, in view of Dong (Detection of Performance of Hybrid Rice Pot-Tray Sowing Utilizing Machine Vision and Machine Learning Approach, Sensors 2019, 19, 5332), hereinafter Dong, further in view of Lu (US Patent Pub. No.: US 2024/0264539 A1), hereinafter Lu.
Regarding claim 5, Witte and Dong teach all of the elements of the claimed invention as stated in claim 4 except for the following limitations as further recited. However, Lu teaches wherein performing the noise filtering comprises: determining distances between the peaks (filtering 912 based on distances 914, 916 between vertices (918 and 920 and 920 and 922) (which reads on “the peaks”) of a contour 924. [0129].
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); identifying one or more of the peaks as outliers (wherein filtering the outlier contours from the set of inspection contours comprises filtering each contour, or portion of a contour, associated with an outlier unit cell or outlier reticle. [0022]) based upon the distances (Referring to filtering 912, distances 914 and 916 between adjacent vertices 918, 920, 922 ( or other points on contour 924) may be determined, and if a distance breaches a given threshold (e.g., a specific distance or process parameter requirement such as edge roughness as one example), contour 924 may cause the corresponding feature ( or at least that local portion of the feature) to be filtered. [0130]), wherein the one or more peaks are identified as outliers based upon one or more of the distances being greater than a first predetermined distance threshold (Referring to filtering 912, distances 914 and 916 between adjacent vertices 918, 920, 922 ( or other points on contour 924) may be determined, and if a distance breaches a given threshold (e.g., a specific distance or process parameter requirement such as edge roughness as one example), contour 924 may cause the corresponding feature (or at least that local portion of the feature) to be filtered. [0130]); and removing the one or more peaks that are identified as the outliers to produce the third processed image (Referring to filtering 912, distances 914 and 916 between adjacent vertices 918, 920, 922 ( or other points on contour 924) may be determined, and if a distance breaches a given threshold (e.g., a specific distance or process parameter requirement such as edge roughness as one example), contour 924 may cause the corresponding feature (or at least that local portion of the feature) to be filtered. [0130]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Witte and Dong to incorporate the teachings of Lu to identify peaks as outliers based upon the distances being greater than a distance threshold and remove the peaks that are identified as the outliers in order to perform the noise filtering.
Claims 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over Witte (US Patent Pub. No.: US 2018/0253873 A1) hereinafter Witte, in view of Dong (Detection of Performance of Hybrid Rice Pot-Tray Sowing Utilizing Machine Vision and Machine Learning Approach, Sensors 2019, 19, 5332), hereinafter Dong, further in view of Khan Academy (Bivariate relationship linearity, strength and direction, https://www.youtube.com/watch?v=30LcZqRfPRY, 2017), hereinafter Khan Academy.
Regarding claim 6, Witte and Dong teach all of the elements of the claimed invention as stated in claim 4 except for the following limitations as further recited. However, Khan Academy teaches wherein performing the pattern recognition comprises: fitting a straight line through the peaks in the third processed image (So, this data right over here, it looks like I could get a, I could put a line through it that gets pretty close through the data. Page 1 1st paragraph); determining distances between the peaks and the straight line (So, because the dots (which reads on “the peaks”) aren't that far from my line. This one gets a little bit further, but it's not, there's not some dots way out there. And so, most of 'em are pretty close to the line. So I would call this a negative, reasonably strong linear relationship. Page 1 1st paragraph.
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); and determining that the image that corresponds to the third processed image is linear scale type (So, because the dots (which reads on “the peaks”) aren't that far from my line. This one gets a little bit further, but it's not, there's not some dots way out there. And so, most of 'em are pretty close to the line. So I would call this a negative, reasonably strong linear relationship. Page 1 1st paragraph) or logarithmic scale type based upon the distances between the peaks and the straight line (So, because the dots (which reads on “the peaks”) aren't that far from my line. This one gets a little bit further, but it's not, there's not some dots way out there. And so, most of 'em are pretty close to the line. So I would call this a negative, reasonably strong linear relationship. Page 1 1st paragraph).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Witte and Dong to incorporate the teachings of Khan Academy to determine that the image is linear scale type or logarithmic scale type based upon the distances between the peaks and the fitted straight line in order to identify the scale type.
Regarding claim 7, Khan Academy in the combination teaches the method of claim 6, wherein the image is determined to be linear scale type based upon an average of the distances between the peaks and the straight line being less than a second predetermined distance threshold (So, because the dots (which reads on “the peaks”) aren't that far from my line. This one gets a little bit further, but it's not, there's not some dots way out there. And so, most of 'em are pretty close to the line. So I would call this a negative, reasonably strong linear relationship. Page 1 1st paragraph. It is common knowledge that the linear scale type can be determined based upon an average of the distances between the dots and the straight line being less than a threshold.
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Regarding claim 8, Khan Academy in the combination teaches the method of claim 6, wherein the images are determined to be logarithmic scale type based upon an average of the distances between the peaks and the straight line being greater than a second predetermined distance threshold (I could try to put a line on it. But if I try to put a line on it, it's actually quite difficult. If I try to do a line like this, you'll notice everything is kind of bending away from the line. It looks like, generally, as one variable increases, the other variable decreases, but they're not doing it in a linear fashion. It looks like there's some other type of curve at play. So, I could try to do a fancier curve that looks something like this (which reads on “logarithmic scale type”), and this seems to fit the data a lot better. So this one, I would describe as non-linear. Page 2 1st paragraph.
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Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Witte (US Patent Pub. No.: US 2018/0253873 A1) hereinafter Witte, in view of Parks (US Patent Pub. No.: US 2006/0015291 A1), hereinafter Parks.
Regarding claim 9, Witte teaches all of the elements of the claimed invention as stated in claim 1 except for the following limitations as further recited. However, Parks teaches further comprising rescaling the image to produce a rescaled image, wherein the image is rescaled based upon the image being determined to be linear scale type or logarithmic scale type (scale the raw data using a scaling function that provides substantially linear transformations for lower data values and substantially logarithmic transformations for higher data values to generate scaled data. Fig. 1 A2).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Witte to incorporate the teachings of Parks to produce a rescaled image based upon the image being determined to be linear scale type or logarithmic scale type for displaying high dynamic range data.
Claims 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Witte (US Patent Pub. No.: US 2018/0253873 A1) hereinafter Witte, in view of Dong (Detection of Performance of Hybrid Rice Pot-Tray Sowing Utilizing Machine Vision and Machine Learning Approach, Sensors 2019, 19, 5332), hereinafter Dong, further in view of Khan Academy (Bivariate relationship linearity, strength and direction, https://www.youtube.com/watch?v=30LcZqRfPRY, 2017), hereinafter Khan Academy.
Apparatus claim 11 is drawn to the apparatus corresponding to the method of using same as claimed in claims 1-4 and 6-8. Therefore apparatus claim 11 corresponds to method claims 1-4 and 6-8, and is rejected for the same reasons of obviousness as used above.
Regarding claim 12, Dong in the combination teaches the computing system of claim 11, wherein identifying the columns comprises: identifying a first portion of the columns having sums that exceed a global threshold (Figure 8b shows a histogram of row numbers and their pixel sums. In the histogram of the row pixel sum, a regulation is found that the pixel sums of the rows in which there are horizontal gridlines are much bigger than those of rows in which there is no horizontal gridline (which reads on “exceed a global threshold”). Page 9 4th paragraph.
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), wherein the global threshold is a first predetermined fraction of a maximum of all of the sums (It is common knowledge that a global threshold can be selected in order to identify peaks.); and identifying a second portion of the columns having sums that exceed a local threshold (It is common knowledge that a local threshold can be selected for a subset of the columns in order to identify peaks.), wherein the local threshold is a second predetermined fraction of a maximum of the sums corresponding to a subset of the columns that are spatially consecutive (
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Claims 13 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Witte (US Patent Pub. No.: US 2018/0253873 A1) hereinafter Witte, in view of Dong (Detection of Performance of Hybrid Rice Pot-Tray Sowing Utilizing Machine Vision and Machine Learning Approach, Sensors 2019, 19, 5332), hereinafter Dong, further in view of Khan Academy (Bivariate relationship linearity, strength and direction, https://www.youtube.com/watch?v=30LcZqRfPRY, 2017), hereinafter Khan Academy, further in view of Lu (US Patent Pub. No.: US 2024/0264539 A1), hereinafter Lu.
Regarding claim 13, Witte, Dong and Khan Academy teach all of the elements of the claimed invention as stated in claim 11 except for the following limitations as further recited. However, Lu teaches wherein performing the noise filtering comprises: determining distances between the peaks (filtering 912 based on distances 914, 916 between vertices (918 and 920 and 920 and 922) (which reads on “the peaks”) of a contour 924. [0129]
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); identifying one or more of the peaks as outliers (wherein filtering the outlier contours from the set of inspection contours comprises filtering each contour, or portion of a contour, associated with an outlier unit cell or outlier reticle. [0022]) based upon the distances between the peaks (Referring to filtering 912, distances 914 and 916 between adjacent vertices 918, 920, 922 ( or other points on contour 924) may be determined, and if a distance breaches a given threshold (e.g., a specific distance or process parameter requirement such as edge roughness as one example), contour 924 may cause the corresponding feature ( or at least that local portion of the feature) to be filtered. [0130]), wherein the one or more peaks are identified as outliers based upon one or more of the distances being greater than a first predetermined distance threshold (Referring to filtering 912, distances 914 and 916 between adjacent vertices 918, 920, 922 ( or other points on contour 924) may be determined, and if a distance breaches a given threshold (e.g., a specific distance or process parameter requirement such as edge roughness as one example), contour 924 may cause the corresponding feature (or at least that local portion of the feature) to be filtered. [0130]); and removing the one or more peaks that are identified as the outliers to produce the plurality of third processed images (Referring to filtering 912, distances 914 and 916 between adjacent vertices 918, 920, 922 ( or other points on contour 924) may be determined, and if a distance breaches a given threshold (e.g., a specific distance or process parameter requirement such as edge roughness as one example), contour 924 may cause the corresponding feature (or at least that local portion of the feature) to be filtered. [0130]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Witte, Dong and Khan Academy to incorporate the teachings of Lu to identify peaks as outliers based upon the distances being greater than a distance threshold and remove the peaks that are identified as the outliers in order to perform the noise filtering.
Regarding claim 14, Lu in the combination teaches the computing system of claim 11, wherein determining the distances between the peaks comprises: determining first distances between each pair of consecutive peaks (filtering 912 based on distances 914, 916 between vertices (918 and 920 and 920 and 922) (which reads on “each pair of consecutive peaks”) of a contour 924. [0129].
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); and determining second distances between each pair of alternating peaks (It is common knowledge that a distance between each pair of alternating peaks can be determined.).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Witte (US Patent Pub. No.: US 2018/0253873 A1) hereinafter Witte, in view of Dong (Detection of Performance of Hybrid Rice Pot-Tray Sowing Utilizing Machine Vision and Machine Learning Approach, Sensors 2019, 19, 5332), hereinafter Dong, further in view of Khan Academy (Bivariate relationship linearity, strength and direction, https://www.youtube.com/watch?v=30LcZqRfPRY, 2017), hereinafter Khan Academy, further in view of Parks (US Patent Pub. No.: US 2006/0015291 A1), hereinafter Parks.
Regarding claim 15, Witte, Dong and Khan Academy teach all of the elements of the claimed invention as stated in claim 11 except for the following limitations as further recited. However, Parks teaches wherein the operations further comprise rescaling the received images to produce a plurality of rescaled images, wherein the received images are rescaled based upon the received images being determined to be linear scale type or logarithmic scale type (scale the raw data using a scaling function that provides substantially linear transformations for lower data values and substantially logarithmic transformations for higher data values to generate scaled data. Fig. 1 A2).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Witte, Dong and Khan Academy to incorporate the teachings of Parks to produce a rescaled image based upon the image being determined to be linear scale type or logarithmic scale type for displaying high dynamic range data.
Claims 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Witte (US Patent Pub. No.: US 2018/0253873 A1) hereinafter Witte, in view of Dong (Detection of Performance of Hybrid Rice Pot-Tray Sowing Utilizing Machine Vision and Machine Learning Approach, Sensors 2019, 19, 5332), hereinafter Dong, further in view of Khan Academy (Bivariate relationship linearity, strength and direction, https://www.youtube.com/watch?v=30LcZqRfPRY, 2017), hereinafter Khan Academy, further in view of Lu (US Patent Pub. No.: US 2024/0264539 A1), hereinafter Lu, further in view of Parks (US Patent Pub. No.: US 2006/0015291 A1), hereinafter Parks.
Regarding claim 16, Witte teaches a computer program comprising instructions that, when executed by a computer processor of a computing device, causes the computing device to perform operations, the operations comprising: receiving a plurality of images (The purpose of the vectorization system and method is to assist geologists or other technicians to rapidly and accurately vectorize well log curves that are visually evident in a raster well log image. [0023]), wherein the images comprise 2D plot segment images of a subterranean formation (FIGS. 2 and 3 illustrate two examples of a raster well log image containing two tracks and multiple well log curves. [0009]
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); performing edge detection on the images to produce a plurality of first processed images (A further advantage of the system and method may be that the image-guided shortest path algorithm can also be used to improve the pre-processing step of image rectification, by automatically tracing the grid lines and track edges of the raster log image. [0027]), wherein performing edge detection comprises detecting edge pixels in each of the images, wherein the edge pixels correspond to gridlines, curves, or both (A further advantage of the system and method may be that the image-guided shortest path algorithm can also be used to improve the pre-processing step of image rectification, by automatically tracing the grid lines and track edges of the raster log image. [0027]), wherein the first processed images are binary images ( 5) optionally enhancing the curves in the image by, for example, simple color separation if each curve is a different color or, for example, by performing a local Hough transform or anisotropic diffusion at each point in a binary or gray-scale image. [0034]), and wherein the binary images comprise the edge pixels and a background (The processing of the rectified image may enhance the well log curves, remove noise and grid lines, and capture image information necessary to separate the curves from the background. [0033]); performing gridlines localizer on the first processed images to produce a plurality of second processed images (A further advantage of the system and method may be that the image-guided shortest path algorithm can also be used to improve the pre-processing step of image rectification, by automatically tracing the grid lines and track edges of the raster log image. [0027]); performing noise filtering on the second processed images to produce a plurality of third processed images ( 2) optionally removing isolated noise by detecting and removing connected components below some number of connected pixels. [0034]); performing pattern recognition on the third processed images (In other embodiments, the system and method may alternatively use other interpolation processes such as the image-guided shortest path, linear or some other common form of curve interpolation, thus degrading to manual vectorization when images are sufficiently noisy to preclude automatic curve extraction. [0028]).
Dong in the combination further teaches wherein performing the gridlines localizer comprises: determining a sum of the edge pixels in each column of the first processed images (Step 2. Get the pixel sum of every row and every column of the binary image. Page 9 4th paragraph); identifying a first portion of the columns having sums that exceed a global threshold (Figure 8b shows a histogram of row numbers and their pixel sums. In the histogram of the row pixel sum, a regulation is found that the pixel sums of the rows in which there are horizontal gridlines are much bigger than those of rows in which there is no horizontal gridline (which reads on “exceed a global threshold”). Page 9 4th paragraph.
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), wherein the global threshold is a first predetermined fraction of a maximum of all of the sums (It is common knowledge that a global threshold can be selected in order to identify peaks.); identifying a second portion of the columns having sums that exceed a local threshold (It is common knowledge that a local threshold can be selected for a subset of the columns in order to identify peaks.), wherein the local threshold is a second predetermined fraction of a maximum of the sums corresponding to a subset of the columns that are spatially consecutive (
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); and determining that the first and second portions of the columns comprise peaks (Figure 8b shows a histogram of row numbers and their pixel sums. In the histogram of the row pixel sum, a regulation is found that the pixel sums of the rows in which there are horizontal gridlines are much bigger than those of rows in which there is no horizontal gridline. Page 9 4th paragraph.
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Khan Academy in the combination further teaches wherein performing the pattern recognition comprises: fitting a straight line through the peaks in the third processed images (So, this data (which reads on “the peaks”) right over here, it looks like I could get a, I could put a line through it that gets pretty close through the data. Page 1 1st paragraph); determining distances between the peaks and the straight line (So, because the dots (which reads on “the peaks”) aren't that far from my line. This one gets a little bit further, but it's not, there's not some dots way out there. And so, most of 'em are pretty close to the line. So I would call this a negative, reasonably strong linear relationship. Page 1 1st paragraph.
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); and determining that the received images that correspond to the third processed images are linear scale type (So, because the dots (which reads on “the peaks”) aren't that far from my line. This one gets a little bit further, but it's not, there's not some dots way out there. And so, most of 'em are pretty close to the line. So I would call this a negative, reasonably strong linear relationship. Page 1 1st paragraph) or logarithmic scale type based upon the distances between the peaks and the straight line (So, because the dots (which reads on “the peaks”) aren't that far from my line. This one gets a little bit further, but it's not, there's not some dots way out there. And so, most of 'em are pretty close to the line. So I would call this a negative, reasonably strong linear relationship. Page 1 1st paragraph), wherein the images are determined to be linear scale type based upon an average of the distances between the peaks and the straight line being less than a second predetermined distance threshold (So, because the dots (which reads on “the peaks”) aren't that far from my line. This one gets a little bit further, but it's not, there's not some dots way out there. And so, most of 'em are pretty close to the line. So I would call this a negative, reasonably strong linear relationship. Page 1 1st paragraph. It is common knowledge that the linear scale type can be determined based upon an average of the distances between the dots and the straight line being less than a threshold.
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), and wherein the images are determined to be logarithmic scale type based upon an average of the distances between the peaks and the straight line being greater than the second predetermined distance threshold (I could try to put a line on it. But if I try to put a line on it, it's actually quite difficult. If I try to do a line like this, you'll notice everything is kind of bending away from the line. It looks like, generally, as one variable increases, the other variable decreases, but they're not doing it in a linear fashion. It looks like there's some other type of curve at play. So, I could try to do a fancier curve that looks something like this (which reads on “logarithmic scale type”), and this seems to fit the data a lot better. So this one, I would describe as non-linear. Page 2 1st paragraph.
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Lu in the combination further teaches wherein performing the noise filtering comprises: determining distances between the peaks (filtering 912 based on distances 914, 916 between vertices (918 and 920 and 920 and 922) (which reads on “the peaks”) of a contour 924. [0129].
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), wherein determining the distances comprises: determining first distances between each pair of consecutive peaks (filtering 912 based on distances 914, 916 between vertices (918 and 920 and 920 and 922) (which reads on “each pair of consecutive peaks”) of a contour 924. [0129]); and determining second distances between each pair of alternating peaks (It is common knowledge that a distance between each pair of alternating peaks can be determined.); identifying one or more of the peaks as outliers (wherein filtering the outlier contours from the set of inspection contours comprises filtering each contour, or portion of a contour, associated with an outlier unit cell or outlier reticle. [0022]) based upon the distances (Referring to filtering 912, distances 914 and 916 between adjacent vertices 918, 920, 922 ( or other points on contour 924) may be determined, and if a distance breaches a given threshold (e.g., a specific distance or process parameter requirement such as edge roughness as one example), contour 924 may cause the corresponding feature ( or at least that local portion of the feature) to be filtered. [0130]), wherein the one or more peaks are identified as outliers based upon one or more of the distances being greater than a first predetermined distance threshold (Referring to filtering 912, distances 914 and 916 between adjacent vertices 918, 920, 922 ( or other points on contour 924) may be determined, and if a distance breaches a given threshold (e.g., a specific distance or process parameter requirement such as edge roughness as one example), contour 924 may cause the corresponding feature (or at least that local portion of the feature) to be filtered. [0130]); and removing the one or more peaks that are identified as the outliers to produce the plurality of third processed images (Referring to filtering 912, distances 914 and 916 between adjacent vertices 918, 920, 922 ( or other points on contour 924) may be determined, and if a distance breaches a given threshold (e.g., a specific distance or process parameter requirement such as edge roughness as one example), contour 924 may cause the corresponding feature (or at least that local portion of the feature) to be filtered. [0130]).
The combination of Witte, Dong, Khan Academy and Lu does not teach the following limitations as further recited, but Parks further teaches rescaling the received images to produce a plurality of rescaled images, wherein the received images are rescaled based upon the received images being determined to be linear scale type or logarithmic scale type (scale the raw data using a scaling function that provides substantially linear transformations for lower data values and substantially logarithmic transformations for higher data values to generate scaled data. Fig. 1 A2); and displaying the rescaled images (Thus, there is a need for display scales that combine the desirable attributes of the log scale for large real signals with those of the linear scale for unstained and near background signals. [0009]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Witte, Dong, Khan Academy and Lu to incorporate the teachings of Parks to produce a rescaled image based upon the image being determined to be linear scale type or logarithmic scale type for displaying high dynamic range data.
Regarding claim 17, Dong in the combination teaches the computer program of claim 16, wherein the global threshold is greater than the local threshold (Figure 8b shows a histogram of row numbers and their pixel sums. In the histogram of the row pixel sum, a regulation is found that the pixel sums of the rows in which there are horizontal gridlines are much bigger than those of rows in which there is no horizontal gridline. Page 9 4th paragraph. It is common knowledge that different global or local threshold can be determined in order to identify different subsets of peaks.
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).
Claims 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Witte (US Patent Pub. No.: US 2018/0253873 A1) hereinafter Witte, in view of Dong (Detection of Performance of Hybrid Rice Pot-Tray Sowing Utilizing Machine Vision and Machine Learning Approach, Sensors 2019, 19, 5332), hereinafter Dong, further in view of Khan Academy (Bivariate relationship linearity, strength and direction, https://www.youtube.com/watch?v=30LcZqRfPRY, 2017), hereinafter Khan Academy, further in view of Lu (US Patent Pub. No.: US 2024/0264539 A1), hereinafter Lu, further in view of Parks (US Patent Pub. No.: US 2006/0015291 A1), hereinafter Parks, further in view of Wright (US Patent No.: US 11,492,900 B2), hereinafter Wright.
Regarding claim 18, Witte, Dong, Khan Academy, Lu and Parks teach all of the elements of the claimed invention as stated in claim 16 except for the following limitations as further recited. However, Wright teaches further comprising performing a wellsite action (For example, the gas data may be monitored to detect deviations from the desired data properties. If a deviation is detected, the drilling operation may be adjusted or corrected to keep the drilling trajectory within the desired layer, formation, or section thereof. Column 9 line 25) based upon the rescaled images (Alternatively, or in addition thereto, in some embodiments, the gas data may be scaled by a scaling factor. Column 8 line 49).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Witte, Dong, Khan Academy, Lu and Parks to incorporate the teachings of Wright to perform a wellsite action based upon the rescaled images in order to control drilling operations.
Regarding claim 19, Wright in the combination teaches the computer program of claim 18, wherein the wellsite action comprises generating and transmitting a signal (Processed data, such as a result of an implemented method, may be transmitted as a signal via a processor output interface to a signal receiving device. Column 13 line 5) that causes a physical action to occur at a wellsite (For example, the gas data may be monitored to detect deviations from the desired data properties. If a deviation is detected, the drilling operation may be adjusted or corrected to keep the drilling trajectory within the desired layer, formation, or section thereof. Column 9 line 25).
Regarding claim 20, Wright in the combination teaches the computer program of claim 19, wherein the physical action comprises varying a weight on a drill bit, varying a torque on the drill bit, varying a drilling trajectory (For example, the gas data may be monitored to detect deviations from the desired data properties. If a deviation is detected, the drilling operation may be adjusted or corrected to keep the drilling trajectory within the desired layer, formation, or section thereof. Column 9 line 25), varying a concentration of a fluid pumped into a wellbore, varying a flow rate of the fluid pumped into the wellbore, or a combination thereof.
Conclusion
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/LEI ZHAO/Examiner, Art Unit 2668
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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