DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 21-40 are pending.
Claims 1-20 are canceled.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claim(s) 21, 27-33, and 38-40 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chien et al (US20130121606) in view of Zimmer (US20100098350).
Regarding claims 21 and 32, Chien teaches a computer implemented method including:
accessing source image data defining a set of source pixels for an image;
(Chien, “Image processing often involves the application of various filtering operations on image data using one or more convolution operations”, [0003]; “The image may contain pixels A1-A10, B1-B10 . . . G1-G10”, [0024]; Fig. 1B, step 102A; Chien’s “image data” and explicit enumeration of image pixels evidences accessing/storing image data that defines a set of source pixels for an image)
selecting a first target pixel from a set of one or more target pixels, the set of one or more target pixels corresponding to one or more unique source pixels of the set of source pixels; and
(Chien, “the method may examine most or all of the pixels in the selected image area and apply the filtering window to these pixels”, [0049]; Fig. 2, step 212; “for each pixel within the selected image area, a three by three filter window (e.g., a blur window) may be applied”, [0049]; Chien teaches iterating through pixels (a “set” of pixels in the selected area) and applying the filter window per pixel, which corresponds to selecting a given “target” pixel (the pixel currently being processed) that corresponds to a unique underlying image pixel)
generating a first output pixel corresponding to the first target pixel by:
(Chien, “the output value of the center pixel may be calculated…”, [0054]; “Red Output of (x,y) pixel = SummationRed/Normalization; Blue Output of (x,y) pixel = SummationBlue/Normalization; Green Output of (x,y) pixel = Summation Green/Normalization”, [0055]; Chien expressly describes computing an output for a pixel (x,y) (i.e., generating an output pixel corresponding to the pixel being processed))
determining a set of sample points having a sample point distribution within a blur sample area corresponding to an area of the image, wherein the sample point distribution has a central skew towards the first target pixel such that the sample points in the sample point distribution are more concentrated in the centre of the blur sample area;
(Chien, "In some embodiments, for each pixel within the selected image area, a three by three filter window (e.g., a blur window) may be applied.", [0049]; "for ( each pixel (i,j) in the blur window ) {", [0055]; Zimmer, "For zoom blur, the sample locations are computed in a different manner. First a vector v is computed, which is a two-dimensional subpixel-accurate displacement representing the specific offset from the center of the zoom blur to the destination sample position ... The sample positions are at locations equal to the center of the zoom blur plus v times the following five factors: f-2, f-1, 1, f, f2.", [0037]; Chien teaches determining a set of sample points (i,j) within a blur sample area (filter window) corresponding to an area of the image, but does not expressly disclose a sample point distribution that has a central skew where points are more concentrated in the center of the blur sample area (as Chien uses a uniform grid). Zimmer teaches computing sample positions for a zoom blur using geometrically spaced fractional factors, causing the distribution of sample points to be skewed and more densely concentrated toward the center)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the teachings of Zimmer into the system or method of Chien in order to accomplish various forms of blur that vary across an image computed for the purpose of simulating depth-of-field, such as a zoom blur. The combination of Chien and Zimmer also teaches other enhanced capabilities.
The combination of Chien and Zimmer further teaches:
determining a set of sample point appearance values based on the sample points and the image;
(Chien, “a bilateral box filter replaces the value at a pixel, (i, j, k), with a weighted sum of the pixel values in the neighborhood of the pixel”, [0003]; “SummationRed += (spatial weight)* (color-range weight)* Red channel value of (i,j);
SummationBlue += (spatial weight)* (color-range weight)* Blue channel value of (i,j); SummationGreen += (spatial weight)* (color-range weight)* Green channel value of (i,j)”, [0055]; Chien’s “pixel values in the neighborhood” and explicit per-sample channel values (Red/Blue/Green at (i,j)) teach determining “appearance values” for sample points based on the image)
determining a first blur appearance value based on the set of sample point appearance values; and
(Chien, Fig. 2, s212; “the output value of the center pixel may be calculated by a summation of the pixel values multiplied by the two coefficients divided by the normalization factor...”, [0054]; weighted-sum-and-normalize computation is a “blur appearance value” (the blurred result) computed from the sample point values; Zimmer , “Each pixel in the output image (FIG. 3) is computed as a weighted average of the values of the corresponding pixel and a predetermined number of surrounding pixels in the input image (FIG. 2)”, [0020]; further confirms the blur value being a weighted average of sample pixel values)
generating the first output pixel based on the first blur appearance value.
(Chien, “Red Output of (x,y) pixel = SummationRed/Normalization; Blue Output of (x,y) pixel = SummationBlue/Normalization; Green Output of (x,y) pixel = Summation Green/Normalization”, [0055]; Chien explicitly generates the output pixel’s appearance (e.g., RGB outputs) from the computed blurred sums (i.e., from the blur appearance values))
Regarding claim 27, the combination of Chien and Zimmer teaches its/their respective base claim(s).
The combination further teaches the computer implemented method of claim 21, wherein the sample point distribution is at least in part a Gaussian distribution.
(Chien, “Pixels in the filter window may have spatial weight coefficients as shown in the exemplary 3 by 3 filter window of FIG. 3B, e.g., the center pixel may have a spatial weight coefficient of 4, whereas the corner pixels may have a spatial weight coefficient of 1”, [0051]; “Spatial weight coefficients may be determined by using Gaussian distribution”, [0052]; Gaussian-weighted spatial sampling is a Gaussian component of the sample-point distribution; Zimmer , “When specifying a Gaussian blur, the key parameter is the blur radius. The blur radius is the standard deviation of the Gaussian distribution convolved with the image to produce the blur”, [0022]; Gaussian blur implies a Gaussian-based sampling/weighting distribution)
Regarding claim 28, the combination of Chien and Zimmer teaches its/their respective base claim(s).
The combination further teaches the computer implemented method of claim 21, wherein determining the set of sample point appearance values includes:
determining, for each sample point, a respective sample point colour based on a location of the sample point with respect to the set of source pixels.
(Chien, “Y channel value of center (x,y) pixel is calculated; for (each pixel (i,j) in the blur window)... Y channel value of the (i,j) pixel is calculated; ...”, “SummationRed += (spatial weight)* (color-range weight)* Red channel value of (i,j);
SummationBlue += (spatial weight)* (color-range weight)* Blue channel value of (i,j); SummationGreen += (spatial weight)* (color-range weight)* Green channel value of (i,j)”, [0055]; RGB channel values of pixel (i,j) are used to calculate the normalized summation of RGB outputs of the center pixel (x,y); this is determining a sample-point “colour/appearance” by looking up the pixel value at the sample location)
Regarding claim 29, the combination of Chien and Zimmer teaches its/their respective base claim(s).
The combination further teaches the computer implemented method of claim 28 wherein determining the first blur appearance value based on the set of sample points includes:
averaging the sample point colour of each of the set of sample points.
(Zimmer, “Each pixel in the output image (FIG. 3) is computed as a weighted average of the values of the corresponding pixel and a predetermined number of surrounding pixels in the input image (FIG. 2)”, [0020]; forming the output pixel by averaging (specifically, a weighted average) the values (color vectors) of surrounding pixels/samples, which corresponds to averaging the sample point colors of the set of sample points; Chien, “the output value of the center pixel may be calculated by a summation of the pixel values multiplied by the two coefficients divided by the normalization factor”, [0054]; summation of sample pixel values and division by a normalization factor is an averaging operation (here, a weighted average) across the sampled pixels in the filter window)
Regarding claim 30, the combination of Chien and Zimmer teaches its/their respective base claim(s).
The combination further teaches the computer implemented method of claim 21, wherein determining the set of sample points includes directly or indirectly determining the number of sample points in the set of sample points, wherein the determination is in part based on a computer processor implementing the method.
(Zimmer, “GPU fragment programs typically only allow a small maximum number of textures. Thus a scheme which minimizes the number of passes and maximizes the blurring work done with each pass is sought”, [0005]; this links implementation constraints of the GPU (processor executing the method) to design choices that limit/shape sampling (e.g., minimizing texture lookups/samples); “the blur of FIG. 1B requires three samples from the source image, while the blur of FIG. 1D requires five samples”, [0022]; choosing an approach with a particular number of samples (3 vs. 5), in the context of computational intensity/efficiency on GPU/fragment programs, i.e., sample count determination influenced by the processor constraints described above)
Regarding claim 31, the combination of Chien and Zimmer teaches its/their respective base claim(s).
The combination further teaches the computer implemented method of claim 21, including the further steps of:
selecting each of the target pixels from the set of one or more target pixels; and
(Chien, “for each pixel within the selected image area, a three by three filter window (e.g., a blur window) may be applied”, [0049]; applying a filter window “for each pixel” corresponds to iterating/selecting target pixels (the pixel currently being processed))
for each target pixel, generating a respective output pixel corresponding to each target pixel by:
(Zimmer, “Each pixel in the output image (FIG. 3) is computed as a weighted average of the values of the corresponding pixel and a predetermined number of surrounding pixels in the input image (FIG. 2)”, [0020]; teaching generating an output pixel for each target pixel using surrounding samples)
determining a set of sample points having a sample point distribution within a blur sample area corresponding to an area of the image,
(Chien, “for each pixel within the selected image area, a three by three filter window (e.g., a blur window) may be applied”, [0049]; the “blur window” defines the blur sample area, and the pixels within it are the set of sample points; Zimmer, “a predetermined number of surrounding pixels”, [0020]; the surrounding pixels are the set of sample points within the blur area around the target pixel)
wherein the sample point distribution has a central skew towards the respective target pixel;
(Chien, “For example, the center pixel may have the largest weight assigned to it, and as pixels deviate from the center, their weight may be smaller”, [0052]; teaching a distribution of weights (and thus effective sampling emphasis) centered on the target/center pixel, i.e., centrally biased toward the target pixel within the blur window)
determining a set of sample point appearance values based on the sample points and the image;
(Chien, “Y channel value of center (x,y) pixel is calculated; for (each pixel (i,j) in the blur window)... Y channel value of the (i,j) pixel is calculated; ...”, “SummationRed += (spatial weight)* (color-range weight)* Red channel value of (i,j); SummationBlue += (spatial weight)* (color-range weight)* Blue channel value of (i,j); SummationGreen += (spatial weight)* (color-range weight)* Green channel value of (i,j)”, [0055]; RGB channel values of pixel (i,j) are used to calculate the normalized summation of RGB outputs of the center pixel (x,y); Chien explicitly determines per-sample-point (i,j) appearance values based on pixel channel values at that sample location; “Each pixel in the image, and thus in the selected image area, may have associated color channels of a respective color space. For example, when using the Red, Green, and Blue (RGB) color space, each pixel may have one or more associated RBG values (such as one value for each of the R, G, and B color channels)”, [0028]; this supports that “pixel appearance value” corresponds to channel/intensity/color values; Zimmer, “Each pixel has a value, which may, for example, be a vector value that specifies color (in red, green, and blue components)”, [0020]; this reinforces that sample point appearance values can be pixel values (e.g., color vectors) at/near the sample points)
determining a blur appearance value based on the sample point appearance values; and
(Chien, “the output value of the center pixel may be calculated by a summation of the pixel values multiplied by the two coefficients divided by the normalization factor”, [0054]; this is a blur value derived from sample pixel values (appearance values) and weighting coefficients; Zimmer, “Each pixel in the output image (FIG. 3) is computed as a weighted average of the values of the corresponding pixel and a predetermined number of surrounding pixels in the input image (FIG. 2)”, [0020]; a weighted average is a blur value based on sampled appearance values)
generating the respective output pixel based on the blur appearance value.
(Chien, “the output value of the center pixel may be calculated...”, [0054]; calculating the output value corresponds to generating the output pixel from the computed blur value)
Regarding claim 33, Chien teaches a computer implemented method for blurring a source image defined by a set of source pixels, the method including:
(Chien, “the tool (e.g., the brush tool) may operate to blur the image substantially contained in the selected area”, [0021]; “The image may contain pixels A1-A10, B1-B10 . . . G1-G10”, [0024]; expressly treats the source image as pixel-defined data and performs a blur operation on it)
for each of a plurality of pixels in the set of source pixels generating an output pixel by:
(Chien, “for (each pixel (x,y) in the affected region)”, computes “Red Output of (x,y) pixel = SummationRed/Normalization; Blue Output of (x,y) pixel = SummationBlue/Normalization; Green Output of (x,y) pixel = Summation Green/Normalization”, [0055]; applying the window to “most or all” pixels teaches per-pixel processing across a plurality)
for a plurality of sample points having a sample point distribution about a respective pixel wherein the sample point distribution has a central skew towards the respective pixel; such that the sample points in the sample point distribution are more concentrated in the centre of the blur sample area:
(Chien, "for ( each pixel (i,j) in the blur window ) {", [0056]; Zimmer, "First a vector v is computed, which is a two-dimensional subpixel-accurate displacement representing the specific offset from the center of the zoom blur to the destination sample position. A fraction f, typically close to but not equal to 1 (e.g. 0.98), is used to compute the sample positions. The sample positions are at locations equal to the center of the zoom blur plus v times the following five factors: f-2, f-1, 1, f, f2.", [0037]; Chien teaches determining a plurality of sample points (i,j) within a blur window about the respective pixel in a uniform grid but does not expressly disclose a skewed distribution where points are more concentrated in the center. Zimmer functionally teaches computing a sample point distribution with geometric fractional factors (f-2, f-1, 1, f, f2), which inherently skews the sample locations to be more concentrated toward the center)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the teachings of Zimmer into the blur method of Chien in order to accomplish zoom blur, radial blur, and various other forms of blur that vary across an image computed for the purpose of simulating depth-of-field. The combination of Chien and Zimmer also teaches other enhanced capabilities.
The combination of Chien and Zimmer further teaches:
determining a sample point appearance value for each said sample point, wherein the sample point appearance value for a sample point is, or is based on, a pixel appearance value for each of one or more pixels in the set of source pixels that are located at or near that sample point;
(Chien, “Y channel value of center (x,y) pixel is calculated; for (each pixel (i,j) in the blur window)... Y channel value of the (i,j) pixel is calculated; ...”, “SummationRed += (spatial weight)* (color-range weight)* Red channel value of (i,j); SummationBlue += (spatial weight)* (color-range weight)* Blue channel value of (i,j); SummationGreen += (spatial weight)* (color-range weight)* Green channel value of (i,j)”, [0055]; RGB channel values of pixel (i,j) are used to calculate the normalized summation of RGB outputs of the center pixel (x,y); Chien explicitly determines per-sample-point (i,j) appearance values based on pixel channel values at that sample location; “Each pixel in the image, and thus in the selected image area, may have associated color channels of a respective color space. For example, when using the Red, Green, and Blue (RGB) color space, each pixel may have one or more associated RBG values (such as one value for each of the R, G, and B color channels)”, [0028]; this supports that “pixel appearance value” corresponds to channel/intensity/color values; Zimmer, “Each pixel has a value, which may, for example, be a vector value that specifies color (in red, green, and blue components)”, [0020]; this reinforces that sample point appearance values can be pixel values (e.g., color vectors) at/near the sample points)
determining a first blur appearance value based on the sample point appearance values; and
(Chien, “the output value of the center pixel may be calculated by a summation of the pixel values multiplied by the two coefficients divided by the normalization factor”, [0054]; this is a blur value computed from the sampled pixel values; “SummationRed += (spatial weight)* (color-range weight)* Red channel value of (i,j); SummationBlue += (spatial weight)* (color-range weight)* Blue channel value of (i,j); SummationGreen += (spatial weight)* (color-range weight)* Green channel value of (i,j)”, [0055]; the normalized weighted sums are blur appearance values derived from sample point appearance values; Zimmer, “Each pixel in the output image (FIG. 3) is computed as a weighted average of the values of the corresponding pixel and a predetermined number of surrounding pixels in the input image (FIG. 2)”, [0020]; computing blur values from nearby sample pixel values)
generating the output pixel based on the first blur appearance value; and
(Chien, “Red Output of (x,y) pixel = SummationRed/Normalization; Blue Output of (x,y) pixel = SummationBlue/Normalization; Green Output of (x,y) pixel = Summation Green/Normalization”, [0055]; generating the output pixel’s channel values from the computed (blurred) normalized sums)
forming a blurred image using the generated output pixels;
(Zimmer, “Each pixel in the output image (FIG. 3) is computed as a weighted average of the values of the corresponding pixel and a predetermined number of surrounding pixels in the input image (FIG. 2)”, [0020]; once per-pixel output values are computed, the set of those output pixels form the output (blurred) image)
wherein the sample point distribution about the respective pixel spans an area of the source image and the pixels corresponding to the sample points are less than all the pixels in the area.
(Chien, “for each pixel within the selected image area, a three by three filter window (e.g., a blur window) may be applied”, [0049]; the distribution spans an “area” (the 3×3 area) and uses the pixels in that local window rather than all pixels in a larger area (e.g., the entire selected region/image); “the tool (e.g., a blur brush tool) may select a portion of the image (e.g., an image as shown in FIG. 3A) to yield a selected image portion (e.g., a 3 by 3 pixel area), on which the filter window of FIG. 3B (e.g., the blur filter) may be applied”, [0050]; blur is computed using a limited subset (window pixels) relative to a broader image area; Zimmer, “Each pixel in the output image (FIG. 3) is computed as a weighted average of the values of the corresponding pixel and a predetermined number of surrounding pixels in the input image (FIG. 2)”, [0020]; “predetermined number” teaches using fewer than all pixels in the surrounding area)
Regarding claims 38-39, the combination of Chien and Zimmer teaches its/their respective base claim(s).
The combination further teaches the computer processing system including:
a processing unit; and
a non-transitory computer-readable storage medium storing instructions, which when executed by the processing unit, cause the processing unit to perform a method according to claim 21.
(Chien, Zimmer, see comments on claim 21; Chien, Fig. 6, [0064])
Regarding claim 40, the combination of Chien and Zimmer teaches its/their respective base claim(s).
The combination further teaches the printed product including an output image printed thereon, the output image generated in accordance with a method according to claim 21.
(Chien, Zimmer, see comments on claim 21; Chien, Fig. 6; “computer system 1000 may be any of various types of devices, including, but not limited to, a personal computer system, desktop computer, laptop or notebook computer, mainframe computer system, handheld computer, workstation, network computer, a consumer device, video game console, handheld video game device, application server, storage device, a peripheral device such as a switch, modem, router, or in general any type of computing device”, [0068]; “resulting image (i.e., the filtered output image)”, [0003]; a computer can connect to a peripheral device such as an external printer for printing an image processed from computer system 1000)
Allowable Subject Matter
Claim(s) 22-26 and 34-37 is/are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening Claim(s).
The following is a statement of reasons for the indication of allowable subject matter:
Claim(s) 22, 25, 34 and 37 recite(s) limitation(s) related to applying a skew to a uniform sample-point distribution so sampling is centrally biased toward the target pixel; randomly jittering sample-point locations to vary the sampling distribution used for blur computation; selecting sample distribution based on blur type, and randomly offset a distribution function; and perform blurring of claim 21 on two systems in which the second system uses more sample points than the first. There are no explicit teachings to the above limitation(s) found in the prior art cited in this office action and from the prior art search.
Claim(s) 23-24, 26, and 35-36 depend on claims 22, 25 and 34, respectively.
Response to Arguments
Applicant's arguments filed on 3/30/2026 with respect to one or more of the pending claims have been fully considered but they are not persuasive.
Regarding claim(s) 21, 32 and 33, Applicant, in the remarks, argues that the combination of the cited reference(s) fails to teach the newly amended limitations in the claims.
The Examiner respectfully disagreed. The office action has been updated to address applicant’s argument. See the updated review comments for details.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JIANXUN YANG whose telephone number is (571)272-9874. The examiner can normally be reached on MON-FRI: 8AM-5PM Pacific Time.
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/JIANXUN YANG/
Primary Examiner, Art Unit 2662 4/6/2026
Prosecution Insights