DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . It is responsive to the submission dated 07/27/2026. Claims 1-23 are presented for examination, of which, claims 1, 11 and 17 are independent claims.
Continued Examination Under 37 CFR 1.114
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after allowance or after an Office action under Ex Parte Quayle, 25 USPQ 74, 453 O.G. 213 (Comm'r Pat. 1935). Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, prosecution in this application has been reopened pursuant to 37 CFR 1.114. Applicant's submission filed on 07/27/2026 has been entered.
Response to Arguments
3. Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Objections
4. Claim 1 is objected to because of the following informalities: to make clear the claimed invention, the applicant is suggested to “a luminance value” with -a respective luminance value-. Appropriate correction is required.
Claim Rejections - 35 USC § 103
5. 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.
6. Claims 1-2 are rejected under 35 U.S.C. 103 as being unpatentable over Maya (JP 2004056783 A) in view of Luo et al. (US 20030011612).
Considering claim 1, Maya discloses an apparatus comprising: a processor; and memory coupled to or included with the processor, the memory storing instructions that, when executed, cause the processor (see paras. 21-24) to:
obtain an image; perform color palette cluster analysis on the image based on an initial set of colors, luminance sorting, and a target number of palettes (for examples, Maya discloses an apparatus includes: a processing logic that divides an image into small blocks (processing block 101). After dividing the image into sub-blocks, processing logic divides the image pixels in each block into one or more groups up to a maximum predetermined number of groups based on the color of the pixels. Grouping (processing block 102). After grouping the pixels of the image, processing logic selects representative color values from the color space for each group (processing block 103). It is possible to do this by examining the possible values available in the color space. In one embodiment of the present invention, processing logic selects a CMYK value (e.g., a δ-bit value representing a combination of C, M, Y and / or K colors) for each group. After selecting a representative color value for each group, processing logic determines, for each group, the number of ink dots in the set of output colors required to obtain the color closest to the target color for the group of blocks. Is calculated (processing block 104). Then, for each group, processing logic constructs the final distribution of print colors, and for each group (eg, C, M, Y, R, G, B, K, and W dots) Are arranged (processing block 105), and for each group, the pixels are arranged in a preset order (eg, sorted by brightness) (or cluster analysis via luminance sorting) according to the number of arrangement masks covering the group (processing block 106). See paras. 26-30. Moreover, Maya discloses: The color selection unit 203 selects a representative color for each group. For example, the color selection unit 203 can select a color value representing a group. The color values are C, M, Y, and / or K when the value of a C, M, Y, and / or K color (e.g., (C, M, Y, K) is equal to (30, 0, 15, 50)) Alternatively, an 8-bit CMYK value representing the average of the values of K may be used. Given an input block color average as a target, the color matching and layout unit 204 calculates the number of dots in the output color space required for each group, so that the set of output colors is as close as possible to the target value. Averaged. In one embodiment of the invention, the color matching and layout unit 204 sets the output color by examining the values achievable in the color space of the output image and identifying the values closest to the achievable target values. Is calculated. For example, the color matching and layout unit 204 calculates how many C, M, Y and K dots are needed for each group. The color matching and layout unit 204 then builds a distribution of dots in the output color set for each group. For example, the color matching and layout unit 204 constructs C, M, Y, R, G, B, K, and W dot distributions for each group. Given a set of output colors, a color matching and layout unit 204 arranges those constructed colors to obtain the desired colors and also to reduce artifacts and graininess and also allow minimization. In one embodiment of the present invention, for each group, the color matching and layout unit 204 arranges the pixels in the order of luminance according to the number of arrangement masks covering the group (e.g., performing cluster analysis via luminance sorting). See paras. 31-33);
produce a compressed set of color palette keys responsive to the color palette cluster analysis; and output a compressed image based on the compressed set of color palette keys (for example, Maya discloses processing logic sets an interval threshold thr and thr2 that specifies how far two color values that are counted as different colors should be (processing block 401); and marks all pixels as belonging to a group according to the determined distance between maxPix and minPix and an interval from the origin (0,0,0); and performs spatial mapping to be used to map between input and output pixels for the block. See paras. 44-54. Maya further teaches generating an output halftone image having different blocks associated with each of the plurality of blocks by using representative color values for the groups of the different blocks approximated in spatial areas covered with the different blocks. See abstract, claim 1 and para. 16 of Maya, wherein the computed representative color values or the marked pixel values or color bitmap values for the groups of the different blocks correspond to the compressed set of color palette keys).
Maya fails to teach colors of the palette cluster analysis are RGB-based color values and converting the RGB-based color values associated with the initial set of colors to luminance values and sorting the luminance values to obtain an index of sorted luminance values, which is disclosed by Luo. See abstract, paras. 9-10, 18-30 of Luo.
Particularly, Luo discloses a method for converting an input digital color image having a set of possible input colors (e.g., the initial set of colors) to an output digital color image having a set of palette colors, the number of palette colors being less than the number of possible input colors (interpreted as perform color palette cluster analysis on the image based on an initial set of colors and converting the color values), wherein the set of palette colors is determined based on the distribution of colors in the input digital image boosted by a distribution of important colors contained in the input digital image (interpreted as converting the color values to include sorted luminance values). This is accomplished using the steps of determining the distribution of colors (e.g., converted luminance values) in the input digital color image, detecting regions of important colors (e.g., index values) in the input digital color image, boosting the distribution of colors in the input digital color image in response to the detected regions of important colors, determining the set of palette colors to be used in the formation of the output digital color image responsive to the boosted distribution of colors, and forming the output digital color image by assigning each color in the input digital color image to one of the colors in the set of palette colors (interpreted as obtaining an index from sorted luminance values, with the boosted distribution of colors correspond to the sorted luminance values). See para. 10.
Moreover, Luo discloses the method operates on an input digital color image 10 having a set of possible input colors. The set of possible input colors will be defined by the color encoding of the input digital color image 10. Typically the input digital color image 10 might be a 24-bit RGB color image having 2.sup.24=16,777,216 different colors. The input digital color image 10 could be at some other bit-depth. A determined distribution of input colors step 11 is used to determine the distribution of input colors 12. In a preferred embodiment of the present invention, the distribution of the input colors is determined by forming a three-dimensional histogram of color values. See para. 18, wherein the above stated processes are construed to encompass that the colors of the palette during cluster analysis are RGB color values that converted to luminance values, as the distribution of color histogram intrinsically includes luminance values associated with the initial colors in the input image.
In addition, Luo discloses: Next, the distribution of input colors 12 is boosted by a distribution of important colors 13 detected in the input color image to form a boosted distribution of colors 14. The distribution of important colors is obtained from a detect regions of important colors step 19. A determine set of palette colors step 15 is then used to determine a set of palette colors 16 responsive to the boosted distribution of colors 14. Since the boosted distribution of colors 14 has been boosted by the distribution of important colors 13, the set of palette colors will contain more colors in the important color regions than would otherwise be the case if the determine set of palette colors step 15 had been applied to the original distribution of input colors 12. The number of colors in the set of palette colors 16 will be less than the number of possible input colors. In a preferred embodiment of the present invention, the number of palette colors will be 256 corresponding to the number of different colors that can be represented with an 8-bit color image. However, it will be obvious to one skilled in the art that the method can be generalized to any number of palette colors. For example, if the output image were a 4-bit color image, the number of corresponding output colors would be 16, or if the output image were a 10-bit color image, the number of corresponding palette colors would be 1024. Once the set of palette colors 16 has been determined, an assign palette color to each image pixel step 17 is used to form an output digital color image 18. The output digital color image 18 will be comprised entirely of colors chosen from the set of palette colors. Generally, the palette color for each pixel of the image will be identified by an index value indicating which palette color should be used for that pixel. For example, if there are 256 palette colors used for a particular image, each pixel of the output image can be represented by an 8-bit number in the range 0-255. The output digital color image 18 will generally be stored in a digital memory buffer, or in a digital image file. In order to properly display the image, a palette index indicating the color value for each of the different palette colors needs to be associated with the image. When the image is displayed, the palette index can be used to determine the corresponding color value for each of the palette colors. See paras. 19-20. See also paras. 21-36.
Accordingly, it would have been obvious to one of the ordinary skilled in the art, before the effective filling date of the invention was made, to have modified the teachings of Maya to include colors of the palette cluster analysis are RGB-based color values and converting the RGB-based color values associated with the initial set of colors to luminance values and sorting the luminance values to obtain an index of sorted luminance values, in the same conventional manner as taught by Luo; in order to to provide an improved method for palette selection which minimizes quanitization artifacts for selected important colors. See para. 9 of Luo.
As per claim 2, Luo, as modified by Maya, discloses each RGB value has a first number of bits, and each luminance value has a second number of bits, the second number of bits being less than the first number of bits. See paras. 19 and 21 of Luo and the rational above with respect to rejections of claim 1 for reason of obviousness.
(Original) The apparatus of claim 1, wherein the instructions, when executed, cause the processor to perform the luminance sorting and the color palette cluster analysis, the color palette cluster analysis including: adding pixels to the color palette cluster analysis based on a spatial pattern that skips over adjacent pixels; and adjusting cluster centroids responsive to each pixel being added.
6. (Previously Presented) The apparatus of claim 1, wherein the instructions, when executed, cause the processor to perform the color palette cluster analysis for each of a plurality of sub-blocks of the image by: performing a target number of clustering iterations; for each clustering iteration, obtaining a set of color palette keys; and for each clustering iteration, dividing the set of color palette keys based on the target number of palettes.
7. (Original) The apparatus of claim 1, wherein the instructions, when executed, cause the processor to: adjust the compressed set of color palette keys, for each of a plurality of sub-blocks of the image, responsive to an overlap averaging analysis, to produce an adjusted set of color palette keys; and output the compressed image based on the adjusted set of color palette keys.
8. (Currently Amended) The apparatus of claim 7, wherein the overlap averaging analysis includes: obtaining the compressed set of color palette keys for each of the plurality of sub-blocks of the image; identifying co-located pixels of the plurality of sub-blocks; and for each co-located pixel, averaging respective color palette keys of the compressed set of color palette keys to obtain [[an]] the adjusted set of color palette keys.
9. (Original) The apparatus of claim 8, wherein the instructions, when executed, further cause the processor to identify the co-located pixels of the sub-blocks based on truth table analysis.
10. (Original) The apparatus of claim 1, wherein the instructions, when executed, cause the processor to perform the color palette cluster analysis by: skipping the color palette cluster analysis for portions of the image below a threshold luminance value; using a traversal look-up table (LUT) to determine an order for adding pixels of the image to the color palette cluster analysis; and using a pixel weight LUT to weight pixels added to the color palette cluster analysis.
Allowable Subject Matter
6. Claims 3-10 and 21-22 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 claims, because the prior art of record fail to teach the apparatus of claim 2, wherein the instructions, when executed, further cause the processor to perform the luminance sorting by: partitioning the sorted index of sorted luminance values into a target number of bins; selecting center values for the bins; and initializing the color palette cluster analysis responsive to each of the center values (as recited in claim 3). The prior art of record fails also to teach the apparatus of claim 1, wherein the instructions, when executed, cause the processor to perform the luminance sorting and the color palette cluster analysis, the color palette cluster analysis including: adding pixels to the color palette cluster analysis based on a spatial pattern that skips over adjacent pixels; and adjusting cluster centroids responsive to each pixel being added (as recited in claim 5), wherein the instructions, when executed, cause the processor to perform the color palette cluster analysis for each sub-block of a plurality of sub-blocks of the image by: performing a target number of clustering iterations; for each clustering iteration, obtaining a set of color palette keys; and for each clustering iteration, dividing the set of color palette keys based on the target number of palettes (as recited in claim 6), wherein the instructions, when executed, cause the processor to: adjust the compressed set of color palette keys, for each of a plurality of sub-blocks of the image, responsive to an overlap averaging analysis, to produce an adjusted set of color palette keys; and output the compressed image based on the adjusted set of color palette keys (as recited in claim 7), and perform the color palette cluster analysis by: skipping the color palette cluster analysis for portions of the image below a threshold luminance value; using a traversal look-up table (LUT) to determine an order for adding pixels of the image to the color palette cluster analysis; and using a pixel weight LUT to weight pixels added to the color palette cluster analysis (as recited in claim 10), wherein the color palette analysis includes splitting a parent cluster into child clusters based on a splitting improvement score that is based on a difference between a summed distance metric of the parent cluster and an average of summed distance metrics of the child clusters (as recited in claim 21); and causing the processor to detect pixels of the image having luminance values below a threshold and omit the detected pixels from the color palette cluster analysis (as recited in claim 22),
7. Claims 11-20 and 23 are allowed, because the prior art of record fail to teach a system and method comprising: averaging, by a processing device, respective color palette keys of the compressed set of color palette keys for co-located pixels in a plurality of sub-blocks of an input image to obtain an adjusted set of color palette keys; and outputting a compressed image based on the adjusted set of color palette keys (as recited in claims 11 and 17).
Conclusion
8. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Dorner et al. (US 20160335784) discloses techniques for generating an image-based color palette based on a color image. A color palette can be a collection of representative colors each associated with a weight or other metadata. A color palette may be generated based on palette generation criteria, which may facilitate or control a palette generation process. Illustratively, the palette generation process may include image pre-processing, color distribution generation, representative color identification, palette candidate generation and palette determination. Representative colors with associated weight can be identified from a distribution of colors depicted by the color image, multiple palette candidates corresponding to the same color image can be generated based on various palette generation criteria, and a color palette can be identified therefrom.
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to WESNER SAJOUS whose telephone number is (571) 272-7791. The examiner can normally be reached on M-F 10:00 TO 7:30 (ET).
Examiner interviews are available via telephone and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice or email the Examiner directly at wesner.sajous@uspto.gov.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Said Broome can be reached on 571-272-2931. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/WESNER SAJOUS/Primary Examiner, Art Unit 2612
WS
09/11/2026
Prosecution Insights