Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/6/2026 has been entered.
Response to Amendment
The amendment filed on 5/6/26 has been entered and made of record. Claims 1, 5-6, 8, 10-12, 17 and 19-20 are amended. Claims 9 and 18 are cancelled. Claims 21-22 are new. Claims 1-8, 10-17, 19-22 are pending.
Response to Arguments
Applicant’s arguments with respect to claims 1, 12 and 17 have been fully considered but they are moot because the arguments do not apply to the references being used in the current rejection.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 8 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 8 cites limitation “wherein the edges are identified as local intensity changes above a threshold value defined by sets of connected pixels forming boundaries between disjoint intensity regions”. Here, it is unclear how the said threshold value is defined by sets of connected pixels.
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 of this title, 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.
Claims 1-2, 4-7, 10, 12-17 and 19-22 are rejected under 35 U.S.C. 103 as being unpatentable over Yamada et al. (US 2023/0419574 A1) in view of Guleryuz et al. (US 2013/0265382 A1) and Jin et al. (CN 113781408 A), further in view of Lim et al. (KR102247778B1).
As to Claim 1, Yamada teaches A method comprising:
receiving, by a processing device, a digital image to be inserted into a digital template (Yamada discloses “The layout unit 2103 generates poster data by laying out the image obtained from the image analysis unit 212 and the text obtained from the text specification unit 202 on each template acquired from the template selection unit 2102” in [0168]);
identifying, using a machine-learning model, first dominant colors of the digital image, and one or more second dominant colors of the digital template from existing graphic content of the digital template (Yamada discloses “In S905, the image analysis unit 212 performs an object recognition process and a main color extraction process on the image acquired in S904. Here, a known method can be used for the object recognition process. In the present embodiment, an object is recognized by a discriminator generated by deep learning” in [0097]; “In a case where a plurality of images are specified by the image specification unit 203, the image analysis unit 212 may extract a plurality of main colors by calculating the main color for each image.” in [0105]; “In the present embodiment, the template refers to a skeleton for which a color scheme and a font have been set in advance” in [0166]; “The template selection unit 2102 selects one or more templates that use a color similar to the main color acquired from the image analysis unit 212 from the templates acquired from the template acquisition unit 2101” in [0167]. Here, it is obvious that the main color of the template is extracted first before being compared with the main color of the image.)
Yamada doesn’t directly teach harmonic color. The combination of Guleryuz further teaches following limitations:
determining, by the processing device, a harmonic match between the first dominant colors of the digital image and one or more second dominant colors of the digital template, the harmonic match including a first harmonic color from the first dominant colors that matches a second harmonic color from the one or more second dominant colors (Yamada discloses “In the creation process, by using a color scheme pattern with colors similar to the main color of the image input by the user, the overall sense of unity of the poster can be achieved” in [0124], see also [0157, 0174]. Guleryuz further discloses “At block 312, a visually pleasing background that matches and highlights the foreground may be selected, e.g., by choosing a background from a multiplicity of backgrounds with known features or by designing a background based on the characteristics of the foreground. Visually pleasing color combinations may include harmonized, matching colors as known to those of skill in art and graphic design” in [0031]; “Process 400 may next select a harmonizing color, hp, that highlights the computed dominant color, DF. A variety of templates may be used to select the harmonizing color, as known in the relevant art … Using the above mechanisms, process 410 thus designs a visually pleasing background b(x) 414 that is complimentary to the foreground image 404” in [0042].);
generating, by the processing device, a modified digital image by applying the first harmonic color as a color effect to the digital image by shifting hue values of the digital image to fit the first harmonic color; and inserting, by the processing device, the modified digital image into the digital template (Yamada discloses “The layout unit 2103 generates poster data by laying out the image obtained from the image analysis unit 212 and the text obtained from the text specification unit 202 on each template acquired from the template selection unit 2102” in [0168]. Guleryuz further discloses “Consequently, process 400 may comprise designing backgrounds having a very low complexity color modulation function that modulates a base background picture's color values to match the foreground picture's color values… Process 400 may next select a harmonizing color, hF, that highlights the computed dominant color, DF. A variety of templates may be used to select the harmonizing color, as known in the relevant art… Once process 400 computes hp, the modulation function may manipulate the base background color values by ensuring that the background dominant color is the computed harmonizing color…” in [0042]. Here, Guleryuz teaches modifying one image by applying harmonic color to achieve a harmonic color matching with another image.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Yamada with the teaching of Guleryuz so as to achieve a visually pleasing image by applying a harmonic color matching between a foreground image and background image (Guleryuz, [0031]).
Yamada and Guleryuz don’t explicitly teach harmonic template. The combination of Jin further teaches following limitations:
determining a harmonic match using one or more harmonic templates that define groups of colors on a color wheel that are harmonically matched, the second harmonic color being a second dominant color closest to a sector boundary of a first harmonic template among the one or more harmonic templates, and the first harmonic color being a first dominant color closest to a sector boundary of the first harmonic template; and shifting hue values of the digital image to fit the first harmonic color (Jin discloses “Color harmony detection module 13: Input the original image into the color harmony model and output the fitting degree of the color harmony template” in [0034]; “Eight color harmony templates are defined using Color Harmonization. The shadow areas of the color harmony templates are the areas where harmonious colors are distributed” in [0039]; “A color harmony template is matched based on the minimum distance between the hue of the original image and the boundary of each hue harmony template sector, as well as the saturation of each pixel” in [0041]; “The shadow area of the color harmony template is the area where harmonious colors are distributed. The angle of this area is fixed and it can be rotated arbitrarily around the center of the color wheel…Based on the minimum distance between the image's color value and the boundary of each template's sector area, as well as the saturation of each pixel, match and display the most suitable color harmony template” in [0042].)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Yamada and Guleryuz with the teaching of Jin so as to explain harmonic types defined over the hue channel of the HSV color wheel and shift hues to match the template sectors.
In response to the argument on using a machine-learning model to identify a dominant color of an image and/or a template image, Yamada discloses “by operating an impression learning application for learning the poster impression in advance” in [0083]; “the image analysis unit 212 may extract a plurality of main colors by calculating the main color for each image” in [0105]; “For example, in a case where a machine learning method such as a decision tree is used, feature values such as an average luminance value, an edge value, and/or the like are extracted from the poster image using image analysis, and a machine learning model may be created which estimates the impression based on these feature values” in [0089]. Here, it is obvious that a main color can be extracted by image analysis unit through a machine learning model. For example, Lim discloses “the determining step analyzes an image from the image source using an image analysis module implemented through machine learning techniques and automatically determines the object, main color, and image shape of the image according to the analysis” in [0014]; “and only the objects, main colors, and image shapes of the images stored in the rawdata database (105) that are automatically determined through machine learning techniques” in [0067], see also [0092]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Yamada, Guleryuz and Jin with the teaching of Lim so as to perform an image analysis module through machine learning techniques to automatically determine the object, main color and shape of the image based on the analysis (Lim, [0092]).
As to Claim 2, Yamada in view of Guleryuz, Jin and Lim teaches The method of claim 1, wherein identifying the first dominant colors of the digital image includes:
computing, for each region of at least one region of the digital image, a color histogram for the region; and assigning, using the machine-learning model, at least one dominant color for the region using the color histogram (Yamada discloses “In S905, the image analysis unit 212 performs an object recognition process and a main color extraction process on the image acquired in S904. Here, a known method can be used for the object recognition process. In the present embodiment, an object is recognized by a discriminator generated by deep learning” in [0097]; “The graph in FIG. 23 is a histogram that schematically represents the frequency of appearance of colors in an image… In the present embodiment, the histogram is generated by dividing the RGB color space into 16 gradations for each channel, but this is by way of example and not limitation” in [0098]; “For example, to extract a second main color, the image analysis unit 212 selects a color that has the second largest local maximum in the three-dimensional (R, G, B) space of the histogram in FIG. 23” in [0105]. Guleryuz also discloses “A separate foreground dominant color may then be calculated for each region, and this foreground dominant color may be used to modulate the background image colors in that region… In one embodiment, the dominant foreground color for region i, e.g., the dominant foreground color DF,i for pi, may be calculated as follows…” in [0042].)
As to Claim 4, Yamada in view of Guleryuz, Jin and Lim teaches The method of claim 2, wherein the color histogram comprises three luminance histograms indicating a brightness distribution of a red, green, or blue color channel, respectively, for each region (Yamada discloses “The graph in FIG. 23 is a histogram that schematically represents the frequency of appearance of colors in an image. In this graph, the horizontal axis represents the color of the pixel in three channels (R, G, B), and the vertical axis represents the number of pixels belonging to the range of ±8 before and after each of the R, G, and B channels” in [0098].)
As to Claim 5, Yamada in view of Guleryuz, Jin and Lim teaches The method of claim 1, wherein the one or more harmonic templates include a radial distribution of colors within a hue-saturation-value (HSV) color wheel that are aesthetically balanced, the one or more harmonic templates including at least one harmonic sector and at least two sector boundaries (Guleryuz discloses “Process 400 may next select a harmonizing color, hF, that highlights the computed dominant color, DF. A variety of templates may be used to select the harmonizing color, as known in the relevant art … Using the above mechanisms, process 410 thus designs a visually pleasing background b(x) 414 that is complimentary to the foreground image 404… When these hints are available, the rendering end may determine the virtual background colors in a way that ensures there is no substantial deviation from the sent color hints, e.g., by selecting a virtual background color which is analogous or split-analogous in color scheme to the actual background, e.g., by being adjacent on a color wheel. This may help in further avoiding artifacts” in [0042]. Jin also discloses “The distribution of hues in the original image in the HSV color space is statistically analyzed and plotted on a circular color wheel diagram” in [0038]; “Eight color harmony templates are defined using Color Harmonization. The shadow areas of the color harmony templates are the areas where harmonious colors are distributed” in [0039]; “Among them, the eight color harmony distribution types are i, V, L, I, T, Y, X and N, totaling eight color harmony templates” in [0040].)
As to Claim 6, Yamada in view of Guleryuz, Jin and Lim teaches The method of claim 5, wherein determining the harmonic match between the first dominant colors and the one or more second dominant colors further includes:
determining, for each first dominant color in the digital image and for each harmonic template, a respective first minimum distance between the first dominant color and the at least two sector boundaries of the one or more harmonic templates; and determining, for each second dominant color in the digital template and for each harmonic template, a respective second minimum distance between the second dominant color and the at least two sector boundaries of the one or more harmonic templates (Guleryuz discloses “Process 400 may next select a harmonizing color, hp, that highlights the computed dominant color, DF. A variety of templates may be used to select the harmonizing color, as known in the relevant art … Using the above mechanisms, process 410 thus designs a visually pleasing background b(x) 414 that is complimentary to the foreground image 404” in [0042]; “At block 312, a visually pleasing background that matches and highlights the foreground may be selected, e.g., by choosing a background from a multiplicity of backgrounds with known features or by designing a background based on the characteristics of the foreground. Visually pleasing color combinations may include harmonized, matching colors as known to those of skill in art and graphic design” in [0031]. Jin, Fig 3.)
As to Claim 7, Yamada in view of Guleryuz, Jin and Lim teaches The method of claim 1, wherein applying the first harmonic color as the color effect includes blending the first harmonic color on the digital image to generate a blended image (Yamada discloses “More specifically, in the present embodiment, a plurality of variations of poster candidates can be created according to the target impression by selecting and combining elements that make up the posters, such as skeletons, color scheme patterns, and fonts, based on the target impression. In the creation process, by using a color scheme pattern with colors similar to the main color of the image input by the user, the overall sense of unity of the poster can be achieved” in [0124]; “In this process, the combination generation unit 1701 uses the color scheme pattern table such that only color scheme patterns that match the main color extracted by the image analysis unit 212 are used in generating the combinations… As a result, the colors assigned to the poster are the same as the colors contained in the image” in [0157]. Guleryuz further discloses “At block 312, a visually pleasing background that matches and highlights the foreground may be selected, e.g., by choosing a background from a multiplicity of backgrounds with known features or by designing a background based on the characteristics of the foreground. Visually pleasing color combinations may include harmonized, matching colors as known to those of skill in art and graphic design” in [0031].)
As to Claim 10, Yamada in view of Guleryuz, Jin and Lim teaches The method of claim 1, wherein the method further includes:
in response to receiving visual edits to the digital template, determining an updated harmonic match between the first dominant colors of the digital image and one or more updated second dominant colors of the digital template; and generating an updated modified digital image in real-time as the digital template is modified by applying an updated first harmonic color from the updated harmonic match as the color effect to the digital image (Yamada discloses “In S1802, the template selection unit 2102 selects one or more templates that use a color similar to the main color acquired from the image analysis unit 212 from the templates acquired from the template acquisition unit 2101. More specifically, the color difference between the main color and a color set in a graphic object in each template is calculated, and only templates including a graphic object having a color difference smaller than or equal to a threshold value are selected” in [0171]; “Although not shown in the figure, the poster creation application may have a function of editing the created poster after the creation result is displayed on the poster display unit 205 such that the layout, the colors, and, the shapes, etc. of the images, the texts (characters), and the graphics (figures, illustrations, photographs, etc.) are edited according to a user operation so as to achieve a design desired by the user.” in [0066]. Guleryuz further discloses “Process 400 may next select a harmonizing color, hp, that highlights the computed dominant color, DF. A variety of templates may be used to select the harmonizing color, as known in the relevant art … Using the above mechanisms, process 410 thus designs a visually pleasing background b(x) 414 that is complimentary to the foreground image 404” in [0042], see also [0031,0046]. Jin, [0039-0042].)
Claim 12 recites similar limitations as claim 1 besides image segmentation (Guleryuz, [0026, 0042]), but in a system form. Therefore, the same rationale used for claim 1 is applied.
Claim 13 is rejected based upon similar rationale as Claim 5.
Claim 14 is rejected based upon similar rationale as Claim 6.
As to Claim 15, Yamada in view of Guleryuz, Jin and Lim teaches The system of claim 12, wherein the processing device is configured to perform image segmentation on the digital image by: applying a median filter to the digital image to create a gray image; and segmenting, using the machine-learning model, the gray image into the multiple segmented regions by assigning a segment label to each pixel in the gray image, wherein pixels with a same segment label share one or more visual characteristics (Guleryuz discloses applying a moving average filters in [0038]; “In some embodiments, gray-scale backgrounds formed with the aid of sampled directional textures that accomplish crosshatching patterns may be used, e.g., by reusing the directional texture LUT to realize the textures and blending… In another embodiment, the background image pixels may be divided into K regions, p, i= 1, ... , K. A separate foreground dominant color may then be calculated for each region, and this foreground dominant color may be used to modulate the background image colors in that region… Regions may also be determined by applying object-based region decomposition algorithms on base background images” in [0042].)
As to Claim 16, Yamada in view of Guleryuz, Jin and Lim teaches The system of claim 15, wherein: the median filter includes parameters to remove noise from the digital image; and the machine-learning model comprises a convolutional neural network (Yamada discloses “for example, a deep learning method using the convolution neural network (CNN) or a machine learning method using a decision tree, or the like. In the present embodiment, the impression learning unit performs supervised deep learning using the CNN with the poster image as input and the four factors as output” in [0087]. Guleryuz discloses a moving average filters to remove segmentation error or noisy segmentation boundary in [0034, 0038].)
Claim 17 recites similar limitations as claims 1 & 10 but in a computer readable medium form. Therefore, the same rationale used for claims 1 & 10 is applied.
Claim 19 is rejected based upon similar rationale as Claim 10.
Claim 20 is rejected based upon similar rationale as Claim 11.
As to Claim 21, Yamada in view of Guleryuz, Jin and Lim teaches The method of claim 1, wherein:
identifying the first dominant colors comprises identifying a respective first dominant color for each of multiple segmented regions of the digital image (Yamada discloses “In S905, the image analysis unit 212 performs an object recognition process and a main color extraction process on the image acquired in S904. Here, a known method can be used for the object recognition process. In the present embodiment, an object is recognized by a discriminator generated by deep learning” in [0097]; “In a case where a plurality of images are specified by the image specification unit 203, the image analysis unit 212 may extract a plurality of main colors by calculating the main color for each image.” in [0105]);
the harmonic match comprises, for each of the multiple segmented regions, a respective first harmonic color matched to a corresponding second harmonic color of the digital template (Yamada discloses “In the creation process, by using a color scheme pattern with colors similar to the main color of the image input by the user, the overall sense of unity of the poster can be achieved” in [0124], see also [0157, 0174]. Guleryuz further discloses “At block 312, a visually pleasing background that matches and highlights the foreground may be selected, e.g., by choosing a background from a multiplicity of backgrounds with known features or by designing a background based on the characteristics of the foreground. Visually pleasing color combinations may include harmonized, matching colors as known to those of skill in art and graphic design” in [0031]; “Process 400 may next select a harmonizing color, hp, that highlights the computed dominant color, DF. A variety of templates may be used to select the harmonizing color, as known in the relevant art … Using the above mechanisms, process 410 thus designs a visually pleasing background b(x) 414 that is complimentary to the foreground image 404” in [0042]. Jin, [0039-0042]); and
applying the first harmonic color as the color effect comprises applying the respective first harmonic color of each segmented region to pixels of that segmented region (Yamada discloses “The layout unit 2103 generates poster data by laying out the image obtained from the image analysis unit 212 and the text obtained from the text specification unit 202 on each template acquired from the template selection unit 2102” in [0168]. Guleryuz further discloses “Consequently, process 400 may comprise designing backgrounds having a very low complexity color modulation function that modulates a base background picture's color values to match the foreground picture's color values… Process 400 may next select a harmonizing color, hF, that highlights the computed dominant color, DF. A variety of templates may be used to select the harmonizing color, as known in the relevant art… Once process 400 computes hp, the modulation function may manipulate the base background color values by ensuring that the background dominant color is the computed harmonizing color…” in [0042]. Here, Guleryuz teaches modifying one image by applying harmonic color to achieve a harmonic color matching with another image.)
As to Claim 22, Yamada in view of Guleryuz, Jin and Lim teaches The method of claim 1, further comprising generating a modified digital template by applying the second harmonic color as a color effect to the digital template by shifting hue values of the digital template to fit the second harmonic color, wherein inserting the modified digital image into the digital template comprises inserting the modified digital image into the modified digital template (Yamada discloses “The layout unit 2103 generates poster data by laying out the image obtained from the image analysis unit 212 and the text obtained from the text specification unit 202 on each template acquired from the template selection unit 2102” in [0168]. Guleryuz further discloses “Consequently, process 400 may comprise designing backgrounds having a very low complexity color modulation function that modulates a base background picture's color values to match the foreground picture's color values… Process 400 may next select a harmonizing color, hF, that highlights the computed dominant color, DF. A variety of templates may be used to select the harmonizing color, as known in the relevant art… Once process 400 computes hp, the modulation function may manipulate the base background color values by ensuring that the background dominant color is the computed harmonizing color…” in [0042]. Here, Guleryuz teaches modifying one image by applying harmonic color to achieve a harmonic color matching with another image. Jin, [0039-0042].)
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Yamada in view of Guleryuz and Jin, further in view of Lim and Schaumberg (US 2025/0086994 A1).
As to Claim 3, Yamada in view of Guleryuz, Jin and Lim teaches The method of claim 2, wherein the method further comprises assigning, using k-nearest neighbors (KNN) classification by the machine-learning model, the region to the at least one dominant color by classifying each pixel of multiple pixels in the region to a particular color based on a most common classification of k nearest neighbor pixels, k being a positive integer value (Schaumberg discloses “The systems's KNN uses this instant lookup to quickly infer "suspect" pixels to pen, tissue, background, etc. types in imask” in [0057]; “This evidence of unsaturated pen color is important for "accelerated scalable K-nearest-neighbor (KNN)" search, because anywhere in the image that identical unsaturated pen pixels are found, the KNN algorithm will then infer those pixels as pen” in [0096].)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Yamada, Guleryuz, Jin and Lim with the teaching of Schaumberg so as to use KNN quickly to infer suspect pixels as background (the white/clear/empty part of a slide), tissue, pen, or marker (Schaumberg, [0096]).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Yamada in view of Guleryuz and Jin and Lim, further in view of Xiang et al. (CN 118134816 A) and Ivanchenko et al. (US 2014/0187223 A1).
As to Claim 8, Yamada in view of Guleryuz and Cohen-Or teaches The method of claim 7, wherein applying the first harmonic color as the color effect further includes:
computing image gradients to detect edges in the blended image; and performing histogram stretching on pixels corresponding to the edges in the blended image to generate the modified digital image, wherein the histogram stretching sharpens the modified digital image (Guleryuz discloses “The backgrounds may include color gradients and/or color palates suitable to color-match the foregrounds” in [0046]. Xiang further discloses “Histogram stretching or linear stretching can enhance edges and textures in underwater images… By applying an edge detection algorithm, an edge image of an underwater image is obtained. Then, the average gradient value in the edge image is calculated. The larger this value is, the higher the edge sharpness and the better the clarity of the underwater image” in [0044].)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Yamada, Guleryuz, Jin and Lim with the teaching of Xiang so as to use histogram stretching to enhance edges and textures in the image (Xiang, [0044]).
The combination of Ivanchenko further teaches following limitations:
wherein the edges are identified as local intensity changes above a threshold value defined by sets of connected pixels forming boundaries between disjoint intensity regions (Ivanchenko discloses “A set of gradients of the image segment can be analyzed, where the gradient represents an amount of change in color value between pixels 606 of the segment. In this example, there would be a set of large gradient values near the transition between the regions… In at least some embodiments, a gradient threshold can be set to determine how much change is necessary to designate the segment as a text candidate” in [0028]; “The image intensity gradients of a plurality of regions in the image are analyzed to identify a set of edge locations 804. Taking an edge to be a change in intensity taking place over a number of pixels, the edge detection algorithm can determine the edge by calculating a derivative of this intensity change, for example, and selecting regions where the calculated value meets or exceeds an edge selection threshold. Pixel values adjacent, or within a determined distance from, each of the set of edge locations are analyzed 806” in [0034].)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Yamada, Guleryuz, Jin, Lim and Xiang with the teaching of Ivanchenko so as to analyze a set of gradients of the image segment to represent an amount of color change at the edge region (Ivanchenko, [0028]).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Yamada in view of Guleryuz and Jin and Lim, further in view of Cohen-Or et al. (Color Harmonization, SIGGRAPH '06: ACM SIGGRAPH 2006 Papers, July 2006).
As to Claim 11, Yamada in view of Guleryuz, Jin and Lim teaches The method of claim 1, wherein: the modified digital image is automatically generated upon insertion into the digital template (Yamada discloses “FIG. 2 shows a software block diagram related, among various units, the poster creation unit 210 that provides the automatic poster creation function” in [0047]; “In this process, the combination generation unit 1701 uses the color scheme pattern table such that only color scheme patterns that match the main color extracted by the image analysis unit 212 are used in generating the combinations… As a result, the colors assigned to the poster are the same as the colors contained in the image” in [0157].); and applying the first harmonic color as the color effect further includes adjusting the hue values of the digital image based on spatial coherence among colors of neighboring pixels
(Cohen-Or discloses “Given a color image, our method finds the best harmonic scheme for the image colors. It then allows a graceful shifting of hue values so as to fit the harmonic scheme while considering spatial coherence among colors of neighboring pixels using an optimization technique” at Abstract; see also E2(V) at p. 627.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Yamada, Guleryuz, Jin and Lim with the teaching of Cohen-Or so as to explain harmonic types defined over the hue channel of the HSV color wheel and shift hues to match the template sectors (Cohen-Or, p. 625).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WEIMING HE whose telephone number is (571)270-1221. The examiner can normally be reached on Monday-Friday, 8:30am-5:00pm.
Examiner interviews are available via telephone, in-person, 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.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tammy Goddard can be reached on 571-272-7773. 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 https://ppair-my.uspto.gov/pair/PrivatePair. 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.
/WEIMING HE/
Primary Examiner, Art Unit 2611
Prosecution Insights