DETAILED ACTION
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 .
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claims 1 are rejected under 35 U.S.C. 103 as being unpatentable over Mironica et al. (Mironica) (US 2024/0420394 A1) in view of Mestha et al. (Mestha) (US 2009/0296110 A1).
1. (Currently Amended) A computer system comprising:
at least one computer processor (e.g., image editing apparatus 200 include several components. The term ‘component’ is used to partition the functionality enabled by the processors and the executable instructions included in the computing device used to implement image editing apparatus 200, paragraph 47); and
a non-transitory, computer-readable storage medium storing a software filter that when executed by the at least one computer processor (e.g., According to some aspects, image editing apparatus 100 includes a non-transitory computer readable medium storing code that is configured to perform the methods described herein. Image editing apparatus 100 is an example of, or includes aspects of, the corresponding element described with reference to FIG. 2, paragraph 45), causes the at least one computer processor to:
receive an image for performing object color management on an object having at least one color in the image (e.g., according to some aspects, machine learning model 230 generates a modified image for the text based on the second color, where the modified image includes the second color in a region corresponding to the text, paragraph 51);
extract, by an object detection and semantic segmentation module, the object from the image (e.g., contrasting color extractor 205 is a component or body of instructions that is configured to extract a color from the input image that contrasts with the input text, paragraph 52);
segment, by the object detection and semantic segmentation module, the object into a first part and a second part (e.g., segmentation component 210 is configured to perform panoptic segmentation on the input image. Panoptic segmentation involves both semantic segmentation and instance segmentation and is considered a “unified segmentation” approach, paragraph 53);
provide, by the object detection and semantic segmentation module, a first part color for the first part and a second part color for the second part based on multiple training images (e.g., segmentation component 210 segments the image to identify one or more objects overlapping the text, i.e., one or more objects in a text region. In some examples, segmentation component 210 applies a contrastive color to the one or more objects to obtain a first modified image, where the modified image is generated based on the first modified image, paragraphs 54, 55),
replace the at least one color of the object in the image with the first part color and the second part color (e.g., In at least one embodiment, a subset of the input image, rather than the whole image, is processed for input to machine learning model 230. In this case, machine learning model 230 performs “inpainting” by generating a modified image smaller than the image and with subset dimensions. According to some aspects, combination component 235 combines the image and the modified image to obtain a combined image, paragraph 59); and
display, on an electronic display of the computer system, the image comprising the object having the first part color and the second part color (e.g., the system described above include a non-transitory computer readable medium storing code, the code comprising instructions executable by a processor to obtain an image and text overlapping the image, wherein the text comprises a first color; select a second color from an area of the image overlapping the text, wherein the second color contrasts with the first color; and generate a modified image for the text based on the second color using a machine learning model, wherein the modified image includes the second color in a region corresponding to the text, paragraph 60).
Mironica does not specifically disclose wherein the first part color and the second part color comprise spot colors associated with the object.
Mestha discloses wherein the first part color and the second part color comprise spot colors associated with the object (e.g., FIG. 8 illustrates an exemplary input image 14 in which various objects in the image have been identified as segments, such as a text object 50, a spot color segment 52, such as a company name or logo, which is out of gamut for the printer, and sky and building regions 54, 56 of a graphic object 58, paragraph 81).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to have modified Minorica to include wherein the first part color and the second part color comprise spot colors associated with the object as taught by Mestha. It would have been obvious to one of ordinary skill in the art at the time of the invention to have modified Mironica by the teaching of Mestha to apply for particular applications.
Regarding claim 2, Mestha discloses wherein the software filter causes the at least one computer processor to: generate a second image in which the object is colored using at least one spot color provided by the object detection and semantic segmentation module (e.g., FIG. 8 illustrates an exemplary input image 14 in which various objects in the image have been identified as segments, such as a text object 50, a spot color segment 52, such as a company name or logo, which is out of gamut for the printer, and sky and building regions 54, 56 of a graphic object 58, paragraph 81).
Regarding claim 3, Mironica discloses wherein the software filter causes the at least one computer processor to: receive, from a user dashboard, information describing a user color; and replace the first part color in the image with the user color for displaying the image on the electronic display of the computer system (e.g., One or more aspects of the method, apparatus, non-transitory computer readable medium, and system include obtaining an image including text and a region overlapping the text, wherein the text comprises a first color; selecting a second color that contrasts with the first color; and generating a modified image including the text and a modified region using a machine learning model that takes the image and the second color as input, wherein the modified region overlaps the text and includes the second color, paragraph 5).
Regarding claim 4, Mironica discloses wherein the image is at least one of a logo or a video frame (e.g., an image editing system is described with reference to FIGS. 1- 2. Methods for generating graphic designs using the image editing system are described with reference to FIGS. 3-8, paragraph 35).
Regarding claim 5, Mironica discloses wherein the computer system is an extended-reality system (e.g., an apparatus for harmonizing text and background images is described. One or more aspects of the apparatus include a processor; a memory including instructions executable by the processor to perform operations including: obtaining an image and text overlapping the image, wherein the text comprises a first color; selecting a second color that contrasts with the first color; and generating a background image for the text based on the second color using a machine learning model, wherein the background image includes the second color in a region corresponding to the text (which is an extended-reality system/apparatus), paragraph 36).
Regarding claim 8, claim 8 is a method claim with limitations similar of limitations of claim 1. Therefore, claim 8 is rejected as set forth above as claim 1.
Regarding claim 10, claim 10 is a method claim with limitations similar of limitations of claim 3. Therefore, claim 10 is rejected as set forth above as claim 3.
Regarding claim 11, claim 11 is a method claim with limitations similar of limitations of claim 4. Therefore, claim 11 is rejected as set forth above as claim 4.
Regarding claim 12, claim 12 is a method claim with limitations similar of limitations of claim 5. Therefore, claim 12 is rejected as set forth above as claim 5.
Regarding claim 15, claim 15 is a non-transitory computer-readable medium claim with limitations similar of limitations of claim 1. Therefore, claim 15 is rejected as set forth above as claim 1.
Regarding claim 17, claim 17 is a non-transitory computer-readable medium claim with limitations similar of limitations of claim 3. Therefore, claim 17 is rejected as set forth above as claim 3.
Regarding claim 18, claim 18 is a non-transitory computer-readable medium claim with limitations similar of limitations of claim 4. Therefore, claim 18 is rejected as set forth above as claim 4.
Regarding claim 9, claim 9 is a method claim with limitations similar of limitations of claim 2. Therefore, claim 9 is rejected as set forth above as claim 2.
Regarding claim 16, claim 16 is a non-transitory computer-readable medium claim with limitations similar of limitations of claim 2. Therefore, claim 16 is rejected as set forth above as claim 2.
Claims 6, 7, 13, 14, 19, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Mironica et al. (Mironica) (US 2024/0420394 A1) and Mestha et al. (Mestha) (US 2009/0296110 A1) as applied to claims 1, 8 above, and further in view of Daher et al. (Daher) (US 2022/0348003 A1).
Regarding claim 6, Mironica does not specifically disclose wherein the software filter causes the at least one computer processor to: send print data, based on the image, to a printing system, wherein specialty inks corresponding to the first part color and the second part color are loaded on the printing system for printing the image on a substrate using the specialty inks.
Daher discloses wherein the software filter causes the at least one computer processor to: send print data, based on the image, to a printing system, wherein specialty inks corresponding to the first part color and the second part color are loaded on the printing system for printing the image on a substrate using the specialty inks (e.g., the methods of the present disclosure consider a reduction in ink demand for a printable page based on different ink demands in different areas of the page. Thus, instead of applying a single approach to an entire page (e.g. greyscale) or only applying corrective measures at the level of the pixel, the methods of the present disclosure provide an intelligent, adaptive approach to reducing ink demand, paragraph 46).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to have modified Mironica to include wherein the software filter causes the at least one computer processor to: send print data, based on the image, to a printing system, wherein specialty inks corresponding to the first part color and the second part color are loaded on the printing system for printing the image on a substrate using the specialty inks as taught by Daher. It would have been obvious to one of ordinary skill in the art at the time of the invention to have modified Mironica by the teaching of Daher to use for particular applications.
Regarding claim 7, Mironica discloses wherein the software filter causes the at least one computer processor to: mask, by a raster image processor, a portion of the print data based on the first part color and the second part color (e.g., system include obtaining an image including text and a region overlapping the text, wherein the text comprises a first color; selecting a second color that contrasts with the first color; and
generating a modified image including the text and a modified region using a machine learning model that takes the image and the second color as input, paragraph 5).
Regarding claim 13, claim 13 is a method claim with limitations similar of limitations of claim 6. Therefore, claim 13 is rejected as set forth above as claim 6.
Regarding claim 14, claim 14 is a method claim with limitations similar of limitations of claim 7. Therefore, claim 14 is rejected as set forth above as claim 7.
Regarding claim 19, claim 19 is a non-transitory computer-readable medium claim with limitations similar of limitations of claim 6. Therefore, claim 19 is rejected as set forth above as claim 6.
Regarding claim 20, claim 20 is a non-transitory computer-readable medium claim with limitations similar of limitations of claim 7. Therefore, claim 20 is rejected as set forth above as claim 7.
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 QUANG N VO whose telephone number is (571)270-1121. The examiner can normally be reached Monday-Friday, 7AM-4PM, EST.
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, Abderrahim Merouan can be reached at 571-270-5254. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/QUANG N VO/ Primary Examiner, Art Unit 2683
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