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 have been considered but are moot because of new grounds of rejection.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Price et al. (US 2018/0253865, hereinafter Price) in view of McHugh et al. (US 2018/0189935, hereinafter McHugh).
Regarding claim 1, Price teaches a computer-implemented method comprising (¶ 0050, Fig. 2):
receiving a training dataset comprising a document (input image; ¶¶ 0021-0022 and ¶ 0113, Fig. 7);
obtaining a mask image based on a layout of content in the document, a content area corresponding to content of the document (trimap; ¶ 0018 and ¶ 0114, Fig. 7);
but doesn’t explicitly teach the mask image having a foreground region with a first set of pixels corresponding with a first value and a background region, outside of the foreground region, with a second set of pixels corresponding with a second value; and training a machine learning model using the mask image to provide a trained machine learning model that generates transparency values for pixels of the second set of pixels of the background region for generating a background image for the document.
However, McHugh teaches the mask image having a foreground region with a first set of pixels corresponding with a first value and a background region, outside of the foreground region, with a second set of pixels corresponding with a second value (initial alpha mask with alpha values for pixels in a foreground portion or background portion; ¶ 0033, ¶ 0046, Fig. 4); and
training a machine learning model using the mask image to provide a trained machine learning model that generates transparency values for pixels of the second set of pixels of the background region for generating a background image for the document (trained improved or final alpha mask based on initial alpha mask to replace background portion of image with another image; ¶ 0028, ¶ 0034, ¶¶ 0036-0039, ¶ 0040, ¶ 0050, Fig. 5B 564).
Price and McHugh are in the same field of endeavor of a method and system for generating a background for an image. Thus, 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 modify the method and system of Price to generate a background image as taught by McHugh. The combination improves the method and system by providing a user a simple means for generating a background image.
Regarding claim 2, Price in view of McHugh teach the method of claim 1, wherein the mask image includes the foreground region corresponding to the area in the document and the background region corresponding to an area in the document without the content (trimap marks regions of an image as pure foreground and/or pure background; ¶ 0018, Price).
Regarding claim 3, Price in view of McHugh teach the method of claim 1, wherein the background image is used in at least one of a cryptographic digital assets and a video (¶ 0072, Price).
Regarding claim 4, Price in view of McHugh teach the method of claim 2, wherein pixels in the foreground region have a value of one and pixels in the background region have a value of zero (¶ 0017, Price).
Regarding claim 5, Price in view of McHugh teach the method of claim 4, wherein the training the machine learning model comprises using a loss function that encourages the machine learning model to generate a transparency value of one for pixels of the background image corresponding to the foreground region and randomly generate a value for pixels of the background image corresponding to the background region (¶¶ 0079-0082, Price).
Regarding claim 6, Price in view of McHugh teach the method of claim 5, wherein the loss function comprises a modified root mean square function (¶¶ 0079-0082, Price).
Regarding claim 7, Price in view of McHugh teach the method of claim 1, further comprising: receiving user input modifying the mask image to provide a modified mask image; and retraining the trained machine learning model using the modified mask image (Trimaps can be generated by a user selecting unknown regions of a training image; ¶ 0074, Price).
Regarding claim 8, Price in view of McHugh teach the method of claim 1, further comprising: generating, using the machine learning model, the transparency values for the background image (generate matte including alpha values based on input image and trimap; ¶ 0021 and ¶ 0095, Price); determining color values for the pixels of the background image (background colors; ¶ 0072, Price); and generating the background image using the transparency values and the color values (generate new background; ¶¶ 0094-0095, Price).
Regarding claim 9, Price in view of McHugh teach the method of claim 1, further comprising: minimizing, during training, a difference between values for pixels in the content area of the mask image and the transparency values for pixels of the background image corresponding to the content area of the mask image using a loss function (¶ 0089, Price).
Regarding claim 10, Price teaches one or more non-transitory computer storage media storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising (¶ 0050, Fig. 2 and ¶¶ 0117-0120, Fig. 8):
generating, using a machine learning model trained on a mask image corresponding to a document, transparency values for pixels for the document (neural network trained to generate matte including alpha values based on input image and trimap; ¶ 0021 and ¶ 0115, Fig. 7);
determining color values for at least the second set of pixels corresponding with the background area of the document (background colors; ¶ 0018, ¶ 0059, ¶ 0072); and generating the background image using the transparency values and the color values (generate new background; ¶¶ 0094-0095);
but does not explicitly teach wherein a first set of pixels corresponding with a foreground area of the document correspond with a first transparency value indicating a unified transparency and at least a portion of pixels of a second set of pixels corresponding with a background area of the document correspond with different transparency values indicating different transparencies;
and generating the background image using the transparency values and the color values associated with the second set of pixels corresponding with the background area of the document.
However, McHugh teaches wherein a first set of pixels corresponding with a foreground area of the document correspond with a first transparency value indicating a unified transparency and at least a portion of pixels of a second set of pixels corresponding with a background area of the document correspond with different transparency values indicating different transparencies (initial alpha mask with alpha values for pixels in a foreground portion or background portion; ¶ 0033, ¶ 0046, Fig. 4);
and generating the background image using the transparency values and the color values associated with the second set of pixels corresponding with the background area of the document(trained improved or final alpha mask based on initial alpha mask to replace background portion of image with another image; ¶ 0028, ¶ 0034, ¶¶ 0036-0039, ¶ 0040, ¶ 0050, Fig. 5B 564).
The motivation applied in claim 1 is incorporated herein.
Regarding claim 11, Price in view of McHugh teach the one or more non-transitory computer storage media of claim 10, wherein the color values for the pixels are randomly selected (¶ 0018, ¶ 0059, Price).
Regarding claim 12, Price in view of McHugh teach the one or more non-transitory computer storage media of claim 10, wherein the color values for the pixels are determined based on at least one of user demographic, time of day, season, and content in the document (¶ 0104, Price).
Regarding claim 13, Price in view of McHugh teach the one or more non-transitory computer storage media of claim 10, wherein the color values for the pixels are determined based on user input (¶ 0074, Price).
Regarding claim 14, Price in view of McHugh teach the one or more non-transitory computer storage media of claim 10, further comprising receiving user input modifying the color values to provide modified color values; and modifying the background image using the modified color values (¶ 0074, Price).
Regarding claim 15, Price in view of McHugh teach the one or more non-transitory computer storage media of claim 10, wherein generating the background image using the transparency values comprises: converting the transparency values to alpha channel values, wherein the background image is generated using the alpha channel values and the color values (¶¶ 0016-0018, Price).
Regarding claim 16, Price in view of McHugh teach the one or more non-transitory computer storage media of claim 10, wherein converting the transparency values to alpha channel values comprises: subtracting the transparency value from one to obtain a subtracted value for each pixel; and multiplying the subtracted value with 255 to provide the alpha channel value for each pixel (¶¶ 0016-0018, Price).
Claims 17-20 recite similar limitations as claims 1, 4, 6, and 7 thus, arguments similar to that presented above for claims 1, 4, 6, and 7 are equally applicable to claims 17-20.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Price et a. (US 2023/0112186) teaches an alpha matting system that utilizes a deep learning model to generate alpha mattes for digital images.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/KENT YIP/Primary Examiner, Art Unit 2681