CTNF 18/965,896 CTNF 101489 DETAILED ACTION Allowable Subject Matter 12-151-08 AIA 07-43 12-51-08 Claim s 5-6 and 13-14 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. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim s 1-2, 9-10, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Liu (CN 114004732 A) in view of Xia (CN 112150580 A) . As per claim 1, Liu teaches the claimed: 1. A method for generating an effect image, comprising: receiving an image to be processed that comprises at least one target object; (Liu (page 3, line 28-29): “the electronic device obtains the first contour data of each object in multiple objects included in the image to be edited ”.) obtaining an image to be used in response to an edit operation from a user for the at least one target object, wherein the image to be used corresponds to the image to be processed and is an image with the at least one target object deformed; and (Liu (page 3, line 30-32): “the electronic device in response to the first deformation operation of the first area of the first object of the plurality of objects by the user, changing the shape of the image area including the first area ; the electronic device obtains the second profile data of each object in the image to be edited after the first deformation operation ”. Liu teaches the user making a deformation operation, which is an edit, that changes the shape of the image, which corresponds to the first image, and the user already implemented a deformation to at least one object, thus is an image with at least one deformed object.) Liu alone does not explicitly teach the remaining claim limitations. However, Liu in combination with Xia teaches the claimed: adding a target material effect to the at least one target object in the image to be used, to generate an effect image corresponding to the image to be processed. (Xia (page 10, line 3-5): “so that when displaying the image , the rendering image is displayed at the corresponding position of the image; so that the rendering image and the target object are better attached, finishing the adding of the effect material .” Xia teaches finishing the adding of the effect material to the rendering image and the target object, and this effect image is displayed at a corresponding position of the original image, which indicates that it corresponds to the previous image. Since the rendering image was displayed, at some point there must have been a generation step to be able to display the image with the effect material, thus teaching the claimed limitation above.) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the effect material as taught by Xia with the system of Liu in order to be able to enhance visual appearance and enables the creation of more engaging and customizable image content. As per claims 9 and 17, these claims are similar in scope to limitations recited in claim 1, and thus is rejected under the same rationale. As per claim 2, Liu teaches the claimed: 2. The method according to claim 1, wherein the obtaining an image to be used in response to an edit operation from a user for the at least one target object comprises: obtaining, in response to an edit operation from the user for at least one part to be edited of the at least one target object, an image to be used in which the at least one part to be edited is deformed, and (Liu (page 3, line 27-33): “wherein the image editing interface displays an image to be edited … the electronic device in response to the first deformation operation of the first area of the first object of the plurality of objects by the user , changing the shape of the image area including the first area; the electronic device obtains the second profile data of each object in the image to be edited after the first deformation operation” Liu teaches the user making a deformation operation, which is an edit, that changes the shape of the image, which corresponds to a first area of an image, and the user already implemented a deformation to at least one object, thus is an image with at least one deformed object with at least one part being deformed.) wherein the edit operation comprises a touch operation on the at least one part to be edited and/or an operation of inputting a deformation parameter corresponding to the at least one part to be edited. (Liu (page 17, line 22-27): “As shown in FIG. 2, the electronic device 100 may include: … a sensor 180,”. Liu (page 24, line 6-9): “sensor 180 can include a … touch sensor 180K”. Liu (page 24, line 19-20): “the electronic device 100 detects the touch operation intensity based on the pressure sensor 180A.”) As per claims 10 and 18, these claims are similar in scope to limitations recited in claim 2, and thus is rejected under the same rationale . 07-21-aia AIA Claim s 3, 11, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Liu in view of Xia in further view of Jefferson (US 20250117991 A1) and in further view of Zhu (CN 115359314 A) . As per claim 3, Liu and Xia alone do not explicitly teach the claimed limitations. However, Liu and Xia in combination with Jefferson and Zhu teaches the claimed: 3. The method according to claim 1, wherein adding a target material effect to the at least one target object in the image to be used, to generate an effect image corresponding to the image to be processed comprises: inputting the image to be used into a pre-trained material generation effect model to perform a material update of at least one part to be edited in the image to be used based on the material generation effect model, and outputting the effect image with the target material effect added to the at least one target object, (Jefferson [0142]: “In an embodiment, machine learning model 720 (with reference to FIG. 7) receives an edit input for the sketch input. Machine learning model 720 modifies the sketch input based on the edit input to obtain a modified sketch input 1205. The image generation model 730 generates a modified image based on the modified sketch input 1205. Machine learning model 720 updates the layered image to include the modified image.” Jefferson [0145]: “FIG. 13 shows an effect of image generation based on a sketch input . An image generation model (with reference to FIG. 7) generates modified image 1305 based on a modified sketch input and a text prompt.” Jefferson [0107]: “For example, machine learning model 720 is a pre-trained model ”) wherein the material generation effect model is trained based on a plurality of pieces of paired data, the paired data comprising input sample data with a target part deformed and output sample data with the target material effect added to the target part. (Zhu (page 4, line 8-16): “a model training device for image editing, comprising: a coding processing module, configured to process the first sample image by using the encoder, obtaining the sample original characteristic corresponding to the first sample image; editing processing module, configured to input the sample original feature input editing model to be trained, editing the sample original feature according to the target text by the editing model to obtain the sample editing feature ; a decoding processing module, configured to process the sample editing feature by using the decoder, obtaining the sample editing image corresponding to the sample editing feature ”. Zhu teaches the input data as the first sample image and the output data as the obtaining of the sample editing feature, and then decoding them together to obtain the sample editing image that fully trains the model.) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the material generation model and training it as taught by Jefferson and Zhu with the system of Liu as modified by Xia in order to enable the automatic creation of image editing materials that allows for desired visual effect to be applied consistently and effectively. As per claims 11 and 19, these claims are similar in scope to limitations recited in claim 3, and thus is rejected under the same rationale . 07-21-aia AIA Claim s 4, 12, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Liu in view of Xia in further view of Kao (US 20210150726 A1) and in further view of Matthews (US 20250166136 A1) . As per claim 4, Liu and Xia alone do not explicitly teach the claimed limitations. However, Liu and Xia in combination with Kao and Matthews teaches the claimed: 4. The method according to claim 1, further comprising: constructing paired data for training a material generation effect model, wherein constructing the paired data for training the material generation effect model comprises: obtaining a plurality of training sample images comprising the at least one target object; (Kao [0091]: “First, sample images are obtained . Each of the sample images includes a depth image of a scene. A label result of each object appears in each of the sample images , and represents a 3D detection result of each object in each of the sample images.”) deforming, for the training sample images, the at least one target object in the training sample images to obtain input sample data corresponding to the training sample images; (Kao [0265]: “The image processing method may determine the transformation relationship by combining 3D pose results corresponding to the target object and the deformation point before the deformation of the target object, when the deformation point after the deformation corresponding to the target object in the original image is determined.”) processing the input sample data based on a pre-trained diffusion model to obtain output sample data with a material update performed on the at least one target object; and (Matthews [0011]: “The operations include receiving the context image. The operations include processing an input with the denoising diffusion model to generate an edited image , where the denoising diffusion model is conditioned on the context image, and where the edited image depicts the object with a modified material property .”) using the input sample data and the output sample data corresponding to the training sample images as the paired data. (Combining the input and output data from the claim limitations above to create a paired data is a well known process in this field of endeavor, especially as the output data depends on the input data, which corresponds to the sample images, so pairing them would be obvious as well. Thus, using the references to obtain the input and output data, then combining them to create a paired data is taught.) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the deformation of the sample images as taught by Kao with the system of Liu as modified by Xia in order to determine a specific area or object where the user can create stylized and customizable effects. Also to use the diffusion model as taught by Matthews with the system of Liu as modified by Xia in order to smoothly apply the changes to the deformed areas of images to output what the user desires. As per claims 12 and 20, these claims are similar in scope to limitations recited in claim 4, and thus is rejected under the same rationale . 07-21-aia AIA Claim s 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Liu in view of Xia in further view of Guo (US 20240193790 A1) . As per claim 7, Liu and Xia alone do not explicitly teach the claimed limitations. However, Liu and Xia in combination with Guo teaches the claimed: 7. The method according to claim 1, wherein the at least one target object comprises a human object and/or an animal object, and at least one part to be edited that corresponds to the at least one target object comprises a face part, a part of facial features, and a limb part. (Guo [0050]: “the target object is a person by way of example, and types of features may include at least one of face features and limb features of the target object occurring in the video frame.”) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the target object as taught by Guo with the system of Liu as modified by Xia in order to create and modify human features realistically and creatively as the user desires. As per claim 15, this claim is similar in scope to limitations recited in claim 7, and thus is rejected under the same rationale . 07-21-aia AIA Claim s 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Liu in view of Xia in further view of Wang (CN 115115753 A) . As per claim 8, Liu and Xia alone do not explicitly teach the claimed limitations. However, Liu and Xia in combination with Wang teaches the claimed: 8. The method according to claim 1, wherein the target material effect is an effect that simulates the outer skin of an animal or a plant, an effect for the skin of a cartoon character, and/or an effect for the skin of a comic character. (Wang (page 20, line 31-35): “A target animation video program file for generating a target animation video is obtained. through the second event processor corresponding to the skin object, using the role information skin by the updating operation, updating the role skin the second animation role in the target animation video program file, obtaining the updated target animation video program file.” Wang teaches the updating of the skin based on an animation video, which can be of a cartoon. Additionally, though Wang teaches being able to change the skin in a video file, not specifically an image, videos are made up of image frames placed right next to each other, so therefore, Wang teaches the target material effect can be implemented in images as well.) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the target material effect as taught by Wang with the system of Liu as modified by Xia in order to create and modify human or animal features creatively and stylistically as the user desires. As per claim 16, this claim is similar in scope to limitations recited in claim 8, and thus is rejected under the same rationale. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSHUA SUO whose telephone number is (571) 272-8387. The examiner can normally be reached Mon-Fri 8am-5pm. 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, Daniel Hajnik can be reached on (571) 272-7642. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. 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. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /JOSHUA SUO/Examiner, Art Unit 2616 /DANIEL F HAJNIK/Supervisory Patent Examiner, Art Unit 2616 Application/Control Number: 18/965,896 Page 2 Art Unit: 2616 Application/Control Number: 18/965,896 Page 3 Art Unit: 2616 Application/Control Number: 18/965,896 Page 4 Art Unit: 2616
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