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 Amendment
The Amendment filed 6/22/2026 has been entered. Claims 1-10 and 12-13, and 15-22 remain pending in the application
Response to Arguments
Applicant’s arguments, see pages 9/10 of remarks/arguments, filed 6/22/2026, with respect to the rejection(s) of claim(s) 1 under 35 USC 102(a)(1) have been fully considered and are persuasive. The amended claim language reciting that a first and second part of a single model will perform the segmenting then the effect applying respectively is not found in the initial provided sources. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Dudovitch (US 20220270261 A1).
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.
Claims 1, 2, 4, 5, 12, 13, 15, 17, 18, are rejected under 35 U.S.C. 102(a)(1) as being unpatentable over Wang (CN 109840881 A) in view of Dudovitch (US 20220270261 A1)
Regarding claims 12, 1, 13,
Wang teaches:
An electronic device, comprising: at least one processor; and a storage apparatus, configured to store at least one program, wherein the at least one program, when executed by the at least one processor, enables the at least one processor (Wang ¶87 “As shown in FIG. 6, the 3D effect image generating apparatus 6 of this embodiment includes a processor 60, a memory 61, and a computer program 62 stored in the memory 61 and operable on the processor 60, such as a 3D effect.”) to implement an effect image processing method and the effect image processing method comprises: determining a to-be-processed effect fusion model (Wang ¶33, cited below, details a fusion unit) according to an effect attribute of an effect to be overlaid; in response to receiving an effect display instruction, (Wang ¶48 “For example, after the user takes a target RGBD image by using the mobile phone, the 3D special effect image generating method described in the present application can perform a personalized processing operation” Wang ¶88 “Illustratively, the computer program 62 can be partitioned into one or more modules/units that are stored in the memory 61 and executed by the processor 60 to complete This application. The one or more modules/units may be a series of computer program instruction segments capable of performing a particular function, the instruction segments being used to describe the execution of the computer program 62 in the 3D effect image generation device 6. For example, the computer program 62 can be segmented into: An original image receiving unit, configured to receive an original image including depth information; An image separating unit, configured to identify a target image included in the original image, and separate the target image from the background image; a special effect processing unit, configured to perform special effect processing on the target image according to the depth information of the target image and/or the background image;” Note: The claims “ effect display instruction” refers to an instruction which once received will initiate additional instructions that will determine a human body segmentation region in the image and determine the effect to be processed, as seen below in the claim. Wang teaches this in its image receiving unit instructions which trigger after the user instructs the program by capturing content with a camera. Once this initial instruction is sent denoting that an effect over the captured image should be displayed Wang teaches that the target, which as seen below can be a human, is segmented from the image and the special effect processing occurs.) determining an effect fusion model according to the to-be-processed effect fusion model and a human body segmentation region in a to-be-processed image corresponding to an object; (Wang ¶33 “And a fusion unit, configured to fuse the target image processed by the special effect with the background image to obtain a 3D special effect image.” ¶50 “The target image described in the present application may be a person, or may also be a specific object. In order to facilitate special effect processing on the target image, the application separates the original image to obtain the target image and the background image. Wherein, the step of identifying the target image included in the original image may include: A, determine the image characteristics of the target image; B. Perform feature matching in the original image according to the determined image feature, and determine the target image according to the feature matching result. For example, when the preset target is a person, the portrait features can be set, the set portrait features can be matched with the original image, and the image area including the portrait feature in the original image can be determined according to the matching result, and the determined image area can be As the target image, a target image of a portrait and a background image without a portrait can be obtained separately.” Note: Wang teaches a special effect to be rendered onto an image where the image contains a target object that can be a human body. To apply the effect the human is separated/segmented out of the image to create an isolated target image. The claims mention a “to-be-processed effect fusion model” that becomes a “target effect fusion model” after a target image has been identified. This is taught in Wang’s fusion unit which fuses the segmented target image of a person, the background the target was segmented out of, and the special effect to apply.) and writing pixel depth information corresponding to the effect fusion model into a rendering engine, to enable the rendering engine to render an image corresponding to the to-be-processed image based on the pixel depth information, wherein the image comprises the target effect. (Wang ¶62 “it is necessary to perform transparency processing with reference to the depth value information. For example, the time-sharing processing method can be used to perform transparency processing when depth writing is turned on: depth writing is started, but color is not output, depth information is written into the depth buffer, and then normal transparency processing is performed”¶91 “A special effect processing unit, configured to perform special effect processing on the target image according to the depth information of the target image and/or the background image; and a fusion unit, configured to fuse the target image after the special effect processing with the background image to obtain a 3D special effect image.” Note: It was previously shown that Wang teaches how a fusion model through its analogous fusion unit. The rendering process of the effect is taught to take into account the pixel depth information that is written in a buffer in use for rendering, resulting in a final image with the effect properly rendered on the target.)
While Wang teaches that an input image can be segmented into a smaller target image to segment a region of the human body and then applying an effect, it does not teach an effect fusion model with two parts, where a first part corresponds to a region of a human body and the second corresponds to the effect. This is taught by Dudovitch which teaches wherein the to-be-processed effect fusion model comprises a first part corresponding to a first region of a human body (Dudovitch ¶16, cited below, teaches a model that employs a first disclosed technique of segmenting a region of a human body out of an image. It is then taught that an effect can be applied to the segmentation.) and a second part corresponding to the effect (Dudovitch ¶16“The disclosed techniques improve the efficiency of using the electronic device by segmenting an image that depicts a whole body of a user or multiple users and applying one or more visual effects to the image” ¶108 “After training, segmentation estimation system 224 receives an input image 501 (e.g., monocular image depicting a real-world body or multiple bodies, such as an image of a user's face, arms, torso, hips and legs) as a single RGB image from a client device 102. The segmentation estimation system 224 applies the first trained machine learning technique module 512 to the received input image 501 to extract one or more features representing the segmentation of the body or bodies depicted in the image 501” ¶111 “Visual effect selection module 519 receives from a client device 102 a selection of a virtualization mode. For example, a user of the AR/VR application may be presented with a list of mode options. In response to receiving a user selection of a given mode option from the list, the given mode is provided to the visual effect selection module 519 as the selection of the virtualization mode. The mode options may include a background removal option (e.g., replacement of a real-world background with a virtual background), an occlusion option (e.g., addition of one or more augmented reality elements to the segmentation or body of the user) … a contour effects option (e.g., to present one or more augmented reality elements, such as a glow or shadow, around the segmentation border), an animated frames option, a body mask option, a recoloring option (e.g., to change a color of portions of the user's body, such as to replace clothing worn by the user), a ripples, particles or sparkles option ” Note: Dudovitch teaches a machine learning model that will first accept an image to segment, and will then apply an effect to the segmented image. Dudovitch teaches a first part of the model, a segmentation estimation system, will first segment the body out of the image. Regions that may be segmented from a given input image are taught specifically to be the “face, arms, torso, hips and legs” in Dudovitch ¶108. It is then taught that a second part of the model, the visual effect selection module, allows a user to select a visual effect then applies the effect to the segmented image of the region of the body.) a to-be-processed image corresponding to specified human body, wherein the human body segmentation region comprises the first region of the specified human body;(Dudovitch ¶16, cited above, teaches that an input image is segmented for an effect to be applied to it. Dudovitch ¶108 teaches that the input images are of real bodies, or of parts of a body, and the body is segmented out of the original image, so that an effect can be applied to it. The “first region of the specified human body”, as seen in the claim above is the region identified by the first part of the fusion model, and is the part of the image that has the effect applied to it, the same teaching of Dudovitch that the segmented body region or body will have the effect applied to it.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Wang with Dudovitch where a region of the human body can be segmented out of an image by a first part of a model, and an effect can applied to the first region of the specified human body in the segmented image by the second part of the model.
There are several reasons that would motivate one to do so, while separated into two separate steps the act of applying an effect to a specific region of the human body requires that the model applying the effect has a solid understanding of that specific, segmented region, or else it will improperly overlay the effect. As the applying of the effect always requires the segmentation to be completed before it, speed and efficiency of the process could be improved by using an efficient pipeline where the same model that applies the effect also makes the segmentation in a prior, first part of the model.
Regarding claims 15, 2,
Wang teaches:
The method according to claim 1, wherein the determining the to-be-processed effect fusion model according to the effect attribute of the effect to be overlaid, comprises: determining an effect fusion model corresponding to the effect as the to-be-processed effect fusion model, according to an effect display shape of the effect and an effect display area of the effect. (Wang ¶78 “After the above-mentioned special effects such as transparency, distortion, and scaling, the processed target image and the background image may be merged and superimposed, or the texture map may be used for rendering to achieve the final 3D effect. This step needs to match each pixel of the target image and the background image, and the local depth image can be registered and fused by a three-dimensional point cloud registration algorithm (for example, ICP algorithm). When the point cloud completes the registration fusion, the 3D point cloud can be finally drawn into a 3D mesh to form a mesh model including vertices, edges, faces, polygons, etc., in order to simplify the rendering process. After the grid representation of the 3D model is realized, in order to visualize, texture information needs to be added to the fused image, and finally a complete 3D texture image is obtained. Users can watch the processed 3D effects directly on smart devices such as mobile phones or on other monitors.” Note:
Wang teaches that before creating the 3D model for the effect over the target a 3D point cloud registration algorithm is used. An algorithm of this type seeks to align, or fuse, two sets of 3D points that are intended to exist in the same area. Once the registration fusing is done to properly align the effect over the target 3D model is made properly placed/fused over the target. As the shape and area the effect will be placed in are considered prior to the fusing and creation of the 3D model over the target Wang teaches that fusion considers the effect shape and area.)
Regarding claims 17, 4,
Wang teaches:
The method according to claim 1, wherein the determining the effect fusion model according to the to-be-processed effect fusion model and the human body segmentation region in the to-be-processed image corresponding to the specified human body, comprises: determining the human body segmentation region in the to-be-processed image corresponding to the specified human body;(Wang ¶50, cited in claim 1, teaches how an object, like a human body, can segmented out of the original image.) and binding the human body segmentation region with the to-be-processed effect fusion model to obtain the effect fusion model. (Wang ¶91, cited in claim 1, details how the segmented human image is fused with the effect)
Regarding claims 18, 5,
Wang teaches:
The method according to claim 4, wherein the determining the human body segmentation region in the to-be-processed image corresponding to the specified human body, comprises: determining the human body segmentation region in the to-be-processed image based on a human body segmentation algorithm or a human body segmentation model. (Wang ¶54 “For example, when the target image is a human body, it can be detected by means of feature extraction, such as sparse representation, dense representation, and spatial pyramid extraction. Due to the particularity of the human body, the pixels of the human body are obviously different from other pixels, and the edge pixels between the human body and other objects can be identified.” Note: Wang teaches several proposed algorithms to segment the human body out of the input image such as spatial pyramid extraction resulting in the edges of the body being determined.)
Claims 19, 6, are rejected under 35 U.S.C. 103 as being unpatentable over Wang (CN 109840881 A), in view of Dudovitch (US 20220270261 A1), and further in view of Pons-Moll (ClothCap: seamless 4D clothing capture and retargeting).
Regarding claims 19, 6,
Wang teaches:
The method according to claim 5, wherein the human body segmentation region
Wang does not however teach making a segmentation region made for a region of the torso specifically, this is taught in Pons-Moll which teaches wherein the human body segmentation region is an area in a torso segmentation region. (
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Pons-Moll 5.2 Body Model Aided Segmentation “This encodes intuitive information such as: the torso nodes are likely to be T-shirt while hands and head have to be unclothed (skin). To that end, we leverage the underlying segmentation of the body into parts that is provided by SMPL … In particular, for the case of a t-shirt and long trousers (first row of Fig. 4), the nodes of the head, hands and feet should be labeled as “skin,” and the nodes of the torso should be labeled “shirt”.” Note: Pons-Moll teaches that the human body is segmented into several regions as seen in Fig. 6. The multiple different segmentation regions of the torso such as the head, arms, and core torso covered by clothing are seen through the color coding in Fig 6.)
It would have been obvious to a person having ordinary skill in the art before the effective filing
date of the claimed invention to combine Wang with Pons-Moll where the human body segmentation performed includes additional segmented regions for areas in the torso region.
There are several reasons that would motivate on to do so, one is gaining higher accuracy for effects intended to be overlayed to specific parts of interest like the hands, head, etc… which are all regions in the torso. By isolating regions of interest and tracking them individually a greater level of information for overlaying effects can be gained as opposed to viewing the entire body in the capture as a single segmentation.
Claims 20, 7, are rejected under 35 U.S.C. 103 as being unpatentable over Wang (CN 109840881 A), in view of Dudovitch (US 20220270261 A1), and further in view of Hua (US 9754410 B2).
Regarding claims 20, 7,
Wang teaches:
The method according to claim 4, wherein the binding the human body segmentation region with the to-be-processed effect fusion model to obtain the effect fusion model, comprises: binding the human body segmentation region with the to-be-processed effect fusion model to obtain a to-be-used effect fusion model;
Wang does not however teach that intersection processing is performed to bind the effect with the human body the effect is being applied to. This is taught in Hua which teaches performing intersection processing on the human body region and the to-be-used effect fusion model to determine the effect fusion model. (Hua Abstract “at least one garment mesh fitted to a template body mesh and deforms the garment mesh to a target body mesh according to a geometrical deformation algorithm. A layering engine receives plural garment meshes that are separately fitted to a target body mesh, and deforms the plural garment meshes according to an iterative layering process that deforms each individual garment mesh according to a layering order while preventing intersections between other garment meshes and the target body mesh.” Col. 4 Line 35 “reconstructing the deformed garment mesh to eliminate any self-intersections of the deformed garment mesh, and any intersections between the garment mesh and the target body mesh.” Col. 8 Line 34 “apply a collision detection and response process to avoid intersections between the length-adjusted garment mesh and the target body mesh” Note: Hua teaches that intersection processing is performed through methods such as collision detection so that an overlayed effect/model, in this case a garment, can be applied to a target human body. This will produce a final result without incorrect intersections that would cause visual errors such as clipping.)
It would have been obvious to a person having ordinary skill in the art before the effective filing
date of the claimed invention to combine Wang with Hua where the process of binding the effect to a target human body involves intersection processing.
There are several reasons that would motivate one to do so, one of which is to remove potential visual errors created by intersections such as models clipping through each other or not meeting cleanly on shared edges.
Claims 21, 8, are rejected under 35 U.S.C. 103 as being unpatentable over Wang (CN 109840881 A) in view of Dudovitch (US 20220270261 A1), further in view of Hua (US 9754410 B2) and further in view of Pons-Moll (ClothCap: seamless 4D clothing capture and retargeting)
Regarding claims 21, 8,
Wang teaches:
The method according to claim 7, wherein the binding the human body segmentation region with the to-be-processed effect fusion model to obtain a to-be-used effect fusion model, comprises:
Wang does not however teach using a specific axis as a reference to determine the motion of the effect, this is taught in Pons-Moll which teaches determining a reference axis corresponding to the specified human body; and using the reference axis to control the human body segmentation region and the to-be-processed effect fusion model to move together, to obtain the to-be-used effect fusion model. (Pons-Moll Abstract “We estimate the garments and their motion from 4D scans; that is, high-resolution 3D scans of the subject in motion at 60 fps.” 4 Body Model “Following this notation, the SMPL body model is a function M(β,θ), parameterized by shape and pose … Every relative rotation between parts is parameterized using the axis-angle representation. Hence, the full pose θ ∈ R72 consists of 23 × 3 + 3 parameters, 3 parameters per joint plus 3 for the global orientation … In our work, the SMPL body model will be used to explain both human body meshes (with no clothes on), as well as clothed meshes. All concepts in Fig. 2 rely on the SMPL model. Thus, here we effectively extend SMPL to also model, manipulate, and pose garments.” Note: Pons-Moll teaches that for specific parts of the body an axis is tracked and used to make the SMPL body model. It is taught that this SMPL model which leverages specific axes for body parts models movement the body takes and uses this to determine the movement of its effects. As different body parts are taught to be tracked and segmented in the previous claim, this teaches the ability for effects to be moved with segmented regions of the body by using a reference axis to determine motion.)
It would have been obvious to a person having ordinary skill in the art before the effective filing
date of the claimed invention to combine Wang with Pons-Moll where the effects being applied have their movement determined by the movement of the body by using a reference axis.
There are several reasons that would motivate one to do so, one of which is more accurate rendering of physics or movement related effects intended to appear attached or fixed to the body. Rather than having to track a whole region of the body’s movement and change an axis provides a simple means of tracking changes in orientation and direction.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Wang (CN 109840881 A) in view of Dudovitch (US 20220270261 A1) and further Li (US 10055895 B2).
Regarding claim 10,
Wang teaches:
The method according to claim 1, further comprising: determining the effect fusion model based on binding of the to-be-processed effect fusion model with the human body segmentation region.
Wang does not however teach determining that the binding of the effect to the human segmentation region can be done in response to the reobtaining the to-be-processed human image after being unable to display the effect, an example of which would be the human target leaving then reentering the frame. This is taught in Li which teaches in response to the effect display instruction being not received again and the to-be-processed image being acquired again, determining the effect fusion model based on binding of the to-be-processed effect fusion model with the human. (Li Col. 6 Line 66 “As video frames are captured with the target 312 moving within the frame, this matching allows tracking of the target 312, and an AR object (not shown) to maintain a constant relative position with respect to the target 312” Col. 9 Line 20 “After an AR object has moved outside of the image in 20 operation 408 with associated global tracking, in operation 410, once a target moves from outside the video frame back inside the video frame, the system resumes tracking the target within the boundary area. When the target moves back into the frame, the device also resumes displaying the AR object display based on the tracking of the target.” Col. 13 Line 27 “If an AR object ( e . g local AR object 512) is attached to an object that moves, such as a book , or a space above a person , the AR object may retain a relative position with respect to the attached object” Note: Li teaches that the display instructions for its effect/AR object will stop if the target, which can be a human, leaves the frame. In response to the display halting and the target reentering the frame Li teaches that the AR object resumes display as its tracking/binding to the target resumes.)
It would have been obvious to a person having ordinary skill in the art before the effective filing
date of the claimed invention to combine Wang with Li where the binding of an effect to a target human can be halted and resumed upon the target reentering frame.
There are several reasons that would motivate on to do so, one is to provide users with an easier user experience by automatically rebinding the effect to a target human when they reenter a frame rather than having the user have to restart the recording or prompt the rebinding effect themselves.
Claim 3, 9, 16, 22, are rejected under 35 U.S.C. 103 as being unpatentable over Wang (CN 109840881 A) in view of Dudovitch (US 20220270261 A1), and further in view of Liu (CN 112464691 A).
Regarding claims 16, 3,
Wang teaches:
The method according to claim 1, further comprising: in response to receiving the effect display instruction, displaying the to-be-processed effect fusion model, wherein a model in the to-be-processed effect fusion model is displayed transparently. (Wang ¶62 “When transparency processing is performed on the human body, if depth writing and color mixing are performed at the same time, the calculation difficulty will increase … it is necessary to refer to the depth value information for transparency processing … Transparency processing can be performed according to the percentage of the depth value of the target image and the background image. Assuming that the depth value of the human body matting image is Z1, and the depth value of the background is Z2, the transparency can be calculated as: P=Z1/Z2*100%. According to the transparency P, the human body can achieve a better transparency effect. Moreover, since the depth information of each target pixel has been known in advance, the transparency processing can be performed in accordance with the depth information of the pixel level when the transparency processing is performed.” ¶78 “After the above-mentioned special effect processing such as transparency, distortion, and scaling, the processed target image and the background image can be fused and superimposed, or texture maps can be used for rendering to achieve the final 3D effect.” Note: Wang teaches the ability for the model of its effects to be transparent.)
While Wang teaches the ability to make transparent effect models it does not directly teach that the rendering or use of a paper model as an effect to apply to the captured frames. The use of a paper model as an effect fused to a target human is taught in Liu which teaches a paper model in the to-be-processed effect fusion model is displayed transparent (Liu ¶50 “The image processing method and device provided by the embodiment of the invention, the terminal device receives the request sent by the user is the image adding user selected sticker request instruction, the identification of the paper and the attribute parameter of the terminal device is sent to the server … determining the target model from the model set according to the attribute parameter. In the process, even if a plurality of terminal devices simultaneously request the same paper model to the server, because different terminal device sends the attribute parameter is different, the server according to the different attribute parameter matching the target model is different, so that different terminal device can shoot the different effect of the photo or video; increasing interest.” Liu ¶5 “The detection algorithm is usually contained on the model corresponding to the sticker, that is to say, when shooting the photo or video, adding sticker, substantially running the detection algorithm in the model corresponding to the sticker. Taking small cat sticker as example, the effect of the paster is adding small cat beard and ear on the proper position of the face, then the process of adding paster substantially uses the human face detection algorithm in the model to identify the human face in the shooting frame, and adding the process of beard and ear of the small cat at the corresponding position.” Note: Liu teaches the processing of a special effect over a human body, in the provided example specifically the face. Liu teaches the processing of a paper model, which it also refers to as a sticker, special effect placed over a human body properly fused to it at corresponding positions.)
It would have been obvious to a person having ordinary skill in the art before the effective filing
date of the claimed invention to combine Wang with Liu where an effect model that is displayed transparently is a paper model.
There are several reasons that would motivate on to do so, paper models as taught in Liu provide an easy way to display effects in a 2D or flat style. Leveraging transparency effects with a paper model allows for a number of stylized special effects to be easily produced such as a stained glass or similar transparent coloration effects.
Regarding claims 22, 9,
Wang teaches:
The method according to claim 1, wherein the writing pixel depth information corresponding to the effect fusion model into the rendering engine, to enable the rendering engine to render the image corresponding to the to-be-processed image based on the pixel depth information, comprises: determining, based on a rendering camera, (A rendering camera, or virtual camera, is simply a view point from which the rendering performed is viewed from and implicit in any view of a rendering. As Wang teaches the creation of 3D models and rendering for the effects, a virtual camera to view these renderings from is implicitly taught.) pixel depth information of a plurality of pixels corresponding to the effect fusion model, and writing, based on the rendering engine, the pixel depth information into the rendering engine, to write the pixel depth information into a model in the to-be-processed effect fusion model to obtain the image. (Wang ¶52 “In the present application, the special effect processing manner on the target image may include one or more of transparency, distortion, and scaling processing. The step of performing transparency processing on the target image may be as shown in FIG. 2, including: … transparency processing when depth writing is turned on: depth writing is started, but color is not output, depth information is written into the depth buffer, and then normal transparency processing is performed … since the depth information of each target pixel has been known in advance, the transparency processing can be performed in accordance with the depth information of the pixel level when the transparency processing is performed” Note: Wang teaches that the 3D special effect is first processed/rendered onto the target image, which is the segmented image of the human body. In order to perform this rendering and implement things such as transparency to the effect each pixel in the target image, meaning each pixel for the human and the effect overlayed has its depth information found and written into a buffer for use in rendering/processing.)
While Wang teaches the ability to write pixel depth information for its rendering process it does not teach the use or rendering of a paper model. This is taught in Liu, which teaches rendering a paper model in the to-be-processed effect fusion model to obtain the image. (Liu ¶65 “effect rendering method provided by the embodiment of the present disclosure. Referring to FIG. 1, the terminal device 10 establishes a network connection with the server 20, the terminal device 10 has the ability of shooting photo or video, … if the user selected sticker is not the terminal device 10 local pre-stored sticker, the terminal device 10 the identification and attribute parameter of the sticker are reported to the server, the server determines the model set corresponding to the identification according to the identification, and then The attribute parameters of the terminal device determine the target model from the model set, and send the target model to the terminal device. After the terminal device receives the target model, it can run the target model to add stickers to the image, and the image can be an image in a photo or video.” Note: Liu previously establishes that the effects it seeks to render are paper models, in the above citation they are referred to alternatively as “stickers”. Here Liu teaches a rendering process involving obtaining the models for the paper model.)
It would have been obvious to a person having ordinary skill in the art before the effective filing
date of the claimed invention to combine Wang with Liu where an effect’s rendering process that involves writing pixel depth information is for the effect of a paper model.
There are several reasons that would motivate on to do so, one is to enable more accurate transparency processing. There are several stylistic benefits of transparency processing that could apply to a paper model discussed previously, to achieve these benefits of accurate high-quality transparency the image’s pixel depth information can be leveraged in the rendering process.
Conclusion
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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/ALAN GREGORY HAKALA/Examiner, Art Unit 2617
/KING Y POON/Supervisory Patent Examiner, Art Unit 2617
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