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 .
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 4-6, 10-12, 14-16, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over SANTESTEBAN et al (Learning-Based Animation of Clothing for Virtual Try-On) in view of BOGO et al (Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image), YU et al (SimulCap: Single-View Human Performance Capture with Cloth Simulation) and DAN CASAS (Learning-Based Animation of Clothing for Virtual Try-On (Eurographics 2019) - https://www.youtube.com/watch?v=o2KJoAhEGg8).
As per claim 1, Santesteban teaches the claimed “system comprising a processor and a non-transitory computer-readable medium storing computing instructions that, when executed on the processor”, cause the processor to perform operations comprising: “extracting shape and pose vectors of an image of a user” (Santesteban, Figure 2 - At runtime, our data-driven cloth deformation model works by computing two corrective displacements on the unposed garment: global fit displacements dependent on the body’s shape, and dynamic wrinkle displacements dependent on the body’s shape and pose) (Noted: the body shape and pose can be captured from an image of the user; see Bogo, Abstract - We describe the first method to automatically estimate the 3D pose of the human body as well as its 3D shape from a single unconstrained image; Yu, 3. Multi-Layer Avatar Digitization - We obtain a double-layer surface using DoubleFusion, which is a single-view, real-time method, which reconstructs the dressed body and undressed body surface at the same time; 1. Introduction - During the performance capture step, we track both skeleton motion of the undressed body and the detailed non-rigid deformation of the cloth sequentially; 4.1. Body Tracking - Iterative closest point algorithm (ICP) is used for skeleton tracking); “generating a virtual image representing the user based on the shape and pose vectors and apparel of interest” (Santesteban, 3.1. Clothing Model - The pipeline Figure 2 shows the template body mesh Tb wearing the template cloth mesh Tc (Figure 2-a), and then the template cloth mesh in isolation (Figure 2-b), with the addition of garment fit (Figure 2-c), with the addition of garment wrinkles (Figure 2-d), and the final deformation after the skinning step (Figure 2-e); Figure 2 - At runtime, our data-driven cloth deformation model works by computing two corrective displacements on the unposed garment: global fit displacements dependent on the body’s shape, and dynamic wrinkle displacements dependent on the body’s shape and pose); “receiving, from the user through an interactive user interface, one or more adjustments on a temporal axis to modify parameters of the virtual image over one or more time periods; updating a model for the user based on the one or more adjustments on the temporal axis and a change model trained on the temporal axis” (Santesteban, 5.3. Qualitative Evaluation, Figure 8 - Generalization to new shapes. In Figure 8, we show the clothing deformations produced by our approach on a static pose while changing the body shape over time) (Noted: the animation, or change in time, of body shape associated with its cloth; see Casas, 00:00-00:26 and 02:20-02:50); “rendering a modified virtual image of the user based on the model for the user, as updated, and the apparel of interest; and sending the modified virtual image of the user for display on the interactive user interface” (Santesteban, 5.3. Qualitative Evaluation, Figure 8 - Generalization to new shapes. In Figure 8, we show the clothing deformations produced by our approach on a static pose while changing the body shape over time).
PNG
media_image1.png
827
1234
media_image1.png
Greyscale
PNG
media_image2.png
809
1232
media_image2.png
Greyscale
PNG
media_image3.png
826
1234
media_image3.png
Greyscale
Thus, it would have been obvious, in view of Bogo, Yu and Casas, to configure Santesteban’s system as claimed by temporal changing the user’s body and pose captured by an image associated to a try-on apparel of interest. The motivation is to represent the fitting of the apparel of interest on the changing user’s body shape and pose.
Claim 2 adds into claim 1 “wherein the change model is trained on the temporal axis for one or more physiological changes” (Santesteban, 5.3. Qualitative Evaluation, Figure 8 - Generalization to new shapes. In Figure 8, we show the clothing deformations produced by our approach on a static pose while changing the body shape over time; the animation, or change in time, of body shape associated with its cloth; see Casas, 00:00-00:26 and 02:20-02:50). Thus, it would have been obvious, in view of Bogo, Yu and Casas, to configure Santesteban’s system as claimed by temporal changing the user’s body and pose captured by an image associated to a try-on apparel of interest. The motivation is to represent the fitting of the apparel of interest on the changing user’s body shape and pose.
Claim 4 adds into claim 1 “wherein the change model for the user is located within the change model based on the shape and pose vectors” (Santesteban, 3.1. Clothing Model - The pipeline Figure 2 shows the template body mesh Tb wearing the template cloth mesh Tc (Figure 2-a; e.g., animation sequences (θ1, θ2,…, θt) and body shapes (β1, β2, …, βn)), and then the template cloth mesh in isolation (Figure 2-b), with the addition of garment fit (Figure 2-c), with the addition of garment wrinkles (Figure 2-d), and the final deformation after the skinning step (Figure 2-e); Figure 2 - At runtime, our data-driven cloth deformation model works by computing two corrective displacements on the unposed garment: global fit displacements dependent on the body’s shape, and dynamic wrinkle displacements dependent on the body’s shape and pose).
Claim 5 adds into claim 1 “wherein the interactive user interface comprises a slider configured to receive the one or more adjustments on the temporal axis to modify the parameters of the virtual image over the one or more time periods” which would have been obvious in Casas’ animation (e.g., body shape changing in time) (Casas, 00:00-00:26 and 02:20-02:50) or Santesteban’s change of body parameters (β1, β2, …, βn) (Santesteban, 3.1. Clothing Model - The pipeline Figure 2 shows the template body mesh Tb wearing the template cloth mesh Tc (Figure 2-a; e.g., animation sequences (θ1, θ2,…, θt) and body shapes (β1, β2, …, βn)), and then the template cloth mesh in isolation (Figure 2-b), with the addition of garment fit (Figure 2-c), with the addition of garment wrinkles (Figure 2-d), and the final deformation after the skinning step (Figure 2-e); Figure 2 - At runtime, our data-driven cloth deformation model works by computing two corrective displacements on the unposed garment: global fit displacements dependent on the body’s shape, and dynamic wrinkle displacements dependent on the body’s shape and pose). Thus, it would have been obvious, in view of Bogo, Yu and Casas, to configure Santesteban’s system as claimed by temporal changing the user’s body and pose captured by an image associated to a try-on apparel of interest. The motivation is to represent the fitting of the apparel of interest on the changing user’s body shape and pose.
Claim 6 adds into claim 1 “wherein the operations further comprise: training the change model using a set of dataset images to generate parametrization of changes to body shapes over the temporal axis for multiple body types” which would have been obvious in Casas’ animation (e.g., body shape changing in time) (Casas, 00:00-00:26 and 02:20-02:50) or Santesteban’s change of body parameters (β1, β2, …, βn) (Santesteban, 3.1. Clothing Model - The pipeline Figure 2 shows the template body mesh Tb wearing the template cloth mesh Tc (Figure 2-a; e.g., animation sequences (θ1, θ2,…, θt) and body shapes (β1, β2, …, βn)), and then the template cloth mesh in isolation (Figure 2-b), with the addition of garment fit (Figure 2-c), with the addition of garment wrinkles (Figure 2-d), and the final deformation after the skinning step (Figure 2-e); Figure 2 - At runtime, our data-driven cloth deformation model works by computing two corrective displacements on the unposed garment: global fit displacements dependent on the body’s shape, and dynamic wrinkle displacements dependent on the body’s shape and pose). Thus, it would have been obvious, in view of Bogo, Yu and Casas, to configure Santesteban’s system as claimed by temporal changing the user’s body and pose captured by an image associated to a try-on apparel of interest. The motivation is to represent the fitting of the apparel of interest on the changing user’s body shape and pose.
Claim 10 adds into claim 1 “wherein updating the model for the user further comprises: generating derivatives of the change model calculated at a first point in time; calculating a shift of the temporal axis to a second point in time for the derivatives of the change model; and applying the shift to the model for the user” (Santesteban, 3.1. Clothing Model - The pipeline Figure 2 shows the template body mesh Tb wearing the template cloth mesh Tc (Figure 2-a; e.g., animation sequences (θ1, θ2,…, θt) and body shapes (β1, β2, …, βn)), and then the template cloth mesh in isolation (Figure 2-b), with the addition of garment fit (Figure 2-c), with the addition of garment wrinkles (Figure 2-d), and the final deformation after the skinning step (Figure 2-e); Figure 2 - At runtime, our data-driven cloth deformation model works by computing two corrective displacements on the unposed garment: global fit displacements dependent on the body’s shape, and dynamic wrinkle displacements dependent on the body’s shape and pose; Casas, 00:00-00:26 and 02:20-02:50).
PNG
media_image1.png
827
1234
media_image1.png
Greyscale
PNG
media_image4.png
581
886
media_image4.png
Greyscale
PNG
media_image5.png
606
905
media_image5.png
Greyscale
Thus, it would have been obvious, in view of Bogo, Yu and Casas, to configure Santesteban’s system as claimed by temporal changing the user’s body and pose captured by an image associated to a try-on apparel of interest. The motivation is to represent the fitting of the apparel of interest on the changing user’s body shape and pose.
Claims 11-12, 14-16 and 19-20 claim a method and a non-transitory computer-readable medium storing computing instructions based on the system of claims 1-2, 4-6, and 10; therefore, they are rejected under a similar rationale.
Claims 3, 7-9, 13, 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over SANTESTEBAN et al in view of BOGO et al, YU et al and DAN CASAS, and further in view of GRABE et al (Fashion Style Generation: Evolutionary Search with Gaussian Mixture Models in the Latent Space).
Claim 3 adds into claim 1, Santesteban does not tech, but Grabe teaches “wherein the change model for the user is derived from a Gaussian Mixture Model configured to output a weighted expectation of the change model for the user” (Grabe, Abstract - A Gaussian mixture model is applied to identify fashion styles based on the higher-layer representations of outfits in a clothing-specific attribute prediction model). Thus, it would have been obvious, in view of Grabe, Bogo, Yu and Casas, to configure Santesteban’s system as claimed by using a Gaussian Mixture Model to generate the changing in shape model of the user. The motivation is to represent the fitting of the apparel of interest on the changing user’s body shape and pose.
Claim 7 adds into claim 6 “wherein training the change model comprises: “splitting the dataset images into bins along the temporal axis; extracting respective shape and pose parameter vectors from the dataset images” (Santesteban, 3.1. Clothing Model - The pipeline Figure 2 shows the template body mesh Tb wearing the template cloth mesh Tc (Figure 2-a; e.g., animation sequences (θ1, θ2,…, θt) and body shapes (β1, β2, …, βn)), and then the template cloth mesh in isolation (Figure 2-b), with the addition of garment fit (Figure 2-c), with the addition of garment wrinkles (Figure 2-d), and the final deformation after the skinning step (Figure 2-e); Figure 2 - At runtime, our data-driven cloth deformation model works by computing two corrective displacements on the unposed garment: global fit displacements dependent on the body’s shape, and dynamic wrinkle displacements dependent on the body’s shape and pose); “modeling the respective shape and pose parameter vectors using a set of Gaussian Mixture Models; applying weighted principal component analysis to the Gaussian Mixture Models” (Grabe, 4.2 Style model - The embedding vectors are scaled to zero mean before principal component analysis (PCA) projects them onto the 135 principal components capturing 90% of the embedding's variance. A Gaussian mixture model (GMM) is applied to find a mixture of Gaussian probability distributions that represent the data distribution); and “fitting changes of the respective shape and pose parameter vectors proportionally along the temporal axis by weighted least squares fitting” (Santesteban, 3.2. Garment Fit Regressor - To compute garment fit displacements in our data-driven model, we use a nonlinear regressor RG, which takes as input the shape of the body β. In particular, we implement the regressor using a single-hidden-layer multilayer perceptron (MLP) neural network. We train the MLP network by minimizing the mean squared error between predicted displacements and ground-truth displacements). Thus, it would have been obvious, in view of Grabe, Bogo, Yu and Casas, to configure Santesteban’s system as claimed by using a Gaussian Mixture Model to generate the changing in shape model of the user. The motivation is to represent the fitting of the apparel of interest on the changing user’s body shape and pose.
Claim 8 adds into claim 7 “wherein extracting the respective shape and pose parameter vectors is performed by a skinned multi-person linear model engine (SMPLify)” in which the claimed “Skinned Multi-Person Linear model (SMPL)” (i.e., a skinned vertex-based model representing a wide variety of body shapes in natural human poses) would have been obvious in the teaching of Santesteban’s skin weighted model (Santesteban, 3.1. Clothing Model - The pipeline Figure 2 shows the template body mesh Tb wearing the template cloth mesh Tc (Figure 2-a), and then the template cloth mesh in isolation (Figure 2-b), with the addition of garment fit (Figure 2-c), with the addition of garment wrinkles (Figure 2-d), and the final deformation after the skinning step (Figure 2-e); Figure 2 - the deformed cloth is skinned on the body to produce the final result).
Claim 9 adds into claim 7 “wherein training the change model further comprises: segmenting the bins by body pose parameter vectors” (Santesteban, 3.1. Clothing Model - The pipeline Figure 2 shows the template body mesh Tb wearing the template cloth mesh Tc (Figure 2-a; e.g., animation sequences (θ1, θ2,…, θt) and body shapes (β1, β2, …, βn)), and then the template cloth mesh in isolation (Figure 2-b), with the addition of garment fit (Figure 2-c), with the addition of garment wrinkles (Figure 2-d), and the final deformation after the skinning step (Figure 2-e); Figure 2 - At runtime, our data-driven cloth deformation model works by computing two corrective displacements on the unposed garment: global fit displacements dependent on the body’s shape, and dynamic wrinkle displacements dependent on the body’s shape and pose).
Claims 13, 17 and 18 claim a method based on the system of claims 3, 7-9; therefore, they are rejected under a similar rationale.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHU K NGUYEN whose telephone number is (571)272-7645. The examiner can normally be reached M-F 8-5pm.
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 F. Hajnik can be reached at (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.
/PHU K NGUYEN/ Primary Examiner, Art Unit 2616
Prosecution Insights