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 .
Claim Objections
Claim 20 and 29 objected to because of the following informalities:
Claims 20 and 29 recite: “wherein the third embedding is represented by a difference between an embedding of the first face an embedding of a mean face”. The claims should be amended to read “wherein the third embedding is represented by a difference between an embedding of the first face and an embedding of a mean face” or similar.
Appropriate correction is required.
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 10, 11, 21, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Chen (US 20190035149 A1) in view of Matsuda (US 12095975 B2; see attachment for paragraph numbers).
Regarding claim 10:
Chen teaches:
An apparatus for generating a mesh model, the apparatus comprising:
at least one processor (Chen: there is provided a computer program product executable on a processor [0061]) configured to:
predict a set of parameters, the set of parameters describing an inner face mesh for a face (Chen: we first analyze the input image and extract the shape features of the user's face [0209]);
generate the inner face mesh based on the predicted set of parameters (Chen: The next step is to reconstruct the user's 3D face model from the face shape features (i.e. the 2D facial landmarks) extracted in Section 2.1.1 [0211]);
join the inner face mesh with an outer face mesh to generate a mesh model of a face (Chen: Although the 3D geometry of the user's face is captured in the input scan data, usually a template mesh fitting process is still required […] we will have to complete the geometry of the head, as the input scan generally only contains the frontal face geometry [0305 – 0306]; see also Chen: Fig. 21); and
output the mesh model of the face (Chen: Output: Rectified 3D face geometry. [0251]).
Chen fails to explicitly teach:
at least one processor coupled to the at least one memory and configured to:
at least one memory; and
Matsuda teaches:
An apparatus for generating a mesh model, the apparatus comprising:
at least one processor (Matsuda: electronics components 20 may include a memory circuit 112 storing instructions and a processor circuit 122 that executes the instructions (27)) coupled to the at least one memory and configured to:
at least one memory (Matsuda: electronics components 20 may include a memory circuit (27)); and
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Masuda with Chen. Utilizing a memory to store instructions, as in Masuda would improve the Chen teachings by enabling the processor to operate on pre-generated instructions.
Regarding claim 11:
Chen in view of Matsuda teaches:
The apparatus of claim 10 (as shown above),
wherein the inner face mesh includes a representation of a forehead, eyes, nose, mouth and portion of a chin of a person (see Note 11A), and
wherein the outer face mesh includes a representation of ears, back of a head, and top of a head of the person (Chen: a two-stage mesh fitting process, for example as shown in FIG. 20. In the first stage, i.e. “coarse fit” stage, we introduce a 3D morphable head model (3DMHM) as the shape prior (see e.g. Equation (1)). [0310]; see Note 11B).
Note 11A: Fig. 5 of Chen showcases that a personalized 3D face model includes a forehead, eyes, nose, mouth, and chin.
Note 11B: Fig. 20 of Chen showcases that a 3D morphable head model includes ears and a back and top of the head of a person.
Regarding claim 21:
Claim 21 is substantially similar to claim 10, and is therefore rejected for similar reasons. Claim 21 contains the following notable differences:
Claim 21 claims a method instead of an apparatus. In the rejection of claim 10, it was shown that Chen in view of Matsuda teaches the claimed apparatus. It follows that Chen in view of Matsuda teaches the corresponding method.
Regarding claim 22:
Claim 22 is substantially similar to claim 11, and is therefore rejected for similar reasons. Claim 22 contains the following notable differences:
Claim 22 claims a method instead of an apparatus. In the rejection of claim 11, it was shown that Chen in view of Matsuda teaches the claimed apparatus. It follows that Chen in view of Matsuda teaches the corresponding method.
Claims 12 and 23 is rejected under 35 U.S.C. 103 as being unpatentable over Chen (US 20190035149 A1) in view of Matsuda (US 12095975 B2) and Schmidt (US 20130314415 A1).
Regarding claim 12:
Chen in view of Matsuda teaches:
The apparatus of claim 10 (as shown above), wherein, to join the inner face mesh with the outer face mesh, the at least one processor is configured to:
join the inner face mesh and the outer face mesh (Chen: Fig. 21; see Note 12A).
Note 12A: In Fig. 21, Chen showcases that the input 3D face scan undergoes a template fitting process to combine the inner face scan with an outer template 3D head model.
Chen in view of Matsuda fails to explicitly teach:
extract first mesh boundary vertices of the inner face mesh;
extract second mesh boundary vertices of the outer face mesh;
deform the second mesh boundary vertices based on the first mesh boundary vertices; and
Schmidt teaches:
extract first mesh boundary vertices of the inner mesh; extract second mesh boundary vertices of the outer mesh (Schmidt: receiving a first mesh boundary and a second mesh boundary, Abstract);
deform the second mesh boundary vertices based on the first mesh boundary vertices (Schmidt: other types of edge operations 200 (e.g., an edge flip operation 202 and/or an edge collapse operation 206) as well as a vertex collapse operation 300 and/or a smoothing operation 400 may be performed on the edges and vertices included in the surface joining the mesh boundaries 710 to further refine the mesh surface [0068]); and
join the inner mesh and the outer mesh (Schmidt: joining a first vertex associated with the first mesh boundary to a first plurality of vertices associated with the second mesh boundary to form a joined surface, Abstract).
Note 12B: When combined with the teachings of Chen in view of Matsuda, it would be obvious for one of ordinary skill in the art to utilize the method of Schmidt with the inner and outer face mesh.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Schmidt with Chen in view of Matsuda. Joining both meshes as in Schmidt would benefit the Chen in view of Matsuda teachings by enabling quick generation of mesh models: “Conventionally, joining two meshes requires the end-user to painstakingly prepare and modify each mesh. For example, joining meshes may require the end-user to manually remove surface(s) at which the meshes are to be joined while at the same time ensuring that the boundaries at which the meshes are to be joined include exactly the same number of vertices. Such preparations are particularly time-consuming when meshes having different triangle and vertex densities are joined” (Schmidt [0006]).
Regarding claim 23:
Claim 23 is substantially similar to claim 12, and is therefore rejected for similar reasons. Claim 23 contains the following notable differences:
Claim 23 claims a method instead of an apparatus. In the rejection of claim 12, it was shown that Chen in view of Matsuda and Schmidt teaches the claimed apparatus. It follows that Chen in view of Matsuda and Schmidt teaches the corresponding method.
Claims 13 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Chen (US 20190035149 A1) in view of Matsuda (US 12095975 B2), Schmidt (US 20130314415 A1) and Tralie (NPL: Laplacian Mesh Editing).
Regarding claim 13:
Chen in view of Matsuda and Schmidt teaches:
The apparatus of claim 12 (as shown above), wherein the at least one processor is configured to
wherein, to deform the second mesh boundary vertices based on the first mesh boundary vertices, the at least one processor is configured to
deform the second mesh boundary vertices to fit the first mesh boundary vertices while minimizing distances between positions of a set of vertices of the vertices (Chen: In the second stage of the fitting process, i.e. “fine fit” stage, we use the result of the coarse fit X* as the starting point, and further apply a non-rigid iterative closest point (N-ICP) algorithm [3], which deforms the resulting mesh to achieve a better surface matching X** with the input face scan data Y, as shown in FIG. 20. [0312]; see Note 13A).
Note 13A: The Examiner interprets the non-rigid iterative closest point (N-ICP) algorithm discussed by Chen to minimize distances between positions of a set of vertices.
Chen in view of Matsuda and Schmidt fails to explicitly teach:
extract static vertices of the outer face mesh, and
wherein, to deform the second mesh boundary vertices based on the first mesh boundary vertices, the at least one processor is configured to
deform the second mesh boundary vertices to fit the first mesh boundary vertices while minimizing distances between positions of a set of vertices of the static vertices.
Tralie teaches:
extract static vertices of the outer face mesh (Tralie: Select multiple "anchor" points and add them as rows to the bottom of the L matrix, Pg. 4; see Note 13B), and
wherein, to deform the second mesh boundary vertices based on the first mesh boundary vertices, the at least one processor is configured to
deform the second mesh boundary vertices to fit the first mesh boundary vertices while minimizing distances between positions of a set of vertices of the static vertices (Update the positions of all vertices based on the obtained solution […] Restore the anchors to the positions that were selected before […] In my implementation I chose to restore them to their original positions. Pg. 4; see Note 13B)
Note 13B: The specification of the present application recites: “In some cases, the deformed outer face mesh 1018 may be joined 1022 to the inner face mesh 1006 using Laplacian mesh editing to blend the outer mesh boundary vertices 1010 and inner face mesh 1006. For example, the Laplacian mesh editing may deform the mesh to minimize distances between positions of pre-defined anchor vertices and their new assigned locations, minimize distance between positions of pre-defined static vertices before and after deformation, and preserver [sic] local geometry” [0127].
Tralie similarly teaches defining anchor vertices, between positions of pre-defined static vertices before and after deformation (via solving the linear system in a “least squares” sense), and restoring the anchor vertices so that they remain static. Therefore, the Examiner understands that Chen in view of Matsuda, Schmidt, and Tralie teaches the limitations of claim 13.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Tralie with Chen in view of Matsuda and Schmidt. Extracting static vertices and deforming based on the static vertices, as in Tralie, would benefit the Chen in view of Matsuda and Schmidt teachings by enabling local deformations of the mesh: “Laplacian meshes store the geometry of triangle meshes in an alternative way by keeping track of differential vertex information instead of absolute information. This turns out to be a much better way to preserve the relationship between vertices when certain transformations (especially deformations) are done on the mesh. It also allows for a very natural way to do smooth function interpolation across the surface, and it allows approximation of a lot of metrics from differential geometry, such as mean curvature, with very little effort” (Tralie, Pg. 1, par. 2)
Regarding claim 24:
Claim 24 is substantially similar to claim 13, and is therefore rejected for similar reasons. Claim 24 contains the following notable differences:
Claim 24 claims a method instead of an apparatus. In the rejection of claim 13, it was shown that Chen in view of Matsuda, Schmidt, and Tralie teaches the claimed apparatus. It follows that Chen in view of Matsuda, Schmidt, and Tralie teaches the corresponding method.
Claims 14, 15, 16, 25, 26, and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Chen (US 20190035149 A1) in view of Matsuda (US 12095975 B2), and Bradley (US 20220301348 A1).
Regarding claim 14:
Chen in view of Matsuda teaches:
The apparatus of claim 10 (as shown above),
Chen in view of Matsuda fails to explicitly teach:
wherein the at least one processor is configured to predict the set of parameters using an encoder and generate the inner face mesh using a decoder.
Bradley teaches:
wherein the at least one processor is configured to
predict the set of parameters using an encoder (Bradley: The encoder 202 outputs the identity encoding 210, the expression encoding 212, and the one or more camera parameters 214 as a representation of the identity, expression, and camera parameters of the face in the image, respectively, in an encoding latent space. [0026]) and
generate the mesh using a decoder (Bradley: The expression mesh decoder 222 receives the expression encoding 212 and the mesh topology 218 and generates an expression mesh 224 [0028]).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Bradley with Chen in view of Matsuda. Predicting parameters with an encoder and generating the mesh with a decoder, as in Bradley, would benefit the Chen in view of Matsuda teachings by enabling construction of the face in arbitrary conditions while using the HMD: “One drawback that exists with many existing facial capture systems is the dependency on controlled settings and the physical presence of the corresponding individuals. Due to these requirements, facial capture systems cannot be used to perform facial reconstruction under various uncontrolled (“in-the-wild”) conditions that include arbitrary human identities and facial expressions” (Bradley [0005]).
Regarding claim 15:
Chen in view of Matsuda and Bradley teaches:
The apparatus of claim 14 (as shown above), wherein the encoder and decoder are trained based on a ground truth face mesh (Bradley: Based on the generated encodings and meshes and the ground truth encodings and meshes, the face reconstruction engine 124 determines a set of losses that can be used to train the encoder and/or decoders [0035]).
Regarding claim 16:
Chen in view of Matsuda and Bradley teaches:
The apparatus of claim 15 (as shown above), wherein the ground truth face mesh is generated by:
extracting a reference outer face mesh from a neutral expression reference mesh (Chen: a geometry of the user's 3D scan is fitted by the morphable head model by a bundle adjustment optimisation process that finds the optimal shape morph parameters of the 3DMHM, and 3D head pose parameters, [0074]);
deforming the reference outer face mesh based on an extracted inner face mesh (Chen: apply a non-rigid iterative closest point (N-ICP) algorithm, which deforms the resulting mesh to achieve a better surface matching with the at least one 3D scan of the user's face. [0074]); and
joining the deformed reference outer face mesh and extracted inner face mesh to form the ground truth face mesh (Chen: Chen: Although the 3D geometry of the user's face is captured in the input scan data, usually a template mesh fitting process is still required […] we will have to complete the geometry of the head, as the input scan generally only contains the frontal face geometry [0305 – 0306]; see also Chen: Fig. 21; see also Note 12A).
Regarding claim 25:
Claim 25 is substantially similar to claim 14, and is therefore rejected for similar reasons. Claim 25 contains the following notable differences:
Claim 25 claims a method instead of an apparatus. In the rejection of claim 14, it was shown that Chen in view of Matsuda and Bradley teaches the claimed apparatus. It follows that Chen in view of Matsuda and Bradley teaches the corresponding method.
Regarding claim 26:
Claim 26 is substantially similar to claim 15, and is therefore rejected for similar reasons. Claim 26 contains the following notable differences:
Claim 26 claims a method instead of an apparatus. In the rejection of claim 15, it was shown that Chen in view of Matsuda and Bradley teaches the claimed apparatus. It follows that Chen in view of Matsuda and Bradley teaches the corresponding method.
Regarding claim 27:
Claim 27 is substantially similar to claim 16, and is therefore rejected for similar reasons. Claim 27 contains the following notable differences:
Claim 27 claims a method instead of an apparatus. In the rejection of claim 16, it was shown that Chen in view of Matsuda and Bradley teaches the claimed apparatus. It follows that Chen in view of Matsuda and Bradley teaches the corresponding method.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Chen (US 20190035149 A1) in view of Matsuda (US 12095975 B2), Bradley (US 20220301348 A1), Hemmer (US 20210349444 A1) and StackExchange (NPL: What is the difference between latent and embedding spaces?).
Regarding claim 17:
Chen in view of Matsuda and Bradley teaches:
The apparatus of claim 14 (as shown above),
Chen in view of Matsuda and Bradley fails to teach:
wherein the decoder is trained based on a training encoder, and wherein the decoder is trained by:
generating, by the training encoder, a first embedding based on an input inner face mesh;
generating, by the decoder, a predicted inner face mesh; and
training the decoder based on a comparison between the input inner face mesh and the predicted inner face mesh.
Hemmer in view of StackExchange teaches:
wherein the decoder is trained based on a training encoder, and wherein the decoder is trained by:
generating, by the training encoder, a first embedding based on an input mesh (Hemmer: the training engine 230 trains the encoder network 214 to generate a latent representation 208 of a received network input 204. [0041]; see Note 17A);
generating, by the decoder, a predicted mesh (Hemmer: The training engine 230 also trains the decoder network 230 to generate high quality reconstructions of the input meshes 202. [0039]); and
training the decoder based on a comparison between the input mesh and the predicted mesh (Hemmer: The quality of the reconstructions can be determined, for example, by using an appropriate metric which measures a difference between the input mesh and the reconstructed mesh. [0039]).
Note 17A: The Examiner interprets a “latent representation” to be analogous to an embedding, because before the effective filing date, one of ordinary skill in the art would find the terms interchangeable: “These two expressions can be used interchangeably, also because the expression "embedding space" is often not formally defined.” (StackExchange: “What is the difference between latent and embedding spaces?” Pg. 3)
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Hemmer with Chen in view of Matsuda and Bradley. Training the encoder based on the comparison of embeddings of images and meshes, as in Hemmer, would benefit the Chen in view of Matsuda and Bradley teachings by enabling quick generation of mesh models: “Conventional approaches to this issue rely on iterative or geometric fitting processes to generate estimated mesh representations of the deformable objects. Robot motions can then be adjusted based on the estimated mesh representations. Such processes, however, can be time-consuming and thus are not suitable for online operations, especially when the robots are moving at high speeds” (Hemmer, [0004]).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Chen (US 20190035149 A1) in view of Matsuda (US 12095975 B2), Bradley (US 20220301348 A1), Hemmer (US 20210349444 A1), StackExchange (NPL: What is the difference between latent and embedding spaces?), and Jones (US 20210335039 A1).
Regarding claim 18:
Chen in view of Matsuda, Bradley, Hemmer, and StackExchange teaches:
The apparatus of claim 17 (as shown above), wherein the encoder is trained by:
Chen in view of Matsuda, Bradley, Hemmer, and StackExchange fails to explicitly teach:
generating, by the encoder, a second embedding based on a synthetic NIR HMD user image corresponding to the inner face mesh; and
training the encoder based on a comparison between the second embedding and the first embedding.
Jones teaches:
generating, by the encoder, a second embedding based on a synthetic image corresponding to the mesh (Jones: generating, using the image encoder, a respective shape space vector for each of the plurality of 2D training images [0009]; see also Note 19B); and
training the encoder based on a comparison between the second embedding and the first embedding (Jones: comparing each generated shape space vector with a corresponding shape space vector for the corresponding target mesh, where the corresponding shape space vector associated with the target mesh is encoded using the mesh encoder; determining a second value for a second error function based on a comparison of the generated shape space vector and the corresponding shape space vector; and updating one or more parameters of the image encoder based on the second value. [0009]; see Note 18A).
Note 18A: In other words, Jones teaches training an image encoder based on a comparison between a shape space vector from the training image and the shape space vector for the target mesh. When the teachings of Jones are combined with the teachings of Chen in view of Matsuda, Bradley, Hemmer, and StackExchange, it would be obvious to one of ordinary skill in the art to substitute the NIR HMD user image for training images and the inner face mesh for the corresponding target mesh.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Jones with Chen in view of Matsuda, Bradley, Hemmer, and StackExchange. Training the encoder based on the comparison of embeddings of images and meshes, as in Jones, would benefit the Chen in view of Matsuda, Bradley, Hemmer, and StackExchange teachings by enabling quick generation of mesh models: “By generating objects (assets) for simulation-based assessments, such as biological assets, costs associated with a traditional art and animation team may be reduced. Further, because it may take some time to generate assets, there is no latency if they are precomputed” (Jones, [0046]).
Claims 19 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Chen (US 20190035149 A1) in view of Matsuda (US 12095975 B2), Bradley (US 20220301348 A1), and Saragih (US 10636192 B1; see attachment for paragraph numbers).
Regarding claim 19:
Chen in view of Matsuda and Bradley teaches:
The apparatus of claim 14,
Chen in view of Matsuda and Bradley fails to teach:
wherein the at least one processor is configured to generate, using the encoder, a third embedding based on NIR HMD user images.
Saragih teaches:
wherein the at least one processor is configured to generate, using the encoder, a third embedding based on user images (Saragih: the controller 220 generates 540 a facial animation model of the portions of the user's face of which the facial sensors 210 captured 510 images […] (47); see Note 19A), wherein the third embedding represents an expression of a first face (Saragih: the facial animation model is an expression parameter comprising a parametric representation of human faces (47)).
Note 19A: Saragih teaches: “When the facial animation model is a blendshape model, the facial animation model calculates a vector of blendshape coefficients that determine the weight of each expression mesh in the linear combination” (47) and that a “trained model maps the the blendshape vectors to animation parameters that render the movement of the portions of the user's face on the three-dimensional model of the face” (7). The Examiner interprets the “vector of blendshape coefficients” to be an embedding, because Cloudflare teaches: “embeddings are vectors created by machine learning models for the purpose of capturing meaningful data about each object.”
Note 19B: Matsuda teaches: “In some embodiments, eye cameras 215 may be infrared cameras collecting images of the user's face in reflection mode” (paragraph 32). Therefore, when combined with the teachings of Chen in view of Matsuda and Bradley, it would be obvious to one of ordinary skill in the art to generate an embedding based on NIR (near infrared) HMD images.
Regarding claim 28:
Claim 28 is substantially similar to claim 19, and is therefore rejected for similar reasons. Claim 28 contains the following notable differences:
Claim 28 claims a method instead of an apparatus. In the rejection of claim 19, it was shown that Chen in view of Matsuda, Bradley, and Saragih teaches the claimed apparatus. It follows that Chen in view of Matsuda, Bradley, and Saragih teaches the corresponding method.
Allowable Subject Matter
Claims 20 and 29 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.
The following is a statement of reasons for the indication of allowable subject matter:
Claim 20 recites the limitation: “wherein the third embedding is represented by a difference between an embedding of the first face an embedding of a mean face”.
Saragih teaches that “a parametric representation of human faces is a blendshape model that models facial expressions of the user as a linear combination of blendshapes” (paragraph 47). Lewis (NPL: Practice and Theory of Blendshape Facial Models) teaches that: “a neutral face shape is designated and the remaining shapes are replaced by the differences between those shapes and the neutral shape”. However, neither Saragih or Lewis teach a difference between an embedding of the first face an embedding of a mean face.
Chen (CN 113763535 A) teaches: “according to the difference between the first face image and the second face image, iteratively adjusting the characteristic latent code the extracting network” (Pg. 2, par. 7). However, Chen does not teach that the second face is a mean or average face.
None of the other prior art searched or on the record teaches, suggests, or renders obvious the limitations of Claim 20.
Claim 29 is substantially similar to claim 20 and is therefore allowable for the same reasons above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Strietzel (US 20090132371 A1) teaches:
extracting a reference outer face mesh from a neutral expression (Strietzel: FIG. 7 illustrates a set of basis head models 710A-710D and a blend fit head model 712 [0142]; see Note 16A) reference mesh (Strietzel: The processor is configured […] create, based on the comparison, a 3D blend fit head model from a combination of selected ones of the plurality of 3D basis head models; and to deform portions of the 3D blend fit head model to approximate a non-uniform surface of the aligned at least one image to generate a personalized 3D head model. [0028]);
deforming the reference outer face mesh based on an extracted inner face mesh (The method further includes deforming portions of the blend fit head model to approximate the non-uniform surface of the aligned 3D face mask [0026]); and
joining the deformed reference outer face mesh and extracted inner face mesh to form the ground truth face mesh (see Note 16B).
Note 16A: In Fig. 7, Strietzel showcases that the basis face mesh used for the outer region of the head has a neutral expression.
Note 16B: Figures 8A, 8B, 9A, and 9B of Strietzel showcase different stages of joining the inner face mesh with the outer face mesh. Fig. 9B showcases that the inner face mesh is joined with the outer face mesh (reproduced below). Notably, the face mesh has a neutral face expression.
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/VINCENT ALEXANDER PROVIDENCE/Examiner, Art Unit 2617 /KING Y POON/Supervisory Patent Examiner, Art Unit 2617
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