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 Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because claim 1 is directed to: A computer-implemented method for generating a facial animation, the computer-implemented method comprising the steps of receiving, receiving, generating, generating and generating which are nothing more than software instructions. Software instructions are non-statutory under 35 U.S.C. 101.
Claims 2-9 depend from claim 1 and comprise additional steps, for example claim 2 comprises the steps of generating and generating; therefore claims 2-9 have the same problem as claim 1 and are rejected under the same rationale.
Allowable Subject Matter
Claims 10-20 are allowed.
The following is an examiner’s statement of reasons for allowance:
Regarding claims 10 (claim 20 is similar in scope), the prior art doesn’t teach:
receiving an identity code including a first set of features describing a neutral facial depiction associated with a particular identity;
receiving an expression code including a second set of features describing a facial expression associated with the particular identity;
generating, via a first machine learning model, an identity-specific facial representation based on a canonical facial representation and the identity code;
generating, via a second machine learning model and based on the identity code, the expression code, and the identity-specific facial representation, a muscle actuation field tensor and one or more bone transformations associated with the canonical facial representation; and
generating, via a physics-based simulator, a facial animation based on at least the muscle actuation field tensor and the one or more bone transformations.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chandran, et al. "Shape Transformers: Topology‐Independent 3D Shape Models Using Transformers." Computer Graphics Forum. Vol. 41. No. 2. 2022, discloses:
Parametric 3D shape models are heavily utilized in computer graphics and vision applications to provide priors on the observed variability of an object’s geometry (e.g., for faces). Original models were linear and operated on the entire shape at once. They were later enhanced to provide localized control on different shape parts separately. In deep shape models, nonlinearity was introduced via a sequence of fully-connected layers and activation functions, and locality was introduced in recent models that use mesh convolution networks. As common limitations, these models often dictate, in one way or another, the allowed extent of spatial correlations and also require that a fixed mesh topology be specified ahead of time. To overcome these limitations, we present Shape Transformers, a new nonlinear parametric 3D shape model based on transformer architectures. A key benefit of this new model comes from using the transformer’s self-attention mechanism to automatically learn nonlinear spatial correlations for a class of 3D shapes. This is in contrast to global models that correlate everything and local models that dictate the correlation extent. Our transformer 3D shape autoencoder is a better alternative to mesh convolution models, which require specially-crafted convolution, and down/up-sampling operators that can be difficult to design. Our model is also topologically independent: it can be trained once and then evaluated on any mesh topology, unlike most previous methods. We demonstrate the application of our model to different datasets, including 3D faces, 3D hand shapes and full human bodies. Our experiments demonstrate the strong potential of our Shape Transformer model in several applications in computer graphics and vision.
CHANDRAN (US2021/0279956A1) discloses:
Techniques are disclosed for training and applying nonlinear face models. In embodiments, a nonlinear face model includes an identity encoder, an expression encoder, and a decoder. The identity encoder takes as input a representation of a facial identity, such as a neutral face mesh minus a reference mesh, and outputs a code associated with the facial identity. The expression encoder takes as input a representation of a target expression, such as a set of blendweight values, and outputs a code associated with the target expression. The codes associated with the facial identity and the facial expression can be concatenated and input into the decoder, which outputs a representation of a face having the facial identity and expression. The representation of the face can include vertex displacements for deforming the reference mesh;
NARUNIEC (US2022/0374649A1) discloses:
Various embodiments set forth systems and techniques for changing a face within an image. The techniques include receiving a first image including a face associated with a first facial identity; generating, via a machine learning model, at least a first texture map and a first position map based on the first image; rendering a second image including a face associated with a second facial identity based on the first texture map and the first position map, wherein the second facial identity is different from the first facial identity;
CHOI (US2022/0092838A1) discloses:
An animation system wherein a machine learning model is adopted to generate animated facial actions based on parameters obtained from a live actor. Specifically, the anatomical structure such as a facial muscle topology and a skull surface that are specific to the live actor may be used. A muscle structure of simplified “pseudo” muscles that approximate the actual muscle topology but with reduced degree of freedom is determined to improve computational efficiency.
ZHAO (CN 112700524 B) discloses:
A 3 D role facial expression animation real-time generating method based on deep learning, comprising: obtaining the training data, and performing enhancement processing to the obtained training data; building generating model; generating model comprises 1 encoder and 3 decoder; the encoder is used for encoding the picture data of the training data to a hidden space; 3 decoder is used for decoding the data of the hidden space into facial action picture of the actor; shooting picture of the animation file; the value of the controller corresponding to the screen picture of the animation file; training the built generating model, obtaining the optimal weight value of encoder and decoder, obtaining the best model; inputting the picture of the actor into the trained generating model, the encoder encodes the picture to the hidden space, and then decoding the data in the hidden space by the corresponding decoder to obtain the value of the corresponding controller; inputting the value of the controller into the animation software, generating the facial action of the model.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAURICE L MCDOWELL, JR whose telephone number is (571)270-3707. The examiner can normally be reached Mon-Fri: 2pm-10pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Said A. Broome can be reached at 571-272-2931. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MAURICE L. MCDOWELL, JR/Primary Examiner, Art Unit 2612
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