DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
2. The information disclosure statements (IDS) submitted on August 8, 2025 and December 2, 2025 are considered by the examiner.
Specification
3. The amended specification was received on September 25, 2025 and entered.
Response to Amendment
4. The amendment filed September 25, 2025 has been entered. Claims 1-18 and 20-21 remain pending in the application. Applicant’s amendments to the Specification and Claims have overcome each and every objection.
Response to Arguments
5. Applicant's arguments filed September 25, 2025 have been fully considered but they are not persuasive.
6. Applicant argues that Sheppard et al. (U.S. Patent Application Publication No. 2016/0078640 A1), hereinafter referred to as Sheppard, in view of Gupta et al. (“3DGen: Triplane Latent Diffusion for Textured Mesh Generation”), hereinafter referred to as Gupta, and Pfister et al. (U.S. Patent Application Publication No. 2006/0028474 A1), hereinafter referred to as Pfister, fail to teach the amended claim 1, 10, and 18 of encoding a precomputed light transport into a neural model having a triplane representation and a machine learning model.
Examiner replies that Applicant’s arguments with respect to claim(s) 1, 10, and 18 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Rainer et al. (“Neural BTF Compression and Interpolation”), hereinafter referred to as Rainer, teaches encoding a precomputed light transport into a neural model as rejected in the 103 rejection below. Gupta teaches encoding into a triplane representation and color prediction MLP. Thus, Sheppard in view of Gupta and Rainer combined teach the amended claim 1, 10, and 18 limitation of encoding a precomputed light transport into a neural model having a triplane representation and a machine learning model.
Furthermore, the Applicant asserts that the precomputed light transport “represents how light interacts with and/or propagates through the geometry of the digital asset” as disclosed in Paragraph 26 of the Applicant’s Specification. However, it is noted that this definition of the precomputed light transport upon which applicant relies are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
7. Applicant argues that dependent claims 2-9, 11-17, 20 and 21 are allowable for depending on independent claims 1, 10, and 18.
Examiner replies that the dependent claims remain rejected since independent claims 1, 10, and 18 stand rejected.
8. Applicant argues that Sheppard, Gupta, and Pfister fail to teach the amended claim 14 limitation of training the neural model using the training dataset. The Applicant asserts that Pfister only uses the images for interpolation purposes and not for training purposes. The Applicant also argues that the reflective images of Pfister are also captured by a physical camera within a physical environment rather than a virtual camera location within a digital scene.
Examiner replies that the Applicant’s arguments with respect to claim(s) 14 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Instead, Rainer et al. (“Neural Precomputed Radiance Transfer”), hereinafter referred to as Rainer 2, in Sections 2 and 6 is used to teach that the neural model is trained using a training dataset.
9. Applicant argues that Sheppard in view of Gupta, Pfister, and Talegaonkar et al. (U.S. Patent Application Publication No. 2025/0131647 A1), hereinafter referred to as Talegaonkar, fail to teach the amended claim 15 limitation of updating “parameters of the triplane representation to incorporate information learned from the training dataset into the triplane representation.” The Applicant asserts that Gupta only teaches that the parameters of the VAE in Gupta are updated rather than the triplane latent features themselves.
Examiner replies that claim 15 does not claim that the triplane latent features are updated, just parameters of the triplane representation. The Applicant does not specify what the parameters of the triplane representation are and thus can be interpretated to be any parameter or value that affects the triplane representation. Furthermore, Gupta Section 2.2 Paragraph 4 discloses “We add a KL divergence loss to ensure the triplane feature distribution … is close to a gaussian prior …” This can be considered the teach that the triplane representation parameters are updated using the loss function. Thus, Gupta teaches the “parameters of the triplane representation to incorporate information learned from the training dataset into the triplane representation” as claimed in claim 15.
10. Applicant argues that Sheppard in view of Gupta, Pfister, and Zheng et al. (“A Self-Occlusion Aware Lighting Model for Real-Time Dynamic Reconstruction”), hereinafter referred to as Zheng, fail to teach using the visibility information to train a neural model in claim 16.
Examiner replies that the rejection for claim 14 has been updated using Sheppard in view of Gupta, Rainer, and Rainer 2. Sheppard in view of Rainer, Gupta, and Rainer 2 is used to teach training a neural model using a training dataset. Zheng is used to teach that the training dataset includes a visibility term that represents self-occlusion properties of the digital asset. Although Zheng does not teach a neural model, Zheng is not used alone. Thus, Sheppard in view of Rainer, Gupta, Rainer 2, and Zheng combined together teach that the visibility information is used to train a neural model.
11. Conclusion: The rejections set in the previous Office Action are shown to have been proper, and the claims are rejected below. New citations and parenthetical remarks can be considered new grounds of rejection and such new grounds of rejection are necessitated by the Applicant’s amendments to the claims. Therefore, the present Office Action is made final.
Claim Rejections - 35 USC § 103
12. 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.
13. The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
14. Claim(s) 1-2, 4-5, 9-11, and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sheppard et al. (U.S. Patent Application Publication No. 2016/0078640 A1), hereinafter referred to as Sheppard, in view of Rainer et al. (“Neural BTF Compression and Interpolation”), hereinafter referred to as Rainer, and Gupta et al. (“3DGen: Triplane Latent Diffusion for Textured Mesh Generation”), hereinafter referred to as Gupta.
15. Regarding claim 1, Sheppard teaches a method comprising: receiving, by a processing device, a digital asset defined by a three-dimensional geometry to be included in a digital scene, the digital scene including at least one digital scene element (Paragraph 87 and Figure 2 teaches the online processing unit 103 receiving a digital asset from the object data library 450; Paragraph 93 teaches including the digital asset from the object data library into a virtual environment or digital scene. The digital scene can be populated with multiple objects from the object library which satisfies including at least one digital scene element);
generating, by the processing device, a compressed representation of the digital asset (Paragraph 11 teaches providing objects or digital assets in a compressed format)
deploying, by the processing device, the compressed representation into the digital scene at a location relative to the at least one digital scene element (Paragraph 11 teaches the virtual environment comprises of a plurality of objects; Paragraph 97 teaches displaying an object from the object library at a particular position. The position is a location relative to other objects because there are other objects in the scene);
and rendering, by the processing device, the digital asset by applying one or more lighting effects to the three-dimensional geometry (Paragraph 174 teaches rendering the 3D object with light effects like shading and highlighting) (Paragraph 11 teaches the virtual environment comprises of a plurality of objects; Paragraph 97 teaches displaying an object from the object library at a particular position. The position is a location relative to other objects because there are other objects in the scene).
However, Sheppard fails to teach generating the compressed representation of the digital asset that encodes a precomputed light transport into a neural model having a triplane representation of the three-dimensional geometry and a machine learning model, the precomputed light transport based on the three-dimensional geometry; and rendering, by the processing device, the digital asset by applying one or more lighting effects to the three-dimensional geometry using the neural model based on: the precomputed light transport.
Rainer teaches generating the compressed representation of the digital asset that encodes a precomputed light transport into a neural model (Section 3.2 and Figure 3 teach using an autoencoder network to encode a BTF. The BTF can be considered the precomputed light transport under broadest reasonable interpretation as it is a function that deals with an object’s appearance under different viewing and lighting directions as defined in the Introduction Paragraph 2. The autoencoder network is the neural model. The encoded result can be considered the compressed representation of a digital asset) (Abstract and Figure 1 teaches the BTF is based on an object under various viewing and lighting conditions; Section 1 Paragraph 2 teaches the BTF or precomputed light transport is based on a real-world object under viewing and lighting directions. Thus, the precomputed light transport is based on three-dimensional geometry) and rendering, by the processing device, the digital asset by applying one or more lighting effects to the three-dimensional geometry using the neural model based on: the precomputed light transport (Section 3.2 Paragraphs 4-5 teach rendering a digital asset using the autoencoder and decoder neural network. The ‘Decoder network’ subsection teaches receiving as input a light and view direction to apply using the encoded precomputed light transport in the neural model. The light and view direction can be considered the lighting effect).
Sheppard and Rainer are considered analogous to the claimed invention as because both are in the same field of rendering and applying a light effect to an object. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the method of rendering a digital asset into a digital scene taught by Sheppard with the precomputed light transport encoding taught by Rainer in order to render objects with realistic material appearance while reducing storage requirements to a practical amount (Rainer Section 1 Paragraph 2).
However, Sheppard and Rainer fail to teach the neural model having a triplane representation of the three-dimensional geometry and a machine learning model.
Gupta teaches the neural model having a triplane representation of the three-dimensional geometry and a machine learning model (Section 2.1 Paragraph 1 teaches Figure 1 uses neural fields that combines trilinear feature interpolation with a MLP decoder. Thus, the neural model has a triplane representation and a machine learning model; Section 2.2 and Figure 1 teach an autoencoder that has a triplane representation of the three-dimensional geometry and various machine learning models like the U-Net, PointNet, and MLP).
Sheppard, Rainer, and Gupta are considered analogous to the claimed invention because both are in the same field of rendering 3D objects. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the method of including a digital asset into a digital scene taught by Sheppard in view of Rainer with the triplane representation and machine learning models taught by Gupta in order to allow for quick generation of textured 3D objects (Gupta Abstract).
16. Regarding claim 2, Sheppard in view of Rainer and Gupta teaches the limitations of claim 1. However, Sheppard and Rainer fail to teach the method wherein the triplane representation includes feature grids that correspond to dimensions of the three- dimensional geometry to represent the digital asset.
Gupta teaches the method wherein the triplane representation includes feature grids that correspond to dimensions of the three-dimensional geometry to represent the digital asset (Section 2.2 and Figure 1 teach the process which converts a 3D object into a triplane representation. Figure 1 shows the feature grids that corresponds to the three-dimensional geometry. This results in the h_xz, h_xy, and h_yz triplane latent features which represent the XZ, XY, and YZ planes or feature grids).
Sheppard, Rainer, and Gupta are considered analogous to the claimed invention because both are in the same field of rendering 3D objects. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the method of including a digital asset into a digital scene taught by Sheppard in view of Rainer with the triplane representation taught by Gupta in order to allow for quick generation of textured 3D objects (Gupta Abstract).
17. Regarding claim 4, Sheppard in view of Rainer and Gupta teaches the limitations of claim 1. However, Sheppard, and Gupta fail to teach the method wherein rendering the digital asset includes determining an intersection point of a ray traced from a virtual camera that defines a view direction with the compressed representation and computing a color value to apply to the compressed representation at the intersection point based on the precomputed light transport.
Rainer teaches wherein rendering the digital asset includes determining an intersection point of a ray traced from a virtual camera that defines a view direction with the compressed representation and computing a color value to apply to the compressed representation at the intersection point based on the precomputed light transport (Section 3.1 teaches the BTF consists of a position p and viewing angles w.sub.i and w.sub.o. The position p can be considered the intersection point from the viewing angle w.sub.i; Section 3.2 ‘Decoder network’ teaches a view direction w.sub.i is passed in when rendering the compressed representation of the precomputed light transport. A RGB value is then output which is the computed color value; Figure 3 also teaches that the view and camera direction is projected onto the space which then outputs a color value. Thus, the view direction is from a camera which can be considered the virtual camera as the output is for a digital reproduction mentioned in Section 1 Paragraph 1).
Sheppard, Rainer, and Gupta are considered analogous to the claimed invention because both are in the same field of rendering 3D objects. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the method of rendering a digital asset into a digital scene taught by Sheppard in view of Gupta with the computing of a color value based on the precomputed light transport taught by Rainer in order to render objects with realistic material appearance while reducing storage requirements to a practical amount (Rainer Section 1 Paragraph 2).
18. Regarding claim 5, Sheppard in view of Rainer and Gupta teaches the limitations of claim 4. However, Sheppard and Rainer fail to teach the method wherein the machine learning model is a multilayer perceptron, and rendering the digital asset includes: generating an input vector that corresponds to the intersection point based on the triplane representation; and evaluating the input vector using the multilayer perceptron to determine the color value.
Gupta teaches the method wherein the machine learning model is a multilayer perceptron, and rendering the digital asset includes: generating an input vector that corresponds to the intersection point based on the triplane representation; and evaluating the input vector using the multilayer perceptron to determine the color value (Section 2.3 teaches using a color prediction multilayer perceptron (MLP) to evaluate the color for a point. Thus, the machine learning model is a multilayer perceptron. It generates the 3D location of the intersection point and the interpolated triplane features as an output from an encoder and passes it into the MLP. Thus, the 3D location of the intersection point and triplane features can be considered the input vector).
Sheppard, Rainer, and Gupta are considered analogous to the claimed invention because both are in the same field of rendering 3D objects. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the method of including a digital asset into a digital scene taught by Sheppard in view of Rainer with the multilayer perceptron to determine a color value using the triplane representation taught by Gupta in order to allow for quick generation of textured 3D objects (Gupta Abstract).
19. Regarding claim 9, Sheppard in view of Rainer and Gupta teaches the limitations of claim 1.
Sheppard further teaches the method wherein the at least one digital scene element includes one or more of a light source or an additional digital asset (Paragraphs 11 and 93 teach the virtual environment comprises of a plurality of objects. Thus, the one digital scene element in the digital scene is an additional digital asset).
20. Regarding claim 10, claim 10 is the system claim (Sheppard Paragraph 51 teaches a tangible non-transient computer readable medium which is a memory and a computer, or processor, that executes instructions) of method claim 1 and is accordingly rejected using substantially similar rationale as to that which is set for with respect to claim 1.
21. Regarding claim 11, Sheppard in view of Rainer and Gupta teaches the limitations of claim 10. Claim 11 is similar in scope to claim 2. Therefore, similar rationale as applied in the rejection of claim 2 applies herein.
22. Regarding claim 17, Sheppard in view of Rainer and Gupta teaches the limitations of claim 10. Claim 10 is similar in scope to claim 9. Therefore, similar rationale as applied in the rejection of claim 9 applies herein.
23. Regarding claim 18, claim 18 is the non-transitory computer-readable medium claim (Sheppard Paragraph 51 teaches a tangible non-transient computer readable medium which is a memory and a computer, or processor, that executes instructions) of method claim 1 and is accordingly rejected using substantially similar rationale as to that which is set for with respect to claim 1.
24. Claim(s) 3, 12, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sheppard et al. (U.S. Patent Application Publication No. 2016/0078640 A1), hereinafter referred to as Sheppard, in view of Rainer et al. (“Neural BTF Compression and Interpolation”), hereinafter referred to as Rainer, Gupta et al. (“3DGen: Triplane Latent Diffusion for Textured Mesh Generation”), hereinafter referred to as Gupta, as applied to claim 1 and 10 above, and further in view of Zheng et al. (“A Self-Occlusion Aware Lighting Model for Real-Time Dynamic Reconstruction”), hereinafter referred to as Zheng.
25. Regarding claim 3, Sheppard in view of Rainer and Gupta teaches the limitations of claim 1. However, Sheppard, Rainer, and Gupta fail to teach the method wherein the one or more lighting effects are based in part on a visibility term included in the compressed representation that represents self-occlusion properties of the digital asset.
Zheng teaches the method wherein the one or more lighting effects are based in part on a visibility term included in the compressed representation that represents self-occlusion properties of the digital asset (Section 3.2 and Equation 6 teach that the radiance for a surface point ‘n’ has a visibility term V(w) that represents the self-occlusion properties of the object. ‘w’ represents the direction of the light source).
Sheppard, Rainer, Gupta, and Zheng are considered analogous to the claimed invention as because both are in the same field of rendering 3D objects. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the method of including a digital asset into a digital scene taught by Sheppard in view of Gupta and Rainer with the visibility term taught by Zheng in order to render objects in varying lighting conditions and avoid a low quality appearance (Zheng Abstract).
26. Regarding claim 12, Sheppard in view of Rainer and Gupta teaches the limitations of claim 10. However, Sheppard, Rainer, and Gupta fail to teach the system wherein the one or more lighting effects are based in part on a visibility term included in the compressed representation that indicates whether portions of the digital asset are occluded by other portions of the digital asset.
Zheng teaches the system wherein the one or more lighting effects are based in part on a visibility term included in the compressed representation that indicates whether portions of the digital asset are occluded by other portions of the digital asset. (Section 3.2 and Equation 6 teach that the radiance for a surface point ‘n’ has a visibility term V(w) that represents the self-occlusion properties of the object. ‘w’ represents the direction of the light source. Self-occlusion means that a portion of the digital asset is occluded by another portion of the asset).
Sheppard, Rainer, Gupta, and Zheng are considered analogous to the claimed invention as because both are in the same field of rendering 3D objects. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the system of including a digital asset into a digital scene taught by Sheppard in view of Rainer and Gupta with the visibility term taught by Zheng in order to render objects in varying lighting conditions and avoid a low quality appearance (Zheng Abstract).
27. Regarding claim 21, Sheppard in view of Rainer and Gupta teach the limitations of claim 1. However, Sheppard fails to teach the method wherein generating the compressed representation includes jointly training the triplane representation and the machine learning model that encodes the precomputed light transport using a training dataset that includes training samples with visibility terms representing self-occlusion properties of the digital asset, wherein the jointly training includes updating parameters of the triplane representation and weights of the machine learning model based on the training samples.
Rainer teaches encoding the precomputed light transport with an autoencoder (Section 3.2 and Figure 3 teach using an autoencoder network to encode a BTF. The BTF can be considered the precomputed light transport under broadest reasonable interpretation as it is a function that deals with an object’s appearance under different viewing and lighting directions as defined in the Introduction Paragraph 2. The autoencoder network is the neural model. The encoded result can be considered the compressed representation of a digital asset).
Sheppard and Rainer are considered analogous to the claimed invention as because both are in the same field of rendering and applying a light effect to an object. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the method of rendering a digital asset into a digital scene taught by Sheppard with the precomputed light transport encoding taught by Rainer in order to render objects with realistic material appearance while reducing storage requirements to a practical amount (Rainer Section 1 Paragraph 2).
However, Sheppard and Rainer fail to teach the method wherein generating the compressed representation includes jointly training the triplane representation and the machine learning model that encodes the precomputed light transport using a training dataset that includes training samples with visibility terms representing self-occlusion properties of the digital asset, wherein the jointly training includes updating parameters of the triplane representation and weights of the machine learning model based on the training samples.
Gupta teaches the method wherein generating the compressed representation includes jointly training the triplane representation and the machine learning model that encodes the precomputed light transport using a training dataset that includes training samples (Section 2.1 Paragraph 1 teaches Figure 1 uses neural fields that combines trilinear feature interpolation with a MLP decoder. Thus, the neural model has a triplane representation and a machine learning model; Section 2.2 and Figure 1 teach an autoencoder that has a triplane representation of the three-dimensional geometry and various machine learning models like the U-Net, PointNet, and MLP) (Section 2.2 teaches the model consists of the triplane representation and machine learning models like the PointNet, UNet, and MLP. Gupta teaches training the model and updating parameters of the triplane representation by using reconstruction loss and KL divergence loss which ensures the triplane feature distribution is close to a gaussian prior; Section 2.3 also teaches training the model through a loss function which compares the output color to the ground truth surface color. The loss function indicates the weights and parameters of the machine learning models are updated in training).
Sheppard, Rainer, and Gupta are considered analogous to the claimed invention because both are in the same field of rendering 3D objects. Rainer teaches in Section 3.2 ‘Encoder Network’ subsection that the encoder has no particular architecture. Thus, the encoder Rainer uses can be substituted with the encoder taught in Gupta as both are autoencoders and result in the same goal of compressing an object and outputting a color value. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the method of including a digital asset into a digital scene taught by Sheppard in view of Rainer with the triplane representation and machine learning models taught by Gupta in order to allow for quick generation of textured 3D objects (Gupta Abstract).
However, Sheppard, Rainer, and Gupta fail to teach the training samples with visibility terms representing self-occlusion properties of the digital asset.
Zheng teaches the training samples with visibility terms representing self-occlusion properties of the digital asset (Section 3.2 and Equation 6 teaches that the radiance for a surface point ‘n’ has a visibility term V(w) that represents the self-occlusion properties of the object. ‘w’ represents the direction of the light source. Each surface point ‘n’ has a visibility term so this means all pixels in the training images will include a visibility term. Thus, since each pixel has a visibility term, each pixel has a training sample with a visibility term that represents the self-occlusion properties).
Sheppard, Rainer, Gupta, and Zheng are considered analogous to the claimed invention as because both are in the same field of rendering 3D objects. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the system of including a digital asset into a digital scene taught by Sheppard in view of Rainer and Gupta with the visibility term taught by Zheng in order to render objects in varying lighting conditions and avoid a low quality appearance (Zheng Abstract).
28. Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sheppard et al. (U.S. Patent Application Publication No. 2016/0078640 A1), hereinafter referred to as Sheppard, in view of Rainer et al. (“Neural BTF Compression and Interpolation”), hereinafter referred to as Rainer, and Gupta et al. (“3DGen: Triplane Latent Diffusion for Textured Mesh Generation”), hereinafter referred to as Gupta, as applied to claim 5, and further in view of Talegaonkar et al. (U.S. Patent Application Publication No. 2025/0131647 A1), hereinafter referred to as Talegaonkar.
Regarding claim 6, Sheppard in view of Rainer and Gupta teaches the limitations of claim 5. However, Sheppard, Rainer, and Gupta fail to teach the method wherein the input vector is a concatenation of a feature vector extracted from the triplane representation and a property vector that defines one or more properties of the digital asset at the intersection point.
Talegaonkar teaches the method wherein the input vector is a concatenation of a feature vector extracted from the triplane representation and a property vector that defines one or more properties of the digital asset at the intersection point (Paragraph 90 and Figure 6 teach concatenating triplane features and general features in step 614 before inputting it into the MLP 618. The general features concatenated with the triplane features can be considered the property vector and the triplane features can be considered the feature vector).
Sheppard, Rainer, Gupta, and Talegaonkar are considered analogous to the claimed invention as because both are in the same field of rendering 3D objects. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the method of including a digital asset into a digital scene taught by Sheppard in view of Rainer and Gupta with the concatenation taught by Talegaonkar in order to generate dynamic and detailed 3D models with MLPs (Talegaonkar Paragraph 35).
29. Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sheppard et al. (U.S. Patent Application Publication No. 2016/0078640 A1), hereinafter referred to as Sheppard, in view of Rainer et al. (“Neural BTF Compression and Interpolation”), hereinafter referred to as Rainer, Gupta et al. (“3DGen: Triplane Latent Diffusion for Textured Mesh Generation”), hereinafter referred to as Gupta, and Talegaonkar et al. (U.S. Patent Application Publication No. 2025/0131647 A1), hereinafter referred to as Talegaonkar, as applied to claim 6 above, and further in view of Pfister et al. (U.S. Patent Application Publication No. 2006/0028474 A1), hereinafter referred to as Pfister, and Zheng et al. (“A Self-Occlusion Aware Lighting Model for Real-Time Dynamic Reconstruction”), hereinafter referred to as Zheng.
Regarding claim 8, Sheppard in view of Rainer, Gupta, and Talegaonkar teaches the limitations of claim 6. However, Sheppard, Rainer, Gupta, and Talegaonkar fail to teach the method wherein the digital asset is a surface-based digital asset and the property vector includes data to represent a view direction, a light direction, a normal, and a visibility term particular to the intersection point.
Pfister teaches the method wherein the digital asset is a surface-based digital asset (Paragraph 23 teaches using surface reflectance fields of objects, which means the object is a surface-based digital asset; Paragraph 46 and 51 teaches the objects, in which the surface reflectance field is calculated for, have surface normals. The Applicant only defines a “surface-based digital asset” to just include one or more surfaces in Paragraph 49 of the Applicant’s Specification. A surface normal indicates a surface) and the property vector includes data to represent a view direction, a light direction, a normal, (Paragraph 42 and Equation 2 teach the look-up function that maps to a vector has data on the view direction ‘v’ and light direction ‘l’ that is particular to the intersection point ‘p’. Paragraph 49 also teaches the directions l and v are unit vectors. Paragraph 51 also teaches the look up function that maps to a vector has data representing the model normal which approximates the surface normal particular to the intersection point ‘p’).
Sheppard, Rainer, Gupta, Talegaonkar, and Pfister are considered analogous to the claimed invention because both are in the same field of rendering 3D objects. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the method of including a digital asset into a digital scene taught by Sheppard in view of Rainer, Gupta, and Talegaonkar with the property vector taught by Pfister in order to render deformed models and reduce the amount of data used when lighting an object under different lightings (Pfister Abstract and Paragraph 18).
However, Sheppard, Rainer, Gupta, Talegaonkar, and Pfister fail to the property vector includes data to represent a visibility term particular to the intersection point.
Zheng teaches the property vector includes data to represent a light direction, a normal, and a visibility term particular to the intersection point (Section 3.2 and Equation 6 teach that the radiance for a surface normal for a particular intersection point ‘n’ has a visibility term V(w) that represents the self-occlusion properties of the object. ‘w’ represents the direction of the light source; Section 4.1 teaches ‘n’ represents the surface normal for a particular intersection point).
Sheppard, Rainer, Gupta, Talegaonkar, Pfister, and Zheng are considered analogous to the claimed invention because all are in the same field of rendering 3D objects. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the method of including a digital asset into a digital scene taught by Sheppard in view of Gupta, Rainer, Talegaonkar, and Pfister with the visibility term taught by Zheng in order to render objects in varying lighting conditions and avoid a low quality appearance (Zheng Abstract).
30. Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sheppard et al. (U.S. Patent Application Publication No. 2016/0078640 A1), hereinafter referred to as Sheppard, in view of Rainer et al. (“Neural BTF Compression and Interpolation”), hereinafter referred to as Rainer, and Gupta et al. (“3DGen: Triplane Latent Diffusion for Textured Mesh Generation”), hereinafter referred to as Gupta, and Zheng et al. (“A Self-Occlusion Aware Lighting Model for Real-Time Dynamic Reconstruction”), hereinafter referred to as Zheng, as applied to claim 12 above, and further in view of Pharr et al. (“Physically Based Rendering: From Theory to Implementation”), hereinafter referred to as Pharr.
Regarding claim 13, Sheppard in view of Rainer, Gupta, and Zheng teaches the limitations of claim 12. However, Sheppard, Rainer, Gupta, and Zheng fail to teach the system the operations further comprising computing the visibility term by tracing an occlusion ray from a first point on the compressed representation to a light source included in the digital scene and determining whether the occlusion ray intersects a second point of the compressed representation.
Pharr teaches the system the operations further comprising computing the visibility term by tracing an occlusion ray from a first point on the compressed representation to a light source included in the digital scene and determining whether the occlusion ray intersects a second point of the compressed representation (Chapter 1, Section 1.2 Page 5 teaches that visibility is determined by tracing a ray from the surface to the light. If the ray is uninterrupted then it is not occluded. This inherently includes determining if the ray intersected a second point).
Sheppard, Rainer, Gupta, Zheng, and Pharr are considered analogous to the claimed invention as because both are in the same field of rendering 3D objects. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the system of including a digital asset into a digital scene taught by Sheppard in view of Rainer, Gupta, and Zheng with the computation of the visibility term taught by Pharr in order to achieve photorealistic rendering (Chapter 1, Section 1.2, Paragraph 1).
31. Claim(s) 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sheppard et al. (U.S. Patent Application Publication No. 2016/0078640 A1), hereinafter referred to as Sheppard, in view of Rainer et al. (“Neural BTF Compression and Interpolation”), hereinafter referred to as Rainer, Gupta et al. (“3DGen: Triplane Latent Diffusion for Textured Mesh Generation”), hereinafter referred to as Gupta, as applied to claim 10 above, and further in view of Rainer et al. (“Neural Precomputed Radiance Transfer”), hereinafter referred to as Rainer 2.
32. Regarding claim 14, Sheppard in view of Rainer and Gupta teaches the limitations of claim 10. However, Sheppard, Rainer, and Gupta fail to teach the system with operations further comprising generating a training dataset that includes training images depicting the digital asset viewed from a plurality of different locations within the digital scene, and training the neural model using the training dataset.
Rainer 2 teaches the system with operations further comprising generating a training dataset that includes training images depicting the digital asset viewed from a plurality of different locations within the digital scene, and training the neural model using the training dataset (Section 6, ‘Data Generation’ subsection teaches randomly placing the cameras and viewing direction to obtain training images depicting the scene and thus objects in the scene from various directions; Section 6 Paragraph 1 teaches using the data generated or training images to train the neural network; Section 2 Paragraph 1 teaches the methods taught by Rainer are run on synthetic data where the scene has been constructed by an artist. Thus, the scene can be considered a digital scene).
Sheppard, Gupta, and Rainer are considered analogous to the claimed invention because both are in the same field of rendering 3D objects. Rainer 2 is considered analogous to the claimed invention because both are in the same field of relighting. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the method of including a digital asset into a digital scene taught by Sheppard in view of Rainer and Gupta with the training dataset taught by Rainer 2 in order to make sure all renderings contribute valuable information to the learning (Rainer 2 Section 6, Paragraph 5).
33. Regarding claim 15, Sheppard in view of Rainer, Gupta, and Rainer 2 teaches the limitations of claim 14. However, Sheppard fails to teach the system wherein the machine learning model is a multilayer perceptron, and generating the compressed representation includes utilizing the training dataset to adjust weights of the multi-layer perceptron, and update parameters of the triplane representation to incorporate information learned from the training dataset into the triplane representation.
Gupta teaches the system wherein the machine learning model is a multilayer perceptron, and generating the compressed representation includes utilizing the training dataset to adjust weights of the multi-layer perceptron (Section 2.3 teaches the MLP is trained by calculating a loss comparing the output to the ground truth surface colors. Thus, the weights or parameters of the MLP is adjusted and trained. The MLP is the machine learning model), and update parameters of the triplane representation to incorporate information learned from the training dataset into the triplane representation (Section 2.2 paragraph 4 teaches the VAE which consists of creating the triplane representation is trained and updated using loss functions. The loss functions are information learned from the training dataset. Thus, the triplane representation is updated by incorporating information learned from the training dataset; Section 3.2 teaches the VAE is trained using multiple camera angles per object which is the training dataset of images from different viewpoint locations).
Sheppard, Rainer, and Gupta are considered analogous to the claimed invention because both are in the same field of rendering 3D objects. Rainer 2 is considered analogous to the claimed invention because both are in the same field of relighting. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the method of including a digital asset into a digital scene taught by Sheppard in view of Rainer and Rainer 2 with the updating of the triplane representation taught by Gupta in order to allow for quick generation of textured 3D objects (Gupta Abstract).
34. Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sheppard et al. (U.S. Patent Application Publication No. 2016/0078640 A1), hereinafter referred to as Sheppard, in view of Rainer et al. (“Neural BTF Compression and Interpolation”), hereinafter referred to as Rainer, Gupta et al. (“3DGen: Triplane Latent Diffusion for Textured Mesh Generation”), hereinafter referred to as Gupta, and Rainer et al. (“Neural Precomputed Radiance Transfer”), hereinafter referred to as Rainer 2, as applied to 14 above, and further in view of Zheng et al. (“A Self-Occlusion Aware Lighting Model for Real-Time Dynamic Reconstruction”), hereinafter referred to as Zheng.
Regarding claim 16, Sheppard in view of Rainer, Gupta, and Rainer 2 teaches the limitations of claim 14. However, Sheppard, Rainer, Gupta, and Rainer 2 fail to teach the system wherein the training dataset includes training samples for each pixel of the training images, the training samples including a visibility term that represents self-occlusion properties of the digital asset.
Zheng teaches the system wherein the training dataset includes training samples for each pixel of the training images, the training samples including a visibility term that represents self-occlusion properties of the digital asset (Section 3.2 and Equation 6 teaches that the radiance for a surface point ‘n’ has a visibility term V(w) that represents the self-occlusion properties of the object. ‘w’ represents the direction of the light source. Each surface point ‘n’ has a visibility term so this means all pixels in the training images will include a visibility term. Thus, since each pixel has a visibility term, each pixel has a training sample with a visibility term that represents the self-occlusion properties).
Sheppard, Rainer, Gupta, and Zheng are considered analogous to the claimed invention as because both are in the same field of rendering 3D objects. Rainer 2 is considered analogous to the claimed invention because both are in the same field of relighting. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the system of including a digital asset into a digital scene taught by Sheppard in view of Rainer, Gupta, and Rainer 2 with the visibility term taught by Zheng in order to render objects in varying lighting conditions and avoid a low quality appearance (Zheng Abstract).
35. Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sheppard et al. (U.S. Patent Application Publication No. 2016/0078640 A1), hereinafter referred to as Sheppard, in view of Rainer et al. (“Neural BTF Compression and Interpolation”), hereinafter referred to as Rainer, and Gupta et al. (“3DGen: Triplane Latent Diffusion for Textured Mesh Generation”), hereinafter referred to as Gupta, as applied to claim 18 above, and further in view of Everitt et al. (U.S. Patent Publication No. 8,102,393 B1), hereinafter referred to as Everitt.
Regarding claim 20, Sheppard in view of Rainer and Gupta teach the limitations of claim 18. However, Sheppard, Rainer, and Gupta fail to teach the non-transitory computer-readable medium wherein the rendering the digital asset includes using one or more of a path tracer or a rasterizer-based renderer.
Everitt teaches the non-transitory computer-readable medium wherein the rendering the digital asset includes using one or more of a path tracer or a rasterizer-based renderer (Column 5 Line 56 – Column 6 Line 5 teaches a rasterizer that renders a digital asset).
Sheppard, Rainer, Gupta, and Everitt are considered analogous to the claimed invention as because both are in the same field of rendering 3D objects. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the non-transitory computer-readable medium of including a digital asset into a digital scene taught by Sheppard in view of Rainer and Gupta with the rasterizer taught by Everitt in order to render a 3D object by optimizing hardware resources and performance (Everitt Abstract).
Allowable Subject Matter
36. Claim 7 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: In claim 7, the prior art fails to teach the method wherein the digital asset is a fiber- based digital asset that includes one or more fiber primitives and the property vector includes data to represent a view direction, a light direction, a tangent, a cross- section offset, and a visibility term particular to the intersection point.
Conclusion
37. 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.
38. Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTINE Y AHN whose telephone number is (571)272-0672. The examiner can normally be reached M-F 8-5pm.
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/CHRISTINE YERA AHN/Examiner, Art Unit 2615
/ALICIA M HARRINGTON/Supervisory Patent Examiner, Art Unit 2615
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