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 .
Response to Amendment
The amendments filed October 29th 2025 have been entered. Applicant’s amendments to the claims have overcome the previously set forth 35 USC 112(b) rejections in the office action mailed July 28th 2025 and are accordingly withdrawn. Applicant’s amendments have overcome the previously set forth 35 USC 103 rejections, however new rejections have been issued as necessitated by amendment.
Claim Objections
Claims 1 objected to because of the following informalities: typographical error in line 10 "at least one encode RGB image". 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.
Claim(s) 1-11, 13-18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yang (US 2018/0234671 A1) in view of Roy (WO 2021/203076 A1), Zakharov et al., "Single-Shot Scene Reconstruction," Proceedings of the 5th Conference on Robot Learning, PMLR 164:501-512, November 2021 (https://proceedings.mlr.press/v164/zakharov22a/zakharov22a.pdf) and Baumgartner (US 11,200,693 B2).
Regarding claims 1, 13, and 20 Yang teaches
A computer-implemented method for single-view three-dimensional (3D) few-view novel-view synthesis of a novel scene (Paragraph [0001] and FIG. 5), comprising:
encoding with an encoder at least one inputted posed RGB image (Paragraphs [0002] & [0067]) generating with a far multi-layer perceptron (MLP) (paragraph [0066] – GFM is ML model that iteratively updates weights until convergence, analogous to MLP) features based on the at least one encoded RGB image wherein the features comprise a representation of a color of background images (Paragraphs [0031], [0099],[0100],[0107]) and generating with a near multi-layer perceptron (MLP) a representation a color of foreground images (Paragraphs [0006] & [0107]) based on the at least one encode RGB image (paragraph [0067] – “may be trained to detect and encode various features in an image.”);
outputting the color of background images from the far MLP (Paragraphs [0099],[0100],[0107]) and volumetrically rendering the color of foreground images generated by the near MLP as foreground images (Paragraphs [0006] & [0107]);
creating a convolutional two-dimensional (2D) feature map from the depth-encoded features and the foreground images (Paragraphs [0031], [0067]);
a feature map is generated by the far MLP (paragraph [0099] – background map generated by GFM is analogous)
extracting local and global features from the global features representation at projected pixel locations (Paragraph [0068], [0069], [0073], [0077]); and
rendering, using a decoder, global feature representations of the novel scene based on the global features representation (paragraph [0067], [0077], [0080]).
Yang fails to describe depth-encoded features; a representation of a density; and volumetrically rendering the density and color of background images and volumetrically rendering the density and color of foreground images
aggregating the depth-encoded features and the foreground images;
generating a triplanar representation from the 2D feature map;
transforming the triplanar representation into a global features representation modeling surroundings of the novel scene, wherein the global features representation comprises a plurality of orthogonal planes, wherein a feature map is generated for each of the plurality of orthogonal planes;
However, Roy teaches aggregating the outputted features and the foreground images (Paragraphs [0012] & [0030]); generating a triplanar representation from the 2D feature map (Paragraph [0009]); transforming the triplanar representation into a global features representation to model 3D surroundings of the novel scene (Paragraph [0004]);
Yang and Roy are considered analogous to the claimed invention as they are in the same field of view synthesis. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings of Yang with Roy to specify the input and output forms of a view synthesis method.
Yang in view of Roy fails to teach depth-encoded features; representing a density and outputting a density; volumetrically rendering the density and color of background images and volumetrically rendering the density and color of foreground images;
wherein the global features representation comprises a plurality of orthogonal planes and wherein a feature map is generated for each of the plurality of orthogonal planes.
However, Zakharov teaches representing a density and outputting a density (Section 2, Sub-Section “Neural Implicit Representations”) and depth-encoded features; (Section 3.1 Sub-Section “3D reasoning block”, paragraph 3, Section 3.2.1, Section 3.2.2) and volumetrically rendering density and color (Section 2, Sub-Section “Neural Implicit Representations”)
Zakharov is considered analogous to the claimed invention as it is in the same field of view synthesis. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify Yang in view of Roy with the teachings of Zakharov to include density as a part of the view synthesis generation process and possibly improve training and rendering performance.
Yang in view of Roy and Zakharov fail to teach wherein the global features representation comprises a plurality of orthogonal planes and wherein a feature map is generated for each of the plurality of orthogonal planes;
Baumgartner teaches wherein the global features representation comprises a plurality of orthogonal planes and wherein a feature map is generated for each of the plurality of orthogonal planes (Col. 4 lines 6-24).
Baumgartner is considered analogous to the claimed invention as it is in the same field of image processing. Therefore it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Baumgartner with Yang in view of Roy and Zakharov to improve object detection and generation
Regarding claim 2, Yang in view of Roy, Zakharov and Baumgartner teach the method of claim 1 and Yang further teaches wherein a number of the posed RGB images input into the encoder is from 1 to 5 (paragraph [0025], [0067]). Yang describes the use of a single image to generate a viewpoint and that image is encoded as 2D data.
Regarding claims 3, and 14, Yang in view of Roy, Zakharov and Baumgartner teach the method of claim 1 and Yang further teaches wherein the encoder uses a trained encoder network (Paragraph [0067]). Yang describes its Geometric Flow Network including “a plurality of convolution (or encoding) layers” which “may include image encoders”. This is a form of a trained encoder network.
Regarding claims 4, and 15, Yang in view of Roy, Zakharov and Baumgartner teach the method of claim 1 and Yang further teaches wherein the near and far MLPs are neural networks (Paragraph [0045]). Yang suggests the use of a deep neural network or artificial neural network its system.
Regarding claims 5, and 16, Yang in view of Roy, Zakharov and Baumgartner teach the method of claim 1 and Roy further teaches wherein the triplanar representation comprises three axis- aligned orthogonal planes (105 & 107 in FIG. 3). Roy uses pose coordinates for in its representation, these are expressed in terms of three axis-aligned orthogonal planes which are the x, y and z axis.
Regarding claims 8, and 17, Yang in view of Roy, Zakharov and Baumgartner teach the method of claim 1 and Yang further teaches wherein the decoder comprises one or more rendering MLPs. (Paragraph [0067]). Yang describes the use of multiple convolutional layers for the decoding portion of its network, this composition of multiple convolutional layers for the decoding portion reconstructs the features and provides information on image completion. The multiple layers of convolution can be considered analogous to one MLP and the reconstruction and production of image completion information can be considered analogous to rendering.
Regarding claims 9, and 18 Yang in view of Roy, Zakharov and Baumgartner teach the method of claim 1. Zakharov further teaches wherein the decoder predicts a color and density for an arbitrary 3D location and a viewing direction from the triplanar representation (Section 2, Sub-Section “Neural Implicit Representations” paragraph 2). Zakharov describes the regression (prediction) of color and density along rays (arbitrary 3D locations and viewing directions) as opposed to predefined 3D coordinates.
Regarding claim 10, Yang in view of Roy, Zakharov and Baumgartner teach the method of claim 1. Zakharov further teaches wherein the decoder uses near and far rendering MLPs to decode color and density used to render the local and global feature representations of the novel scene (Section 2, Sub-Section “Neural Implicit Representations”, Section 3.1, Sub-Section “3D Reasoning Block”). Zakharov describes a decoder calculating information for foreground and background information. Zakharov also describes regressing density and color values. Furthermore, Zakharov uses a "GAN-like encoder-decoder" which could be considered analogous an MLP. This is used for determining various features such as the object’s geometry and pose (global) and 3D points of the partial object shape in the frame of reference (local).
Regarding claim 11, Yang in view of Roy, Zakharov and Baumgartner teach the method of claim 9. Zakharov further teaches wherein the near and far rendering MLPs output density and color for a 3D point and a viewing direction (Section 2 Sub-Section “6DoF Object Detection via Correspondence Regression” and “Neural Implicit Representation”, Section 3.1, Sub-Sections “Object Reasoning Block” and “3D Reasoning Block Zakharov describes the regression (prediction) of color and density along rays (arbitrary 3D locations and viewing directions). Zakharov describes the use of multiple MLPs. Zakharov describes calculating information for foreground and background information. While Zakharov doesn’t explicitly say the MLPs output density and color for a 3d point and viewing direction, it does say they are used to “predict important object properties” which implies prediction of the previously mentioned density and color values. Therefore it would have been obvious to one of ordinary skill in the art to specify the ”important object properties” as object or image properties that are present elsewhere in the reference. Furthermore, it would have been obvious to incorporate these teachings with Yang in view of Roy to possibly improve training and rendering performance.
Claim(s) 12, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yang in view of Roy, Zakharov and Baumgartner and in further view of Alexander (US 2020/0184221 A1).
Regarding claim 12, Yang in view of Roy, Zakharov and Baumgartner teach the method of claim 1. However, Yang in view of Roy, Zakharov and Baumgartner fail to teach wherein the novel scene is rendered in a 360 degree view.
However, Alexander teaches wherein the novel scene is rendered in a 360 degree view (paragraph [0036]).
Alexander is considered analogous to the claimed invention, Yang, Roy and Zakharov as they are all in the same field of computer graphics. Therefore it would have been obvious to modify the teachings of Yang in view of Roy, Zakharov and Baumgartner with the 360 degree view of Alexander to improve user experience and better illustrate features of the scene.
Regarding claim 19, Yang in view of Roy, Zakharov and Baumgartner teach the system of claim 13. However, they fail to teach A vehicle comprising the system of claim 13. Alexander teaches a vehicle comprising a system (paragraph [0026]). Alexander describes a high performance computing module “positioned on (or embodied at) an aircraft”. This is analogous to a vehicle comprising a system. It would be obvious to one of ordinary skill in the art to substitute the system of Alexander, with the system of Yang in view of Roy to implement a view reconstruction system in a vehicle.
Claim(s) 6, 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yang in view of Roy Zakharov and Baumgartner and in further view of and Hinek (US 2021/0407124 A1).
Regarding claim 6, Yang in view of Roy, Zakharov and Baumgartner teach the method of claim 5.
Baumgartner further teaches obtaining a respective feature map for each of the axis-aligned orthogonal planes (Col. 4 lines 6-18). Baumgartner describes saliency maps being generated for the respective orthogonal planes. The saliency maps of Baumgartner are analogous to feature maps.
Baumgartner is considered analogous to the claimed invention as it is in the same field of image processing. Therefore it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Baumgartner with Yang in view of Roy and Zakharov to improve object detection and generation.
Yang in view of Roy, Zakharov and Baumgartner fails to teach three axis-aligned orthogonal planes.
However, Hinek teaches three axis-aligned orthogonal planes (paragraph [0016]). Hinek describes gathering training images from various angles. Some of the suggested angles of Hinek disclose three axis-aligned orthogonal planes, these being top-down, front and side views.
Hinek is considered analogous to the claimed invention as it is in the same field of image processing. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Hinek to improve the performance of generating image data for occluded views.
Regarding claim 7, Yang in view of Roy, Zakharov and Baumgartner teach the method of claim 1. Roy further teaches modeling the 3D surroundings from a corresponding perspective (Paragraphs [0004], [0006]).
Baumgartner further teaches wherein the triplanar representation comprises perpendicular cross-planes, each cross-plane modeling the 3D surroundings from a corresponding perspective, and the method further comprises merging the cross-planes to produce the global features (Col. 4 lines 6-24).
Baumgartner fails to teach wherein the triplanar representation comprises three perpendicular cross-planes.
However, Hinek teaches three perpendicular cross-planes (paragraph [0016]). Hinek describes gathering training images from various angles. A subset of the suggested angles of Hinek are three perpendicular cross-planes, these being top-down, front and side views.
Response to Arguments
Applicant's arguments filed October 29th 2025 have been fully considered but they are not persuasive. Applicant argues on pages 9 and 10 that Baumgartner does not remedy the deficiencies of Yang, Roy and Zakharov with respect to the amended subject matter of feature maps being generated by a far MLP. Applicant has a good point in their assertion that Baumgartner does not teach using a far MLP to generate feature maps. However, this argument is not persuasive as Baumgartner is not relied upon for this limitation. Yang teaches generating a feature map using a far MLP and Baumgartner teaches generating a feature map for each of the plurality of orthogonal planes. It would have been obvious to one of ordinary skill in the art to combine these references in order to improve object detection.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/AIDAN W MCCOY/Examiner, Art Unit 2611
/TAMMY PAIGE GODDARD/Supervisory Patent Examiner, Art Unit 2611
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