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 April 28th 2026 have been entered. Applicant’s amendments to the drawings and specification have overcome the drawing objections previously set forth in the office action mailed January 27th 2026. Applicant’s amendments to the claims have overcome the previously set forth 35 USC 103 rejections, however new rejections have been entered as necessitated by amendment.
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 1, 2, 4-7, 9, 10, 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over Wu, et al., "4D Gaussian Splatting for Real-Time Dynamic Scene Rendering," arXiv preprint arXiv:2310.08528v2, December 2023 (Hereinafter "Wu") in view of Zheng, Zerong, et al. "Structured local radiance fields for human avatar modeling." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022 (hereinafter "Zheng) and Z. Yang, et al, "Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction," 2023 arXiv, arxiv.org/abs/2309.13101 (Hereinafter “Yang”).
Regarding claim 1, Wu teaches A method comprising:
receiving a video input (section 5 subsection “Real-world Datasets”) comprising a scene (Abstract, introduction paragraph 1, figs 4, 7 & 8) and a subject (Figs. 1, 3-5);
obtaining a three-dimensional (3D) reconstruction of the subject and the scene from the video input (section 2.2 paragraph 2 "Dynamic3DGS [33] models dynamic scenes by tracking the position and variance of each 3D Gaussian at each timestamp ti.", figs 3-5 7, 8);
generating a 3D Gaussian representation of each of the scene and the subject based on the 3D reconstruction of the subject and scene (section 3.1, fig 3-5, 7, 8);
generating a deformed 3D Gaussian representation of the subject by adapting the 3D Gaussian representation of the subject to the 3D reconstruction of the subject (fig. 3, section 4.2); and
rendering a visual output comprising at least one of the subject or the scene (fig 1, 3-5, 7 & 8, last paragraph of section 1) based at least in part on the deformed 3D Gaussian representation of the subject and the 3D Gaussian representation of the scene (fig. 3, section 4).
Wu fails to teach rendering a visual output comprising at least one of an animatable avatar of the subject or the scene
However Zheng teaches a video input (Abstract, fig 1 – “driving video”, section 2 paragraph 1 –“In contrast, our method bypasses the reconstruction step and directly learns an animatable avatar from RGB videos.”) and rendering a visual output comprising an animatable avatar of the subject (figs. 1, 2, 5-8, section 2 paragraph 1 and subsection “Animatable Human Avatars”, section 6 conclusion).
Zheng is considered analogous to the claimed invention as it is in the same field of three-dimensional animation. Therefore, it would have been obvious to one of ordinary skill in the art to combine the teachings of Wu with the teachings of Zheng in order to implement the improved rendering quality, speed, and storage consumption of Wu (section 5.2 paragraph 2) for use in animatable avatars.
Wu in view of Zheng fails to teach generating a deformed 3D Gaussian representation of the subject by adapting the 3D Gaussian representation of the subject based on the 3D reconstruction of the subject.
However, Yang teaches generating a deformed 3D Gaussian representation of the subject by adapting the 3D Gaussian representation of the subject based on the 3D reconstruction of the subject (fig. 2, section 3.2). Yang describes learning a 3D gaussian representation of a scene by independently learning a deformation field and 3D Gaussian reconstructions and utilizing the deformation as a transform for the 3D Gaussian reconstructions to determine a deformed 3D Gaussian representation. Yang is considered analogous to the claimed invention as it is in the same field of scene reconstruction. Therefore it would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Yang with Wu in view of Zheng in order to achieve higher rendering quality and real-time rendering speeds.
Regarding claim 2, Wu in view of Zheng and Yang teaches the method of claim 1. Wu further teaches wherein the obtaining the 3D reconstruction of the subject and the scene comprises performing a structure-from-motion operation (section 4.3) to a sequence of frames in the video input (section 5 subsection “Real-world Datasets”) to obtain point cloud data of the scene (section 3.1 paragraph 1, Appendix pg. 12 col. 2 “The Neu3D dataset [25] includes 15 – 20 fixed camera setups, so it’s easy to get the SfM [47] point in the first frame. We utilize the dense point-cloud reconstruction and downsample it lower than 100k to avoid out of memory error.) and the 3D reconstruction of the subject (section 3.1 paragraph 1). Zheng further teaches pose estimation (Fig. 2, 4-8, tables 1 & 2, section 4.1 – novel pose synthesis/generation can be considered pose estimation).
Regarding claim 4, Wu in view of Zheng and Yang teaches the method of claim 1, Wu further teaches generating the deformed 3D Gaussian representation (fig. 3, section 4.2). Zheng further teaches wherein generating the representation comprises applying a forward deformation module (fig. 2 “Forward Skinning”, section 3 subsection “Discussion”) to facilitate learning of pose correctives (Fig. 4 desc – calculating local coordinate of posed space points) and linear skinning weights (section 3 – “we can transform node i to the posed space using linear blending skinning (LBS)”).
Regarding claim 5, Wu in view of Zheng and Yang teach the method of claim 4. Wu further teaches wherein the deformed 3D Gaussian representation is generated based at least in part on the pose correctives (section 5.3 subsection “Gaussian Deformation Decoder” – adjusting 3D gaussians to accurately model movements, section 5.4 subsection “Tracking with 3D Gaussians.”). Zheng further teaches wherein the representation is generated based at least in part on the pose correctives and the linear skinning weights (Fig. 2, fig. 4 desc., section 3).
Regarding claim 6, Wu in view of Zheng and Yang teaches the method of claim 5. Wu further teaches wherein the generating the deformed 3D Gaussian representation of the subject comprises applying the pose correctives to the 3D Gaussian representation of the subject (section 5.3 subsection “Gaussian Deformation Decoder” – adjusting 3D gaussians to accurately model movements, section 5.4 subsection “Tracking with 3D Gaussians.”).
Regarding claim 7, Wu in view of Zheng and Yang teaches The method of claim 6. Wu further teaches generating the deformed 3D Gaussian representation of the subject (fig. 3). Zheng further teaches wherein generating the representation of the subject comprises applying the linear skinning weights to the representation of the subject applied with the pose correctives (fig, 2 – forward skinning (applying linear weights) done after body pose 𝜽 applied (pose correctives)).
Apparatus claims 9, 10, 12-15 are drawn to the apparatus of the corresponding method claimed in claims 1, 2, 4-7. Therefore, the apparatus claims 9, 10, 12-15 correspond to the method claims 1, 2, 4-7, and are rejected for the same reasons of obviousness as used above.
Claims 3, 11 are rejected under 35 U.S.C. 103 as being unpatentable over Wu in view of Zheng and Yang and in further view of Kim (US 20130182894 A1).
Regarding claim 3, Wu in view of Zheng and Yang teach the method of claim 2. Wu in view of Zheng fail to teach wherein the structure-from-motion operation and the pose estimation are performed concurrently.
However Kim teaches wherein the structure-from-motion operation and the pose estimation are performed concurrently (paragraph [0173]). Kim describes estimating camera pose at the same time as a structure from motion operation estimates coordinates. While the teachings of Kim are directed towards a camera pose rather than a subject’s pose, the simultaneous execution described is analogous to what is claimed in claim 3. Furthermore, Kim is considered analogous to the claimed invention as it is in the same field of computer graphics. Therefore it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Kim with Wu and Zheng and Yang in order to perform the SfM and pose estimation operations concurrently in order to enable faster processing times.
Apparatus claim 11 is drawn to the apparatus of the corresponding method claimed in claim 3. Therefore, the apparatus claim 11 corresponds to the method claim 3, and is rejected for the same reasons of obviousness as used above.
Claims 8, 16, 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wu in view of Zheng and Yang and in further of Kerbl et al., "3D Gaussian Splatting for Real-Time Radiance Field Rendering" SIGGRAPH 2023, volume 42, July 2023 (hereinafter "Kerbl") .
Regarding claim 8, Wu in view of Zheng and Yang teaches the method of claim 1. Zheng further teaches the visual output of the subject or the scene comprising the at least one of the animatable avatar.
Wu in view of Zheng and Yang fail to teach wherein the visual output of the subject or the scene is rendered using differentiable Gaussian rasterization.
However, Kerbl teaches wherein the visual output of the subject or the scene is rendered using differentiable Gaussian rasterization (fig. 2, section 6). Kerbl describes rendering visual output by differentially rasterizing the Gaussian representation of a given scene which can be considered analogous to differentiable Gaussian rasterization. Kerbl is considered analogous to the claimed invention as it is in the same field of three dimensional computer graphics. Therefore it would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Kerbl with Wu in view of Zheng and Yang to improve rasterization speeds (Kerbl, section 8 paragraphs 1 & 2).
CRM claim 16 is drawn to the CRM of the corresponding method claimed in claim 8. Therefore, the CRM claim 16 corresponds to the method claim 8, and is rejected for the same reasons of obviousness as used above.
Regarding claim 18, Wu in view of Zheng and Yang and Kerbl teach the non-transitory computer-readable medium of claim 16.
Wu further teaches generating the deformed 3D Gaussian representation (fig. 3, section 4.2). Zheng further teaches wherein the instructions that cause the one or more processors to generate the representation of the subject further cause the one or more processors to apply a forward deformation module (fig. 2 “Forward Skinning”, section 3 subsection “Discussion”) to facilitate learning of pose correctives (Fig. 4 desc – calculating local coordinate of posed space points) and linear skinning weights (section 3 – “we can transform node i to the posed space using linear blending skinning (LBS)”).
Regarding claim 19, Wu in view of Zheng and Yang and Kerbl teach The non-transitory computer-readable medium of claim 18. Wu further teaches wherein the deformed 3D Gaussian representation is generated based at least in part on the pose correctives (section 5.3 subsection “Gaussian Deformation Decoder” – adjusting 3D gaussians to accurately model movements, section 5.4 subsection “Tracking with 3D Gaussians.”). Zheng further teaches wherein the representation is generated based at least in part on the pose correctives and the linear skinning weights (Fig. 2, fig. 4 desc., section 3).
Regarding claim 20, Wu in view of Zheng and Yang and Kerbl teach The non-transitory computer-readable medium of claim 19. Wu further teaches wherein the instructions that cause the one or more processors to generate the deformed 3D Gaussian representation of the subject further cause the one or more processors to apply the pose correctives to the 3D Gaussian representation of the subject (section 5.3 subsection “Gaussian Deformation Decoder” – adjusting 3D gaussians to accurately model movements, section 5.4 subsection “Tracking with 3D Gaussians.”). Zheng further teaches wherein the instructions that cause the one or more processors to generate the representation of the subject further cause the one or more processors to apply the linear skinning weights to representation of the subject applied with the pose correctives (fig, 2 – forward skinning (applying linear weights) done after body pose 𝜽 applied (pose correctives)).
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Wu in view of Zheng and Yang and Kerbl and in further view Kim.
Regarding claim 17, Wu in view of Zheng and Yang and Kerbl teach the non-transitory computer-readable medium of claim 16. Wu in view of Zheng and Yang and Kerbl fail to teach wherein the structure-from-motion operation and the pose estimation are performed concurrently.
However Kim teaches wherein the structure-from-motion operation and the pose estimation are performed concurrently (paragraph [0173]). The motivation to combine Kim with Wu in view of Zheng and Yang and Kerbl would have been the same as that of claim 3.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1 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.
Conclusion
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.
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/AIDAN W MCCOY/Examiner, Art Unit 2611
/TAMMY GODDARD/Supervisory Patent Examiner, Art Unit 2611
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