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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on May 1, 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant's arguments filed 6/22/2026 have been fully considered but they are not persuasive. Applicant argues that prior art Quach fails to teach decoding a number of samples, N and dynamically sampling said pre-defined 2D area. Examiner respectfully disagrees. Quach does teach these features by the nature of the lossy point cloud. Thus, even though the 2D grids are fixed with n’ = w x h points (Quach Section “3.2 Folding Refinement”), it is irregularly sampled representation of 3D space requires attribute compression via dynamic sampling or dynamic attribute coding, as Quach discloses. Thus, Quach is still an effective prior art for rejecting all amended claims.
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Claim Rejections - 35 USC § 102
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 17, 22-23, 26, 30-33 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Quach, Maurice et al. “Folding-Based Compression Of Point Cloud Attributes.” 2020 IEEE International Conference on Image Processing (ICIP) (2020): 3309-3313.
Regarding claim 1, Quach et. al. discloses a method for decoding point cloud data (Quach et. al., method implemented by the decoder in figure 2, bottom), comprising: decoding a codeword that provides a representation of a point cloud (Quach et. al. image decompression); obtaining a pre-defined 2D area; decoding a number of samples, N (Quach et. al. grid folding, figure 1); generating an adaptive point set having N samples by dynamically sampling said pre-defined 2D area (Quach et. al. folding grid refinement is an adaptive adjustment of the N samples, section 3.2); and reconstructing said point cloud responsive to said codeword and said adaptive point set, using a neural network-based module (Quach et. al. the decoder reconstructing the point cloud attributes using the inverse mapping and the decompression of the compressed codeword to obtain the decompressed attributes).
Regarding claim 21, Quach et. al. discloses a method for encoding point cloud data (Quach et. al., method implemented by the encoder in figure 2, top), comprising: generating a codeword, by a neural network-based module, which provides a representation of an input point cloud associated with said point cloud data (Quach et. al. codeword with original attributes obtained through optimized mapping, figure 2); compressing said codeword (Quach et. al. image compression); encoding a number of samples, N (Quach et. al. grid folding); obtaining a pre-defined 2D area; generating an adaptive point set having N samples by dynamically sampling said pre-defined 2D area (Quach et. al. folding grid refinement is an adaptive adjustment of the N samples, section 3.2); and reconstructing a first point cloud, by another neural network-based module, based on said codeword and said adaptive point set (Quach et. al., see 3.1, first sentence “We propose a grid folding composed of two steps, namely, an initial folding step to get a rough reconstruction of X and a folding refinement step to improve the reconstruction”).
Regarding claim 26, Quach et. al. discloses an apparatus for decoding point cloud data (Quach et. al., method implemented by the decoder in figure 2, bottom), comprising one or more processors and at least one memory coupled to said one or more processors, wherein said one or more processors are configured to: decode a codeword that provides a representation of a point cloud (Quach et. al. image decompression); obtain a pre-defined 2D area; decode a number of samples, N (Quach et. al. grid folding, figure 1); generate an adaptive point set having N samples by dynamically sampling said pre-defined 2D area (Quach et. al. folding grid refinement is an adaptive adjustment of the N samples, section 3.2); and reconstruct said point cloud responsive to said codeword and said adaptive point set, using a neural network-based module (Quach et. al. the decoder reconstructing the point cloud attributes using the inverse mapping and the decompression of the compressed codeword to obtain the decompressed attributes).
Regarding claim 31, Quach et. al. discloses an apparatus for encoding point cloud data (Quach et. al., method implemented by the encoder in figure 2, top), comprising one or more processors and at least one memory coupled to said one or more processors, wherein said one or more processors are configured to: generate a codeword, by a neural network-based module, which provides a representation of an input point cloud associated with said point cloud data (Quach et. al. codeword with original attributes obtained through optimized mapping, figure 2); compress said codeword (Quach et. al. image compression); encode a number of samples, N (Quach et. al. grid folding); obtain a pre-defined 2D area; generate an adaptive point set having N samples by dynamically sampling said pre-defined 2D area (Quach et. al. folding grid refinement is an adaptive adjustment of the N samples, section 3.2); and reconstruct a first point cloud, by another neural network-based module, based on said codeword and said adaptive point set (Quach et. al., see 3.1, first sentence “We propose a grid folding composed of two steps, namely, an initial folding step to get a rough reconstruction of X and a folding refinement step to improve the reconstruction”).
Regarding claim 17 and 30, Quach et. al. discloses the method of claim 1 and 26, wherein samples for said adaptive point set are drawn from a grid of pre-defined size, further comprising: accessing a density distribution function to obtain scores for samples in said adaptive point set, wherein said generating for samples subsequent to the first sample is based on distances and scores (Quach et. al. the folding grid points are drawn from a continuous 2D area, section 3, Proposed Method, section 2, last paragraph “we employ the folding network as a parametric function that maps an input 2D grid to points in 3D space. The parameters of this function (i.e., the weights of the network) are obtained by overfitting the network to a specific point cloud”).
Regarding claims 22 and 32, Quach et. al. discloses the method of claim 21 and the apparatus of claim 31, wherein said one or more processors are further configured to, further comprising: adjusting said adaptive point set to generate another point set, by a third neural network- based module, based on said first reconstructed point cloud, said codeword, and said input point cloud (Quach et. al., see folding refinement, sections 3.1 and 3.2).
Regarding claims 23 and 33, Quach et. al. discloses the method of claim 22, and the apparatus of claim 32, wherein said one or more processors are further configured to, further comprising: compressing said another point set, wherein said another point set is for refining said representation of said input point cloud (Quach et. al., see folding refinement, sections 3.1 and 3.2).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 2, 10, 25, 27, 29, 35 are rejected under 35 U.S.C. 103 as being unpatentable over Quach, Maurice et al. “Folding-Based Compression Of Point Cloud Attributes.” 2020 IEEE International Conference on Image Processing (ICIP) (2020): 3309-3313 in view of Zong, Daoming et al. “ASHF-Net: Adaptive Sampling and Hierarchical Folding Network for Robust Point Cloud Completion.” AAAI Conference on Artificial Intelligence (2021).
Regarding claim 2 and 27, Quach et. al. discloses the method of claim 1 and 26. However, Quach et. al. fails to disclose further comprising: obtaining another number of samples, N'; generating another adaptive point set having N' samples by dynamically sampling said pre-defined 2D area; and reconstructing another version of said point cloud responsive to said codeword and said another point set, using said neural network-based module.
Zong et. al. teaches further comprising: obtaining another number of samples, N'; generating another adaptive point set having N' samples; and reconstructing another version of said point cloud responsive to said codeword and said another point set, using said neural network-based module (Zong et. al. figure 2). Adaptively using different numbers of grid points for down or upsampling is well known in the art and important for varies sizes of point cloud data. Thus, it would have been obvious for one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Quach et. al. and the teachings of Zong et. al. so that these features are included in the method.
Regarding claim 10, 25, 29, and 35 Quach et. al. discloses the method of claim 1, 21, 26, and 31. However, Quach et. al. fails to disclose wherein a Farthest Point Sampling (FPS) procedure is used to generate said adaptive point set.
Zong et. al. teaches wherein a Farthest Point Sampling (FPS) procedure is used to generate said adaptive point set (Zong et. al., page 3626, second paragraph, page 3627, Point Cloud Denoising Auto-Encoder section, first paragraph). FPS is a common general knowledge procedure for adaptive sampling in the context of the claimed invention. Thus, it would have been obvious for one skilled in the art prior to the effective filing date to have combined the features of Quach et. al. and Zong et. al. so that the method includes FPS.
Claim(s) 8, 24, 28, 34 are rejected under 35 U.S.C. 103 as being unpatentable over Quach, Maurice et al. “Folding-Based Compression Of Point Cloud Attributes.” 2020 IEEE International Conference on Image Processing (ICIP) (2020): 3309-3313 in view of Yang, Yaoqing et al. “FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation.” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (2017): 206-215.
Regarding claim 8, 24, 28, and 34, Quach et. al. discloses the method of claim 1, 21, 26, and 31. However, Quach et. al. fails to disclose wherein said neural network-based module corresponds to a FoldingNet.
Yang et. al. teaches wherein said neural network-based module corresponds to a FoldingNet (Yang et. al., see section 2. “The initial folding in our work is inspired by [6] where an autoencoder network is trained on a dataset to learn how to fold a 2D grid onto a 3D point cloud. In our work, we build on this folding idea;” wherein [6] corresponds to Yang et. al.). This is an important aspect of the claimed invention because the neural network carries out the folding of the point cloud from 2D to 3D. Thus, it would have been obvious to one skilled in the art prior to the effective filing date to have combined the teachings of Quach et. al. and the teachings of Yang et. al. so that the method includes the FoldingNet neural network architecture.
Conclusion
Response to Amendment
Examiner has carefully considered the amendments to the claims; however, the prior art Quach is still effective in maintaining the prior rejection.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESSICA YIFANG LIN whose telephone number is (571)272-6435. The examiner can normally be reached M-F 7:00am-6:15pm, with optional day off.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vu Le can be reached at 571-272-7332. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JESSICA YIFANG LIN/Examiner, Art Unit 2668 July 15, 2026
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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