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 .
Election/Restrictions
Claims 19, 21, 22, 25, 26, and 44-48 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention and/or species, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 4/2/2026.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 06/12/2024, 08/05/2025, and 06/02/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, they are being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3, 5-7, and 39-43 are rejected under 35 U.S.C. 101.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of decoding data, without significantly more.
The claim recites: “A method of decoding point cloud data for a point cloud, comprising: decoding a first set of data using a first decoding strategy, wherein said first set of data corresponds to a first subset of bit levels of point coordinates of said point cloud data; and decoding a second set of data using a second decoding strategy, wherein said second set of data corresponds to a second subset of bit levels of point coordinates of said point cloud data.”
The limitations, as drafted, are processes that, under their broadest reasonable interpretation, cover performance of the limitations using mathematical equations. In particular, decoding a point cloud can be performed mathematically in multiple ways, amounting to first and second decoding strategies, and can be done based on subsets of bit levels.
This judicial exception is not integrated into a practical application. In particular, the claim does not recite any additional elements. Accordingly, there are no additional elements to integrate the abstract idea into a practical limitation by imposing meaningful limits. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. It is therefore a judicial exception that is not integrated into a practical application, and does not include additional elements that are sufficient to amount to significantly more than the judicial exception. This claim is not patent eligible.
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to describing parameters of the mathematical functions of the abstract idea of claim 1. The claim is not patent eligible.
Claims 5-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to specifying decoding strategies, all of which are performable using mathematical calculations. The claims are not patent eligible.
Claims 39-43 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a generically recited apparatus for performing the abstract idea of claims 1, 3, and 5-7. The apparatus contains elements (processor and memory) that are recited at a high level of generality, and therefore do not provide meaningful limits on the performance of the abstract idea. The claims are not patent eligible.
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.
Claim(s) 1, 3, 5-7, and 39-43 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang (Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression).
Regarding claim 1, Wang teaches “A method of decoding point cloud data for a point cloud, comprising: decoding a first set of data using a first decoding strategy, wherein said first set of data corresponds to a first subset of bit levels of point coordinates of said point cloud data; and decoding a second set of data using a second decoding strategy, wherein said second set of data corresponds to a second subset of bit levels of point coordinates of said point cloud data.” (Wang, Figure 10(a) shows that from 1st to mth scale (scale corresponds to to bit levels), a first decoding strategy (lossless phase) is used, and from mth scale to Nth scale, a second strategy (lossy phase) is used. Section 5.2 Paragraphs 3-4 describes: “In lossy mode of Fig. 10, the SparsePCGC devises the lossless coding from the first to mth scale, and the lossy mode to remaining scales. Adapting m is to best balance the rate and distortion for lossy compression. As reported in[23],having lossy coded PCG for all scales would produce severe degradation of reconstruction quality due to scale by-scale refinement, but could not bring noticeable rate reductions. Note that the highest scale N is typically determined by the geometry precision of input point clouds. For example, N=10, or 11 for dense 8iVFB and Owlii, and N=18 for KITTI and Ford sequences.” Section 9.1 and Figure 15 further confirm that the scale corresponds to bit level (“And the number of points is almost the same on high-precision scales (e.g., from 14 bit to 18 bit), suggesting that we just need to estimate the position of occupied voxels.”), and that the bit levels correspond to point coordinates of the point cloud data. This is additionally supported by Section 5.2 Subsection KITTI: “KITTI (SemanticKITTI) [38] is a large-scale LiDAR dataset used for semantic scene understanding. It contains 22 sequences, a total of 43,552 scans of outdoor scenes collected using the Velodyne HDL 64E LiDAR sensor. There are around 120k points on average per frame. These raw floating-point coordinates are quantized to millimeter scale (e.g.,1mm as unit precision), requiring 18-bit geometry precision for storage.”)
Regarding claim 3, Wang teaches “The method of claim 1,”
“wherein said first subset of bit levels are bit levels 0 to da - 1 and said second subset of bit levels are bit levels da to db - 1, wherein bit 0 represents the most significant bit, da and db are integers, and da < db.” (Wang, Figure 10a shows that the first and second subset bit level ranges (see 1st scale, mth scale, Nth scale, etc.). Accordingly, the claimed terms da and db are being precisely mapped to the terms and ranges provided in Figure 10a. Figure 15 confirms that all scales are integers. Lastly, 1st scale corresponds to bit 0: The Specification at Paragraph 71 defines bit 0 (d = 0) as the most significant bit. This makes it the coarsest bit. Wang describe that strategy of Figure 10 operates from coarsest (left) to finest (right), as each successive scale comes from subdividing the voxel from the previous scale into 8 sub-voxels, creating an additional bit (Section 4.1). Therefore, 1st scale, at the left-most position in figure 10a, corresponds to the coarsest bit. Thus, “bit 0” and “1st scale” amount to two naming conventions for the same first and coarsest unit. The 1st scale is therefore also the most significant bit because it is required to obtain increasingly finer details as the process proceeds from right to left.)
Regarding claim 5, Wang teaches “The method of claim 1,”
“wherein said first decoding strategy is a lossless decoding strategy.” (Wang, Figure 10(a) and Section 5.2 Paragraphs 3-4, “In lossy mode of Fig. 10, the SparsePCGC devises the lossless coding from the first to mth scale, and the lossy mode to remaining scales. Adapting m is to best balance the rate and distortion for lossy compression. As reported in[23],having lossy coded PCG for all scales would produce severe degradation of reconstruction quality due to scale by-scale refinement, but could not bring noticeable rate reductions. Note that the highest scale N is typically determined by the geometry precision of input point clouds. For example, N=10, or 11 for dense 8iVFB and Owlii, and N=18 for KITTI and Ford sequences.”)
Regarding claim 6, Wang teaches “The method of claim 1,”
“wherein said second decoding strategy is a voxel-based decoding strategy or a point-based decoding strategy.” (Wang teaches both alternatives in Figure 10(b-c) and section 7.2.2, “As in Fig. 10, for losslessphase,wedirectlyapplythe8 Stage SOPA model to exploit cross-scale and multi-stage correlations; while in lossy phase, we use different methods for dense and sparse point clouds due to their diverse geometry precision and relevant voxel density.
• For dense PCG, we suggest the SLNE-enhanced One Stage SOPA to upscale sparse tensor from m-th to (m+1)-th scale, and then apply the One-Stage SOPA from(m+1) till the highest scale N;
• For sparse PCG, we apply the SOPA(Position)model to directly upscale m-th scale sparse tensor to its highest scale N;
Recalling that m is adapted for various rate-distortion trade-off of lossy SparsePCGC, in our experiments, we set the factor m as{N-1,N-2,N-3} for dense point clouds, and {N-2,N-3, ···,N-8}for sparse LiDAR point clouds.” Note that the first bullet is voxel-based and the second bullet teaches point-based, see section 5.1 and Figure 8 for additional description that One-Stage SOPA operates on voxels and SOPA (Position) operates on points.)
Regarding claim 7, Wang teaches “The method of claim 6,”
“wherein said voxel-based decoding strategy comprises: a plurality of sub-blocks, wherein each sub-block of said plurality of sub-blocks includes an upsampling function, a convolution layer, and an activation function,” (Wang, Figure 3b shows a respective sub-block containing upsampling function (voxel sampling), convolution layer (deep feature aggregation), and an activation function (sigmoid layer of the occupancy probability generation). Figure 10b shows that the one-stage SOPA is repeated (i.e. the plurality of sub-blocks).
“and wherein a number of sub-blocks in said plurality of sub-blocks is the same as a number of bit levels in said second subset of bit levels.” (Wang, figure 10b shows that there is one sub-block (1-stage SOPA) for each scale of the second subset of bit levels.).
Regarding claims 39-43, these claims recite an apparatus with a processor and memory for performing the steps recited in claims 1, 3, and 5-7. Therefore, the recited elements of these claims are mapped to the analogous steps in the corresponding method claims. Wang additionally teaches an apparatus with a processor and memory (Wang, Section 7.1. Subsection “Test Conditions”, Our prototype is implemented using PyTorch and MinkowskiEninge [43], which is tested on a computer with an Intel Xeon 6226R CPU and an Nvidia GeForce RTX 3090 GPU. We record the encoding and decoding time following the methodology used in G-PCC. Because of the platform diversity, e.g., CPU vs. GPU, Python vs. C/C++, etc, the running time comparison only serves as the intuitive reference to have a general idea about the computational complexity.” Note that a memory is inherent for these processors to function and perform the steps of the invention of Wang.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wang2 (Multiscale Point Cloud Geometry Compression) teaches a multiscale framework that reconstructs 3D point cloud geometry using progressive resampling.
Kato (US 20200273211 A1) teaches octree pattern encoding of data and bit stream encoding and decoding.
Kim (US20200021847A1) teaches point cloud data compression wherein bit stream encodes point cloud data.
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/AARON JOSEPH SORRIN/
Examiner, Art Unit 2672
/SUMATI LEFKOWITZ/Supervisory Patent Examiner, Art Unit 2672
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