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 .
Claim Rejections - 35 USC § 102
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, 7, 18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by He et al. (Y. He, X. Ren, D. Tang, Y. Zhang, X. Xue and Y. Fu, "Density-preserving Deep Point Cloud Compression," 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, 2022, pp. 2323-2332, doi: 10.1109/CVPR52688.2022.00237.).
Regarding claim 1, He et al. discloses an electronic device comprising: circuitry configured to: acquire a reference point cloud of an object (pg. 2324 section 3: system architecture to input a point cloud);
encode the reference point cloud to generate encoded point cloud data (Fig. 2, pg. 2324 section 3: encoding the point cloud to generate encoded data);
decode the encoded point cloud data to generate a test point cloud (Fig. 2, pg. 2324 section 3: decoding the encoded data);
generate a first local density map of the reference point cloud, wherein the first local density map represents a local density value at each three-dimensional (3D) point of the reference point cloud (pg. 2327 section 4.1: density of neighbors within a certain radius of each point in the original point cloud are determined as part of a density metric (i.e. local density map));
determine 3D locations in the test point cloud that correspond to locations of 3D points of the reference point cloud (pg. 2327 section 4.1: points b of the encoded cloud correspond to points a of the original point cloud);
generate a second local density map of the test point cloud, wherein the second local density map represents a local density value at each 3D location of the determined 3D locations in the test point cloud (pg. 2327 section 4.1: density of neighbors within a certain radius of each point in the encoded point cloud are determined as part of a density metric (i.e. local density map));
compute a value of a density distortion metric for the test point cloud based on the first local density map and the second local density map (Fig. 7, pg. 2327 section 4.1: equation 7, computing density metric comparing density maps of original and encoded point clouds); and
control a display device to render information associated with a reconstruction quality of the test point cloud based on the computed value (Fig. 7, pg. 2329 section 4.2: output results as computer-generated graphs based on the density metrics indicating reconstruction quality).
Regarding claim 7, He et al. discloses the electronic device according to claim 1 as applied above. He et al. further discloses wherein the density distortion metric indicates a peak signal-to-noise ratio (PSNR) associated with the test point cloud (Fig. 6-7, pg. 2327 section 4.1: PSNR and density distortion metrics are calculated for the test point cloud).
Regarding claim 18, He et al. discloses everything claimed as applied above (see rejection of claim 1).
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) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over He et al. (Y. He, X. Ren, D. Tang, Y. Zhang, X. Xue and Y. Fu, "Density-preserving Deep Point Cloud Compression," 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, 2022, pp. 2323-2332, doi: 10.1109/CVPR52688.2022.00237.).
Regarding claim 20, He et al. discloses everything claimed as applied above (see rejection of claim 1).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to combine He et al. with generic computer components such as non-transitory computer-readable medium having stored thereon, computer- executable instructions that when executed by an electronic device, causes the electronic device to execute operations for the purpose of effectively implementing point cloud compression in applications such as autonomous driving.
Allowable Subject Matter
Claims 2-6, 8-17, 19 are 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.
Regarding claim 2, He et al. discloses the electronic device according to claim 1 as applied above. He et al. fails to disclose determine a bounding box for the reference point cloud; determine a number of 3D points in the reference point cloud; compute a radius to be used to sample the 3D points of the reference point cloud, wherein the radius is computed based on the bounding box and the number of the 3D points in the reference point cloud; and compute a spherical volume based on the radius.
Claims 3-4 are dependent on claim 2 and thus similarly incorporate allowable subject matter.
Regarding claim 5, He et al. discloses the electronic device according to claim 1 as applied above. He et al. fails to disclose wherein the circuitry is further configured to quantize each of the first local density map and the second local density map based on a defined number of quantization levels, and wherein the value of the density distortion metric is computed further based on the quantization.
Regarding claim 6, He et al. discloses the electronic device according to claim 1 as applied above. He et al. fails to disclose wherein the circuitry is further configured to compute a mean square error based on the first local density map and the second local density map, and wherein the value of the density distortion metric is computed further based on the computed mean square error.
Regarding claim 8, He et al. discloses the electronic device according to claim 1 as applied above. He et al. fails to disclose wherein the circuitry is further configured to select, from a plurality of rate distortion (RD) points, an RD point as an optimal rate to be used to encode the reference point cloud, and wherein the selection is performed based on a determination that the computed value of the density distortion metric is above a threshold value.
Regarding claim 9, He et al. discloses the electronic device according to claim 1 as applied above. He et al. fails to disclose further comprising a memory configured to store a point cloud codec that includes a machine learning-based encoder and a machine learning-based decoder.
Claims 10-11 are dependent on claim 9 and thus similarly incorporate allowable subject matter.
Regarding claim 12, He et al. discloses the electronic device according to claim 1 as applied above. He et al. fails to disclose wherein the circuitry is further configured to: select the reference point cloud as reference data; select the test point cloud as test data; and compute a first mean square error based on the first local density map and the second local density map.
Claims 13-17, 19 are dependent on claim 12 and thus similarly incorporate allowable subject matter.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gao (US 20210049790) discloses determining points in a processed point cloud which correspond to points in an original point cloud and determining error for each point to calculate a quality metric for the processed point cloud.
Mammou (US 20190311502) discloses encoding a point cloud and determining distortion between the original and a reconstructed point cloud based on points with corresponding locations and including generating a bounding box.
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/CAROLINE E. DEPALMA/Examiner, Art Unit 2675
/SJ Park/Primary Examiner, Art Unit 2675