DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. In a preliminary amendment applicant has canceled claims 2, 6-12, 15-18 and 23-30 and added new claims 31-40. Currently, claims 1, 3-5, 13, 14, 19-22, and 31-40 are pending and being considered.
Claim Rejections - 35 USC § 102
3. 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.
Claims 1, 3-5, 13, and 31-35 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Pang, et al., ("TearingNet: Point Cloud Autoencoder to Learn Topology-Friendly Representations", 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 1 April 2021, pages 7449-7458).
Regarding claim 1: Pang discloses a method for decoding point cloud data (abstract, section 1, and fig. 2), comprising:
accessing-decoding a data array with samples on a regular grid, by a decoder associated with a neural network-based autoencoder, or an image or video decoder, wherein each sample in said data array indicates at least a position of a point in a point cloud (section 1, fig. 2, and section 3.1 to section 5.2); and
reconstructing said point cloud responsive to said data array (sections 4.1 to 4.3 and 5.3).
Regarding claim 3: The method for claim 1, further comprising: accessing a codeword that provides a representation of said point cloud, wherein said point cloud is reconstructed further responsive to said codeword (sections 4.1-4.3).
Regarding claim 4: The method of claim 3, wherein each sample in said data array indicates a difference between a position of a point in said point cloud and a position of a respective point in an initial version of said reconstructed point cloud (section 1, fig. 2, and section 3.1 to section 5.2).
Regarding claim 5: The method of claim 3, further comprising:
generating said initial version of said reconstructed point cloud, based on said regular grid and said codeword, using a neural network-based module, wherein said initial version of said reconstructed point cloud is added to said data array to reconstruct said point cloud (section 1, fig. 2, and section 3.1 to section 5.2).
Regarding claim 13: The method of claim 1, further comprising:
decoding at least an image or a video by said image or video decoder (section 1, fig. 2, and section 3.1 to section 5.2) ; and
decoding data indicative of a range of positions of said point cloud data, wherein said decoded image or video is scaled responsive to said range of positions to reconstruct said point cloud (section 1, fig. 2, and section 3.1 to section 5.2. See section 3.2 for stretching of the grid, i.e. read as scaling).
Regarding claim 31: See claim 1.
Regarding claim 32: See claim 3.
Regarding claim 33: See claim 4.
Regarding claim 34: See claim 5.
Regarding claim 35: See claim 13.
Allowable Subject Matter
4. Claims 14, 19-22, and 36-40 are allowed.
5. The following is a statement of reasons for the indication of allowable subject matter: The closest prior art of Pang, et al., ("TearingNet: Point Cloud Autoencoder to Learn Topology-Friendly Representations", 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 1 April 2021, pages 7449-7458) discloses a point cloud topology learning autoencoder. Pang et al. nor any other prior art of record, regarding claim 14 and similarly claim 36, teaches the features of “reconstructing a first point cloud, by a second neural network-based module, based on said codeword and a grid; and generating a data array with samples on said grid, wherein each sample in said data array indicates a position of a point in said input point cloud, based on said reconstructed first point cloud, said grid, and said input point cloud; and compressing said data array by an encoder associated with a neural network-based autoencoder, or an image or video encoder,” these, in combination with the other claim limitations. Regarding claims 19-22 and 37-40, these claims are directly or indirectly dependent from allowable independent claims 14 or 36, respectively, therefore, these claims are allowed.
Contact Information
6. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANAND BHATNAGAR whose telephone number is (571)272-7416. The examiner can normally be reached on M-F 7:30am-4:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vu Le can be reached on 571-272-4650. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANAND P BHATNAGAR/
Primary Examiner, Art Unit 2668
January 4, 2026