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 . 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 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. There are a total of 20 claims and claims 1-20 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on LO do is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d) which papers have been placed of record in the file.
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.
Claims 1-9, and 12-20 are rejected under 35 U.S.C. 103 as being unpatentable over ZHU (CN116033186 A; given by the applicant in the IDS) in view of Hur et al.( US 2021/0320960 A1).
Regarding claim 1, ZHU discloses a method comprising: determining first information associated with a first geometry model representing a first portion of a point cloud geometry contained in a first sub-volume of the point cloud geometry([abstract]- a point cloud data processing method, device, device and medium, the method comprises: obtaining the compressed code stream of the point cloud data; when the node to be decoded is the first node in the first geometric structure of point cloud data, determining the decoded neighbor node with spatial adjacent relation with the node to be decoded; when the decoded neighbor node is different from the second node in the second geometric structure of the first geometric structure, determining the target occupied information of the decoded neighbor node; decoding the node geometrical information of the node to be decoded in the compressed code stream based on the target occupying information; The node geometry information includes any one of a partition tree occupancy information); reconstructing the first portion of the point cloud geometry using the first information([0006-0007]- Obtain the compressed code stream of point cloud data, and determine a decoded neighbor node having a spatial adjacent relationship with the node to be decoded)); determining, based on a second geometry model representing a second portion of the point cloud geometry contained in a second sub-volume neighboring the first sub-volume, second information associated with the second geometry model representing the reconstructed first portion, wherein the first geometry model is different from the second geometry model([0006-0008]- When the node to be decoded is the first node in the first geometric structure of the point cloud data, and [0008], determining target occupancy information of the decoded neighbor node when the decoded neighbor node is a second node in a second geometric structure different from the first geometric structure).
However, ZHU does not explicitly disclose decoding, based on the second information, third information associated with the second geometry model representing the second portion of the point cloud geometry.
In an analogous art, Hur discloses decoding, based on the second information, third information associated with the second geometry model representing the second portion of the point cloud geometry([see in Fig. 1, 6-8]- The dotted lines in FIG. 1 represent a process of transmitting the feedback information secured by the renderer 10007. The point cloud content providing system may process (encode/decode) point cloud data based on the feedback information. Accordingly, the point cloud video data decoder 10006 may perform a decoding operation based on the feedback information. The reception device 10004 may transmit the feedback information to the transmission device 10000. The transmission device 10000 (or the point cloud video data encoder 10002) may perform an encoding operation based on the feedback information. Accordingly, the point cloud content providing system may efficiently process necessary data (e.g., point cloud data corresponding to the user's head position) based on the feedback information rather than processing (encoding/decoding) the entire point cloud data, and provide point cloud content to the user.). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the technique of Hur to the modified system of ZHU a method and device for processing point cloud content for efficiently processing a large amount of point data [Hur, 00003 ].
Regarding claim 2, ZHU discloses determining fourth information associated with the second geometry model representing a third portion of the point cloud geometry contained in a third sub-volume neighboring the second sub-volume; and wherein the decoding the third information, from a bitstream, is based on the second information and the fourth information([0006-0008]- When the node to be decoded is the first node in the first geometric structure of the point cloud data, and [0008], determining target occupancy information of the decoded neighbor node when the decoded neighbor node is a second node in a second geometric structure different from the first geometric structure).
Regarding claim 3, Hur discloses decoding, from a bitstream, a first indication indicating the second geometry model, wherein: the first indication further indicates at least one of: a tree-based geometry model associated with the second sub-volume; or a triangle-based geometry model associated with the second sub-volume; and the first indication is signaled in the bitstream for one or more sub-volumes containing a portion of the point cloud geometry([see in Fig. 4]- FIG. 6 shows an octree structure. The 3D space of the point cloud content according to the embodiments is represented by axes (e.g., X-axis, Y-axis, and Z-axis) of the coordinate system).
Regarding claim 4, ZHU disclose binarizing the third information([0031;0151]- binary tree (BT)); and decoding, based on contextual information derived from the second information, the binarized third information([0154-0155]- compared to the octree partitioning method shown in Figure 4a above, as shown in the first figure in Figure 6b, the placeholder information obtained by binary tree partitioning along the x-axis can encode only the bits corresponding to positions 0 and 4, while the remaining 6 skipped bits can be inferred as 0 at the decoding end.).
Regarding claim 6, ZHU discloses wherein: the first geometry model is a triangle-based geometry model representing the first portion of the point cloud geometry([0150]- three modes for geometry coding); and the second geometry model is a tree-based geometry model representing the reconstructed first portion of the point cloud geometry, the second portion of the point cloud geometry, and a third portion of the point cloud geometry([0150-0151 and 0156]- three modes for geometry coding; Geometric coding based on partition tree; [0004]-discloses information of neighboring nodes using different types of geometric structures.).
Regarding claim 7, ZHU discloses wherein: the reconstructed first portion of the point cloud geometry comprises a set of points defined by voxelizing at least one triangle of the triangle-based geometry model([0177]- when aiming to code a current sub-volume based on a partition tree model using a neighbor coded based on a different geometry coding mode, it is proposed to represent the decoded neighbor node); and the tree-based geometry model representing the reconstructed first: portion of the point cloud geometry comprises a local space-partitioning of first sub-volume determined by recursively splitting the first: sub-volume into local sub-volumes containing at least one point of the set of points([0177 and 0215]- the reconstructed points of the neighboring sub-volume are represented as a partition tree e.g. by dividing its local space according to the tree-based model.).
Regarding claim 8, ZHU discloses wherein: the local space-partitioning is a local space-partitioning tree; and the recursive splitting of the first: sub-volume is stopped based on: a local sub-volume corresponding to each leaf node of the local space partitioning tree contains at least one point of the set of points; or a size of a local sub-volume corresponding to one leaf node of the local space partitioning tree is below a minimum size([0177 and 0215]- the reconstructed points of the neighboring sub-volume are represented as a partition tree e.g. by dividing its local space according to the tree-based model.).
Regarding claim 9, the claim is interpreted and rejected for the same reason as set forth in claim 1. Hence; all limitations for claim 9 have been met in method claim 1.
Regarding claim 12, the claim is interpreted and rejected for the same reason as set forth in claim 5.
Regarding claim 13,ZHU discloses wherein: the reconstructed first portion of the point cloud geometry is a set of points derived based on points defined as centers of sub-volumes of a space-partitioning of tree-based geometry representation model representing the first portion of the point cloud geometry([0150-0151 and 0156]- three modes for geometry coding; Geometric coding based on partition tree; [0004]-discloses information of neighboring nodes using different types of geometric structures, ([0177]- when aiming to code a current sub-volume based on a partition tree model using a neighbor coded based on a different geometry coding mode, it is proposed to represent the decoded neighbor node)).
Regarding clam 14, Hur discloses wherein: triangle-based geometry representation model representing the reconstructed first portion of the point cloud geometry contained in the first sub-volume comprises at least one triangle defined by vertices located on edges of the first sub-volume([see in Fig. 4]- FIG. 6 shows an octree structure. The 3D space of the point cloud content according to the embodiments is represented by axes (e.g., X-axis, Y-axis, and Z-axis) of the coordinate system).
Regarding claim 15, the claim is interpreted and rejected for the same reason as set forth in claim 1. Hence; all limitations for claim 15 have been met in method claim 1.
Regarding claim 16, the claim is interpreted and rejected for the same reason as set forth in claim 3.
Regarding claim 17, ZHU disclose wherein: the third information indicates vertex information of at least one vertex along edge of the second sub-volume; and the vertex information is encoded based on the second information indicating vertex information of at least one vertex along at least one edge of the first sub-volume([0158, 0219-0220]- n Figure 7b, the root vertex can be selected first based on the specified root node selection rules. Then, the nearest neighbor in the constructed tree can be searched sequentially as the parent (i.e., ancestor). If the number of children (child nodes) of the parent is less than the preset maxChildNum (i.e., the maximum number of child nodes, also known as the child node number threshold)).
Regarding claim 18, the third information indicates a presence flag of at least one vertex on an edge of the second sub-volume([0109-0112]- A vertex present along an edge is detected when there is at least one occupied voxel adjacent to the edge among all blocks sharing the edge. The occupied voxel according to the embodiments refers to a voxel containing a point); and the presence flag is encoded based on the second information indicating a presence flag of at least one vertex along at least one edge of the first sub-volume([0109-0112]- A vertex present along an edge is detected when there is at least one occupied voxel adjacent to the edge among all blocks sharing the edge. The occupied voxel according to the embodiments refers to a voxel containing a point).
Regarding claim 19, the claim is interpreted and rejected for the same reason as set forth in claim 2.
Regarding claim 20, the claim is interpreted and rejected for the same reason as set forth in claim 4.
Claims 11 is rejected under 35 U.S.C. 103 as being unpatentable over ZHU in view of Hur as applied to claim 9 above and further in view of Platonov et al. ( US 2007/0182739 A1).
Regarding claim 11, the combination of ZHU and Hur do not explicitly disclose the fourth information indicates occupancy bit associated with each leaf node of a local spanning-tree representing the third portion of the point cloud geometry; and the third information is decoded based on the second information and the fourth information.
In an analogous art, Platonov discloses the fourth information indicates occupancy bit associated with each leaf node of a local spanning-tree representing the third portion of the point cloud geometry; and the third information is decoded based on the second information and the fourth information([see in claim text 13]- a first graph for determining said Euclidean minimum spanning tree (201) of a point cloud (200).). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the technique of Platonov to the modified system of ZHU and Hur to provide a semi-automatic or a fully automatic method of providing contour models intended for being superposed with an image of a real object. In addition thereto, as compared to manual generation of such a data model, higher accuracy is obtained on the basis of the CAD model which as a result will lead to an improved and more exact tracking method when the augmented reality system is used. An idea of the invention consists in determining, by way of the CAD model, which edges of the object to be tracked are of relevance to the subsequent optical contour-based tracking, in order to transform the same thereafter to a three-dimensional contour mode [Platonov; para 0013 ].
Allowable Subject Matter
Claim 10 is 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.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 10, The method of claim 9, wherein: the third information indicates occupancy bit associated with each leaf node of a local spanning-tree representing the second portion of the point cloud geometry; and the third information is decoded based on the second information indicating occupancy bits of leaf nodes of a local space-partitioning tree representing the reconstructed first portion of the point cloud geometry.
Citation of Pertinent Prior Art
The prior art are made of record and not relied upon but considered pertinent to applicant’s disclosure:
1. HU et al., US 2025/0373832 A1, discloses point cloud media technologies, including a point cloud file processing method and apparatus, a computer-readable medium, an electronic device, and a computer program product.
2. XU et. al., US 2024/0013477A1, discloses machine learning models for 3D scene modeling using neural point clouds.
3. NEILL; Richard, US 2022/0219402 A1, discloses methods and systems for fabrication of complex multi-dimensional objects, parts, assemblies, and structures.
4. SHAYANI et al., US 2020/0050710 A1, discloses techniques for generating designs that reflect stylistic preferences.
5. OH et al., US 2022/0360797 A1, discloses point cloud contents to provide a user with various services such as virtual reality (VR), augmented reality (AR), mixed reality (MR), and self-driving services.
6. PARK et al., US 2022/0239946 A1, discloses method for providing point cloud contents to provide a user with various services such as virtual reality (VR), augmented reality (AR), mixed reality (MR), and autonomous driving services.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MD NAZMUL HAQUE whose telephone number is (571)272-5328. The examiner can normally be reached IFW.
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/MD N HAQUE/Primary Examiner, Art Unit 2487
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