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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/1/26 has been entered.
Response to Arguments
Applicant’s arguments filed 4/14/26 with respect to claims 1, 8, 11 and 16 have been read and considered but are moot because claims 1, 8, 11 and 16 are now rejected with Tourapis (US 2020/0021856), Lasserre (US 2020/0211232) and Rudolf (US 2018/0137671) in view of Shin (US 2018/0174353). Peruse the rejection below for elaboration.
Claim Rejections - 35 USC § 103
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 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.
Claims 1, 8, 11 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Tourapis (US 2020/0021856), Lasserre (US 2020/0211232) and Rudolf (US 2018/0137671) in view of Shin (US 2018/0174353).
Regarding claim 1, Tourapis discloses a method (paragraph [53], Tourapis discloses element 104 is an encoder for executing a method of encoding point cloud data) comprising:
encoding a geometry representing a position of a point of point cloud data based on an octree including the point cloud data (paragraph [154], Tourapis discloses geometry encoder for encoding geometry of a point cloud data, wherein paragraph [67], Tourapis discloses spatial encoder is implemented to utilize octrees for compressing spatial information for points of a point cloud data); and
encoding an attribute of the point of the point cloud data (paragraph [53], Tourapis discloses encoding or compressing attribute information of a point cloud data),
wherein the encoding the attribute includes generating a level of detail (paragraph [57], Tourapis discloses LODs (level of details) are generated for point cloud data, and paragraph [151], Tourapis discloses encoder for generating compressed attribute information that includes LOD information as illustrated in fig.8), and
searching a neighbor point for the point in the level of detail based on a search range (paragraph [203], Tourapis discloses search ranges (SR1 and SR2) is applied to find points including a level of detail and a range of SR2 is applied to search for points in a simplified nearest neighbor search for attribute value prediction, wherein paragraph [392], Tourapis discloses utilizing adaptive scanning offset mode for determining a sampling offset or a shift value to apply for determining a sampling mode or search range of points to be sampled for providing better rate distortion performance during the coding and prediction process for each LOD (level of detail), and the sampling can begin at offset or shifted value at second or third Morton code, etc.),
wherein a bitstream includes information related to the search range for searching the neighbor point (paragraph [119], Tourapis discloses an encoder for encoding a bitstream that includes values defining the neighborhood size for signaling the range or area around point P for searching the neighbor point, wherein paragraph [203], Tourapis discloses search ranges (SR1 and SR2) is applied to find points including a level of detail and a range of SR2 is applied to search for points in a simplified nearest neighbor search for attribute value prediction, wherein paragraph [392], Tourapis discloses utilizing adaptive scanning offset mode for determining a sampling offset or a shift value to apply for determining a sampling mode or search range of points to be sampled for providing better rate distortion performance during the coding and prediction process for each LOD (level of detail), and the sampling can begin at offset or shifted value at second or third Morton code, etc.).
Tourapis does not disclose encoding a geometry representing a position of a point of point cloud data based on an octree including nodes including the point cloud data. However, Lasserre teaches encoding a geometry representing a position of a point of point cloud data based on an octree including nodes including the point cloud data (paragraph [123], Lasserre discloses the encoding of octree structures for representing a geometry of points for a point cloud, wherein the octree structure comprises leaf nodes, and paragraph [171], in fig.6, Lasserre discloses that octree structure can be subdivided into multiple nodes, and paragraph [172], Lasserre discloses that the nodes can comprise multiple depths or levels, with “depth 1”, “depth 2”, etcetera, and paragraph [234], Lasserre discloses that octree-based structure comprises nodes that includes depth levels for representing point cloud data). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Tourapis and Lasserre together as a whole for permitting high coding efficiency of point cloud data (Lasserre’s paragraph [22]).
Tourapis and Lasserre do not disclose “searching a neighboring point for the point in the level of detail based on a center point and a search range around the center point”. However, Rudolf teaches searching a neighbor point for the point in the level of detail based on a center point (paragraph [52], Rudolf discloses the construction of LODs (level of detail), with LOD1, LOD2 and LOD3, wherein the most important points are chosen during the search for searching the nearest point that is closest to the center of the voxel 506, and determining the criteria for picking points around the geometric center of each quadrant/octant, thus, Rudolf discloses searching a neighbor point for the point in the level of detail based on a center point).
Since Tourapis discloses “…searching a neighboring point for the point in the level of detail based on a search range”, and Rudolf discloses “…searching a neighbor point for the point in the level of detail based on a center point”, therefore, by simple substitution, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Tourapis, Lasserre and Rudolf together as a whole for ascertaining the limitation “searching a neighboring point for the point in the level of detail based on a center point and a search range around the center point” in order to render high quality images while maintaining high-frequency detail in dense parts of a scene (Rudolf’s paragraph [11]).
Tourapis, Lasserre and Rudolf do not disclose wherein the center point is related to a Morton code of the point. However, Shin teaches wherein the center point is related to a Morton code of the point (paragraph [83], Shin discloses generating a Morton code indicating a location of each of the primitives, wherein the location of the primitive refers to a location of a center of the primitive in a 3D space, in that the coordinate of a center point of each of the primitives is obtained, thus Shin discloses the center point of the primitive is related with a Morton code of the point). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Tourapis, Lasserre, Rudolf and Shin together as a whole for reducing computation and power consumption while generating three-dimensional image data of the object for display (Shin’s paragraph [169]).
Regarding claim 8, Tourapis discloses a method (paragraph [53], Tourapis discloses element 116 is a decoder for executing a method of decoding point cloud data) comprising:
obtaining a bitstream including point cloud data (paragraph [154], Tourapis discloses receiving a bitstream that includes a geometry bitstream representing point cloud positions of a point cloud data to be decoded by a geometry decoder);
decoding a geometry representing a position of a point of the point cloud data based on an octree including the point cloud data (paragraph [154], Tourapis discloses decoding a geometry bitstream representing point cloud positions of a point cloud data, wherein paragraph [67], Tourapis discloses implementation of octrees for spatially compressing points of a point cloud to determine the spatial structural makeup of the points of the point cloud as illustrated in figs.6A-B and fig.7, and transmit the encoded octree structure to a decoder); and
decoding an attribute of the point of the point cloud data (paragraph [155], Tourapis discloses compressed attributes of point cloud data is received by a decoder to be decoded; paragraph [238], Tourapis discloses attribute information of a point cloud data is received at the decoder at step 1102),
wherein the decoding the attribute includes generating a level of detail (paragraph [57], Tourapis discloses LODs (level of details) are generated for point cloud data, and paragraph [156], Tourapis discloses encoder for generating compressed attribute information that includes LOD information to be decoded at the decoder 220 for decoding the compressed attribute information that includes the LOD structure and algorithm to generate the level of details of the compressed point cloud data for reorganizing the points of the point cloud data for decompression),
searching a neighbor point for the point in the level of detail based on a search range (paragraph [203], Tourapis discloses search ranges (SR1 and SR2) is applied to find points including a level of detail and a range of SR2 is applied to search for points in a simplified nearest neighbor search for attribute value prediction, wherein paragraph [392], Tourapis discloses utilizing adaptive scanning offset mode for determining a sampling offset or a shift value to apply for determining a sampling mode or search range of points to be sampled for providing better rate distortion performance during the coding and prediction process for each LOD (level of detail), and the sampling can begin at offset or shifted value at second or third Morton code, etc.),
wherein the bitstream includes information related to the search range for searching the neighbor point (paragraph [119], Tourapis discloses an encoder for encoding a bitstream that includes values defining the neighborhood size for signaling the range or area around point P for searching the neighbor point, wherein paragraph [203], Tourapis discloses search ranges (SR1 and SR2) is applied to find points including a level of detail and a range of SR2 is applied to search for points in a simplified nearest neighbor search for attribute value prediction, wherein paragraph [392], Tourapis discloses utilizing adaptive scanning offset mode for determining a sampling offset or a shift value to apply for determining a sampling mode or search range of points to be sampled for providing better rate distortion performance during the coding and prediction process for each LOD (level of detail), and the sampling can begin at offset or shifted value at second or third Morton code, etc.).
Tourapis does not disclose decoding a geometry representing a position of a point of the point cloud data based on an octree including nodes including the point cloud data. However, Lasserre teaches decoding a geometry representing a position of a point of the point cloud data based on an octree including nodes including the point cloud data (paragraph [275], Lasserre discloses the decoding of point cloud data based on geometry obtained from the encoder, wherein paragraph [123], Lasserre discloses the encoding of octree structures for representing a geometry of points for a point cloud, wherein the octree structure comprises leaf nodes, and paragraph [171], in fig.6, Lasserre discloses that octree structure can be subdivided into multiple nodes, and paragraph [172], Lasserre discloses that the nodes can comprise multiple depths or levels, with “depth 1”, “depth 2”, etcetera, and paragraph [234], Lasserre discloses that octree-based structure comprises nodes that includes depth levels for representing point cloud data). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Tourapis and Lasserre together as a whole for permitting high coding efficiency of point cloud data (Lasserre’s paragraph [22]).
Tourapis and Lasserre do not disclose searching a neighbor point for the point in the level of detail based on a center point and a search range around the center point. However, Rudolf teaches searching a neighbor point for the point in the level of detail based on a center point (paragraph [52], Rudolf discloses the construction of LODs (level of detail), with LOD1, LOD2 and LOD3, wherein the most important points are chosen during the search for searching the nearest point that is closest to the center of the voxel 506, and determining the criteria for picking points around the geometric center of each quadrant/octant, thus, Rudolf discloses searching a neighbor point for the point in the level of detail based on a center point).
Since Tourapis discloses “…searching a neighbor point for the point in the level of detail based on a search range”, and Rudolf discloses “…searching a neighbor point for the point in the level of detail based on a center point”, therefore, by simple substitution, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Tourapis, Lasserre and Rudolf together as a whole for ascertaining the limitation “searching a neighboring point for the point in the level of detail based on a center point and a search range around the center point” in order to render high quality images while maintaining high-frequency detail in dense parts of a scene (Rudolf’s paragraph [11]).
Tourapis, Lasserre and Rudolf do not disclose wherein the center point is related to a Morton code of the point. However, Shin teaches wherein the center point is related to a Morton code of the point (paragraph [83], Shin discloses generating a Morton code indicating a location of each of the primitives, wherein the location of the primitive refers to a location of a center of the primitive in a 3D space, in that the coordinate of a center point of each of the primitives is obtained, thus Shin discloses the center point of the primitive is related with a Morton code of the point). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Tourapis, Lasserre, Rudolf and Shin together as a whole for reducing computation and power consumption while generating three-dimensional image data of the object for display (Shin’s paragraph [169]).
Regarding claim 11, Tourapis and Lasserre do not disclose wherein the center point selected based on the Morton code of the point is a point having a Morton code value closest to the Morton code of the point.
However, Rudolf discloses wherein the center point selected is based on the point closest to the center of the voxel (paragraph [52], Rudolf discloses the construction of LODs (level of detail), with LOD1, LOD2 and LOD3, wherein the most important points are chosen during the search for searching the nearest point that is closest to the center of the voxel 506, and determining the criteria for picking points around the geometric center of each quadrant/octant, thus, Rudolf discloses searching a neighbor point for the point in the level of detail based on a center point). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Tourapis, Lasserre and Rudolf together as a whole for rendering high quality images while maintaining high-frequency detail in dense parts of a scene (Rudolf’s paragraph [11]).
Tourapis, Lasserre and Rudolf do not disclose wherein the center point selected based on the Morton code of the point is a point having a Morton code value closest to the Morton code of the point. However, Shin teaches the center point is related to a Morton code of the point (paragraph [83], Shin discloses generating a Morton code indicating a location of each of the primitives, wherein the location of the primitive refers to a location of a center of the primitive in a 3D space, in that the coordinate of a center point of each of the primitives is obtained, thus Shin discloses the center point of the primitive is related with a Morton code of the point).
Since Rudolf discloses “wherein the center point selected is based on the point closest to the center of the voxel”, and Shin discloses “the center point is related to a Morton code of the point”, therefore, by simple substitution, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Tourapis, Lasserre, Rudolf and Shin together as a whole for ascertaining the limitation “wherein the center point selected based on the Morton code of the point is a point having a Morton code value closest to the Morton code of the point” in order to reduce computation and power consumption while generating three-dimensional image data of the object for display (Shin’s paragraph [169]).
Regarding claim 16, Tourapis discloses a method of transmitting data for point cloud data (paragraph [53], Tourapis discloses element 104 is an encoder for executing a method of encoding point cloud data to transmit the point cloud data to decoder 116) comprising:
obtaining a bitstream (paragraph [53], Tourapis discloses element 104 is an encoder for executing a method of encoding point cloud data to transmit the point cloud data to be received and obtained by a decoder 116) generated by:
encoding a geometry representing a position of a point of the point cloud data based on an octree including the point cloud data (paragraph [154], Tourapis discloses geometry encoder for encoding geometry of a point cloud data, wherein paragraph [67], Tourapis discloses implementation of octrees for spatially compressing points of a point cloud to determine the spatial structural makeup of the points of the point cloud as illustrated in figs.6A-B and fig.7); and
encoding an attribute of the point of the point cloud data (paragraph [53], Tourapis discloses encoding or compressing attribute information of a point cloud data),
wherein the encoding the attribute includes generating a level of detail (paragraph [57], Tourapis discloses LODs (level of details) are generated for point cloud data, and paragraph [151], Tourapis discloses encoder for generating compressed attribute information that includes LOD information as illustrated in fig.8), and searching a neighbor point in the levels of detail based on a search range (paragraph [203], Tourapis discloses search ranges (SR1 and SR2) is applied to find points including a level of detail and a range of SR2 is applied to search for points in a simplified nearest neighbor search for attribute value prediction, wherein paragraph [392], Tourapis discloses utilizing adaptive scanning offset mode for determining a sampling offset or a shift value to apply for determining a sampling mode or search range of points to be sampled for providing better rate distortion performance during the coding and prediction process for each LOD (level of detail), and the sampling can begin at offset or shifted value at second or third Morton code, etc.), and
searching a neighbor point for the point in the level of detail based on a search range (paragraph [203], Tourapis discloses search ranges (SR1 and SR2) is applied to find points including a level of detail and a range of SR2 is applied to search for points in a simplified nearest neighbor search for attribute value prediction, wherein paragraph [392], Tourapis discloses utilizing adaptive scanning offset mode for determining a sampling offset or a shift value to apply for determining a sampling mode or search range of points to be sampled for providing better rate distortion performance during the coding and prediction process for each LOD (level of detail), and the sampling can begin at offset or shifted value at second or third Morton code, etc.),
wherein the encoded geometry and the encoded attribute are included in a bitstream (paragraph [149], Tourapis discloses an encoder is utilized for encoding attribute information for a point cloud data into a bitstream to be transmitted to a decoder, and paragraph [154], Tourapis discloses that geometry encoder is utilized for encoding the geometry information of a point cloud data into a bitstream to be transmitted to a decoder), and
wherein the bitstream includes information related to the search range for searching the neighbor point (paragraph [119], Tourapis discloses an encoder for encoding a bitstream that includes values defining the neighborhood size for signaling the range or area around point P for searching the neighbor point, wherein paragraph [203], Tourapis discloses search ranges (SR1 and SR2) is applied to find points including a level of detail and a range of SR2 is applied to search for points in a simplified nearest neighbor search for attribute value prediction, wherein paragraph [392], Tourapis discloses utilizing adaptive scanning offset mode for determining a sampling offset or a shift value to apply for determining a sampling mode or search range of points to be sampled for providing better rate distortion performance during the coding and prediction process for each LOD (level of detail), and the sampling can begin at offset or shifted value at second or third Morton code, etc.); and
transmitting data for the point cloud data including the bitstream (paragraph [53], Tourapis discloses element 104 is an encoder for executing a method of encoding point cloud data to transmit the point cloud data to decoder 116, wherein paragraph [149], Tourapis discloses an encoder is utilized for encoding attribute information for a point cloud data into a bitstream to be transmitted to a decoder, and paragraph [154], Tourapis discloses that geometry encoder is utilized for encoding the geometry information of a point cloud data into a bitstream to be transmitted to a decoder).
Tourapis and Lasserre do not disclose searching a neighbor point for the point in the level of detail based on a center point and a search range around the center point. However, Rudolf teaches searching a neighbor point for the point in the level of detail based on a center point (paragraph [52], Rudolf discloses the construction of LODs (level of detail), with LOD1, LOD2 and LOD3, wherein the most important points are chosen during the search for searching the nearest point that is closest to the center of the voxel 506, and determining the criteria for picking points around the geometric center of each quadrant/octant, thus, Rudolf discloses searching a neighbor point for the point in the level of detail based on a center point).
Since Tourapis discloses “…searching a neighbor point for the point in the level of detail based on a search range”, and Rudolf discloses “…searching a neighbor point for the point in the level of detail based on a center point”, therefore, by simple substitution, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Tourapis, Lasserre and Rudolf together as a whole for ascertaining the limitation “searching a neighboring point for the point in the level of detail based on a center point and a search range around the center point” in order to render high quality images while maintaining high-frequency detail in dense parts of a scene (Rudolf’s paragraph [11]).
Tourapis, Lasserre and Rudolf do not disclose wherein the center point is related to a Morton code of the point. However, Shin teaches wherein the center point is related to a Morton code of the point (paragraph [83], Shin discloses generating a Morton code indicating a location of each of the primitives, wherein the location of the primitive refers to a location of a center of the primitive in a 3D space, in that the coordinate of a center point of each of the primitives is obtained, thus Shin discloses the center point of the primitive is related with a Morton code of the point). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Tourapis, Lasserre, Rudolf and Shin together as a whole for reducing computation and power consumption while generating three-dimensional image data of the object for display (Shin’s paragraph [169]).
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALLEN C WONG whose telephone number is (571)272-7341. The examiner can normally be reached on Flex Monday-Thursday 9:30am-7:30pm.
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/ALLEN C WONG/Primary Examiner, Art Unit 2488
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