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 .
Response to Arguments
Applicant’s arguments filed 11/27/25 with respect to claims 1 and 8 have been read and considered but are moot because claims 1 and 8 are now rejected with Tourapis (US 2020/0021856) in view of Lasserre (US 2020/0211232). Peruse the rejection below for elaboration.
With regards to amendment to claims 1 and 8, Applicant asserts that allowable subject matter of previously dependent claim 2 is included onto claim 1. The Examiner respectfully disagrees. As filed in 10/7/24, previous claim 2 discloses “…wherein: the encoding of the geometry comprises: generating an octree of the geometry, wherein the octree includes one or more nodes corresponding to each of one or more spaces generated by recursively subdividing a bounding box including the points, wherein each of the nodes corresponds to any one of one or more levels of the octree, the one or more nodes including a root node corresponding to a lowest one of the one or more levels, and a leaf node corresponding to a highest one of the one or more levels, wherein the level corresponding to each of the nodes represents the number of hops from the root node to each of the nodes, wherein a bitstream including the point cloud data includes neighbor point set generation information”. Amended claim 1 does not include the limitations of previous claim 2. Neither amended claim 8 or new claim 16 discloses the limitations of previous claim 2.
More specifically, in previous rejection mailed 9/12/25, Examiner specifically states that claim 2 is objected to as comprising allowable subject matter because of “wherein the octree includes one or more nodes corresponding to each of one or more spaces generated by recursively subdividing a bounding box including the points”, and Applicant did not include the aforementioned limitation onto claims 1, 8 and 16.
For independent claims 1, 8, and new claim 16, Applicant just added the limitations “…based on an octree including nodes including one or more levels including the point cloud data” and “wherein a bitstream includes information related to a range for searching the neighbor point”.
With regards to the limitation “wherein a bitstream includes information related to a range for searching the neighbor point”, in 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. Then in 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. And in paragraph [392], Tourapis discloses implementing the 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, etcetera. Thus, Tourapis discloses “wherein a bitstream includes information related to a range for searching the neighbor point”.
And with regards to the limitation “…based on an octree including nodes including one or more levels including the point cloud data”, a new reference of Lasserre (US 2020/0211232) was included with this rejection for claims 1, 8 and 16. Thus, claims 1, 8 and 16 are now rejected under Tourapis (US 2020/0021856) in view of Lasserre (US 2020/0211232). Peruse the rejection below for elaboration.
Also, claims 1 and 8 are now rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 6 of U.S. Patent No. 11,601,488 and Tourapis (US 2020/0021856) in view of Lasserre (US 2020/0211232). Peruse the rejection below for elaboration.
And, claims 1 and 8 are now rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 8 of U.S. Patent No. 12,149,751 and Tourapis (US 2020/0021856) in view of Lasserre (US 2020/0211232). Peruse the rejection below for elaboration.
Claims 3 and 10-12 are still 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.
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 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Tourapis (US 2020/0021856) in view of Lasserre (US 2020/0211232).
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 one or more points of point cloud data based on an octree (paragraph [154], Tourapis discloses geometry encoder for encoding geometry of a point cloud data, wherein ); and
encoding an attribute of the one or more points 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 levels of detail of the one or more points (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.),
wherein a shift value is used to the search range (paragraph [458], Tourapis discloses that points can be shifted slightly, 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.; 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 a bitstream includes information related to a 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 one or more points of point cloud data based on an octree including nodes including one or more levels including the point cloud data. However, Lasserre teaches encoding a geometry representing a position of one or more points of point cloud data based on an octree including nodes including one or more levels 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]).
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 one or more points of the point cloud data () based on an octree (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 one or more points 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 levels of detail of the one or more points (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 in the level of details 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 shift value is used to the search range (paragraph [458], Tourapis discloses that points can be shifted slightly, 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.; 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 the bitstream includes information related to a 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 one or more points of the point cloud data based on an octree including nodes including one or more levels including the point cloud data. However, Lasserre teaches decoding a geometry representing a position of one or more points of the point cloud data based on an octree including nodes including one or more levels 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]).
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 one or more points of the point cloud data based on an octree (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 one or more points 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 levels of detail of the one or more points (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.),
wherein a shift value is used to the search range (paragraph [458], Tourapis discloses that points can be shifted slightly, 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.; 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 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 a 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 does not disclose encoding a geometry representing a position of one or more points of the point cloud data based on an octree including nodes including one or more levels including the point cloud data. However, Lasserre teaches encoding a geometry representing a position of one or more points of the point cloud data based on an octree including nodes including one or more levels 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]).
Allowable Subject Matter
Claims 3 and 10-12 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.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1 and 8 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 6 of U.S. Patent No. 11,601,488 and Tourapis (US 2020/0021856) in view of Lasserre (US 2020/0211232).
Regarding claim 1, claim 1 of present Application ’494 is similar to claim 1 of Patent ‘488 in that claim 1 of Patent ‘488 discloses most of the limitations of claim 1 of present Application ‘494. Peruse the table below.
Claim 1 of Patent ‘488 does not disclose wherein a shift value is used to the search range. However, Tourapis teaches wherein a shift value is used to the search range (paragraph [458], Tourapis discloses that points can be shifted slightly, 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.; 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). 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 claim 1 of Patent ‘488 and Tourapis together as a whole for reducing costs, resources and time for compressing and transporting point cloud data (Tourapis’ paragraph [39]).
Claim 1 of Patent ‘488 and Tourapis do not disclose encoding a geometry representing a position of one or more points of point cloud data based on an octree including nodes including one or more levels including the point cloud data. However, Lasserre teaches encoding a geometry representing a position of one or more points of point cloud data based on an octree including nodes including one or more levels 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 claim 1 of Patent ‘488, Tourapis and Lasserre together as a whole for permitting high coding efficiency of point cloud data (Lasserre’s paragraph [22]).
Regarding claim 8, claim 8 of present Application ‘494 is similar to claim 6 of Patent ‘488 in that claim 6 of Patent ‘488 discloses most of the limitations of claim 8 of present Application ‘494. Peruse the table below.
Claim 6 of Patent ‘488 does not disclose wherein a shift value is used to the search range. However, Tourapis teaches wherein a shift value is used to the search range (paragraph [458], Tourapis discloses that points can be shifted slightly, 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.; 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). 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 claim 6 of Patent ‘488 and Tourapis together as a whole for reducing costs, resources and time for compressing and transporting point cloud data (Tourapis’ paragraph [39]).
Claim 6 of Patent ‘488 and Tourapis do not disclose decoding a geometry representing a position of one or more points of the point cloud data based on an octree including nodes including one or more levels including the point cloud data. However, Lasserre teaches decoding a geometry representing a position of one or more points of the point cloud data based on an octree including nodes including one or more levels 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 claim 6 of Patent ‘488, Tourapis and Lasserre together as a whole for permitting high coding efficiency of point cloud data (Lasserre’s paragraph [22]).
Claims 1 and 8 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 8 of U.S. Patent No. 12,149,751 and Tourapis (US 2020/0021856) in view of Lasserre (US 2020/0211232).
Regarding claim 1, claim 1 of present Application ’494 is similar to claim 1 of Patent ‘751 in that claim 1 of Patent ‘751 discloses most of the limitations of claim 1 of present Application ‘494. Peruse the table below.
Claim 1 of Patent ‘751 does not disclose searching a neighbor point in the levels of detail based on a search range, wherein a shift value is used to the search range. However, Tourapis teaches 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.), wherein a shift value is used to the search range (paragraph [458], Tourapis discloses that points can be shifted slightly, 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.; 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). 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 claim 1 of Patent ‘751 and Tourapis together as a whole for reducing costs, resources and time for compressing and transporting point cloud data (Tourapis’ paragraph [39]).
Claim 1 of Patent ‘751 and Tourapis do not disclose encoding a geometry representing a position of one or more points of point cloud data based on an octree including nodes including one or more levels including the point cloud data. However, Lasserre teaches encoding a geometry representing a position of one or more points of point cloud data based on an octree including nodes including one or more levels 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 claim 1 of Patent ‘751, Tourapis and Lasserre together as a whole for permitting high coding efficiency of point cloud data (Lasserre’s paragraph [22]).
Regarding claim 8, claim 8 of present Application ‘494 is similar to claim 8 of Patent ‘751 in that claim 8 of Patent ‘751 discloses most of the limitations of claim 8 of present Application ‘494. Peruse the table below.
Claim 8 of Patent ‘751 does not disclose searching a neighbor point in the levels of detail based on a search range, wherein a shift value is used to the search range. However, Tourapis teaches 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.), wherein a shift value is used to the search range (paragraph [458], Tourapis discloses that points can be shifted slightly, 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.; 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). 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 claim 8 of Patent ‘751 and Tourapis together as a whole for reducing costs, resources and time for compressing and transporting point cloud data (Tourapis’ paragraph [39]).
Claim 8 of Patent ‘751 and Tourapis do not disclose decoding a geometry representing a position of one or more points of the point cloud data based on an octree including nodes including one or more levels including the point cloud data. However, Lasserre teaches decoding a geometry representing a position of one or more points of the point cloud data based on an octree including nodes including one or more levels 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 claim 8 of Patent ‘751, Tourapis and Lasserre together as a whole for permitting high coding efficiency of point cloud data (Lasserre’s paragraph [22]).
Peruse table below.
Present Application 18/908,494
US Patent No. 11,601,488
US Patent No. 12,149,751
Claim 1.
A method comprising: encoding a geometry representing a position of one or more points of point cloud data based on an octree including nodes including one or more levels including the point cloud data; and encoding an attribute of the one or more points of the point cloud data, wherein the encoding the attribute includes generating levels of detail of the one or more points, and searching a neighbor point in the levels of detail based on a search range, wherein a shift value is used to the search range, wherein a bitstream includes information related to a range for searching the neighbor point.
Claim 1.
A method for transmitting point cloud data, the method comprising: encoding point cloud data including geometry and attribute, the geometry representing positions of points of the point cloud data and the attribute including at least one of color and reflectance of the points, wherein: the attribute is encoded based on one or more LODs (Level Of Details) that are generated by reorganizing the points, one or more neighbor points of a point of a LOD of the one or more LODs are selected based on a maximum neighbor distance; and transmitting a bitstream including the encoded point cloud data, wherein the maximum neighbor distance is generated based on a LOD and a neighbor search range for the point, wherein the maximum neighbor distance is represented as 2.sup.LoD.sup.2×3×NN_range, and wherein the LoD represents a level of LOD of the point, and the NN_range is a neighbor point search range that represents a number of one or more octree nodes around the point.
Claim 1.
A method of transmitting point cloud data by an apparatus, the method comprising: encoding a geometry representing a position of one or more points of the point cloud data; encoding an attribute of the one or more points of the point cloud data; and transmitting a bitstream including the geometry and the attribute, wherein the encoding the attribute includes generating level of details of the one or more points, wherein the one or more points are sorted based on Morton codes, wherein the encoding the attribute further includes generating neighbor points for the one or more points, wherein a position of a point in a level of the level of details is shifted.
Claim 8.
A method comprising: obtaining a bitstream including point cloud data; decoding a geometry representing a position of one or more points of the point cloud data based on an octree including nodes including one or more levels including the point cloud data; and decoding an attribute of the one or more points of the point cloud data, wherein the decoding the attribute includes generating levels of detail of the one or more points, searching a neighbor point in the level of details based on a search range, wherein a shift value is used to the search range, wherein the bitstream includes information related to a range for searching the neighbor point.
Claim 6.
A method for processing point cloud data, the method comprising: receiving a bitstream including the point cloud data, the point cloud data including geometry and attribute, the geometry representing positions of points of the point cloud data, and the attribute including at least one of color and reflectance of the points; and decoding the point cloud data, wherein the attribute is decoded based on one or more LODs (Level Of Details) that are generated by reorganizing the points, one or more neighbor points of a point of a LOD of the one or more LODs are selected based on a maximum neighbor distance, wherein the maximum neighbor distance is generated based on a LOD and a neighbor search range for the point, wherein the maximum neighbor distance is represented as 2.sup.LoD.sup.2×3×NN_range, and wherein the LoD represents a level of LOD of the point, and the NN_range is a neighbor point search range that represents a number of one or more octree nodes around the point.
Claim 8.
A method of receiving point cloud data by an apparatus, the method comprising: receiving a bitstream including point cloud data; and decoding a geometry representing a position of one or more points of the point cloud data; decoding an attribute of the one or more points of the point cloud data, wherein the decoding the attribute includes generating level of details of the one or more points, wherein the one or more points are sorted based on Morton codes, wherein the decoding the attribute further includes generating neighbor points for the one or more points, wherein a position of a point in a level of the level of details is shifted.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Contact Information
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sath V Perungavoor can be reached on 571-272-7455. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ALLEN C WONG/Primary Examiner, Art Unit 2488