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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 03/12/2024 and 10/27/2025 are being considered by the examiner.
Claim Objections
Claims 1, 2, 7, 10, and 20 are objected to because of the following informalities:
In claim 1, lines 4-22, the bullets/dashes should be removed in order to have the claim written in a proper format with only indentations to indicate sub-clauses.
In claim 2, lines 4-22, the bullets/dashes should be removed in order to have the claim written in a proper format with only indentations to indicate sub-clauses.
In claim 7, lines 4-11, the bullets/dashes should be removed in order to have the claim written in a proper format with only indentations to indicate sub-clauses.
In claim 10, lines 5-23, the bullets/dashes should be removed in order to have the claim written in a proper format with only indentations to indicate sub-clauses.
In claim 20, lines 3-10, the bullets/dashes should be removed in order to have the claim written in a proper format with only indentations to indicate sub-clauses.
Appropriate correction is required.
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 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 of this title, 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 1-11, 13, and 15-22 are rejected under 35 U.S.C. 103 as being unpatentable over OH (US 20250322549 A1), hereinafter referenced as OH in view of ZHANG (US 20210383575 A1), hereinafter referenced as ZHANG.
Regarding claim 1, OH teaches a method of encoding, into a bitstream (Figs. 1-2, Paragraph [0318] – OH discloses the encoded point cloud data (bitstream) according to the embodiments may be generated by the point cloud video encoder 10002 in FIG. 1, the encoding process 20001 in FIG. 2.),
point cloud geometry data (Fig. 15, Paragraph [0247] – OH discloses the point cloud data transmission method 15000 according to the embodiments includes encoding geometry information of point cloud data and encoding attribute information of the point cloud data.)
represented by geometrical elements occupying some discrete positions of a set of discrete positions of a multi-dimensional space (Fig. 15, Paragraph [0243] – OH discloses the geometry data represents three-dimensional (3D) position information (e.g., a coordinate value of X, Y, and Z axes) of each point. That is, the position of each point is represented by parameters in a coordinate system representing a 3D space (e.g., parameters (x, y, z) of three axes, i.e., X, Y, and Z axes, representing a space).), wherein the method comprises:
obtaining a series of at least one binary data (fj,n) (Fig. 4, Paragraph [0149] – OH discloses the point cloud content providing system or the point cloud encoder may perform entropy encoding (intra encoding) based on the occupancy code of the current node and the occupancy of neighboring nodes, or perform entropy encoding (inter encoding) based on the occupancy code of the previous frame [wherein the occupancy codes are a series of at least one binary data].)
representative of an occupancy data of at least one neighboring geometrical element belonging to a causal neighborhood of a current geometrical element of the multi-dimensional space (Fig. 4, Paragraph [0137] – OH discloses the occupancy code is represented as an 8-bit code. That is, when at least one point is contained in the space corresponding to a child node, the node is assigned a value of 1. When no point is contained in the space corresponding to the child node (the space is empty), the node is assigned a value of 0. The point cloud encoder (for example, the arithmetic encoder 40004) according to the embodiments may perform entropy encoding on the occupancy codes.);
obtaining a first index (I1) from the series of at least one binary data (fj,n) (Fig. 7, Paragraph [0150] – OH discloses FIG. 7 illustrates a process of obtaining an occupancy pattern based on the occupancy of neighbor nodes.),
Although OH further teaches the first index (I1) being representative of a neighborhood occupancy configuration among a first set of neighborhood occupancy configuration indices representative of potential neighborhood occupancy configurations (Fig. 7, Paragraph [0150] – OH discloses the point cloud encoder according to the embodiments determines occupancy of neighbor nodes of each node of the octree and obtains a value of a neighbor pattern. The neighbor node pattern is used to infer the occupancy pattern of the node.);
OH fails to explicitly teach obtaining a second index (I2) by applying an index range reduction function (F) to the first index (I1); the second index (I2) being representative of the neighborhood occupancy configuration among a second set of neighborhood occupancy configuration indices representative of the potential neighborhood occupancy configurations; each index of the first set of neighborhood occupancy configuration indices being mapped to an index of the second set of neighborhood occupancy configuration indices according to the index range reduction function; and the range of the second set of neighborhood occupancy configuration indices being lower than the range of the first set of neighborhood occupancy configuration indices; and entropy encoding, into the bitstream, at least one binary data (fj) representative of an occupancy data of the current geometrical element based on the second index (I2).
However, ZHANG explicitly teaches obtaining a second index (I2) by applying an index range reduction function (F) to the first index (I1) (Fig. 3B, Paragraph [0058] – ZHANG discloses gps_context_reduction_level [wherein gps_context_reduction_level is an index range reduction function] may specify the context reduction level in geometry parameter set [wherein geometry parameter set is the first index]. Paragraph [0059] - ZHANG further discloses the hash table 300B may be used to cache occupancy information [wherein occupancy information is second index].);
the second index (I2) being representative of the neighborhood occupancy configuration among a second set of neighborhood occupancy configuration indices representative of the potential neighborhood occupancy configurations (Fig. 3A-B, Paragraph [0059] – ZHANG discloses when encoding/decoding the occupancy value of current node, the occupancy information of neighboring nodes is obtained from the hash table H.sub.d. After encoding/decoding an occupancy value of the current node, the coded occupancy value is then stored in H.sub.d.);
each index of the first set of neighborhood occupancy configuration indices being mapped to an index of the second set of neighborhood occupancy configuration indices according to the index range reduction function (Fig. 4, Paragraph [0062] – ZHANG discloses at 404, the method 400 may include reducing a number of contexts associated with the received data based on occupancy data corresponding to one or more parent nodes and one or more child nodes within the received data. See also Paragraph [0059].);
and entropy encoding, into the bitstream, at least one binary data (fj) representative of an occupancy data of the current geometrical element based on the second index (I2) (Fig. 3B, Paragraph [0059] – ZHANG discloses when encoding/decoding the occupancy value of current node, the occupancy information of neighboring nodes is obtained from the hash table H.sub.d. After encoding/decoding an occupancy value of the current node, the coded occupancy value is then stored in H.sub.d.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of OH of having a method of encoding, into a bitstream, point cloud geometry data represented by geometrical elements occupying some discrete positions of a set of discrete positions of a multi-dimensional space, wherein the method comprises: obtaining a series of at least one binary data (fj,n) representative of an occupancy data of at least one neighboring geometrical element belonging to a causal neighborhood of a current geometrical element of the multi-dimensional space; obtaining a first index (I1) from the series of at least one binary data (fj,n), the first index (I1) being representative of a neighborhood occupancy configuration among a first set of neighborhood occupancy configuration indices representative of potential neighborhood occupancy configurations; with the teachings of ZHANG having obtaining a second index (I2) by applying an index range reduction function (F) to the first index (I1); the second index (I2) being representative of the neighborhood occupancy configuration among a second set of neighborhood occupancy configuration indices representative of the potential neighborhood occupancy configurations; each index of the first set of neighborhood occupancy configuration indices being mapped to an index of the second set of neighborhood occupancy configuration indices according to the index range reduction function; and the range of the second set of neighborhood occupancy configuration indices being lower than the range of the first set of neighborhood occupancy configuration indices; and entropy encoding, into the bitstream, at least one binary data (fj) representative of an occupancy data of the current geometrical element based on the second index (I2).
Wherein having OH's method of encoding, into a bitstream, point cloud geometry data wherein the method comprises: obtaining a second index (I2) by applying an index range reduction function (F) to the first index (I1); the second index (I2) being representative of the neighborhood occupancy configuration among a second set of neighborhood occupancy configuration indices representative of the potential neighborhood occupancy configurations; each index of the first set of neighborhood occupancy configuration indices being mapped to an index of the second set of neighborhood occupancy configuration indices according to the index range reduction function; and the range of the second set of neighborhood occupancy configuration indices being lower than the range of the first set of neighborhood occupancy configuration indices; and entropy encoding, into the bitstream, at least one binary data (fj) representative of an occupancy data of the current geometrical element based on the second index (I2).
The motivation behind the modification would have been to obtain a method of encoding point cloud geometry data for efficiently processing a large amount of point cloud data using point cloud compression by considering a relevant subset of neighboring nodes from among all neighboring nodes, since both OH and ZHANG relate to point cloud data processing methods and systems, wherein OH has a point cloud data processing method and device for addressing latency and encoding/decoding complexity, and reduce the data error, while ZHANG has a system, method and computer program that compresses and decompresses point cloud data based on reducing an expanded set of contexts associated with the point cloud data, thus improving the performance of parent-node-level context while keeping the number of contexts small by reducing the number of contexts presented. Please see OH (US 20250322549 A1), Paragraph [0149, 0511], and ZHANG (US 20210383575 A1), Paragraph [0026, 0028].
Regarding claim 2, OH teaches a method of decoding, from a bitstream (Figs. 1-2, Paragraph [0320] – OH discloses the decoded point cloud data (bitstream) according to the embodiments may be decoded by the point cloud video decoder 10006 in FIG. 1, the decoding process 20003 in FIG. 2.),
point cloud geometry data (Fig. 16, Paragraph [0253] – OH discloses FIG. 16 is a block diagram illustrating a point cloud data reception method 16000 according to embodiments. The reception method 16000 includes decoding geometry information and then decoding attribute information.)
represented by geometrical elements occupying some discrete positions of a set of discrete positions of a multi-dimensional space (Fig. 16, Paragraph [0243] – OH discloses the geometry data represents three-dimensional (3D) position information (e.g., a coordinate value of X, Y, and Z axes) of each point. That is, the position of each point is represented by parameters in a coordinate system representing a 3D space (e.g., parameters (x, y, z) of three axes, i.e., X, Y, and Z axes, representing a space)), wherein the method comprises:
obtaining a series of at least one binary data (fj,n) (Fig. 11, Paragraph [0174] - OH discloses the point cloud decoder may receive a geometry bitstream [wherein geometry bitstream is a series of at least one binary data].)
based on occupancy data of precedingly decoded geometrical elements belonging to a causal neighborhood of a current geometrical element of the multi-dimensional space (Fig. 11, Paragraph [0181] - OH discloses the octree synthesizer 11001 according to the embodiments may generate an octree by acquiring an occupancy code from the decoded geometry bitstream (or information on the geometry secured as a result of decoding).);
obtaining, a first index (I1) from the series of at least one binary data (fj,n) (Fig. 7, Paragraph [0150] – OH discloses FIG. 7 illustrates a process of obtaining an occupancy pattern based on the occupancy of neighbor nodes.),
Although OH further teaches the first index (I1) being representative of a neighborhood occupancy configuration among a first set of neighborhood occupancy configuration indices representative of potential neighborhood occupancy configurations (Fig. 7, Paragraph [0150] – OH discloses the point cloud encoder according to the embodiments determines occupancy of neighbor nodes of each node of the octree and obtains a value of a neighbor pattern. The neighbor node pattern is used to infer the occupancy pattern of the node.);
OH fails to explicitly teach obtaining a second index (I2) by applying an index range reduction function (F) to the first index (I1); the second index (I2) is representative of the neighborhood occupancy configuration among a second set of neighborhood occupancy configuration indices representative of the potential neighborhood occupancy configurations; each index of the first set of neighborhood occupancy configuration indices being mapped to an index of the second set of neighborhood occupancy configuration indices according to the index range reduction function; and the range of the second set of neighborhood occupancy configuration indices being lower than the range of the first set of neighborhood occupancy configuration indices; and the range of the second set of neighborhood occupancy configuration indices being lower than the range of the first set of neighborhood occupancy configuration indices; and entropy decoding, from the bitstream, at least one binary data (fj) representative of an occupancy data of the current geometrical element based on the second index (I2).
However, ZHANG explicitly teaches obtaining a second index (I2) by applying an index range reduction function (F) to the first index (I1) (Fig. 3B, Paragraph [0058] – ZHANG discloses gps_context_reduction_level [wherein gps_context_reduction_level is an index range reduction function] may specify the context reduction level in geometry parameter set [wherein geometry parameter set is the first index]. Paragraph [0059] - ZHANG further discloses the hash table 300B may be used to cache occupancy information [wherein occupancy information is second index].);
the second index (I2) is representative of the neighborhood occupancy configuration among a second set of neighborhood occupancy configuration indices representative of the potential neighborhood occupancy configurations (Fig. 3A-B, Paragraph [0059] – ZHANG discloses when encoding/decoding the occupancy value of current node, the occupancy information of neighboring nodes is obtained from the hash table H.sub.d. After encoding/decoding an occupancy value of the current node, the coded occupancy value is then stored in H.sub.d.);
each index of the first set of neighborhood occupancy configuration indices being mapped to an index of the second set of neighborhood occupancy configuration indices according to the index range reduction function (Fig. 4, Paragraph [0062] – ZHANG discloses at 404, the method 400 may include reducing a number of contexts associated with the received data based on occupancy data corresponding to one or more parent nodes and one or more child nodes within the received data. See also Paragraph [0059].);
and the range of the second set of neighborhood occupancy configuration indices being lower than the range of the first set of neighborhood occupancy configuration indices (Fig. 7, Paragraph [0095]) – ZHANG discloses Point Cloud Compression 96 may reduce an expanded set of neighboring nodes for a current node for compression and decompression of point cloud data.);
and entropy decoding, from the bitstream, at least one binary data (fj) representative of an occupancy data of the current geometrical element based on the second index (I2) (Fig. 4, Paragraph [0063] - ZHANG discloses at 406, the method 400 may include decoding the data corresponding to the point cloud based on the reduced number of contexts. See also Fig. 3B, Paragraph [0059].).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of OH of having a method of decoding, from a bitstream, point cloud geometry data represented by geometrical elements occupying some discrete positions of a set of discrete positions of a multi-dimensional space, wherein the method comprises: obtaining, a series of at least one binary data (fj,n) based on occupancy data of precedingly decoded geometrical elements belonging to a causal neighborhood of a current geometrical element of the multi-dimensional space; obtaining, a first index (I1) from the series of at least one binary data (fj,n), the first index (I1) being representative of a neighborhood occupancy configuration among a first set of neighborhood occupancy configuration indices representative of potential neighborhood occupancy configurations; with the teachings of ZHANG having obtaining a second index (I2) by applying an index range reduction function (F) to the first index (I1); the second index (I2) is representative of the neighborhood occupancy configuration among a second set of neighborhood occupancy configuration indices representative of the potential neighborhood occupancy configurations; each index of the first set of neighborhood occupancy configuration indices being mapped to an index of the second set of neighborhood occupancy configuration indices according to the index range reduction function; and the range of the second set of neighborhood occupancy configuration indices being lower than the range of the first set of neighborhood occupancy configuration indices; and entropy decoding, from the bitstream, at least one binary data (fj) representative of an occupancy data of the current geometrical element based on the second index (I2).
Wherein having OH's method of decoding, from a bitstream, point cloud geometry data wherein the method comprises: obtaining a second index (I2) by applying an index range reduction function (F) to the first index (I1); the second index (I2) is representative of the neighborhood occupancy configuration among a second set of neighborhood occupancy configuration indices representative of the potential neighborhood occupancy configurations; each index of the first set of neighborhood occupancy configuration indices being mapped to an index of the second set of neighborhood occupancy configuration indices according to the index range reduction function; and the range of the second set of neighborhood occupancy configuration indices being lower than the range of the first set of neighborhood occupancy configuration indices; and entropy decoding, from the bitstream, at least one binary data (fj) representative of an occupancy data of the current geometrical element based on the second index (I2).
The motivation behind the modification would have been to obtain a method of decoding point cloud geometry data for efficiently processing a large amount of point cloud data using point cloud compression by considering a relevant subset of neighboring nodes from among all neighboring nodes, since both OH and ZHANG relate to point cloud data processing methods and systems, wherein OH has a point cloud data processing method and device for addressing latency and encoding/decoding complexity, and reduce the data error, while ZHANG has a system, method and computer program that compresses and decompresses point cloud data based on reducing an expanded set of contexts associated with the point cloud data, thus improving the performance of parent-node-level context while keeping the number of contexts small by reducing the number of contexts presented. Please see OH (US 20250322549 A1), Paragraph [0149, 0511], and ZHANG (US 20210383575 A1), Paragraph [0026, 0028].
Regarding claim 3, OH in view of ZHANG teach the method of claim 1,
OH fails to explicitly teach wherein the index range reduction function (F) is a hashing function.
However, ZHANG explicitly teaches wherein the index range reduction function (F) is a hashing function (Fig. 3A-B, Paragraph [0058] – ZHANG discloses gps_context_reduction_level [wherein gps_context_reduction_level is an index range reduction function] may specify the context reduction level in geometry parameter set. Paragraph [0059] - ZHANG further teaches the hash table 300B may be used to cache occupancy information.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of OH in view of ZHANG of having a method of encoding, into a bitstream, point cloud geometry data, wherein the method comprises: obtaining a second index (I2) by applying an index range reduction function (F) to the first index (I1), with the teachings of ZHANG having wherein the index range reduction function (F) is a hashing function.
Wherein having OH’s method of encoding, into a bitstream, point cloud geometry data, wherein the index range reduction function (F) is a hashing function.
The motivation behind the modification would have been to obtain a method of encoding point cloud geometry data for efficiently processing a large amount of point cloud data using point cloud compression by considering a relevant subset of neighboring nodes from among all neighboring nodes, since both OH and ZHANG relate to point cloud data processing methods and systems, wherein OH has a point cloud data processing method and device for addressing latency and encoding/decoding complexity, and reduce the data error, while ZHANG has a system, method and computer program that compresses and decompresses point cloud data based on reducing an expanded set of contexts associated with the point cloud data, thus improving the performance of parent-node-level context while keeping the number of contexts small by reducing the number of contexts presented. Please see OH (US 20250322549 A1), Paragraph [0149, 0511], and ZHANG (US 20210383575 A1), Paragraph [0026, 0028].
Regarding claim 4, OH in view of ZHANG teach the method of claim 1,
Although OH explicitly teaches wherein said at least one binary data (fj) is encoded by a binary arithmetic coder (Fig. 4, Paragraph [0137] - OH discloses point cloud encoder (for example, the arithmetic encoder 40004) according to the embodiments may perform entropy encoding on the occupancy codes.).
OH fails to explicitly teach wherein said at least one binary data (fj) is encoded by a binary arithmetic coder using an internal probability based on at least the second index.
However, ZHANG explicitly teaches wherein said at least one binary data (fj) is encoded by a binary arithmetic coder (Fig. 2B, Paragraph [0036] - ZHANG discloses the occupancy code 204 of each node is then compressed by an arithmetic encoder. The occupancy code 204 can be denoted as S which is an 8-bit integer, and each bit in S indicates the occupancy status of each child node.)
using an internal probability based on at least the second index (Fig. 2B, Paragraph [0037] - ZHANG discloses for bit-wise encoding, eight bins in S are encoded in a certain order where each bin is encoded by referring to the occupancy status of neighboring nodes and child nodes of neighboring nodes, where the neighboring nodes are in the same level of current node. For byte-wise encoding, S is encoded by referring to an adaptive look up table (A-LUT), which keeps track of the N (e.g., 32) most frequent occupancy codes and a cache which keeps track of the last different observed M (e.g., 16) occupancy codes.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of OH in view of ZHANG of having a method of encoding, into a bitstream, point cloud geometry data, wherein the method comprises: obtaining a series of at least one binary data (fj,n) representative of an occupancy data of at least one neighboring geometrical element, with the teachings of ZHANG having wherein said at least one binary data (fj) is encoded by a binary arithmetic coder using an internal probability based on at least the second index.
Wherein having OH’s method of encoding, into a bitstream, point cloud geometry data, wherein said at least one binary data (fj) is encoded by a binary arithmetic coder using an internal probability based on at least the second index.
The motivation behind the modification would have been to obtain a method of encoding point cloud geometry data for efficiently processing a large amount of point cloud data using point cloud compression by considering a relevant subset of neighboring nodes from among all neighboring nodes, since both OH and ZHANG relate to point cloud data processing methods and systems, wherein OH has a point cloud data processing method and device for addressing latency and encoding/decoding complexity, and reduce the data error, while ZHANG has a system, method and computer program that compresses and decompresses point cloud data based on reducing an expanded set of contexts associated with the point cloud data, thus improving the performance of parent-node-level context while keeping the number of contexts small by reducing the number of contexts presented. Please see OH (US 20250322549 A1), Paragraph [0149, 0511], and ZHANG (US 20210383575 A1), Paragraph [0026, 0028].
Regarding claim 5, OH in view of ZHANG teach the method of claim 4,
Although OH explicitly teaches wherein encoding said at least one binary data (fj) (Fig. 4, Paragraph [0137] - OH discloses point cloud encoder (for example, the arithmetic encoder 40004) according to the embodiments may perform entropy encoding on the occupancy codes.).
OH fails to explicitly teach wherein encoding said at least one binary data (fj) comprises selecting a context among a set of contexts based on at least the second index.
However, ZHANG explicitly teaches wherein encoding said at least one binary data (fj) (Fig. 2B, Paragraph [0036] - ZHANG discloses the occupancy code 204 of each node is then compressed by an arithmetic encoder. The occupancy code 204 can be denoted as S which is an 8-bit integer, and each bit in S indicates the occupancy status of each child node.)
comprises selecting a context among a set of contexts based on at least the second index (Fig. 2B, Paragraph [0037] - ZHANG discloses S is encoded by referring to an adaptive look up table (A-LUT), which keeps track of the N (e.g., 32) most frequent occupancy codes and a cache which keeps track of the last different observed M (e.g., 16) occupancy codes [wherein a cache is a set of contexts based on at least the second index].).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of OH in view of ZHANG of having a method of encoding, into a bitstream, point cloud geometry data, wherein the method comprises: obtaining a series of at least one binary data (fj,n) representative of an occupancy data of at least one neighboring geometrical element, with the teachings of ZHANG having wherein encoding said at least one binary data (fj) comprises selecting a context among a set of contexts based on at least the second index.
Wherein having OH’s method of encoding, into a bitstream, point cloud geometry data, wherein encoding said at least one binary data (fj) comprises selecting a context among a set of contexts based on at least the second index.
The motivation behind the modification would have been to obtain a method of encoding point cloud geometry data for efficiently processing a large amount of point cloud data using point cloud compression by considering a relevant subset of neighboring nodes from among all neighboring nodes, since both OH and ZHANG relate to point cloud data processing methods and systems, wherein OH has a point cloud data processing method and device for addressing latency and encoding/decoding complexity, and reduce the data error, while ZHANG has a system, method and computer program that compresses and decompresses point cloud data based on reducing an expanded set of contexts associated with the point cloud data, thus improving the performance of parent-node-level context while keeping the number of contexts small by reducing the number of contexts presented. Please see OH (US 20250322549 A1), Paragraph [0149, 0511], and ZHANG (US 20210383575 A1), Paragraph [0026, 0028].
Regarding claim 6, OH in view of ZHANG teach the method of claim 5,
OH further teaches wherein selecting the context further depends on predictors of the at least one binary data (fj) (Fig. 4, Paragraph [0159] – OH discloses the point cloud encoder according to the embodiments may generate a predictor for points to perform prediction transform coding for setting a predicted attribute (or predicted attribute value) of each point. That is, N predictors may be generated for N points. The predictor according to the embodiments may calculate a weight (=1/distance) based on the LOD value of each point, indexing information about neighboring points present within a set distance for each LOD, and a distance to the neighboring points.).
Regarding claim 7, OH in view of ZHANG teach the method of claim 5,
Although OH explicitly teaches wherein encoding the at least one binary data (fj) representative of the occupancy data (Fig. 4, Paragraph [0137] - OH discloses point cloud encoder (for example, the arithmetic encoder 40004) according to the embodiments may perform entropy encoding on the occupancy codes.).
OH fails to explicitly teach wherein encoding the at least one binary data (fj) representative of the occupancy data of the current geometrical element comprises: obtaining a context index (Ctxldx) as an entry of a context index table determined from at least the second index (I2); obtaining a context (Ctx) associated with a probability (pctxldx) as the entry, associated with the context index, of a context table comprising the set of contexts; entropy encoding into the bitstream, the at least one binary data (fj) using the probability (pctxldx); and updating the entry of the context index table based on the encoded binary data (fj) to a new value.
However, ZHANG explicitly teaches wherein encoding the at least one binary data (fj) representative of the occupancy data of the current geometrical element (Fig. 2B, Paragraph [0039] - ZHANG discloses an occupancy code of current node typically has 8 bits, where each bit represents whether its i.sup.th child node is occupied or not. When coding the occupancy code of the current node, all the information from neighboring coded nodes can be used for context modeling.) comprises:
obtaining a context index (Ctxldx) as an entry of a context index table determined from at least the second index (I2) (Fig. 2B, Paragraph [0039] - ZHANG discloses when coding the occupancy code of the current node, all the information from neighboring coded nodes can be used for context modeling. The context information can be further grouped in terms of the partition level and distance to current node. Without loss of generality, the context index of the i.sup.th child node in current node can be obtained as follows, idx=LUT[i][ctxIdxParent][ctxIdxChild], where LUT is a look-up table of context indices.);
obtaining a context (Ctx) associated with a probability (pctxldx) as the entry, associated with the context index, of a context table comprising the set of contexts (Fig. 2B, Paragraph [0037] - ZHANG discloses for bit-wise encoding, eight bins in S are encoded in a certain order where each bin is encoded by referring to the occupancy status of neighboring nodes and child nodes of neighboring nodes, where the neighboring nodes are in the same level of current node. For byte-wise encoding, S is encoded by referring to an adaptive look up table (A-LUT), which keeps track of the N (e.g., 32) most frequent occupancy codes and a cache which keeps track of the last different observed M (e.g., 16) occupancy codes. See also Paragraph [0039].);
entropy encoding into the bitstream, the at least one binary data (fj) using the probability (pctxldx) (Fig. 3A-B, Paragraph [0057] - ZHANG discloses the encoder and decoder can then decide how to utilize context information [wherein context information is probability] from neighboring coded nodes.);
and updating the entry of the context index table based on the encoded binary data (fj) to a new value (Fig. 3A-B, Paragraph [0059] - ZHANG discloses when encoding/decoding the occupancy value of current node, the occupancy information of neighboring nodes is obtained from the hash table H.sub.d. After encoding/decoding an occupancy value of the current node, the coded occupancy value is then stored in H.sub.d.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of OH in view of ZHANG of having a method of encoding, into a bitstream, point cloud geometry data, wherein the method comprises: obtaining a series of at least one binary data (fj,n) representative of an occupancy data of at least one neighboring geometrical element, with the teachings of ZHANG having wherein encoding the at least one binary data (fj) representative of the occupancy data of the current geometrical element comprises: obtaining a context index (Ctxldx) as an entry of a context index table determined from at least the second index (I2); obtaining a context (Ctx) associated with a probability (pctxldx) as the entry, associated with the context index, of a context table comprising the set of contexts; entropy encoding into the bitstream, the at least one binary data (fj) using the probability (pctxldx); and updating the entry of the context index table based on the encoded binary data (fj) to a new value.
Wherein having OH’s method of encoding, into a bitstream, point cloud geometry data, wherein encoding the at least one binary data (fj) representative of the occupancy data of the current geometrical element comprises: obtaining a context index (Ctxldx) as an entry of a context index table determined from at least the second index (I2); obtaining a context (Ctx) associated with a probability (pctxldx) as the entry, associated with the context index, of a context table comprising the set of contexts; entropy encoding into the bitstream, the at least one binary data (fj) using the probability (pctxldx); and updating the entry of the context index table based on the encoded binary data (fj) to a new value.
The motivation behind the modification would have been to obtain a method of encoding point cloud geometry data for efficiently processing a large amount of point cloud data using point cloud compression by considering a relevant subset of neighboring nodes from among all neighboring nodes, since both OH and ZHANG relate to point cloud data processing methods and systems, wherein OH has a point cloud data processing method and device for addressing latency and encoding/decoding complexity, and reduce the data error, while ZHANG has a system, method and computer program that compresses and decompresses point cloud data based on reducing an expanded set of contexts associated with the point cloud data, thus improving the performance of parent-node-level context while keeping the number of contexts small by reducing the number of contexts presented. Please see OH (US 20250322549 A1), Paragraph [0149, 0511], and ZHANG (US 20210383575 A1), Paragraph [0026, 0028].
Regarding claim 8, OH in view of ZHANG teach the method of claim 1,
OH further teaches wherein the geometrical elements are defined in a two-dimensional space (Fig. 4, Paragraph [0114] - OH discloses in the case of a pixel, which is the minimum unit containing 2D image/video information, points of point cloud content (or 3D point cloud video) according to the embodiments may be included in one or more voxels.).
Regarding claim 9, OH in view of ZHANG teach the method of claim 1,
OH further teaches wherein the geometrical elements are defined in a three-dimensional space (Fig. 15, Paragraph [0243] – OH discloses the geometry data represents three-dimensional (3D) position information (e.g., a coordinate value of X, Y, and Z axes) of each point. That is, the position of each point is represented by parameters in a coordinate system representing a 3D space (e.g., parameters (x, y, z) of three axes, i.e., X, Y, and Z axes, representing a space).).
Regarding claim 10, OH teaches an apparatus of encoding, into a bitstream (Fig. 1, Paragraph [0095] – OH discloses the point cloud content providing system (for example, the transmission device 10000 or the point cloud video encoder 10002) according to the embodiments may encode the point cloud data (20001).),
point cloud geometry data (Fig. 1, Paragraph [0096] – OH discloses the point cloud data may include the geometry and attributes of a point. Accordingly, the point cloud content providing system may perform geometry encoding of encoding the geometry and output a geometry bitstream.)
represented by geometrical elements occupying some discrete positions of a set of discrete positions of a multi-dimensional space (Fig. 15, Paragraph [0243] – OH discloses the geometry data represents three-dimensional (3D) position information (e.g., a coordinate value of X, Y, and Z axes) of each point. That is, the position of each point is represented by parameters in a coordinate system representing a 3D space (e.g., parameters (x, y, z) of three axes, i.e., X, Y, and Z axes, representing a space).),
wherein the apparatus comprises at least one processor (Fig. 1, Paragraph [0091] - OH discloses the elements of the point cloud content providing system illustrated in FIG. 1 may be implemented by hardware, software, a processor, and/or a combination thereof.) configured to:
obtaining a series of at least one binary data (fj,n) (Fig. 4, Paragraph [0149] – OH discloses the point cloud content providing system or the point cloud encoder may perform entropy encoding (intra encoding) based on the occupancy code of the current node and the occupancy of neighboring nodes, or perform entropy encoding (inter encoding) based on the occupancy code of the previous frame [wherein the occupancy codes are a series of at least one binary data].)
representative of an occupancy data of at least one neighboring geometrical element belonging to a causal neighborhood of a current geometrical element of the multi-dimensional space (Fig. 4, Paragraph [0137] – OH discloses the occupancy code is represented as an 8-bit code. That is, when at least one point is contained in the space corresponding to a child node, the node is assigned a value of 1. When no point is contained in the space corresponding to the child node (the space is empty), the node is assigned a value of 0. The point cloud encoder (for example, the arithmetic encoder 40004) according to the embodiments may perform entropy encoding on the occupancy codes.);
obtaining a first index (I1) from the series of at least one binary data (fj,n) (Fig. 7, Paragraph [0150] – OH discloses FIG. 7 illustrates a process of obtaining an occupancy pattern based on the occupancy of neighbor nodes.),
Although OH further teaches the first index (I1) being representative of a neighborhood occupancy configuration among a first set of neighborhood occupancy configuration indices representative of potential neighborhood occupancy configurations (Fig. 7, Paragraph [0150] – OH discloses the point cloud encoder according to the embodiments determines occupancy of neighbor nodes of each node of the octree and obtains a value of a neighbor pattern. The neighbor node pattern is used to infer the occupancy pattern of the node.);
OH fails to explicitly teach obtaining a second index (I2) by applying an index range reduction function (F) to the first index (I1); the second index (I2) is representative of the neighborhood occupancy configuration among a second set of neighborhood occupancy configuration indices representative of the potential neighborhood occupancy configurations; each index of the first set of neighborhood occupancy configuration indices being mapped to an index of the second set of neighborhood occupancy configuration indices according to the index range reduction function; and the range of the second set of neighborhood occupancy configuration indices being lower than the range of the first set of neighborhood occupancy configuration indices; and entropy encoding, into the bitstream, at least one binary data (fj) representative of an occupancy data of the current geometrical element based on the second index (I2).
However, ZHANG explicitly teaches obtaining a second index (I2) by applying an index range reduction function (F) to the first index (I1) (Fig. 3B, Paragraph [0058] – ZHANG discloses gps_context_reduction_level [wherein gps_context_reduction_level is an index range reduction function] may specify the context reduction level in geometry parameter set [wherein geometry parameter set is the first index]. Paragraph [0059] - ZHANG further discloses the hash table 300B may be used to cache occupancy information [wherein occupancy information is second index].);
the second index (I2) is representative of the neighborhood occupancy configuration among a second set of neighborhood occupancy configuration indices representative of the potential neighborhood occupancy configurations (Fig. 3A-B, Paragraph [0059] – ZHANG discloses when encoding/decoding the occupancy value of current node, the occupancy information of neighboring nodes is obtained from the hash table H.sub.d. After encoding/decoding an occupancy value of the current node, the coded occupancy value is then stored in H.sub.d.);
each index of the first set of neighborhood occupancy configuration indices being mapped to an index of the second set of neighborhood occupancy configuration indices according to the index range reduction function (Fig. 4, Paragraph [0062] – ZHANG discloses at 404, the method 400 may include reducing a number of contexts associated with the received data based on occupancy data corresponding to one or more parent nodes and one or more child nodes within the received data. See also Paragraph [0059].);
and the range of the second set of neighborhood occupancy configuration indices being lower than the range of the first set of neighborhood occupancy configuration indices (Fig. 7, Paragraph [0095]) – ZHANG discloses Point Cloud Compression 96 may reduce an expanded set of neighboring nodes for a current node for compression and decompression of point cloud data.);
and entropy encoding, into the bitstream, at least one binary data (fj) representative of an occupancy data of the current geometrical element based on the second index (I2) (Fig. 3B, Paragraph [0059] – ZHANG discloses when encoding/decoding the occupancy value of current node, the occupancy information of neighboring nodes is obtained from the hash table H.sub.d. After encoding/decoding an occupancy value of the current node, the coded occupancy value is then stored in H.sub.d.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of OH of having an apparatus of encoding, into a bitstream, point cloud geometry data represented by geometrical elements occupying some discrete positions of a set of discrete positions of a multi-dimensional space, wherein the apparatus comprises at least one processor configured to: obtaining a series of at least one binary data (fj,n) representative of an occupancy data of at least one neighboring geometrical element belonging to a causal neighborhood of a current geometrical element of the multi-dimensional space; obtaining a first index (I1) from the series of at least one binary data (fj,n), the first index (I1) being representative of a neighborhood occupancy configuration among a first set of neighborhood occupancy configuration indices representative of potential neighborhood occupancy configurations; with the teachings of ZHANG having obtaining a second index (I2) by applying an index range reduction function (F) to the first index (I1); the second index (I2) is representative of the neighborhood occupancy configuration among a second set of neighborhood occupancy configuration indices representative of the potential neighborhood occupancy configurations; each index of the first set of neighborhood occupancy configuration indices being mapped to an index of the second set of neighborhood occupancy configuration indices according to the index range reduction function; and the range of the second set of neighborhood occupancy configuration indices being lower than the range of the first set of neighborhood occupancy configuration indices; and entropy encoding, into the bitstream, at least one binary data (fj) representative of an occupancy data of the current geometrical element based on the second index (I2).
Wherein having OH's apparatus of encoding, into a bitstream, point cloud geometry data wherein the apparatus comprises at least one processor configured to: obtaining a second index (I2) by applying an index range reduction function (F) to the first index (I1); the second index (I2) is representative of the neighborhood occupancy configuration among a second set of neighborhood occupancy configuration indices representative of the potential neighborhood occupancy configurations; each index of the first set of neighborhood occupancy configuration indices being mapped to an index of the second set of neighborhood occupancy configuration indices according to the index range reduction function; and the range of the second set of neighborhood occupancy configuration indices being lower than the range of the first set of neighborhood occupancy configuration indices; and entropy encoding, into the bitstream, at least one binary data (fj) representative of an occupancy data of the current geometrical element based on the second index (I2).
The motivation behind the modification would have been to obtain an apparatus of encoding point cloud geometry data for efficiently processing a large amount of point cloud data using point cloud compression by considering a relevant subset of neighboring nodes from among all neighboring nodes, since both OH and ZHANG relate to point cloud data processing methods and systems, wherein OH has a point cloud data processing method and device for addressing latency and encoding/decoding complexity, and reduce the data error, while ZHANG has a system, method and computer program that compresses and decompresses point cloud data based on reducing an expanded set of contexts associated with the point cloud data, thus improving the performance of parent-node-level context while keeping the number of contexts small by reducing the number of contexts presented. Please see OH (US 20250322549 A1), Paragraph [0149, 0511], and ZHANG (US 20210383575 A1), Paragraph [0026, 0028].
Regarding claim 11, OH in view of ZHANG teach the method of claim 2,
OH further teaches an apparatus of decoding, from a bitstream, point cloud geometry data (Fig. 1, Paragraph [0098] - OH discloses point cloud content providing system (e.g., the reception device 10004 or the point cloud video decoder 10005) may decode the encoded point cloud data (e.g., the geometry bitstream, the attribute bitstream) transmitted in the bitstream.)
represented by geometrical elements occupying some discrete positions of a set of discrete positions of a multi-dimensional space (Fig. 15, Paragraph [0243] – OH discloses the geometry data represents three-dimensional (3D) position information (e.g., a coordinate value of X, Y, and Z axes) of each point. That is, the position of each point is represented by parameters in a coordinate system representing a 3D space (e.g., parameters (x, y, z) of three axes, i.e., X, Y, and Z axes, representing a space).),
wherein the apparatus comprises at least one processor configured to perform the method of claim 2 (Fig. 11, Paragraph [0190] - OH discloses the elements of the point cloud decoder of FIG. 11 may be implemented by hardware including one or more processors or integrated circuits configured to communicate with one or more memories included in the point cloud providing device, software, firmware, or a combination thereof. See also Paragraph [0091].).
Regarding claim 13, OH in view of ZHANG teach the method of claim 1,
OH further teaches a non-transitory computer-readable storage medium carrying instruction of program code for executing the method of claim 1 (Fig. 1, Paragraph [0586] - OH discloses executable instructions for performing the method/operations of the device according to the embodiments may be stored in a non-transitory CRM or other computer program products configured to be executed by one or more processors, or may be stored in a transitory CRM or other computer program products configured to be executed by one or more processors.).
Regarding claim 15, OH in view of ZHANG teach the method of claim 2,
OH further teaches OH further teaches a non-transitory computer-readable storage medium carrying instruction of program code for executing the method of claim 2 (Fig. 1, Paragraph [0586] - OH discloses executable instructions for performing the method/operations of the device according to the embodiments may be stored in a non-transitory CRM or other computer program products configured to be executed by one or more processors, or may be stored in a transitory CRM or other computer program products configured to be executed by one or more processors.).
Regarding claim 16, OH in view of ZHANG teach the method of claim 2,
OH fails to explicitly teach wherein the index range reduction function (F) is a hashing function.
However, ZHANG explicitly teaches wherein the index range reduction function (F) is a hashing function (Fig. 3A-B, Paragraph [0058] – ZHANG discloses gps_context_reduction_level [wherein gps_context_reduction_level is an index range reduction function] may specify the context reduction level in geometry parameter set. Paragraph [0059] - ZHANG further teaches the hash table 300B may be used to cache occupancy information.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of OH in view of ZHANG of having a method of decoding, from a bitstream, point cloud geometry data, wherein the method comprises: obtaining a second index (I2) by applying an index range reduction function (F) to the first index (I1), with the teachings of ZHANG having wherein the index range reduction function (F) is a hashing function.
Wherein having OH’s method of decoding, from a bitstream, point cloud geometry data, wherein the index range reduction function (F) is a hashing function.
The motivation behind the modification would have been to obtain a method of encoding point cloud geometry data for efficiently processing a large amount of point cloud data using point cloud compression by considering a relevant subset of neighboring nodes from among all neighboring nodes, since both OH and ZHANG relate to point cloud data processing methods and systems, wherein OH has a point cloud data processing method and device for addressing latency and encoding/decoding complexity, and reduce the data error, while ZHANG has a system, method and computer program that compresses and decompresses point cloud data based on reducing an expanded set of contexts associated with the point cloud data, thus improving the performance of parent-node-level context while keeping the number of contexts small by reducing the number of contexts presented. Please see OH (US 20250322549 A1), Paragraph [0149, 0511], and ZHANG (US 20210383575 A1), Paragraph [0026, 0028].
Regarding claim 17, OH in view of ZHANG teach the method of claim 2,
Although OH explicitly teaches wherein said at least one binary data (fj) is decoded by a binary arithmetic decoder (Fig. 11, Paragraph [0179] - OH discloses the arithmetic decoder 11000 according to the embodiments decodes the received geometry bitstream based on the arithmetic coding.).
OH fails to explicitly teach wherein said at least one binary data (fj) is decoded by a binary arithmetic decoder using an internal probability based on at least the second index.
However, ZHANG explicitly teaches wherein said at least one binary data (fj) is decoded by a binary arithmetic decoder (Fig. 4, Paragraph [0063] - ZHANG discloses at 406, the method 400 may include decoding the data corresponding to the point cloud based on the reduced number of contexts.)
using an internal probability based on at least the second index (Fig. 4, Paragraph [0038] - ZHANG discloses a binary flag indicating whether S is the A-LUT or not is encoded. If S is in the A-LUT, the index in the A-LUT is encoded by using a binary arithmetic encoder. If S is not in the A-LUT, then a binary flag indicating whether S is in the cache or not is encoded. If S is in the cache, then the binary representation of its index is encoded by using a binary arithmetic encoder. Otherwise, if S is not in the cache, then the binary representation of S is encoded by using a binary arithmetic encoder. The decoding process starts by parsing the dimensions of the bounding box B from bitstream. The same octree structure is then built by subdividing B according to the decoded occupancy codes. See also Fig. 2B.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of OH in view of ZHANG of having a method of decoding, from a bitstream, point cloud geometry data, wherein the method comprises: obtaining a series of at least one binary data (fj,n) representative of an occupancy data of at least one neighboring geometrical element, with the teachings of ZHANG having wherein said at least one binary data (fj) is decoded by a binary arithmetic decoder using an internal probability based on at least the second index.
Wherein having OH’s method of decoding, from a bitstream, point cloud geometry data, wherein said at least one binary data (fj) is decoded by a binary arithmetic decoder using an internal probability based on at least the second index.
The motivation behind the modification would have been to obtain a method of decoding point cloud geometry data for efficiently processing a large amount of point cloud data using point cloud compression by considering a relevant subset of neighboring nodes from among all neighboring nodes, since both OH and ZHANG relate to point cloud data processing methods and systems, wherein OH has a point cloud data processing method and device for addressing latency and encoding/decoding complexity, and reduce the data error, while ZHANG has a system, method and computer program that compresses and decompresses point cloud data based on reducing an expanded set of contexts associated with the point cloud data, thus improving the performance of parent-node-level context while keeping the number of contexts small by reducing the number of contexts presented. Please see OH (US 20250322549 A1), Paragraph [0149, 0511], and ZHANG (US 20210383575 A1), Paragraph [0026, 0028].
Regarding claim 18, OH in view of ZHANG teach the method of claim 17,
Although OH explicitly teaches wherein decoding said at least one binary data (fj) (Fig. 11, Paragraph [0179] - OH discloses the arithmetic decoder 11000 according to the embodiments decodes the received geometry bitstream based on the arithmetic coding.).
OH fails to explicitly teach wherein decoding said at least one binary data (fj) comprises selecting a context among a set of contexts based on at least the second index.
However, ZHANG explicitly teaches wherein decoding said at least one binary data (fj) (Fig. 4, Paragraph [0063] - ZHANG discloses at 406, the method 400 may include decoding the data corresponding to the point cloud based on the reduced number of contexts.)
comprises selecting a context among a set of contexts based on at least the second index (Fig. 4, Paragraph [0063] - ZHANG discloses at 406, the method 400 may include decoding the data corresponding to the point cloud based on the reduced number of contexts.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of OH in view of ZHANG of having a method of decoding, from a bitstream, point cloud geometry data, wherein the method comprises: obtaining a series of at least one binary data (fj,n) representative of an occupancy data of at least one neighboring geometrical element, with the teachings of ZHANG having wherein decoding said at least one binary data (fj) comprises selecting a context among a set of contexts based on at least the second index.
Wherein having OH’s method of decoding, from a bitstream, point cloud geometry data, wherein decoding said at least one binary data (fj) comprises selecting a context among a set of contexts based on at least the second index.
The motivation behind the modification would have been to obtain a method of decoding point cloud geometry data for efficiently processing a large amount of point cloud data using point cloud compression by considering a relevant subset of neighboring nodes from among all neighboring nodes, since both OH and ZHANG relate to point cloud data processing methods and systems, wherein OH has a point cloud data processing method and device for addressing latency and encoding/decoding complexity, and reduce the data error, while ZHANG has a system, method and computer program that compresses and decompresses point cloud data based on reducing an expanded set of contexts associated with the point cloud data, thus improving the performance of parent-node-level context while keeping the number of contexts small by reducing the number of contexts presented. Please see OH (US 20250322549 A1), Paragraph [0149, 0511], and ZHANG (US 20210383575 A1), Paragraph [0026, 0028].
Regarding claim 19, OH in view of ZHANG teach the method of claim 18,
OH further teaches wherein selecting the context further depends on predictors of the at least one binary data (fj) (Fig. 4, Paragraph [0159] – OH discloses the point cloud encoder according to the embodiments may generate a predictor for points to perform prediction transform coding for setting a predicted attribute (or predicted attribute value) of each point. That is, N predictors may be generated for N points. The predictor according to the embodiments may calculate a weight (=1/distance) based on the LOD value of each point, indexing information about neighboring points present within a set distance for each LOD, and a distance to the neighboring points. See also Fig. 11, Paragraph [0180].).
Regarding claim 20, OH in view of ZHANG teach the method of claim 18,
Although OH explicitly teaches wherein decoding the at least one binary data (fj) (Fig. 11, Paragraph [0179] - OH discloses the arithmetic decoder 11000 according to the embodiments decodes the received geometry bitstream based on the arithmetic coding.).
OH fails to explicitly teach wherein decoding the at least one binary data (fj) representative of the occupancy data of the current geometrical element comprises: obtaining a context index (Ctxldx) as an entry of a context index table determined from at least the second index (I2); obtaining a context (Ctx) associated with a probability (pctxldx) as the entry, associated with the context index, of a context table comprising the set of contexts; entropy decoding from the bitstream, the at least one binary data (fj) using the probability (pctxldx); and updating the entry of the context index table based on the decoded binary data (fj) to a new value.
However, ZHANG explicitly teaches wherein decoding the at least one binary data (fj) representative of the occupancy data of the current geometrical element (Fig. 4, Paragraph [0038] - ZHANG discloses the decoding process starts by parsing the dimensions of the bounding box B from bitstream. The same octree structure is then built by subdividing B according to the decoded occupancy codes. See also paragraph [0039].) comprises:
obtaining a context index (Ctxldx) as an entry of a context index table determined from at least the second index (I2) (Fig. 2B, Paragraph [0039] - ZHANG discloses when coding the occupancy code of the current node, all the information from neighboring coded nodes can be used for context modeling. The context information can be further grouped in terms of the partition level and distance to current node. Without loss of generality, the context index of the i.sup.th child node in current node can be obtained as follows, idx=LUT[i][ctxIdxParent][ctxIdxChild], where LUT is a look-up table of context indices.);
obtaining a context (Ctx) associated with a probability (pctxldx) as the entry, associated with the context index, of a context table comprising the set of contexts (Fig. 2B, Paragraph [0037] - ZHANG discloses for bit-wise encoding, eight bins in S are encoded in a certain order where each bin is encoded by referring to the occupancy status of neighboring nodes and child nodes of neighboring nodes, where the neighboring nodes are in the same level of current node. For byte-wise encoding, S is encoded by referring to an adaptive look up table (A-LUT), which keeps track of the N (e.g., 32) most frequent occupancy codes and a cache which keeps track of the last different observed M (e.g., 16) occupancy codes. Paragraph [0039] - ZHANG further discloses the decoding process starts by parsing the dimensions of the bounding box B from bitstream. The same octree structure is then built by subdividing B according to the decoded occupancy codes.);
entropy decoding from the bitstream, the at least one binary data (fj) using the probability (pctxldx) (Fig. 3A-B, Paragraph [0057] - ZHANG discloses the encoder and decoder can then decide how to utilize context information [wherein context information is probability] from neighboring coded nodes.);
and updating the entry of the context index table based on the encoded binary data (fj) to a new value (Fig. 3A-B, Paragraph [0059] - ZHANG discloses when encoding/decoding the occupancy value of current node, the occupancy information of neighboring nodes is obtained from the hash table H.sub.d. After encoding/decoding an occupancy value of the current node, the coded occupancy value is then stored in H.sub.d.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of OH in view of ZHANG of having a method of decoding, from a bitstream, point cloud geometry data, wherein the method comprises: obtaining a series of at least one binary data (fj,n) representative of an occupancy data of at least one neighboring geometrical element, with the teachings of ZHANG having wherein decoding the at least one binary data (fj) representative of the occupancy data of the current geometrical element comprises: obtaining a context index (Ctxldx) as an entry of a context index table determined from at least the second index (I2); obtaining a context (Ctx) associated with a probability (pctxldx) as the entry, associated with the context index, of a context table comprising the set of contexts; entropy decoding from the bitstream, the at least one binary data (fj) using the probability (pctxldx); and updating the entry of the context index table based on the decoded binary data (fj) to a new value.
Wherein having OH’s method of decoding, from a bitstream, point cloud geometry data, wherein decoding the at least one binary data (fj) representative of the occupancy data of the current geometrical element comprises: obtaining a context index (Ctxldx) as an entry of a context index table determined from at least the second index (I2); obtaining a context (Ctx) associated with a probability (pctxldx) as the entry, associated with the context index, of a context table comprising the set of contexts; entropy decoding from the bitstream, the at least one binary data (fj) using the probability (pctxldx); and updating the entry of the context index table based on the decoded binary data (fj) to a new value.
The motivation behind the modification would have been to obtain a method of decoding point cloud geometry data for efficiently processing a large amount of point cloud data using point cloud compression by considering a relevant subset of neighboring nodes from among all neighboring nodes, since both OH and ZHANG relate to point cloud data processing methods and systems, wherein OH has a point cloud data processing method and device for addressing latency and encoding/decoding complexity, and reduce the data error, while ZHANG has a system, method and computer program that compresses and decompresses point cloud data based on reducing an expanded set of contexts associated with the point cloud data, thus improving the performance of parent-node-level context while keeping the number of contexts small by reducing the number of contexts presented. Please see OH (US 20250322549 A1), Paragraph [0149, 0511], and ZHANG (US 20210383575 A1), Paragraph [0026, 0028].
Regarding claim 21, OH in view of ZHANG teach the method of claim 2,
OH further teaches wherein the geometrical elements are defined in a two-dimensional space (Fig. 4, Paragraph [0114] - OH discloses in the case of a pixel, which is the minimum unit containing 2D image/video information, points of point cloud content (or 3D point cloud video) according to the embodiments may be included in one or more voxels.).
Regarding claim 22, OH in view of ZHANG teach the method of claim 2,
OH further teaches wherein the geometrical elements are defined in a three-dimensional space (Fig. 15, Paragraph [0243] – OH discloses the geometry data represents three-dimensional (3D) position information (e.g., a coordinate value of X, Y, and Z axes) of each point. That is, the position of each point is represented by parameters in a coordinate system representing a 3D space (e.g., parameters (x, y, z) of three axes, i.e., X, Y, and Z axes, representing a space).).
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered pertinent to applicant’s disclosure.
POIRIER et al. (US 20220345728 A1) - A method for coding/decoding a large field of view video into a bitstream in an immersive rendering system is disclosed. At least one picture of said large field of view video is represented as a surface, said surface being projected onto at least one 2D picture using a projection function. For at least one current block of said at least one 2D picture, at least one item of information representative of a modification of a 2D spatial neighborhood is determined according to said projection function. A group of neighboring blocks using said at least on item of information representative of a modification is determined and at least one part of encoding/decoding of said current block is performed using said determined group of neighboring blocks.… Fig. 1, Abstract.
YEA et al. (US 20210112276 A1) - A method of point cloud attribute coding performed by at least one processor, including obtaining an encoded bitstream corresponding to a point cloud; determining whether the encoded bitstream was encoded using an inter-channel tool for inter-channel decorrelation; based on determining that the encoded bitstream was encoded using the inter-channel tool, decoding the encoded bitstream using the inter-channel tool to reconstruct an attribute signal corresponding to the point cloud; and reconstructing the point cloud using the reconstructed attribute signal..… Figs. 3, 4, Abstract.
VOSOUGHI et al. (US 20200013215 A1) - An electronic apparatus and method for adaptive sub-band based coding of hierarchical transform coefficients of a 3D point cloud, is provided. The electronic apparatus stores the 3D point cloud and generates a plurality of voxels from the 3D point cloud. The electronic apparatus generates a plurality of hierarchical transform coefficients by application of a hierarchical transform scheme on the generated plurality of voxels and classifies the plurality of hierarchical transform coefficients into a plurality of sub-bands of hierarchical transform coefficients. The plurality of hierarchical transform coefficients are classified based on a weight of each of the plurality of hierarchical transform coefficients...… Figs. 3B, 5, 6, Abstract.
MAMMOU et al. (US 20190075320 A1) - A system comprises an encoder configured to compress a point cloud comprising a plurality of points each point comprising spatial information for the point. The encoder is configured to sub-sample the points and determine subdivision locations for the subsampled points. Also, the encoder is configured to determine, for respective subdivision location, if a point is to be included, not included, or relocated relative to the subdivision location. The encoder encodes spatial information for the sub-sampled points and encodes subdivision location point inclusion/relocation information to generate a compressed point cloud. A decoder recreates an original or near replica of an original point cloud based on the spatial information and the subdivision location inclusion/relocation information included in the compressed point cloud… Figs. 1A-C, 4, Abstract.
PARK et al. (US 20220383553 A1) - A point cloud data transmission method according to embodiments may comprise the steps of: encoding point cloud data; and transmitting a bitstream comprising the point cloud data. A point cloud data reception method according to embodiments may comprise the steps of: receiving a bitstream comprising point cloud data; and decoding the point cloud data… Fig. 1, Abstract.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BEZAWIT N SHIMELES whose telephone number is (571)272-7663. The examiner can normally be reached M-F 7:30am-5pm.
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, Chineyere Wills-Burns can be reached at (571) 272-9752. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/BEZAWIT NOLAWI SHIMELES/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673