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 2/25/2026 have been fully considered but they are not persuasive.
Applicant argues that the combination of Lee and Shi does not explicitly teach grouping said point-wise features into windows according to a number of points to be contained in each window.
In response, the examiner respectfully disagrees. Lee teaches Referring to FIG. 16, points in the same PU may be categorized into three attribute groups based on attribute values. As shown in FIG. 16, a histogram may be generated according to the range of attribute values to show the data distribution. Additional methods may be applied to group points in a PU based on the criteria described later.
To classify point positions based on attribute values, the bin of the histogram with the highest frequency is found, and the median, mode, or average of the bin is selected as a reference value (group_anchor_attr) to classify the first geometry group. Depending on the coverage of the anchor value, the method may be divided into the following two methods: 1) unstructured and 2) structured.
To classify points after selecting the anchor value, points that fall within a specific range of attribute values from the reference value (group_anchor_attr) may be grouped.
Points that fall within the specific range of attribute values from the point position having the same or closest attribute information to the reference value (group_anchor_attr) may be categorized into a group.
Referring to FIG. 16, one and the same PU may include points, and the points may be categorized based on attribute values. For example, when the attribute is the color of points, points 1601 with the attribute blue, points 1602 with the attribute yellow, and points 1603 with the attribute green may be distributed according to their frequency.
To select a reference value (anchor value) for grouping, the anchor value may be selected based on the most frequent attribute in the group of points with the same attribute. A first anchor value and a second anchor value 1604 may each be selected according to the distribution of points, as shown in FIG. 16.
FIG. 17 illustrates a method of grouping points according to embodiments.
The method/device according to the embodiments may select a reference value in FIG. 16 and group points according to the reference value, as shown in FIG. 17.
Based on the reference value (anchor), the method of grouping points may be carried out based on the shape of the region. The region may have 1) an irregular shape, and/or 2) a regular shape.
In the first case, when point positions are classified based on a specific range of attribute values, grouping points in the PU space into a group causes points present in a region of an irregular shape to be grouped together.
In the second case, when grouping points that fall within a specific range based on a point that has a reference attribute value, points contained in a relatively regular region may be bundled into a group.
Referring to FIG. 17-(a), as described above, when points that fall within a specific range of attribute values are grouped based on a first reference value (first anchor), the region of the grouped points may be irregular. For each of the first anchor and second anchor, a first group, a second group, or the like may be generated.
Referring to FIG. 17-(b), as described above, when the points included in a specific range of geometry values are grouped based on the first reference value (first anchor), the region of the grouped points may have a regular shape, such as a circle or the like. For each of the first anchor and the second anchor, a first group, a second group, or the like may be generated. [0237] – [0249].
Classifying (grouping) points after selecting the anchor value. To select a reference value (anchor value) for grouping, the anchor value may be selected based on the most frequent attribute in the group of points with the same attribute. The groups are considered as windows. As shown in Fig. 17, each anchor value corresponds to a group. Thus, Lee teaches the claim limitation “grouping said point-wise features into windows according to a number of points to be contained in each window.”
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., the grouping is based on a number of points contained in each window, such that all windows (except possibly one) have exactly the same number of points and the grouping is to ensure that dense regions have smaller windows while sparse regions have large windows) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
It is strongly recommended that applicant explicitly recite these features in the claim.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 4, 11, 12, 14, 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US 2024/0397093 A1) in view of Shi et al. (US 2025/0148628 A1).
Consider claim 1, Lee teaches a method for decoding 3D data (point cloud is a set of points positioned in a 3D space. [0044]. The point cloud decoder decodes the bitstream containing the point cloud video data. [0049]), comprising: obtaining sorted point-wise features associated with said 3D data (the point cloud encoder may classify (reorganize) points by LOD…. The LOD is generated by reorganizing the points into a set of refinement levels according to a set LOD distance value…. The LOD generation process is also performed by the point cloud decoder. [0130] – [0133]; Fig. 9 shows an example of points being reorganized by LOD.); grouping said point-wise features into windows according to a number of points to be contained in each window (classifying points after selecting the anchor value. To select a reference value (anchor value) for grouping, the anchor value may be selected based on the most frequent attribute in the group of points with the same attribute. [0237] – [0249], Fig. 16-17); and decoding said 3D data based on said point-wise features (The point cloud decoder includes a geometry decoder and an attribute decoder. The geometry decoder performs geometry decoding on the geometry bitstream and outputs decoded geometry. The attribute decoder performs attribute decoding based on the decoded geometry and the attribute bitstream, and outputs decoded attributes. The decoded geometry and decoded attributes are used to reconstruct point cloud content (a decoded point cloud). [0148] – [0164], Fig. 10-11).
However, Lee does not explicitly teach performing self-attention in each window according to an attention mechanism to update said point-wise features.
In an analogous art, Shi discloses systems and methods for generating 3D point cloud based on visual features of the input. Abstract.
Shi teaches performing self-attention in each window according to an attention mechanism to update said point-wise features (3D self-attention engine performs attention-based refinement for the 3D point cloud point-wise features. The 3D self-attention engine 640 can generate a corresponding plurality of refined 3D depth features corresponding to the plurality of uplifted 3D depth features included in the 3D point cloud 635. [0082] – [0084]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the known technique of performing self-attention to update the point-wise features because such incorporation would facilitate the generation of high-quality completed depth maps. [0039] and [0073].
Consider claim 4, Lee teaches said 3D data corresponds to point cloud data at a current octree level or location data of one or more 3D meshes (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) [0148] – [0164], Fig. 10-11).
Consider claim 11, Lee teaches a method for encoding 3D data (point cloud is a set of points positioned in a 3D space. [0044]. The point cloud video encoder 10002 may encode the point cloud video data based on point cloud compression coding. [0045]), comprising: obtaining points in said 3D data with associated point-wise features (he point cloud video acquirer 10001 according to the embodiments acquires a point cloud video. [0044]. The point cloud video is represented by a point cloud belonging to a coordinate system for expressing a 3D space. The point cloud video according to the embodiments may include a Ply (Polygon File format or the Stanford Triangle format) file. When the point cloud video has one or more frames, the acquired point cloud video may include one or more Ply files. The Ply files contain point cloud data, such as point geometry and/or attributes. The geometry includes positions of points. The position of each point may be represented by parameters (e.g., values of the X, Y, and Z axes) representing a three-dimensional coordinate system (e.g., a coordinate system composed of X, Y and Z axes). The attributes include attributes of points (e.g., information about texture, color (in YCbCr or RGB), reflectance r, transparency, etc. of each point). A point has one or more attributes. [0058].); sorting said points and associated point-wise features according to an order (the point cloud encoder may classify (reorganize) points by LOD…. The LOD is generated by reorganizing the points into a set of refinement levels according to a set LOD distance value…. The LOD generation process is also performed by the point cloud decoder. [0130] – [0133]; Fig. 9 shows an example of points being reorganized by LOD.); grouping said sorted points with associated point-wise features into windows according to a number of points to be contained in each window (classifying points after selecting the anchor value. To select a reference value (anchor value) for grouping, the anchor value may be selected based on the most frequent attribute in the group of points with the same attribute. [0237] – [0249], Fig. 16-17); encoding said 3D data based on said updated point-wise features (The point cloud video encoder 10002 may output a bitstream containing the encoded point cloud video data. The bitstream may contain not only the encoded point cloud video data, but also signaling information related to encoding of the point cloud video data. [0045]. The point cloud video encoder 10002 of FIG. 1. The point cloud encoder reconstructs and encodes point cloud data (e.g., positions and/or attributes of the points) to adjust the quality of the point cloud content. [0073]. See also [0130] – [0133]. Fig. 1 and Fig. 4).
However, Lee does not explicitly teach performing self-attention in each window according to an attention mechanism to update point-wise features.
Shi teaches performing self-attention in each window according to an attention mechanism to update point-wise features (3D self-attention engine performs attention-based refinement for the 3D point cloud point-wise features. The 3D self-attention engine 640 can generate a corresponding plurality of refined 3D depth features corresponding to the plurality of uplifted 3D depth features included in the 3D point cloud 635. [0082] – [0084]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the known technique of performing self-attention to update the point-wise features because such incorporation would facilitate the generation of high-quality completed depth maps. [0039] and [0073].
Consider claim 12, Lee teaches said encoding said 3D data is lossy ([0073]), and wherein said updated point-wise features are encoded in a bitstream (The point cloud encoder reconstructs and encodes point cloud data (e.g., positions and/or attributes of the points) to adjust the quality of the point cloud content. [0073]. See also [0130] – [0133]. Fig. 1 and Fig. 4).
Consider claim 14, claim 14 recites an apparatus for decoding 3D data (Fig. 1 of Lee), comprising one or more processors and at least one memory coupled to said one or more processors (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. The one or more processors may perform at least one or more of the operations and/or functions of the elements of the point cloud decoder of FIG. 11 described above. Additionally, the one or more processors may operate or execute a set of software programs and/or instructions for performing the operations and/or functions of the elements of the point cloud decoder of FIG. 11. [0165] and Fig. 11. [0450]), wherein said one or more processors are configured to perform the method recited in claim 1 (see rejection for claim 1).
Consider claim 18, claim 18 recites an apparatus for encoding 3D data (Fig. 1), comprising one or more processors and at least one memory coupled to said one or more processors (the elements of the point cloud encoder of FIG. 4 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. The one or more processors may perform at least one of the operations and/or functions of the elements of the point cloud encoder [0094], Fig. 4, and [0450]), wherein said one or more processors are configured to perform the method recited in claim 11 (see rejection for claim 11).
Consider claim 19, claim 19 recites the method recited in claim 12. Thus, it is rejected for the same reasons.
Claim(s) 2, 13, 15, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US 2024/0397093 A1) in view of Shi et al. (US 2025/0148628 A1) and Park et al. (US 2025/0218049 A1).
Consider claim 2, the combination of Lee and Shi teaches all the limitations in claim 1 and said 3D data is losslessly decoded (arithmetic decoding is lossless decoding [0148] – [0164] and Fig. 10-11 of Lee) but does not explicitly teach, further comprising: obtaining previously decoded points in said 3D data with associated point-wise features; sorting said decoded points and associated point-wise features according to an order, to obtain said sorted point-wise features; and obtaining probabilities based on said updated point-wise features, wherein point locations of said 3D data are decoded based on said probabilities.
In an analogous art, Park teaches method of decoding point cloud data. [0005].
Park teaches obtaining previously decoded points in said 3D data with associated point-wise features (In order to encode or decode the current point contained in the current frame, the method/device according to the embodiments may find, in the reference frame for the current frame, a reference point close to the previously decoded (restored) point. The reference point may have the same laser ID and the same scaled azimuth as the previously decoded point. [0218]. Currently, geometry compression using an intra-frame predictive tree is available. The inter-frame geometry predictive tree may reference up to two previously decoded points in the current frame, and select a mode according to a calculation equation and deliver the same as signaling information to the decoder. At this time, one inter pred point corresponding to the current point in the previous frame is selected as a point with the closest x, y, and z geometry values (reference point). At this time, one more point may be selected in the reference frame. As shown in FIG. 16, an inter pred point and an additional inter pred point are selected as candidates. In one of the inter pred point and the additional inter pred point is selected as a predictor for the current point, a point with a smaller (current point-predictor) difference may be selected. Based on the selected point in the reference frame, up to three previously decoded points in the reference frame may be selected as predictors to perform inter-frame geometry predictive coding. [0229]); sorting said decoded points and associated point-wise features according to an order, to obtain said sorted point-wise features (The decoding of the point cloud data may include sorting points of the point cloud data based on an azimuth according to a laser ID. Points contained in a current frame containing the point cloud data and a reference frame for the current frame may be sorted based on the laser ID and the azimuth, and a predictor for the points in the current frame may be selected from the sorted points in the current frame and the reference frame. The decoding of the point cloud data may include decoding an attribute of the point cloud data, wherein the attribute may be predicted based on an attribute and a weight contained in the reference frame. Based on that a value generated based on an attribute for the current frame, an attribute for the reference frame, and the weight is greater than or equal to a threshold, the attribute may be predicted based on the attribute for the reference frame. Based on that the value generated based on the attribute for the current frame, the attribute for the reference frame, and the weight is less than the threshold, the attribute may be predicted based on the attribute for the current frame. For example, the points may be sorted in an ascending order of the laser ID. Points belonging to a laser ID may be sorted in an ascending order of the azimuth. The attribute may include reflectance. From the points sorted according to the laser ID and the azimuth, a point having a similar reflectance to the current point may be selected as a predictor for the current point. An efficient prediction mode may be selected between intra-frame prediction and/or inter-frame prediction. The cases based on the threshold may be reversed. [0441]); and obtaining probabilities based on said updated point-wise features (arithmetic decoding process obtain probabilities [0133] – [0144], Fig. 10-11), wherein point locations of said 3D data are decoded based on said probabilities (The coordinate inverse transformer 11004 according to the embodiments may acquire positions of the points by transforming the coordinates based on the reconstructed geometry. [0133] – [0144], Fig. 10-11).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the known technique of obtaining previously decoded points because such incorporation would facilitate geometry coding using a predictive tree that enables low-latency coding. [0227].
Consider claim 13, the combination of Lee and Shi teaches said encoding said 3D data is lossless (arithmetic decoding is lossless decoding [0148] – [0164] and Fig. 10-11 of Lee).
However, the combination does not explicitly teach obtaining probabilities based on said updated point-wise features of previously coded points, wherein point locations of said 3D data are encoded based on said probabilities.
Park teaches obtaining probabilities based on said updated point-wise features of previously coded points (In order to encode or decode the current point contained in the current frame, the method/device according to the embodiments may find, in the reference frame for the current frame, a reference point close to the previously decoded (restored) point. The reference point may have the same laser ID and the same scaled azimuth as the previously decoded point. [0218]. Currently, geometry compression using an intra-frame predictive tree is available. The inter-frame geometry predictive tree may reference up to two previously decoded points in the current frame, and select a mode according to a calculation equation and deliver the same as signaling information to the decoder. At this time, one inter pred point corresponding to the current point in the previous frame is selected as a point with the closest x, y, and z geometry values (reference point). At this time, one more point may be selected in the reference frame. As shown in FIG. 16, an inter pred point and an additional inter pred point are selected as candidates. In one of the inter pred point and the additional inter pred point is selected as a predictor for the current point, a point with a smaller (current point-predictor) difference may be selected. Based on the selected point in the reference frame, up to three previously decoded points in the reference frame may be selected as predictors to perform inter-frame geometry predictive coding. [0229]. Arithmetic decoding process obtain probabilities [0133] – [0144], Fig. 10-11), wherein point locations of said 3D data are encoded based on said probabilities (The point cloud encoder reconstructs and encodes point cloud data (e.g., positions and/or attributes of the points) to adjust the quality of the point cloud content (to, for example, lossless, lossy, or near-lossless) according to the network condition or applications. [0070]. the coordinate transformer according to the embodiments receives positions and transforms the same into coordinates. For example, the positions may be transformed into position information in a three-dimensional space (e.g., a three-dimensional space represented by an XYZ coordinate system). The position information in the three-dimensional space according to the embodiments may be referred to as geometry information. [0074] and Fig. 4).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the known technique of obtaining previously decoded points because such incorporation would facilitate geometry coding using a predictive tree that enables low-latency coding. [0227].
Consider claim 15, claim 15 recites the method recited in claim 2. Thus, it is rejected for the same reasons.
Consider claim 20, claim 20 recites the method recited in claim 13. Thus, it is rejected for the same reasons.
Claim(s) 5-6 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US 2024/0397093 A1) in view of Shi et al. (US 2025/0148628 A1) and Lee et al. (US 2025/0168337 A1) (hereinafter “Lee II”)
Consider claim 5, the combination of Lee and Shi teaches all the limitations in claim 1 but does not explicitly teach said point-wise features are sorted based on spherical sorting.
Lee II teaches said point-wise features are sorted based on spherical sorting ([0288] – [0289], [0295] – [0297], [0304] – [0306], [0392] – [0393]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the known technique of sorting point-wise features based on spherical sorting because such incorporation would allow variety of sorting options. [0288] – [0289].
Consider claim 6, Lee II teaches an order for sorting said point-wise features is switched among a plurality of sorting methods ([0288] – [0289], [0295] – [0297], [0304] – [0306], [0392] – [0393]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the known technique of sorting point-wise features based on spherical sorting because such incorporation would allow variety of sorting options. [0288] – [0289].
Consider claim 17, claim 17 recites the method recited in claim 5. Thus, it is rejected for the same reasons.
Claim(s) 7-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US 2024/0397093 A1) in view of Shi et al. (US 2025/0148628 A1) and Ryder et al. (US 2025/0119592 A1).
Consider claim 7, the combination of Lee and Shi teaches all the limitations in claim 1 but does not explicitly teach said self-attention is based on a focused linear attention where 1D depth-wise convolution is used under each attention head.
Ryder teaches said self-attention is based on a focused linear attention where 1D depth-wise convolution is used under each attention head ([0174], [0193], [0259], [0287]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the known element of a self-attention that is based on focused linear attention where 1D depth-wise convolution is used because such incorporation would reduce computation complexity and memory bandwidth requirements. [0006].
Consider claim 8, Ryder teaches shifting said windows ([0272]); and performing self-attention in each shifted window according to said attention mechanism, wherein said features are further updated ([0105], [0136] – [0137], [0187], [0272]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the known element of a self-attention that is based on focused linear attention where 1D depth-wise convolution is used because such incorporation would reduce computation complexity and memory bandwidth requirements. [0006].
Consider claim 9, Shi teaches downsampling said point-wise features ([0087] – [0096] and Fig. 7A); performing grouping and self-attention to obtain features at a different resolution ([0087] – [0096] and Fig. 7A); and combining said point-wise features with said features at said different resolution ([0087] – [0096] and Fig. 7A).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the known technique of performing self-attention to update the point-wise features because such incorporation would facilitate the generation of high-quality completed depth maps. [0039] and [0073].
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US 2024/0397093 A1) in view of Shi et al. (US 2025/0148628 A1) and Song, Rui, et al. "Efficient hierarchical entropy model for learned point cloud compression." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023 (hereinafter “Song”).
Consider claim 10, the combination of Lee and Shi teaches all the limitations in claim 1 but does not explicitly teach splitting said point-wise features into two parts in each window, wherein said self-attention is performed on a first part in a window according to said attention mechanism; performing convolution-based feature aggregation on a second part in a window; concatenating features from said self-attention and convolution-based feature aggregation; and performing feature mixing through a convolution-based module.
Song teaches splitting said point-wise features into two parts in each window, wherein said self-attention is performed on a first part in a window according to said attention mechanism (Section 4.2 Grouped context; Fig. 2-3); performing convolution-based feature aggregation on a second part in a window (Section 4.2 Grouped context; Fig. 2-3); concatenating features from said self-attention and convolution-based feature aggregation (Section 4.2 Grouped context; Fig. 2-3); and performing feature mixing through a convolution-based module (Section 4.2 Grouped context; Fig. 2-3).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the known technique of splitting point-wise features into two parts in each window because such incorporation would lead to better compression performance. Section 4.2.
Claim(s) 3 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US 2024/0397093 A1) in view of Shi et al. (US 2025/0148628 A1) and Taquet et al. (US 2025/0131597 A1).
Consider claim 3, the combination of Lee and Shi teaches all the limitations in claim 1 but does not explicitly teach said 3D data is lossy decoded, and wherein point locations of said 3D data are predicted from said updated point-wise features.
Taquet teaches said 3D data is lossy decoded, and wherein point locations of said 3D data are predicted from said updated point-wise features ([0011], [0015] – [0016], [0023] – [0024]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the known technique of predicting point locations of the 3D data because such incorporation would allow for the quasi 1D property to be exploited in G-PCC in both the occupancy tree and the predictive tree. [0023].
Consider claim 16, claim 16 recites the method recited in claim 3. Thus, it is rejected for the same reasons.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/TAT C CHIO/Primary Examiner, Art Unit 2486
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