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 July 25, 2024 and January 26, 2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the Examiner.
Response to Amendment
Applicant’s Preliminary Amendment to the specification and the claims, filed on July 8, 2024, has been entered and made of record.
Currently pending Claim(s) 1-3, 5-7, 9-12, 14-17, and 19
Independent Claim(s) 1, 9-10, and 19
Amended Claim(s) 3, 5, 7, 12, 14, and 17
Canceled Claim(s) 4, 8, 13, and 18
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 7 and 16 are rejected under 35 U.S.C. 112(b). Claim 7 recites the limitation of “the bounding boxes of the subgroups,” and claim 16 recites the limitation of “the bounding box information about the second subgroup.” There is insufficient antecedent basis for these limitations, and the details regarding the bounding boxes is not introduced until claim 17.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 9-12, and 19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Hur et al. (US 2021/0319581 A1), hereafter Hur.
Regarding claim 1, Hur teaches a method of transmitting point cloud data, the method comprising: encoding point cloud data; and transmitting a bitstream containing the point cloud data (Figs. 2 and 4 show methods for encoding and transmitting data from an acquired .ply file containing geometry and attribute data of a point cloud.).
Regarding claim 2, Hur teaches the method of claim 1, wherein the encoding of the point cloud data comprises: encoding geometry; and encoding attributes (Fig. 2 shows that .ply files include geometry information and attribute information. Fig. 4 shows an encoder for point position data and attribute date, which results in a geometry bitstream and an attribute bitstream.),
wherein the encoding of the attributes comprises: generating levels of detail (LoDs) (Figs. 8-9 and [0160-180] disclose the process of generating LoDs.),
wherein the LoDs are divided into a plurality of subgroups (Hut teaches using bounding boxes to select portions of the point cloud data, which can divide an octree layer into subgroups. Figs. 16(a-c) and [0256-0264] discloses dividing the point cloud data into a layer-group and subgroup structure. [0261] “The point cloud video encoder according to the embodiments may encode point cloud data on a slice-by-slice basis or a tile-by-tile basis, wherein a tile includes one or more slices. In addition, the point cloud video encoder according to the embodiments may perform different quantization and/or transformation for each tile or each slice.”).
Regarding claim 3, Hur teaches the method of claim 2, wherein the generating of the LoDs comprises: searching for neighbor nodes, wherein the searching comprises: searching for neighbors of a first node within a boundary of a first subgroup, the first node belonging to the first subgroup (Hur teaches searching for a neighbor node within the diagonal distance of a bounding box region. [0027] “According to an embodiment, when the one or more LODs are generated based on an octree, the base neighbor point distance may be determined based on a diagonal distance of one node in a specific LOD.” [0028] “The attribute decoder may estimate a density of the point cloud data based on the base neighbor point distance and a diagonal length of a bounding box of the point cloud data, and automatically calculate the maximum neighbor point range according to the estimated density.” [0445] “That is, the neighbor points registered as the neighbor point set of the point Px are limited to points within the maximum neighbor point distance at the LOD to which the point Px belongs among the X points.”),
wherein the encoding of the attributes further comprises: deriving weights, wherein the deriving of the weights comprises: deriving a weight for a second node belonging to a parent subgroup based on a boundary of a child subgroup (The neighborhood search distance may be set based on the diagonal distance to a node in a parent level. [0353] “According to embodiments, when searching for neighbor points in previous LODs (e.g., LOD0 to LODl−1 in the retained list) based on points belonging to a current LOD (e.g., LOD1 set in the index list), a diagonal distance of a higher node (parent node) of an octree node of the current LOD may be a base distance for acquiring the maximum neighbor point distance.”).
Regarding claim 9, Hur teaches a device for transmitting point cloud data, the device comprising: an encoder configured to encode point cloud data; and a transmitter configured to transmit a bitstream containing the point cloud data (Fig. 1 shows a device for encoding and transmitting data from an acquired .ply file containing geometry and attribute data of a point cloud.).
Regarding claim 10, Hur teaches a method of receiving point cloud data, the method comprising: receiving a bitstream containing point cloud data; and decoding the point cloud data; comprising (Figs. 2 and 4 show methods for receiving and decoding data from an encoded .ply file containing geometry and attribute data of a point cloud.).
Regarding claim 11, Hur teaches the method of claim 10, wherein the encoding of the point cloud data comprises: decoding geometry; and decoding attributes (Fig. 2 shows that .ply files include geometry information and attribute information. Fig. 10 shows a decoder for point position data and attribute date, which results in a geometry bitstream and an attribute bitstream.),
wherein the decoding of the attributes comprises: generating levels of detail (LoDs) (Fig. 8-9; [0160-180] discloses the process of generating LoDs.),
wherein the LoDs are divided into a plurality of subgroups (Hut teaches using bounding boxes to select portions of the point cloud data, which can divide an octree layer into subgroups. Figs. 16(a-c) and [0256-0264] discloses dividing the point cloud data into a layer-group and subgroup structure. [0261] “The point cloud video encoder according to the embodiments may encode point cloud data on a slice-by-slice basis or a tile-by-tile basis, wherein a tile includes one or more slices. In addition, the point cloud video encoder according to the embodiments may perform different quantization and/or transformation for each tile or each slice.”).
Regarding claim 12, Hur teaches the method of claim 11, wherein the generating of the LoDs comprises: searching for neighbor nodes, wherein the searching comprises: searching for neighbors of a first node within a boundary of a first subgroup, the first node belonging to the first subgroup (Hur teaches searching for a neighbor node within the diagonal distance of the bounding box. [0027] “According to an embodiment, when the one or more LODs are generated based on an octree, the base neighbor point distance may be determined based on a diagonal distance of one node in a specific LOD.” [0028] “The attribute decoder may estimate a density of the point cloud data based on the base neighbor point distance and a diagonal length of a bounding box of the point cloud data, and automatically calculate the maximum neighbor point range according to the estimated density.” [0445] “That is, the neighbor points registered as the neighbor point set of the point Px are limited to points within the maximum neighbor point distance at the LOD to which the point Px belongs among the X points.”),
wherein the decoding of the attributes further comprises: deriving weights, wherein the deriving of the weights comprises: deriving a weight for a second node belonging to a parent subgroup based on a boundary of a child subgroup (The neighborhood search distance may be set based on the diagonal distance to a node in a parent level. [0353] “According to embodiments, when searching for neighbor points in previous LODs (e.g., LOD0 to LODl−1 in the retained list) based on points belonging to a current LOD (e.g., LOD1 set in the index list), a diagonal distance of a higher node (parent node) of an octree node of the current LOD may be a base distance for acquiring the maximum neighbor point distance.”).
Regarding claim 19, Hur teaches a device for receiving point cloud data, the device comprising: a receiver configured to receive a bitstream containing point cloud data; and a decoder configured to decode the point cloud data (Fig. 1 shows a device for encoding/decoding and transmitting data from an acquired .ply file containing geometry and attribute data of a point cloud.).
Allowable Subject Matter
Claims 5-7 and 14-17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claim 5, the closest prior art of record, Hur (US 2021/0319581 A1), teaches the method of claim 3. Hur further teaches setting a weight for a node ([0447] “According to embodiments, a predictor of the point may register a ½ distance (=weight) based on a distance value to each neighbor point by a registered neighbor point set. For example, the predictor of the P3 node calculates a weight based on the distance value to each neighbor point by (P2 P4 P6) as the neighbor point set. According to embodiments, weights of neighbor points may be (1/√(P2−P3)2, 1/√(P4−P3)2, 1/√(P6−P3)2).”).
However, Hur fails to teach wherein the weight for the second node is based on a number of nodes referencing the second node as a neighbor among nodes included within a boundary of a child subgroup, the second node being positioned in the child subgroup. As shown in [0447], Hur’s method involves setting a weight for a node based on the distances to neighboring nodes, which may be in the same subgroup and/or bounding box region. Thus, the weight for a node is based on the distances to neighboring nodes rather than the number of neighboring nodes.
Furthermore, other cited prior art references (such as Yea (US 2021/0217136 A1) and Zhang (WO 2021/062736 A1) from the IDS dated July 25, 2024, and the Korean ISR dated July 8, 2024) also fail to teach all of the limitations of claim 5. Although these references teach G-PCC encoding of point clouds and dividing the point cloud data using octree division and subgroups (using slices, bounding boxes, etc.), these references teach setting a weight for a node based on distances to neighboring nodes instead of setting a weight based on the number of neighboring nodes. See [0007-0010] and [0052] of Yea and [Fig. 5, Step S503] of Zhang for further details about setting a weight for a node.
Other prior art references found during the Examiner’s search, such as Sugio (US 2022/0191545 A1), teach a similar method to the claimed invention where neighbor nodes are selected during attribute encoding. However, Sugio also teaches that weights are based on the distance to neighboring nodes rather than teaching that weights are based on the number of neighboring nodes.
Thus, for the reasons stated above, claim 5 would be allowable over the prior art of record if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Similarly, claim 14 would also be allowable, because claim 14 contains the same limitations as claim 5 applied to decoding instead of encoding. Claims 6-7 and 15-17 would be allowable due to their dependence on claims 5 and 14, respectively.
The Examiner suggests amending the independent claims to include the limitations from at least claims 2-3 and 5. For example, independent claims 1 and 9 could be amended to include all limitations from claims 2-3 and 5, and independent claims 10 and 19 could be amended to include all limitations from claims 11-12 and 14.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Oh (US 2024/0323434 A1) teaches a method for encoding and decoding point cloud data. The method involves splitting the octree layers into subgroups and generating LoDs. Although these methods are very similar to the claimed invention, Oh names the same inventor and applicant as the claimed invention and claims priority within 1-year of the priority date of the claimed invention. Thus, Oh is not used in prior art rejections.
Sugio et al. (US 2022/0191545 A1) teaches a method for encoding and decoding point cloud data. Regarding attribute encoding, the method involves generating a predicted value of attribute information for a current node in an octree and performing a transform process for separating input signals into multiple components. Attribute information for a node is predicted from attribute information of first nodes, including a parent node and another node in the same layer as the parent node.
Mammou et al. (US 11,010,928 B2) teaches a method for compressing attribute information for a point cloud during encoding and decoding. The method provides several prediction strategies for predicting attribute values.
Vorobyov et al. (US 10,825,244 B1) teaches methods for constructing LoDs using a visualization tree based on the location of the viewer [Figs. 7-8].
Iguchi et al. (US 12,340,543 B2) teaches methods for encoding and decoding point cloud data. The methods include partitioning point cloud data into octrees and generating LoDs.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC JAMES SHOEMAKER whose telephone number is (571)272-6605. The examiner can normally be reached Monday through Friday from 8am to 5pm ET.
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, JENNIFER MEHMOOD, can be reached at (571)272-2976. 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.
/Eric Shoemaker/
Patent Examiner
/JENNIFER MEHMOOD/ Supervisory Patent Examiner, Art Unit 2664
Prosecution Insights