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 .
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)(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 1-4, 6-7, 14-15, 17-18, and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Li et al (US 20220005230 A1).
Referring to claims 1-2 and 7:
Li et al disclose a point cloud encoding and decoding method, and encoding device and decoding device for performing these methods (see abstract and summary, and par. 168), the method comprising:
determining, for a point P to be encoded to the bitstream, a predictor list of k predictor points of the point cloud including k points of the point cloud nearest to the point P to be encoded, wherein the k points are selected according to their relative position to each other and wherein k is equal to or larger than 3 (reads on method 1000 in Fig. 1: determining a point set composed of K nearest neighbor points of a current point is determined (S1100); determining a point set composed of L next nearest neighbor points of the current point (S1200); and determining a preferred neighbor point set of the current point according to the point set composed of K nearest neighbor points of the current point and the point set composed of L next nearest neighbor points of the current point (S1300); par. 81: K is 3); and
encoding the point attributes associated to the point P to be encoded by predictive encoding based on attributes of the predictor points of the predictor list (reads on method 1000 in Fig. 1: encoding according to the preferred neighbor point set of the current point (S1400) – shown in more detail in Fig. 5 as including:
determining a weighted mean of reconstructed attribute values of all points in the preferred neighbor point set of the current point as a candidate predictor 1, and a corresponding mode is determined as a prediction mode 1 (S1401);
sequentially determining the reconstructed attribute values of all points in the preferred neighbor point set of the current point as candidate predictor 2 to candidate predictor N+1, and the corresponding modes are sequentially determined as prediction mode 2 to prediction mode N+1, wherein N is the number of points in the point set (S1402);
calculating a cost value Jn of the prediction mode 1 to the prediction mode N+1 is calculated, n is from 1 to N+1, a minimum value in Jn is determined as the minimum cost value, and the corresponding mode of the minimum cost value as a preferred or optimum prediction mode (S1403);
determining the predicted value of the current point according to the preferred or optimum prediction mode (S1404);
determining a predicted residual of the current point according to a difference between an attribute value of the current value and the predicted value of the current point (S1405); and
encoding the preferred or optimum prediction mode and the predicted residual (S1406)).
The reconstructed attribute value is a reconstructed attribute value that is encoded, and the reconstructed attribute may be a color attribute, a reflectivity or other attribute.
The relevant operations of the decoding end are essentially correspond to those of the encoding operations (see Fig. 7).
Referring to claims 3 and 17:
Li et al disclose said determining the predictor list comprising determining an initial list of k predictor points of the point cloud including the k points of the point cloud nearest to the point P to be encoded (point set composed of K nearest neighbor points) and selecting the predictor points of the initial list as a predictor list according to their relative position to each other (point set composed of L next nearest neighbor points).
Referring to claims 4, 6, 18, and 20:
Li et al disclose determining a candidate list of n predictor points by nearest points of the point cloud, replacing at least one predictor point of the initial list by a predictor point of the candidate list to acquire a predictor list, and selecting predictor points of the acquired predictor list as a predictor list according to their relative position to each other (determining a preferred neighbor point set of the current point according to the point set composed of K nearest neighbor points of the current point and the point set composed of L next nearest neighbor points), wherein only a last predictor point of the initial list and/or the predictor list can be replaced.
Referring to claims 14 and 15:
Li et al disclose an encoder/decoder for encoding/decoding, in a bitstream, point attributes associated to a point of a point cloud, comprising: a processor and a memory storage device, wherein in the memory storage device instructions executable by the processor are stored that, when executed, cause the processor to perform the method according to claims 1 and 2 (par. 168, 173).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 5 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al as applied to claims 1 and 2 above, and further in view of well-known prior art (MPEP 2144.03).
Referring to claims 5 and 19:
Li et al do not disclose iteratively replacing at least one predictor point by each predictor point of a candidate list to acquire a predictor list until predictor points of the predictor list are selected. However, iteratively replacing one or more a predictor points in a point cloud is well-known to minimize prediction errors (optimally matching predicted and actual points) in point cloud compression thereby achieving higher compression rates and better reconstruction quality. For that reason, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified Li et al to iteratively replacing at least one predictor point by each predictor point of a candidate list to acquire a predictor list until predictor points of the predictor list are selected.
Allowable Subject Matter
Claims 8-13 and 21 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.
Referring to claims 8-10, while it is known in the prior art the prior art to separate the space around the point P to be encoded into octants along the X, Y, and Z-axis, the prior art searched and of record neither anticipates nor suggests the added limitation in the combination, wherein the predictor points are selected if at least two predictor points are located in opposite octants, wherein opposite octants share only the common point P to be encoded or wherein loose opposite octants share only one common edge.
Referring to claims 11-13 and 21, the prior art searched and of record neither anticipates nor suggests in the claimed combination, wherein at least one threshold Ti is defined by Ti = W x dist(P, Pi), with a weight W>1 and Pi a predictor point of the initial list, wherein predictor points of the candidate list are eligible for replacing the at least one predictor point of the initial list if the distance between the predictor point of the candidate list to the point P to be encoded/decoded is smaller than Ti.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed (i.e., a descriptive title that distinguishes the invention and is not a generic or general description). The new title should take into account any amendments to the claims to best indicate the claimed invention.
The title must be as short and specific as possible (see 37 CFR 1.72(a)). Applicant should distill a description of the claimed invention into as few words as possible to capture the essence of the claimed invention. Rather than reciting statutory categories (apparatus, method, product) and some generic descriptor (e.g., information processing), a title that is specific, but characterizes the essence or key aspect(s) of the claimed invention, should be submitted.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 29 December 2023 and 07 March 2025 were filed in compliance with the provisions of 37 CFR 1.97 and 1.98. Accordingly, the statements have been considered by the examiner as indicated below.
The relevance of the cited document in the first IDS, in addition to the US equivalent publication applied above, can be found in the International Search Report and/or Written Opinion from the ISA dated 24 March 2022 for PCT/CN2021/104335 (of record).
Applicant has not provided an explanation of relevance of cited document(s) summarized below.
Yea et al (US 20200311984 A1) disclose a method of adaptive point cloud attribute coding includes obtaining an attribute of a current point included in point cloud data, and obtaining candidate predicted values of the obtained attribute, the candidate predicted values including any one or any combination of a weighted average value of a plurality of distances from the current point respectively to other points included in the point cloud data, a first predicted value of a first distance from the current point to a first nearest point among the other points and a second predicted value of a second distance from the current point to a second nearest point after the first nearest point among the other points. The method further includes selecting, for the obtained attribute, one among the obtained candidate predicted values, using rate-distortion optimization, and setting, for a decoder, a flag indicating whether the obtained candidate predicted values includes the weighted average value.
Cited Art
The prior art and other references made of record and not relied upon are considered pertinent to applicant's disclosure.
Mammou et al (US 20190075320 A1) disclose a system having multi-sensors for capturing a set of points that make up a point cloud. An encoder (202) generates a compressed point cloud, identifies a location between a respective point of a sub-sampled point cloud and a neighboring point in the sub-sampled point cloud, encodes data for the compressed point cloud comprising spatial information for points of the sub-sampled point cloud, and data indicating for each of the respective locations, and determines whether the respective point is to be included at the location or relocated relative to the location in a decompressed point cloud.
Wang et al (US 20240348772 A1) disclose a method for point cloud coding that comprises: determining, during a conversion between a current frame of a point cloud sequence and a bitstream of the point cloud sequence, at least one neighbor list comprising a set of neighbor points of a current point of the current frame based on geometry information of the set of neighbor points; and performing the conversion based on the at least one neighbor list. Compared with the conventional solution, the proposed method can advantageously improve the point cloud coding efficiency and coding quality.
Xu et al (US 20240314359 A1) disclose a method for point cloud coding that comprises: determining, for a current point in a current point cloud (PC) sample of a point cloud sequence during a conversion between the current PC sample and a bitstream of the point cloud sequence, at least one neighboring point from a set of points in a reference PC sample of the current PC sample, the set of points being in a group of level of details (LODs) of the reference PC sample; and performing the conversion based on the at least one neighboring point. Compared with the conventional solution, the proposed method can advantageously improve the accuracy of the nearest neighbor search.
Xu et al (US 20250142121 A1 / US 20250039448 A1 / US 20240357172 A1 / US 20240357174 A1 / US 20240320866 A1) disclose a method for point cloud coding and describe in G-PCC, performing the nearest neighbors search considering the point distribution information where the concept of strict opposite and loose opposite are defined. According to the relative position with the current point (x, y, z), every nearest neighbor point is assigned to a direction index according which, strict opposite and loose opposite are defined. (see section 3.1.1.2 Nearest Neighbors Search Considering Point Distribution).
Ramasubramonian et al (US 20240185470 A1) disclose a device for processing point cloud data is configured to determine a first attribute value for a first point of a point cloud, the first point being a closest already-decoded point to a current point of the point cloud; determine second and third attribute values for second and third points of the point cloud, the second and third points being second and third closest already-decoded points; determine a fourth attribute value for a fourth point of the point cloud, the fourth point being an already-decoded point that is either further from, or the same distance to, the current point as the third point; generate a set of predictor candidates with a subset of the first attribute value, the second attribute value, the third attribute value, and the fourth attribute value based on a comparison of a location of the second point to a location of the third point.
Zhu (CN 115086658 B) disclose a processing method of point cloud data, wherein the method comprises the following steps: acquiring point cloud data, wherein the point cloud data comprises a current point to be processed and a plurality of processed points; selecting N processed points from the plurality of processed points as N candidate points; according to the distance between N candidate points and the current point, k candidate points are selected from the N candidate points to serve as k neighbor points of the current point, wherein N and k are positive integers larger than 1; and determining the attribute predicted value of the current point according to the attribute values of the k neighbor points. According to the embodiment of the application, the candidate points can be selected by utilizing the geometric relationship and the attribute relationship of the processed points, and the specific neighbor points are selected from the candidate points, so that a richer neighbor point selection mode is provided, and the prediction efficiency of the point cloud attribute is improved.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Scott Rogers whose telephone number is 571-272-7467. The examiner can normally be reached 8 am to 7 pm flex.
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/Scott A Rogers/
Primary Examiner, Art Unit 2683
23 December 2025