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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/27/2026 has been entered.
Response to Arguments
Applicant's arguments filed 4/27/2026 have been fully considered but they are not persuasive. The Applicant argues with respect to the independent claims that Zhang fails to disclose,
generating residual information, the residual information including residual information related to the geometry information and residual information related to the attribute information, wherein different weights are assigned to the residual information related to the geometry information and the residual information related to the attribute information.
Following review of the Zhang reference, the Examiner determined that Zhang anticipates this limitation, by disclosing in [0060]-[0061] a distance metric for forming a predictive tree that contains a weighting factor that changes the relative weighting importance between geometry and attribute information. Further below, in [0074]-[0075], Zhang discloses, “The prediction can be applied to both the geometry and attribute of the points. Geometry and attribute may construct their own candidate list and the prediction index may be signaled separately as well. [0075] The prediction residual can be directly signaled. Alternatively, transforms can be conducted to the prediction residuals.” Thus, Zhang discloses that the geometry and attribute information is on separate channels and is weighted relative to one another at the time of forming a prediction tree. This differential relative weighting of the geometry and attribute distance information in forming a predictive tree necessarily carries over to the weighting of the respective residual information for these two channels, because the residual information is created from the predictive tree. Applicant also notes in the response that, “The previous first Office Action dated April 29, 2024 indicated that claims 5-7 and 12-14 would be allowable if rewritten in independent form. In response, in the Amendment filed on July 29, 2024, portions of original claim 5 were deleted and claims 6 and 7 were canceled.” The Examiner appreciates Applicant’s effort to advance prosecution, but respectfully notes that a 2nd non-final action vacated the allowability of claims 5-7 and 12-14 with newly found prior art, and that the reference cited in the current office action was not of record at the time of the 4/29/2025 office action.
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, 2, 5, 8, 9, 12, 15, and 18 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Zhang, US 2021/0248785 A1.
Regarding claim 1, Zhang discloses: a method of transmitting point cloud data, the method comprising:
encoding the point cloud data, wherein the point cloud data includes geometry information including positions of points of the point cloud data (See [0058]-[0059], disclosing predictive tree coding for geometry information using position.) and attribute information including attribute values of the points (See [0060]-[0061].); and
transmitting the encoded point cloud data and signaling data (See figure 2, and [0027]-[0028], disclosing transmission over network 230.),
wherein the encoding the point cloud data comprises:
generating a predictive tree based on the points of the point cloud data (See [0052].); and
compressing the point cloud data by performing prediction based on the predictive tree (See figure 4, and [0048], specifically, “generate predictive tree” in step 440.),
wherein the compressing comprises:
predicting the geometry information and the attribute information based on the predictive tree (See [0058], “To construct a predictive tree, the geometry structure and attribute information may be utilized, and [0074], “The prediction can be applied to both the geometry and attribute of the points.”), and
generating residual information, the residual information including residual information related to the geometry information and residual information related to the attribute information (See [0074]-[0075], respectively disclosing that “The prediction can be applied to both the geometry and attribute of the points.” and “The prediction residual can be directly signaled. Alternatively, transforms can be conducted to the prediction residuals.”.), and
wherein different weights are assigned to the residual information related to the geometry information and the residual information related to the attribute information (See [0061]-[0062]. The equation in [0060] has a weighting factor β that changes the that changes the relative importance between geometry and attribute in forming a predictive tree. The prediction process for geometry and attribute information produces prediction residuals for each type of information, and these residuals inherit the respective weightings assigned to the geometry and attribute information, because the residual data is formed from the prediction tree, and because the channel division between geometry information and attribute information persists throughout the encoding process, as disclosed by [0074]-[0075].).
Regarding claim 2, Zhang discloses: the method of claim 1, wherein the encoding the geometry information further comprises:
clustering the points of the point cloud data based on at least one of similarity of the geometry information or similarity of the attribute information related to the points of the point cloud data (See [0058]-[0063], disclosing different ways of clustering points of point cloud data, Typically, during the tree construction process, two points are connected if they are found to be close to each other.” [0063] gives example of points closes to a straight line being connected (“clustered”) to form a branch of the predictive tree.); and
dividing the points of the point cloud data into a plurality of compression units (See [0019], disclosing arithmetically coding occupancy data of each node.).
Regarding claim 5, Zhang discloses: the method of claim 1, wherein the compressing further comprises:
assigning either a same predictive coding parameter to both the geometry information and the attribute information, or different predictive coding parameters respectively to the geometry information and the attribute information (See [0061]. The weighting factor β applied to affect the relative importance of geometry and attribute data in forming the predictive tree is a different coding parameter.).
Decoding method claim 15 is directed to a method of receiving point cloud data that substantially corresponds to the transmitting method claim in claim 1. Therefore, method claim 15 is rejected for the same reasons of obviousness as set forth above with respect to claim 1.
Device claims 8, 9, 12, and 18 are drawn to the method of using the corresponding apparatus claimed in method claims 1, 2, 5, and 15, respectively. Therefore, device claims 8, 9, 12, and 18, correspond, respectively, to method claims 1, 2, 5, and 15, and are rejected for the same reasons anticipation as used above.
Conclusion
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/KYLE M LOTFI/Examiner, Art Unit 2425
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