Prosecution Insights
Last updated: April 19, 2026
Application No. 18/560,287

POINT CLOUD DATA TRANSMISSION METHOD, POINT CLOUD DATA TRANSMISSION DEVICE, POINT CLOUD DATA RECEPTION METHOD, AND POINT CLOUD DATA RECEPTION DEVICE

Non-Final OA §103
Filed
Nov 10, 2023
Examiner
BRANIFF, CHRISTOPHER
Art Unit
2484
Tech Center
2400 — Computer Networks
Assignee
LG Electronics Inc.
OA Round
3 (Non-Final)
85%
Grant Probability
Favorable
3-4
OA Rounds
2y 2m
To Grant
96%
With Interview

Examiner Intelligence

Grants 85% — above average
85%
Career Allow Rate
544 granted / 637 resolved
+27.4% vs TC avg
Moderate +10% lift
Without
With
+10.2%
Interview Lift
resolved cases with interview
Typical timeline
2y 2m
Avg Prosecution
28 currently pending
Career history
665
Total Applications
across all art units

Statute-Specific Performance

§101
4.1%
-35.9% vs TC avg
§103
55.3%
+15.3% vs TC avg
§102
16.3%
-23.7% vs TC avg
§112
7.7%
-32.3% vs TC avg
Black line = Tech Center average estimate • Based on career data from 637 resolved cases

Office Action

§103
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 . 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 December 22, 2025 has been entered. 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. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. Claims 1, 8, 15 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Tourapis et al. (US 2020/0021856A1, already of record, referred to herein as “Tourapis”) in view of Chou et al. (US 2019/0228050 A1, referred to herein as “Chou”). Regarding claim 1, Tourapis discloses: A method of encoding point cloud data (Tourapis: paragraph [0039], disclosing encoding methods of a point cloud), the method comprising: encoding geometry information of point cloud data (Tourapis: paragraph [0039], disclosing that geometry information—also referred to as spatial information—associated with the point cloud may be compressed by an encoder); and encoding attribute information of the point cloud data (Tourapis: paragraph [0039], disclosing that attribute information of the point cloud may be compressed by the encoder), wherein the encoding attribute information includes: generating levels of detail (LoDs) for reconstructed geometry information (Tourapis: paragraphs [0005] and [0057], disclosing sorting of points into multiple levels of detail (LODs); paragraphs [0154] – [0156], disclosing levels of detail associated with geometry information); searching neighbor points for points of a level of LoDs (Tourapis: Fig. 10B, paragraphs [0005] and [0210], disclosing search of neighbor points and inclusion of the neighboring points in a current level of detail); predicting attribute information for the points based on the searched neighbor points (Tourapis: paragraph [0046], disclosing that the encoder may include a predictor that determines a predicted attribute value of a point in the point cloud; paragraph [0150], disclosing that the level of detail structure may be used to compress attributes associated with the point cloud; paragraphs [0101] – [0103], disclosing that the attribute prediction is based on the searched neighbor points), and wherein the searching neighbor points for the points of the level includes: searching first neighbor points based on relative locations related a block for a point of the points (Tourapis: paragraphs [0214] – [0230], disclosing searching for neighbor points based on relative locations; paragraph [0104], disclosing block-based coding)…; and searching second neighbor points based on a… Morton order (Tourapis: paragraphs [0208] and [0214] – [0216], disclosing searching of multiple points—e.g. including a second neighbor point—based on index values according to a Morton order). Tourapis does not explicitly disclose: a size of the block; and a scaled Morton order However, Chou discloses: a size of the block (Chou: paragraph [0016], disclosing coding of point cloud attribute data; paragraph [0075], disclosing block-based coding of blocks of various size) and a scaled Morton order (Chou: paragraph [0061], disclosing that the point locations are scaled and a Morton order determined). At the time the application was effectively filed, it would have been obvious for a person having ordinary skill in the art to use the scaled Morton order during block-based coding of Chou in the method of Tourapis. One would have been motivated to modify Tourapis in this manner in order to better code point cloud data at reduced bit rates (Chou: paragraphs [0003] – [0006] and [0016] – [0018]). Additionally, Tourapis and Chou are directed to the same field of endeavor—namely, coding of video data by use of point clouds (Tourapis: paragraph [0002]; Chou: paragraph [0002]). Regarding claim 8, the claim recites analogous limitations to claim 1, above, and is therefore rejected on the same premise. (Note that Tourapis discloses implementation via memory and processor in paragraphs [0007] and [0474] – [0476].) Regarding claim 15, Tourapis and Chou disclose: A method decoding point cloud data (Tourapis: paragraph [0039], disclosing decoding methods of a point cloud), the method comprising: decoding geometry information about of point cloud data (Tourapis: paragraph [0039], disclosing that geometry information—also referred to as spatial information—associated with the point cloud may be compressed by an encoder and decompressed by a decoder); and decoding attribute information of the point cloud data (Tourapis: paragraph [0039], disclosing that attribute information of the point cloud may be compressed by the encoder and decompressed by the decoder), wherein the decoding attribute information includes: generating levels of detail (LoDs) for the decoded geometry information; (Tourapis: paragraphs [0005] and [0057], disclosing sorting of points into multiple levels of detail (LODs)), searching neighbor points for points of a level of LoDs (Tourapis: Fig. 10B, paragraphs [0005] and [0210], disclosing search of neighbor points and inclusion of the neighboring points in a current level of detail); predicting attribute information for the points based on the searched neighbor points (Tourapis: paragraph [0046], disclosing that the encoder may include a predictor that determines a predicted attribute value of a point in the point cloud; paragraph [0150], disclosing that the level of detail structure may be used to compress attributes associated with the point cloud; paragraphs [0101] – [0103], disclosing that the attribute prediction is based on the searched neighbor points), and wherein the searching neighbor points for the points of the level includes: searching first neighbor points based on relative locations related a block for a point of the points (Tourapis: paragraphs [0214] – [0230], disclosing searching for neighbor points based on relative locations; paragraph [0104], disclosing block-based coding) and a size of the block (Chou: paragraph [0016], disclosing coding of point cloud attribute data; paragraph [0075], disclosing block-based coding of blocks of various size); and searching second neighbor points based on a scaled Morton order (Tourapis: paragraphs [0208] and [0214] – [0216], disclosing searching of multiple points—e.g. including a second neighbor point—based on index values according to a Morton order; Chou: paragraph [0061], disclosing that the point locations are scaled and a Morton order determined). The motivation for combining Tourapis and Chou has been discussed in connection with claim 1, above. Regarding claim 22, the claim recites analogous limitations to claim 15, above, and is therefore rejected on the same basis. (Note that Tourapis discloses implementation via memory and processor in paragraphs [0007] and [0474] – [0476].) Claims 5, 6, 7, 12, 13, 14, 19, 20, 21, 25, 26 and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Tourapis in view of Chou as applied to claim 1 above, and further in view of Wan et al. (US 2023/0101072A1, already of record, referred to herein as “Wan”). Regarding claim 5, Tourapis and Chou disclose: The method of claim 1, wherein a neighbor of the first neighbor is searched based on acquiring information about the neighbor for the point (Tourapis: Fig. 10B, paragraph [0210], disclosing identification of a set of neighboring points based on a search range)… the points having a Euclidean distance from the point within a specific distance among points outside the specific range from the point (Tourapis: paragraph [0049], disclosing use of information about distances in X, Y, Z space—e.g., a Euclidean distance—between a point being evaluated and a neighboring point). Tourapis and Chou do not disclose: from a neighbor node table, wherein the neighbor node table contains information about the points having a Euclidian distance. However, Wan discloses: from a neighbor node table (Wan: paragraph [0101], disclosing use of a lookup table for neighboring nodes), wherein the neighbor node table contains information about the points having a Euclidian distance (Wan: paragraph [0101], disclosing that the neighbor node lookup table is established using coordinate differences). At the time the application was effectively filed, it would have been obvious for a person having ordinary skill in the art to use the neighboring information of Wan in the method of Tourapis and Chou. One would have been motivated to modify Tourapis and Chou in this manner in order to better convey compressed point cloud data to a decoding device (Wan: paragraphs [0048] – [0049]). Additionally, Tourapis, Chou, and Wan are directed to the same endeavor; namely, the coding of point cloud video data (Tourapis: paragraph [0002]; Chou: paragraph [0002]; Wan: paragraph [0002]). Regarding claim 6, Tourapis, Chou, and Wan disclose: The method of claim 5, wherein the neighbor node table is an array containing rows and columns (Wan: paragraph [0101], disclosing use of a lookup table— e.g., with rows and columns—for neighboring nodes information), wherein the searching the first neighbor points comprises: acquiring information about the neighbor by referencing the rows and columns of the neighbor node table based on an index of the point according to the specific sorting method (Wan: paragraph [0101], disclosing use of the lookup table—including its rows and columns—for neighboring node information sorted by Morton code). The motivation for combining Tourapis, Chou, and Wan has been discussed in connection with claim 5, above. Regarding claim 7, Tourapis, Chou, and Wan disclose: The method of claim 6, wherein the specific sorting method is sorting the points based on a Morton curve, a Hilbert curve, a Peano curve, or a Gray curve (Tourapis: paragraphs [0057] and [0058], disclosing use of a Morton curve, a Hilbert curve, or a Peano curve to order points). Regarding claim 12, the claim recites analogous limitations to claim 5, above, and is therefore rejected on the same premise. Regarding claim 13, the claim recites analogous limitations to claim 6, above, and is therefore rejected on the same premise. Regarding claim 14, the claim recites analogous limitations to claim 7, above, and is therefore rejected on the same premise. Regarding claim 19, the claim recites analogous limitations to claim 5, above, and is therefore rejected on the same premise. Regarding claim 20, the claim recites analogous limitations to claim 6, above, and is therefore rejected on the same premise. Regarding claim 21, the claim recites analogous limitations to claim 7, above, and is therefore rejected on the same premise. Regarding claim 25, the claim recites analogous limitations to claim 19, above, and is therefore rejected on the same basis. Regarding claim 26, the claim recites analogous limitations to claim 20, above, and is therefore rejected on the same basis. Regarding claim 27, the claim recites analogous limitations to claim 21, above, and is therefore rejected on the same basis. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to Christopher Braniff whose telephone number is (571) 270-5009. The examiner can normally be reached M-F 7AM to 4PM. 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, Thai Tran can be reached at (571) 272-7382. 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. CHRISTOPHER T. BRANIFF Primary Examiner Art Unit 2484 /CHRISTOPHER BRANIFF/Primary Examiner, Art Unit 2484
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Prosecution Timeline

Nov 10, 2023
Application Filed
Feb 27, 2025
Non-Final Rejection — §103
Jun 03, 2025
Response Filed
Sep 05, 2025
Final Rejection — §103
Dec 22, 2025
Request for Continued Examination
Jan 09, 2026
Response after Non-Final Action
Jan 20, 2026
Non-Final Rejection — §103 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

3-4
Expected OA Rounds
85%
Grant Probability
96%
With Interview (+10.2%)
2y 2m
Median Time to Grant
High
PTA Risk
Based on 637 resolved cases by this examiner. Grant probability derived from career allow rate.

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