Prosecution Insights
Last updated: August 17, 2026
Application No. 18/847,815

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

Non-Final OA §103§112
Filed
Sep 17, 2024
Priority
Mar 21, 2022 — RE 10-2022-0034808 +1 more
Examiner
HAGHANI, SHADAN E
Art Unit
2485
Tech Center
2400 — Computer Networks
Assignee
LG Electronics Inc.
OA Round
3 (Non-Final)
61%
Grant Probability
Moderate
3-4
OA Rounds
1y 0m
Est. Remaining
79%
With Interview

Examiner Intelligence

Grants 61% of resolved cases
61%
Career Allowance Rate
232 granted / 380 resolved
+3.1% vs TC avg
Strong +18% interview lift
Without
With
+17.8%
Interview Lift
resolved cases with interview
Typical timeline
2y 11m
Avg Prosecution
34 currently pending
Career history
413
Total Applications
across all art units

Statute-Specific Performance

§101
2.4%
-37.6% vs TC avg
§103
65.3%
+25.3% vs TC avg
§102
11.5%
-28.5% vs TC avg
§112
15.1%
-24.9% vs TC avg
Black line = Tech Center average estimate • Based on career data from 380 resolved cases

Office Action

§103 §112
DETAILED ACTION 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 6/5/2026 has been entered. 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. The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. Claims 5-7 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. 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. Claim(s) 1-4, 11-15 are rejected under 35 U.S.C. 103 as being unpatentable over Kim (NPL: “LiDAR Point Cloud Compression by Vertically Placed Objects Based on Global Motion Prediction,” IEEE 2022) in view of Inter-EM (MPEG-N00189, “Inter-prediction Exploration Model (Inter-EM) v3.0,” ISO/IEC JTC 1/SC 29/WG 7, MPEG 3D Graphics coding, N00189, 25 September 2021), MPEG-N00155 (NPL: “Description of Exploration Experiment 13.2 on inter prediction,” ISO/IEC JTC 1/SC 29/WG 7, MPEG 3D Graphics Coding, N00155, 6 August 2021), and G-PCC (MPEG-N0215, “G-PCC codec description,” ISO/IEC JTC 1/SC 29/WG 7 N 0215, 29 December 2021). Regarding Claim 1, Kim (NPL: “LiDAR Point Cloud Compression by Vertically Placed Objects Based on Global Motion Prediction,” IEEE 2022) discloses reference frame (686th frame, Figure 2 caption) is generated based on a road (road, paragraph under Figure 2) for point cloud data (LiDAR point cloud, Fig. 2 caption); generating a compensated reference frame (motion compensated point cloud, page 15300, left column above Fig. 2) … based on … road (objects placed horizontally such as road, page 15300, left column; horizontally placed objects, page 15300 right column) and object (vertically placed objects, page 15300 both columns) partitioning (point cloud classification classifies horizontal and vertical objects, page 15300 right column) of points of the reference frame (point cloud t-1, page 15300 right column). Kim discloses the remaining claim elements because Kim’s disclosure is based on the Inter-EM model, based on the G-PCC reference software (See, “proposed modules were applied to the Inter-EM v2.0 based on the G-PCC reference software v7,” Section IV). Citations to the Inter-EM model and G-PCC indicate that Kim includes the following features: Inter-EM (MPEG-N00189, “Inter-prediction Exploration Model (Inter-EM) v3.0,” ISO/IEC JTC 1/SC 29/WG 7, MPEG 3D Graphics coding, N00189, 25 September 2021) provides evidence that Kim discloses a method (MPEG 3D Graphics coding, cover page) comprising: generating first information (--interPredictionEnabled=0|1, page 18) for representing that a reference frame is generated (When inter prediction is enabled, inter predicted frames use the previous frame as reference frame, page 18) based on … point cloud data (point cloud sequence, page 6) in a bitstream (output of the encoder is a binary bitstream, Page 6); generating second information (--motionParamPreset=0|1|3|6, page 18) for representing whether global motion compensation is applied (global motion is enabled, page 18) to a partition block (frame partitioned according to the partitioning method defined by --partitionMethod=0|2|3|4|5, page 9) in the bitstream (output of the encoder is a binary bitstream, Page 6); encoding geometry data (geometry tree coding, page 11) of a point cloud frame (number of frames encoded are N, page 19) for the point cloud data (encoder takes as input one or more PLY files describing a point cloud sequence, page 6) based on an occupancy tree (predictive geometry tree, page 11); and encoding attribute data (attribute coding, page 14) of the point cloud frame (number of frames encoded are N, page 19); wherein the encoding the geometry data of the point cloud frame includes: generating a compensated reference frame (Inter-EM, title—see evidentiary citation below) for the point cloud frame (number of frames encoded are N, page 19) based on global motion parameters (global motion parameters, page 19) including a rotation component (3x3 motion matrix, page 19) and a translation component (3 translation parameters, page 19). MPEG-N00155 (NPL: “Description of Exploration Experiment 13.2 on inter prediction,” ISO/IEC JTC 1/SC 29/WG 7, MPEG 3D Graphics Coding, N00155, 6 August 2021) provides evidence that Kim discloses wherein the encoding the geometry data of the point cloud frame includes: generating a compensated reference frame (in Inter-EM, global motion is applied to the previous point cloud to create a compensated previous point cloud, page 3) for the point cloud frame (point cloud, page 3) based on global motion parameters (global motion, page 3). Where Inter-EM v2.0 and v3.0 are different, one of ordinary skill in the art before the application was filed would have been motivated to replace Inter-EM v2.0 of Kim with Inter-EM v3.0 of MPEG because Inter-EM v3.0 is the updated working model of inter-predictive geometry coding and is expected to provide superior performance, improving the system. Where G-PCC v7 and v12 are different, one of ordinary skill in the art before the application was filed would have been motivated to replace G-PCC v7 with G-PCC v12 because G-PCC v12 is the updated codec and is expected to provide superior performance, improving the system. Regarding Claim 2, Kim (NPL: “LiDAR Point Cloud Compression by Vertically Placed Objects Based on Global Motion Prediction,” IEEE 2022) discloses the method of claim 1, wherein the point cloud data comprises points related to a road (road, Fig. 2) and points related to an object (vehicle, building, tree, Fig. 2), wherein, in a frame containing the points related to the road, the points related to the road are changed by the object related to the road, or are missing (inherent: objects in the path of lidar prevent the lidar from extracting data behind the object). Regarding Claim 3, Kim (NPL: “LiDAR Point Cloud Compression by Vertically Placed Objects Based on Global Motion Prediction,” IEEE 2022) discloses the method of claim 1, wherein the point cloud data is acquired by LiDAR (LiDAR, Fig. 2). G-PCC (MPEG-N0215, “G-PCC codec description,” ISO/IEC JTC 1/SC 29/WG 7 N 0215, 29 December 2021) provides evidence that Kim discloses wherein the point cloud data comprises points based on a laser ID of the LiDAR (angle correction based on particular laser L, Section 3.2.5.1, 3.2.5.2; laserindex, Section 3.2.5.5). Where Inter-EM v2.0 and v3.0 are different, one of ordinary skill in the art before the application was filed would have been motivated to replace Inter-EM v2.0 of Kim with Inter-EM v3.0 of MPEG because Inter-EM v3.0 is the updated working model of inter-predictive geometry coding and is expected to provide superior performance, improving the system. Where G-PCC v7 and v12 are different, one of ordinary skill in the art before the application was filed would have been motivated to replace G-PCC v7 with G-PCC v12 because G-PCC v12 is the updated codec and is expected to provide superior performance, improving the system. Regarding Claim 4, Kim (NPL: “LiDAR Point Cloud Compression by Vertically Placed Objects Based on Global Motion Prediction,” IEEE 2022) discloses the method of Claim 1. Inter-EM (MPEG-N00189, “Inter-prediction Exploration Model (Inter-EM) v3.0,” ISO/IEC JTC 1/SC 29/WG 7, MPEG 3D Graphics coding, N00189, 25 September 2021) provides evidence that Kim discloses wherein a point for inter-prediction is updated based on a threshold (upper and lower thresholds for ground/object classification, page 19). Regarding Claim 10, Kim (NPL: “LiDAR Point Cloud Compression by Vertically Placed Objects Based on Global Motion Prediction,” IEEE 2022) discloses a device comprising: a memory; and at least one processor connected to the memory, the at least one processor configured (software, Section IV). The remainder of claim 10 is rejected on the grounds provided in Claim 1. Regarding Claim 11, the claim is rejected on the grounds provided in Claim 1. Regarding Claim 12, the claim is rejected on the grounds provided in Claim 2. Regarding Claim 13, the claim is rejected on the grounds provided in Claim 3. Regarding Claim 14, the claim is rejected on the grounds provided in Claim 4. Regarding Claim 15, the claim is rejected on the grounds provided in Claim 10. Claim(s) 5-7 are rejected under 35 U.S.C. 103 as being unpatentable over Kim (NPL: “LiDAR Point Cloud Compression by Vertically Placed Objects Based on Global Motion Prediction,” IEEE 2022) in view of Inter-EM (MPEG-N00189, “Inter-prediction Exploration Model (Inter-EM) v3.0,” ISO/IEC JTC 1/SC 29/WG 7, MPEG 3D Graphics coding, N00189, 25 September 2021), MPEG-N00155 (NPL: “Description of Exploration Experiment 13.2 on inter prediction,” ISO/IEC JTC 1/SC 29/WG 7, MPEG 3D Graphics Coding, N00155, 6 August 2021), G-PCC (MPEG-N0215, “G-PCC codec description,” ISO/IEC JTC 1/SC 29/WG 7 N 0215, 29 December 2021), Pham Van (US Patent 11,949,909), and Liu (NPL “Extending the Detection Range for Low-Channel Roadside LiDAR by Static Background Construction,” IEEE 2022). Regarding Claim 5, Kim (NPL: “LiDAR Point Cloud Compression by Vertically Placed Objects Based on Global Motion Prediction,” IEEE 2022) discloses the method of claim 1. MPEG-N00155 (NPL: “Description of Exploration Experiment 13.2 on inter prediction,” ISO/IEC JTC 1/SC 29/WG 7, MPEG 3D Graphics Coding, N00155, 6 August 2021) provides evidence that Kim discloses wherein the encoding of the geometry data of the point cloud frame further includes: predicting (local motion is searched, Section 3.2) a road frame (points corresponding to roads, Section 3.2) of the point cloud frame (current point cloud, inferred) based on a reference road frame (previous point cloud, Section 3.2) of the compensated reference frame (previous compensated point cloud, Section 3.2); Kim does not disclose but Pham Van (US Patent 11,949,909) teaches calculating points (residual values, Column 15 lines 14-25) for each laser ID (laser index, Column 15 lines 14-25) based on a spherical coordinate system (code residual values in the in r, φ, i domain, Column 15 lines 15-30; where I is laserID, Column 26 lines 40-45) with origin coordinate information (the origin of the frame may be the center of LIDAR system, Column 22 lines 43-50; lasers spinning around the Z axis according to an azimuth angle, Column 15 lines 35-40; LIDAR system positioned at point 476, Column 27 lines 62-end) about the reference road frame being changed (apply estimated global motion to the prediction reference frame, Column 17 lines 58 - end); searching the reference road frame for a [] point (match feature points between the prediction frame (reference) and the current frame, Column 18 lines 1-10) in the road frame (To derive motion set for ground/road, only the points with the label of "ground/road" may be used, Column 20 lines 22-30) based on at least one of the laser ID or an angle (certain lasers identify ground points, Column 30 lines 32-36). Kim does not disclose, but Liu (NPL “Extending the Detection Range for Low-Channel Roadside LiDAR by Static Background Construction,” IEEE 2022) teaches searching the reference road frame for a missing point (in the set of distances at horizontal angle j and vertical angle i, p.4 right column, select the maximum distance Dij, p.5 left column, equation 5) … and updating the missing point in the road frame (set the point cloud measurement dij to the maximum distance Dij, p.5 left column, equation 5; p. 4 left column). One of ordinary skill in the art before the application was filed would have been motivated to predict the point cloud of Kim using the spherical system of Phan Vam because the MPEG team is experimenting with using spherical coordinates to encode LiDAR data for compression efficiency, expecting improvements in the system. One of ordinary skill in the art before the application was filed would have been motivated to predict the point cloud of Kim using the background construction algorithm of Liu because Liu teaches that doing so would enable the usage of low channel LiDAR to achieve high accuracy point maps that will become useful for autonomous vehicle applications (p. 3 left column). Regarding Claim 6, Pham Van (US Patent 11,949,909) teaches rotating the reference road frame (motion parameters are defined as a rotation matrix and translation vector, which will be applied on all the points in a prediction reference frame, Column 17 lines 45-57) with origin coordinate information (Compute the original coordinates (x, y, z), Column 17 lines 1-30) about the reference road frame being changed (applied on all points in the prediction reference frame, Column 18 lines 45 - 56) The remainder of the claim is rejected on the grounds provided in Claim 5. One of ordinary skill in the art before the application was filed would have been motivated to predict the point cloud of Kim using the spherical system of Phan Vam because the MPEG team is experimenting with using spherical coordinates to encode LiDAR data for compression efficiency. One of ordinary skill in the art before the application was filed would have been motivated to predict the point cloud of Kim using the background construction algorithm of Liu because Liu teaches that doing so would enable the usage of low channel LiDAR to achieve high accuracy point maps that will become useful for autonomous vehicle applications (p. 3 left column). Regarding Claim 7, the claim is rejected on the grounds provided in Claim 6. Response to Arguments Applicant’s remarks filed 6/5/2026 are moot because they do not apply to the combination of references cited in this office action. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 20190281274 A1 – two model distribution modes based on amount of change in model Jin, “An Improved Coarse-to-fine Motion Estimation Scheme for LiDAR Point Cloud Geometry Compression,” IEEE 2021 – segmenting the road point cloud from the object point cloud and inter-predicting the current frame THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHADAN E HAGHANI whose telephone number is (571)270-5631. The examiner can normally be reached M-F 9AM - 5PM. 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, Jay Patel can be reached at 571-272-2988. 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. /SHADAN E HAGHANI/ Examiner, Art Unit 2485
Read full office action

Prosecution Timeline

Sep 17, 2024
Application Filed
Oct 01, 2025
Non-Final Rejection mailed — §103, §112
Dec 31, 2025
Response Filed
Feb 05, 2026
Final Rejection mailed — §103, §112
Jun 05, 2026
Request for Continued Examination
Jun 15, 2026
Response after Non-Final Action
Jul 23, 2026
Non-Final Rejection mailed — §103, §112 (current)

Precedent Cases

Applications granted by this same examiner with similar technology

Patent 12701251
V-DMC DISPLACEMENT VECTOR INTEGER QUANTIZATION
2y 3m to grant Granted Aug 04, 2026
Patent 12695903
IMAGE ENCODING/DECODING METHOD AND DEVICE USING SAME
3y 0m to grant Granted Jul 28, 2026
Patent 12677070
MEDICAL CONTROL DEVICE AND MEDICAL OBSERVATION SYSTEM
3y 1m to grant Granted Jul 07, 2026
Patent 12666163
Automated Room-Specific White Balance Correction In A Building Image With Visual Data Showing Multiple Rooms
2y 0m to grant Granted Jun 23, 2026
Patent 12647584
DISTRIBUTED ANALYSIS OF A MULTI-LAYER SIGNAL ENCODING
3y 1m to grant Granted Jun 02, 2026
Study what changed to get past this examiner. Based on 5 most recent grants.

Strategy Recommendation AI-generated — please review before filing

Get a prosecution strategy drawn from examiner precedents, rejection analysis, and claim mapping.
Typically takes 5-10 seconds — AI-generated, attorney review required before filing

Prosecution Projections

3-4
Expected OA Rounds
61%
Grant Probability
79%
With Interview (+17.8%)
2y 11m (~1y 0m remaining)
Median Time to Grant
High
PTA Risk
Based on 380 resolved cases by this examiner. Grant probability derived from career allowance rate.

Sign in with your work email

Enter your email to receive a magic link. No password needed.

Personal email addresses (Gmail, Yahoo, etc.) are not accepted.

Free tier: 3 strategy analyses per month