Prosecution Insights
Last updated: April 19, 2026
Application No. 18/992,903

DEEP DISTRIBUTION-AWARE POINT FEATURE EXTRACTOR FOR AI-BASED POINT CLOUD COMPRESSION

Non-Final OA §103
Filed
Jan 09, 2025
Examiner
WALKER, JARED T
Art Unit
2426
Tech Center
2400 — Computer Networks
Assignee
Interdigital Vc Holdings Inc.
OA Round
1 (Non-Final)
84%
Grant Probability
Favorable
1-2
OA Rounds
2y 4m
To Grant
94%
With Interview

Examiner Intelligence

Grants 84% — above average
84%
Career Allow Rate
414 granted / 490 resolved
+26.5% vs TC avg
Moderate +10% lift
Without
With
+10.0%
Interview Lift
resolved cases with interview
Typical timeline
2y 4m
Avg Prosecution
18 currently pending
Career history
508
Total Applications
across all art units

Statute-Specific Performance

§101
5.1%
-34.9% vs TC avg
§103
58.1%
+18.1% vs TC avg
§102
19.3%
-20.7% vs TC avg
§112
11.1%
-28.9% vs TC avg
Black line = Tech Center average estimate • Based on career data from 490 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 . 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 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. 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. 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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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. Claim(s) 1,2,6-8,10,11,18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Choi US 20220051017 in view of Meltzer US 20220156415. Regarding claim 1, Choi disclose(s) the following claim limitations: A learning-based point cloud geometry processing block method, the method comprising: accessing a first feature map (i.e. feature map determined using an image. If the feature map was coded, decoding would be required to access.) [64; fig. 2], wherein the first feature map has a quantity of C channels and is an input to the processing block (i.e. feature maps has a number of channels) [64], and wherein the first feature map is generated by a first set of neural network layers (i.e. object detection layer use neural networks to generate probability distribution) [67]; accessing a set of distribution parameters (i.e. probability distribution used to classify objects in an image) [67]; and Choi do/does not explicitly disclose(s) the following claim limitations: transforming the first feature map to a second feature map based on the set of distribution parameters. However, in the same field of endeavor Meltzer discloses the deficient claim limitations, as follows: transforming the first feature map to a second feature map based on the set of distribution parameters (i.e. normalize feature map using distribution parameters from a neural network processed feature map) [80]. It would have been obvious to one with ordinary skill in the art at the time of filing to modify the teachings of Choi with Meltzer to transform the first feature map to a second feature map based on the set of distribution parameters. It would be advantageous because "Accordingly, unlike prior-art approaches, the disclosed techniques can be used to compare the geometric styles of pairs of different 3D CAD objects represented by B-reps, thereby increasing the accuracy of geometric style comparisons relative to the prior art. These technical advantages provide one or more technological advancements over prior art approaches.” [9]. Therefore, it would have been obvious to one with ordinary skill, in the art at the time of filing, to modify the teachings of Choi with Meltzer to obtain the invention as specified in claim 1. Regarding claim 2, Meltzer meets the claim limitations, as follows: The method of claim 1, further comprising updating the first feature map by normalizing vector elements of the first feature map (i.e. normalize feature map using distribution parameters from a neural network processed feature map) [80,89]. Regarding claim 6, Meltzer meets the claim limitations, as follows: The method of claim 1,wherein the set of distribution parameters is determined using a back-propagation technique during a training period [124]. Regarding claim 7, Choi meets the claim limitations, as follows: The method of claim 1,wherein the set of distribution parameters is determined on a per feature channel basis [64]. Regarding claim 8, Choi meets the claim limitations, as follows: The method of claim 1,further comprising updating the second feature map by performing downsampling using a function of average pooling or max pooling [65]. Regarding claim 10, Choi meets the claim limitations, as follows: The method of claim 1, further comprising: wherein prior to encoding the second feature map, performing a process comprising: accessing a second set of distribution parameters; and updating the second feature map by transforming the second feature map based on the second set of distribution parameters; and encoding the second feature map into a bitstream (i.e. multiple feature maps may be used and would produce different parameters and be encoded into a bitstream) [61]. Regarding claim 11, Choi meets the claim limitations, as follows: The method of claim 1, further comprising: aggregating the feature map using a second neural network; and encoding the second feature map into a bitstream [61,85]. Claim 18 is rejected using similar rationale as claim 1 and further below. Choi teaches the processor and storage medium in fig. 6b and 10. Claim 20 is rejected using similar rationale as claim 2. Claim(s) 9,14, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Choi and Meltzer in view of Kim US 11200639. Regarding claim 9, Choi and Meltzer do/does not explicitly disclose(s) the following claim limitations: determining a third feature map by filtering the second feature map using a smoothing filter; and updating the second feature map by concatenating the third feature map to the second feature map. However, in the same field of endeavor Kim discloses the deficient claim limitations, as follows: determining a third feature map by filtering the second feature map using a smoothing filter; and updating the second feature map by concatenating the third feature map to the second feature map [31:42-52]. It would have been obvious to one with ordinary skill in the art at the time of filing to modify the teachings of Choi and Meltzer with Kim to determine a third feature map by filtering the second feature map using a smoothing filter; and updating the second feature map by concatenating the third feature map to the second feature map. It would be advantageous because there is a need for improving image encoding and decoding efficiency [5:60-64]. Therefore, it would have been obvious to one with ordinary skill, in the art at the time of filing, to modify the teachings of Choi and Meltzer with Kim to obtain the invention as specified in claim 9. Regarding claim 14, Kim meets the claim limitations, as follows: The method of claim 1, further comprising: determining a fourth feature map by aggregating the first feature map using a neural network in parallel to transforming the first feature map to the second feature map; and updating the second feature map by concatenating the fourth feature map to the second feature map (i.e. fourth reduced image would produce a fourth feature map) [31:42-52]. Claim 19 is rejected using similar rationale as claim 1 and further below. Kim teaches decoding and reconstructing [6:7-29] Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Choi and Meltzer in view of Lee US 20210160522. Regarding claim 12, Choi and Meltzer do/does not explicitly disclose(s) the following claim limitations: wherein the second neural network is selected from the group consisting of a sparse convolutional neural network (CNN) and multi-perceptron layers (MLP) However, in the same field of endeavor Lee discloses the deficient claim limitations, as follows: wherein the second neural network is selected from the group consisting of a sparse convolutional neural network (CNN) and multi-perceptron layers (MLP) [120,122]. It would have been obvious to one with ordinary skill in the art at the time of filing to modify the teachings of Choi and Meltzer with Lee to have the second neural network is selected from the group consisting of a sparse convolutional neural network (CNN) and multi-perceptron layers (MLP). It would be advantageous because "By performing prediction based on a trained deep neural network (DNN), signaling of prediction information may be omitted and encoding and decoding efficiencies may be increased.” [22]. Therefore, it would have been obvious to one with ordinary skill, in the art at the time of filing, to modify the teachings of Choi and Meltzer with Lee to obtain the invention as specified in claim 12. Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Choi and Meltzer in view of Yang US 20240223790. Regarding claim 13, Choi and Meltzer do/does not explicitly disclose(s) the following claim limitations: wherein aggregating the second feature map comprises using a Residual Network (ResNet) architecture. However, in the same field of endeavor Yang discloses the deficient claim limitations, as follows: wherein aggregating the second feature map comprises using a Residual Network (ResNet) architecture [114]. It would have been obvious to one with ordinary skill in the art at the time of filing to modify the teachings of Choi and Meltzer with Yang to have the wherein aggregating the second feature map comprises using a Residual Network (ResNet) architecture. It would be advantageous because "In a case of a limited network resource and a continuously increasing requirement for higher video quality, compression and decompression technologies need to be improved. The improved technologies can improve a compression rate almost without affecting picture quality.” [4]. Therefore, it would have been obvious to one with ordinary skill, in the art at the time of filing, to modify the teachings of Choi and Meltzer with Yang to obtain the invention as specified in claim 13. Allowable Subject Matter Claims 3-5 and 21-23 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. Contact Information Any inquiry concerning this communication or earlier communications from the examiner should be directed to JARED T WALKER whose telephone number is (571)272-1839. The examiner can normally be reached M-F: 8:00 - 4:30 Mountain. 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, Nasser Goodarzi can be reached on 571-272-4195. 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. /Jared Walker/Primary Examiner, Art Unit 2426
Read full office action

Prosecution Timeline

Jan 09, 2025
Application Filed
Mar 04, 2026
Non-Final Rejection — §103 (current)

Precedent Cases

Applications granted by this same examiner with similar technology

Patent 12586380
IMAGE ANALYSIS DEVICE, IMAGE ANALYSIS METHOD FOR TELECOMMUTING WORK SECURITY AND TERMINAL DEVICE INCLUDING THE SAME
2y 5m to grant Granted Mar 24, 2026
Patent 12581178
Camera Assembly Arrangement for Vehicle Rear View Cover and Rear View Device Therewith
2y 5m to grant Granted Mar 17, 2026
Patent 12563304
MEASUREMENT DEVICE, MEASUREMENT METHOD, PROGRAM
2y 5m to grant Granted Feb 24, 2026
Patent 12555383
VIDEO SURVEILLANCE SYSTEM FOR CAMERA-RICH AREAS
2y 5m to grant Granted Feb 17, 2026
Patent 12556718
ELECTRONIC DEVICE AND METHOD WITH IMAGE ENCODING AND DECODING
2y 5m to grant Granted Feb 17, 2026
Study what changed to get past this examiner. Based on 5 most recent grants.

AI Strategy Recommendation

Get an AI-powered prosecution strategy using examiner precedents, rejection analysis, and claim mapping.
Powered by AI — typically takes 5-10 seconds

Prosecution Projections

1-2
Expected OA Rounds
84%
Grant Probability
94%
With Interview (+10.0%)
2y 4m
Median Time to Grant
Low
PTA Risk
Based on 490 resolved cases by this examiner. Grant probability derived from career allow 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