Prosecution Insights
Last updated: July 17, 2026
Application No. 18/975,182

LOCALLY ADAPTIVE COLOR CORRECTION IN IMAGE PROCESSING FOR RGB-IR SENSORS

Final Rejection §102
Filed
Dec 10, 2024
Examiner
JEBARI, MOHAMMED
Art Unit
2482
Tech Center
2400 — Computer Networks
Assignee
NVIDIA Corporation
OA Round
2 (Final)
55%
Grant Probability
Moderate
3-4
OA Rounds
2y 2m
Est. Remaining
71%
With Interview

Examiner Intelligence

Grants 55% of resolved cases
55%
Career Allowance Rate
275 granted / 499 resolved
-2.9% vs TC avg
Strong +16% interview lift
Without
With
+15.6%
Interview Lift
resolved cases with interview
Typical timeline
3y 9m
Avg Prosecution
33 currently pending
Career history
543
Total Applications
across all art units

Statute-Specific Performance

§101
0.4%
-39.6% vs TC avg
§103
90.9%
+50.9% vs TC avg
§102
6.9%
-33.1% vs TC avg
§112
0.8%
-39.2% vs TC avg
Black line = Tech Center average estimate • Based on career data from 499 resolved cases

Office Action

§102
Notice of Pre-AIA or AIA Status 1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Response to Arguments 2. Applicant's arguments filed 03/30/2026 have been fully considered but they are not persuasive. On page 9 of the amendment, Applicant argued that Hung does not disclose the features of “the first part of the local color correction applies a first portion of a local color operation matrix to the first pixel” and that “the global color correction applies a second portion of the local color operation matrix to a pixel in the second image corresponding to the first pixel.” While Applicant’s arguments are understood, the claim language are broad enough to interpret Hung’s white balance correction 114, as the first part of the local correction which applies color operation matrix to input red, green and blue values for a pixel as taught in paragraph 0063, and interpret Hung’s color correction 118, as the global color correction which applies color operation matrix to input red, green and blue values for a pixel of the full color image outputted by the standard demosaicing 116 as taught in paragraph 0067. Claim Rejections - 35 USC § 102 3. 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 4. Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hung et al. (US 2023/0360178) hereinafter “Hung”. As per claim 1, Hung discloses a method comprising: receiving a first image depicting a scene (Fig. 1 shows image data 102 received by processing unit 104; see also paragraph 0024), the first image being captured using an image sensor (paragraph 0040, Image data 102 may include intensity values from many sensor pixels of an RGB-IR camera); removing, from a first pixel in the first image, a contribution of infrared radiation to a color channel of the first pixel based at least on an estimated amount of residual infrared radiation present in the color channel when the first image was captured using the image sensor (paragraph 0047, an IR subtraction factor (e.g., quantity 110A) is determined based on the scene detection value 108. The IR subtraction factor controls an amount of infrared correction 112 to perform. Accordingly, the IR subtraction factor controls an amount of an IR contribution to subtract from a visible light sensor pixel value for a given image. Based on the IR subtraction factor (quantity 110A), a fractional amount of the IR contribution to the visible light sensor pixel value may be subtracted out for infrared correction 112. In one embodiment, the IR subtraction value is a factor having a value of between 0 and 1); performing a first part of local color correction for the color channel of the first pixel (Fig. 1, White balance correction 114) based at least on the estimated amount of residual infrared radiation (paragraph 0062, Performing correct white balance correction 114 is made more challenging when an IR signal is not removed from visible light sensor pixel values. The IR signal acts as an offset, interpreted as residual. As a result, when AWB is performed on an image in which an IR signal remains, a different amount of gain may be applied to the IR offset in each of the color channels), wherein the first part of the local color correction applies a first portion of a local color operation matrix to the first pixel (paragraph 0063 teaches color correction operation based on using white balance matrix that is applied to red, green and blue pixel values); and performing global color correction (Fig. 1, Color correction 118) for a plurality of pixels in a second image that is reconstructed from the first image (Fig. 1, color correction 118 is applied to the output of demosaicing 116, that is a reconstructed full color image from incomplete color samples output from an image sensor, taught in paragraph 0064), wherein the global color correction applies a second portion of the local color operation matrix to a pixel in the second image corresponding to the first pixel (see Fig. 1, color correction 118, considered as a second correction that is applied after white balance correction to the full color image, uses color correction matrix applied to red, green and blue pixel values as taught in paragraph 0067). As per claim 2, Hung discloses the method of claim 1, wherein the image sensor is equipped with a plurality of color and infrared filters (paragraph 0025). As per claim 3, Hung discloses the method of claim 1, wherein the removal of the contribution of infrared radiation to the color channel of the first pixel is based further on a scale factor (i.e., IR subtraction factor; see paragraph 0047). As per claim 4, Hung discloses the method of claim 1, wherein the local color operation matrix is computed based on a local color correction matrix (CCM) and a local automatic white balance (AWB) matrix (the local color correction is based on a first part of local color correction, uses matrix related to AWB as taught in paragraph 0063, and a second part of local color correction, uses matrix related to CCM as taught in paragraph 0067). As per claim 5, Hung discloses the method of claim 1, wherein the first part of the local color correction is based on the local color operation matrix combined with an inverse matrix of a global color operation matrix (see paragraph 0063). As per claim 6, Hung discloses the method of claim 1, wherein the global color operation matrix is based on a global color correction matrix (CCM) (see paragraph 0067) and a global automatic white balance (AWB) matrix (as shown from Fig. 1, color correction 118 is based on white balance correction 114, which uses AWB matrix taught in paragraph 0063). As per claim 7, arguments analogous to those applied for the last limitation of claim 1 are applicable for claim 7. As per claims 8 and 16, arguments analogous to those applied for claim 1 are applicable for claims 8 and 16. In addition, Hung discloses at least one processor comprising: one or more circuit to perform the claimed method (Fig. 1, processing unit 104; paragraph 0037) As per claim 9, Hung discloses the at least one processor of claim 8, wherein the processor is comprised in at least one of: a control system for an autonomous or semi-autonomous machine (see Figs. 8A-8B; paragraph 0012); a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing deep learning operations; a system implemented using an edge device; a system for generating or presenting at least one of virtual reality content, augmented reality content, or mixed reality content; a system implemented using a robot; a system for performing conversational AI operations; a system for generating synthetic data; a system incorporating one or more virtual machines (VMs);a system implementing one or more machine learning models using as an inference microservice including the one or more machine learning models and one or more operation system (OS)-level virtualization packages; a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources. As per claims 10-15, arguments analogous to those applied for claims 2-7 are applicable for claims 10-15. As per claim 17, arguments analogous to those applied for claim 9 are applicable for claim 17. As per claims 18-19, arguments analogous to those applied for claims 2-3 are applicable for claims 18-19. As per claim 20, arguments analogous to those applied for claim 4 are applicable for claim 20. Conclusion 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 MOHAMMED JEBARI whose telephone number is (571)270-7945. The examiner can normally be reached Mon-Fri: 09:00am-06:00pm. 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, Chris Kelley can be reached at 571-272-7331. 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. /MOHAMMED JEBARI/Primary Examiner, Art Unit 2482
Read full office action

Prosecution Timeline

Dec 10, 2024
Application Filed
Dec 31, 2025
Non-Final Rejection mailed — §102
Mar 26, 2026
Examiner Interview Summary
Mar 26, 2026
Applicant Interview (Telephonic)
Mar 30, 2026
Response Filed
Jun 11, 2026
Final Rejection mailed — §102 (current)

Precedent Cases

Applications granted by this same examiner with similar technology

Patent 12666021
DETERMINING REGIONS OF INTEREST USING LEARNED IMAGE CODEC FOR MACHINES
2y 2m to grant Granted Jun 23, 2026
Patent 12659448
IMAGE DISPLAY WITHIN A THREE-DIMENSIONAL ENVIRONMENT
2y 9m to grant Granted Jun 16, 2026
Patent 12651360
MONOCULAR DEPTH ESTIMATION SYSTEM
3y 1m to grant Granted Jun 09, 2026
Patent 12652396
METHOD AND APPARATUS FOR VIDEO CODING USING MOTION VECTOR WITH COMPONENT-WISE ADAPTIVE SPATIAL RESOLUTION
2y 8m to grant Granted Jun 09, 2026
Patent 12641238
METHOD AND APPARATUS FOR TEMPORAL RESAMPLING
2y 5m to grant Granted May 26, 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
55%
Grant Probability
71%
With Interview (+15.6%)
3y 9m (~2y 2m remaining)
Median Time to Grant
Moderate
PTA Risk
Based on 499 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