Prosecution Insights
Last updated: April 19, 2026
Application No. 18/653,872

REPROJECTION FALLBACK TOPOLOGY

Non-Final OA §101§103
Filed
May 02, 2024
Examiner
NGUYEN, HAU H
Art Unit
2611
Tech Center
2600 — Communications
Assignee
Qualcomm Incorporated
OA Round
1 (Non-Final)
90%
Grant Probability
Favorable
1-2
OA Rounds
2y 9m
To Grant
99%
With Interview

Examiner Intelligence

Grants 90% — above average
90%
Career Allow Rate
807 granted / 892 resolved
+28.5% vs TC avg
Moderate +9% lift
Without
With
+8.9%
Interview Lift
resolved cases with interview
Typical timeline
2y 9m
Avg Prosecution
22 currently pending
Career history
914
Total Applications
across all art units

Statute-Specific Performance

§101
5.5%
-34.5% vs TC avg
§103
58.0%
+18.0% vs TC avg
§102
19.2%
-20.8% vs TC avg
§112
3.8%
-36.2% vs TC avg
Black line = Tech Center average estimate • Based on career data from 892 resolved cases

Office Action

§101 §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 § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 20 describes a computer-readable medium. Further, Applicant's specification, at paragraph [0033], fails to explicitly define the scope of computer-readable medium to exclude transitory signals. Thus, in giving the term its plain meaning (see MPEP 2111.01), the claimed computer-readable medium is considered to include data signals per se. Data signals per se are not statutory as they fail to fall into one of the four statutory categories of invention. As an additional note, a non-transitory computer readable medium having executable programming instructions stored thereon is considered statutory as non-transitory computer readable media excludes data signals (data signals are transitory computer readable media). 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. 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. Claims 1, 5-6, 8-14, 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bastani et al. (US. Patent App. Pub. No. 2021/0407212, “Bastani”, hereinafter) in view of Sevostianov et al. (US. Patent App. Pub. No. 2023/0030247, “Sevostianov”) further in view of Raut et al. (US. Patent App. Pub. No. 2019/0045213, “Raut”). As per claim 1, as shown in Fig. 1 and 8, Bastani teaches an apparatus for graphics processing, comprising: a memory (818); and a processor (816) coupled to the memory and, based at least in part on information stored in the memory, the processor is configured to: determine that a first reprojection process for a frame will not complete within a time period (¶ [40], “In one aspect, the process of detecting, by the HWD 150, the location and the orientation of the HWD 150 and/or the gaze direction of the user wearing the HWD 150, and generating and transmitting, by the console 110, a high resolution image (e.g., 1920 by 1080 pixels) corresponding to the detected location and the gaze direction to the HWD 150 may be computationally exhaustive and may not be performed within a frame time (e.g., less than 11 ms)”), wherein the first reprojection process is associated with a first set of characteristics (addressed below referring to Sevostianov); perform, based on the determination, a second reprojection process for the frame (see ¶ [62], referring to Fig. 6A-B, “For example, the console 110 can delay or expedite (or hasten) the start time and/or the completion time of generating the first updated image frame during the time period 530 for the subsequent image frame, such that the HWD 150 can receive the image data and perform an image processing (e.g., time warp processing or reprojection) to generate a second image frame for the subsequent image frame at a completion time within the completion time range 590 from the display time 580”), wherein the second reprojection process is associated with a second set of characteristics that is different than the first set of characteristics (also addressed below referring to Sevostianov). Bastani does not expressly teach the first reprojection process is associated with a first set of characteristics and the second reprojection process is associated with a second set of characteristics that is different than the first set of characteristics. However, Sevostianov teaches a very similar method of reprojecting image frame when a reprojected frame does not complete with in time period (¶ [112]) with the first reprojection and second reprojection described in ¶ [114]. Sevostianov further teaches the above features, i.e., the first reprojection process is associated with a first set of characteristics, and the second reprojection process is associated with a second set of characteristics that is different than the first set of characteristics (see ¶ [112], “Each flow is operated at its own rate caused by parameters of corresponding process like 3D scene complexity, tracking speed, computational resources dedicated to this process, etc. Rates of the flows may be different, both in terms of average and instant values, i.e., the rate may vary and may depend on, e.g., 3D scene complexity”). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method as taught by Sevostianov into the method as taught by Bastani as addressed above, the advantage of which is to decrease image distortions (¶ [28-29]). The combined teachings of Bastani and Sevostianov does not expressly comprises output an indication of the performed second reprojection process for the frame. However, Raut teaches an image reprojection method similar to that of the combined Bastani-Sevostianov (see ¶ [25]), and further teaches output an indication of the performed second reprojection process for the frame (see ¶ [47], “In another embodiment, reprojected reconstructed reference frame 226 may be added to available frames and an extension of the standard may be required such that an indicator or the like of reprojected reconstructed reference frame 226 may be provided via bitstream 235”). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the method as taught by Raut to the combined Bastani-Sevostianov as addressed above, the advantage of which is to increase the compression efficiency, video quality, and computational efficiency (¶ [3]). As per claim 5, the combined Bastani-Sevostianov-Raut also teaches wherein the first set of characteristics comprises at least one of: a frame rate (Sevostianov, ¶ [112]); a resolution (Sevostianov, ¶ [73]); a composition of layers; or a number of points in a sparse warp grid. Thus, claim 5 would have been obvious over the combined references for the reason above. As per claim 6, the combined Bastani-Sevostianov-Raut also teaches wherein the second set of characteristics comprises at least one of: a second frame rate lower than the frame rate; a second resolution lower than the resolution (Sevostianov, ¶ [104], reduced resolution); a second composition of layers having a second number of layers less than a first number of layers of the composition of layers; or a second number of points in a second sparse warp grid less than a first number of points in a sparse warp grid. Thus, claim 6 would have been obvious over the combined references for the reason above. As per claim 8, the combined Bastani-Sevostianov-Raut does also teach wherein the processor is further configured to: perform the first reprojection process for a second frame before the performance of the second reprojection process for the frame (as addressed in claim 1), wherein the second set of characteristics comprises at least one of a planar-warp or a time-warp of the frame (Bastani, Fig. 5, ¶ [60-61], “During a time period 530, according to sensor measurements 505B, the console 110 may perform an image processing (e.g., time warp processing or reprojection) on the image frame generated during the time period 520 to generate a first updated image frame”); and output a second indication of the performed first reprojection process for the second frame (as addressed in claim 1). Thus, claim 8 would have been obvious over the combined references for the reason above. As per claim 9, as addressed above, the combined Bastani-Sevostianov-Raut does impliedly teach wherein the processor is further configured to: perform the first reprojection process for a third frame; and output a third indication of the performed first reprojection process for the third frame after the output of the indication of the performed second reprojection process for the frame (at best understood by the examiner since it is not clear what frame is referred to. As addressed in claim 1, the combined teachings comprise performing the first reprojection frame by frame and receiving feedback for every frame as shown in Fig. 7 of Bastani. Also addressed in claim 1, Raut teaches outputting the indication of the performed reprojection process for each frame). Thus, claim 9 would have been obvious over the combined references for the reason above. As per claim 10, as addressed in claim 8 and 9 above, the combined Bastani-Sevostianov-Raut does teach to perform the first reprojection process for the third frame, the processor is configured to: perform the first reprojection process for the third frame before completing the performance of the second reprojection process for the frame. Thus, claim 10 would have been obvious over the combined references for the reason above. As per claim 11, the combined Bastani-Sevostianov-Raut does also teach wherein, to perform the first reprojection process, the processor is configured to reproject the second frame based on the first set of characteristics using a first set of hardware, wherein, to perform the second reprojection process, the processor is configured to reproject the frame based on the second set of characteristics using a second set of hardware different from the first set of hardware. As per claim 12, the combined Bastani-Sevostianov-Raut does also teach wherein, to output the indication of the performed second reprojection process for the frame, the processor is configured to: output, to a frame buffer, the indication of the performed second reprojection process for the frame; transmit the indication of the performed second reprojection process for the frame; or store, in the memory, the indication of the performed second reprojection process for the frame (see Raut, ¶ [55-56], storing in a frame buffer along with the reconstructed reference frame). Thus, claim 12 would have been obvious over the combined references for the reason above. As per claim 13, the combined Bastani-Sevostianov-Raut does further teach wherein the processor comprises a graphics processing unit (GPU) (Bastani, ¶ [40], “…the adaptive image renderer 170 is implemented as a processor (or a graphical processing unit (GPU))…”). Claim 14, which is similar in scope to claim 1 as addressed above, is thus rejected under the same rationale. Claim 17, which is similar in scope to claim 8 as addressed above, is thus rejected under the same rationale. Claim 18, which is similar in scope to claim 9 as addressed above, is thus rejected under the same rationale. Claim 19, which is similar in scope to claim 11 as addressed above, is thus rejected under the same rationale. Claim 20, which is similar in scope to claim 1 as addressed above, is thus rejected under the same rationale. Claims 2-3, 7, 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Bastani et al. (US. Patent App. Pub. No. 2021/0407212) in view of Sevostianov et al. (US. Patent App. Pub. No. 2023/0030247) further in view of Raut et al. (US. Patent App. Pub. No. 2019/0045213) and further in view of Haraden et al. (US. Patent App. Pub. No. 2018/0275748, “Haraden”). As per claim 2, the combined Bastani-Sevostianov-Raut fails to explicitly teach wherein the first set of characteristics comprises a first composition process, wherein the second set of characteristics does not include the first composition process. However, in a similar method of reprojecting images (see Abstract), Haraden teaches the Late Stage Reprojection (LSR) (interpreted as second reprojection process) includes the second composition process that does not include the first composition process (see Fig. 3, ¶ [63-67], i.e., the second composition process for input frame 340 is different from the first composition process 310). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method as taught by Haraden into the combined Bastani-Sevostianov-Raut method as addressed above, the advantage of which is to efficiently improve the power consumption of systems (¶ [8]). As per claim 3, as addressed in claim 2, the combined Bastani-Sevostianov-Raut-Haraden does teach wherein the second set of characteristics comprises a second composition process different from the first composition process. Thus, claim 3 would have been obvious over the combined references for the reason above. As per claim 7, the combined Bastani-Sevostianov-Raut-Haraden does teach wherein the first set of characteristics comprises a composition of layers, wherein the second set of characteristics does not comprise any composition of layers (see Haraden, ¶ [93], “…applying late stage reprojection processing to the isolated sub-region while refraining from applying late stage reprojection processing to the at least one other region of the one or more layers”). Thus, claim 7 would have been obvious over the combined references for the reason above. Claim 15, which is similar in scope to claim 2 as addressed above, is thus rejected under the same rationale. Claim 16, which is similar in scope to claim 7 as addressed above, is thus rejected under the same rationale. Allowable Subject Matter Claim 4 is 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. The following is a statement of reasons for the indication of allowable subject matter: The prior art taken singly or in combination does not teach or suggest, an apparatus for graphics processing, among other things, comprising: …wherein the first composition process comprises a first composition of a set of layers, wherein the second composition process comprises a second composition of a subset of the set of layers, wherein the subset of the set of layers has less layers than the set of layers. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to Hau H. Nguyen whose telephone number is: 571-272-7787. The examiner can normally be reached on MON-FRI from 8:30-5:30. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tammy Goddard, can be reached on (571) 272-7773. The fax number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). /HAU H NGUYEN/Primary Examiner, Art Unit 2611
Read full office action

Prosecution Timeline

May 02, 2024
Application Filed
Nov 14, 2025
Non-Final Rejection — §101, §103 (current)

Precedent Cases

Applications granted by this same examiner with similar technology

Patent 12597194
METHOD FOR OBTAINING IMAGE RELATED TO VIRTUAL REALITY CONTENT AND ELECTRONIC DEVICE SUPPORTING THE SAME
2y 5m to grant Granted Apr 07, 2026
Patent 12591435
DEVICE LINK MANAGEMENT
2y 5m to grant Granted Mar 31, 2026
Patent 12586288
DEVICE AND METHOD FOR GENERATING DYNAMIC TEXTURE MAP FOR 3 DIMENSIONAL DIGITAL HUMAN
2y 5m to grant Granted Mar 24, 2026
Patent 12573135
GENERATION OF A DENSE POINT CLOUD OF A PHYSICAL OBJECT
2y 5m to grant Granted Mar 10, 2026
Patent 12573141
METHOD AND DEVICE FOR LEARNING 3D MODEL RECONSTRUCTION
2y 5m to grant Granted Mar 10, 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
90%
Grant Probability
99%
With Interview (+8.9%)
2y 9m
Median Time to Grant
Low
PTA Risk
Based on 892 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