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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 06/23/2026 has been entered.
Response to Amendment
Applicant’s amendment filed on 06/23/2026 has been entered. Claims 1-20 are still pending in this application.
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) 1-2, 4-7, 11-13, 17 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20060238445) in view of Davis et al. (US 20230195816) and Noorkami et al. (US 20230300338).
Regarding claim 16, Wang teaches a system, comprising: one or more processors (fig. 4) to calculate quality assessment metrics, for individual regions of the plurality of regions (46 in fig. 4: ROI weights Calculator), as a weighted combination of the two or more weight-based quality metrics for the individual regions of the plurality of regions (74 in fig. 9), and further to provide the quality assessment metrics for the individual regions to an optimization process used to determine how to compress the image (80 in fig. 9).
Wang does not teach compress the image comprising all of the individual regions.
Davis teaches compress the image comprising all of the individual regions (p0106:the regional accumulation system 102 can determine accumulating metric values (and corresponding regions) of the accumulating regional metric map 502, flatten the information to a single digital image (e.g., an image of different fog areas outlining the regions and values), compress the digital image).
Wang and Davis are combinable because they both deal with compress image. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Wang with the teaching of Davis for purpose of generate region-based metrics for provider devices based on movement of provider devices through various regions and transportation requests within the various regions (p0002).
Wang in view of Davis does not teach the two of more weight-based quality metrics being weighted according to identified content of the individual regions.
Noorkami teaches the two of more weight-based quality metrics being weighted according to identified content of the individual regions (p0073: the weighted metric may be a weighted signal-to-noise ratio (SNR) or a weighted peak SNR (PSNR), in which the weighting of the ratio is different in different regions of the video frame).
Wang in view of Davis and Noorkami are combinable because they both deal with compress image. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Wang in view of Davis with the teaching of Noorkami for purpose of provide efficient video encoding for video frames that have content with variable intra-frame resolution (p0019).
Regarding claim 1, The structural elements of apparatus claim 16 perform all of the steps of method claim 1. Thus, claim 1 is rejected for the same reasons discussed in the rejection of claim 16.
Regarding claim 2, Wang teaches the computer-implemented method of claim 1, wherein the weighted combination of the two or more weight-based quality metrics is performed using a linear function or a non-linear function (p0086: If it is assumed that the relationship among the aspects mentioned above can be simplified into a linear function in video quality evaluation).
Regarding claim 4, Wang teaches the computer-implemented method of claim 1, wherein the weighted combination includes a determined combination factor to be applied to values for the two or more weight-based quality metrics (80 in fig. 9: allocation based on ROI quality metric).
Regarding claim 5, Wang teaches the computer-implemented method of claim 4, wherein the determined combination factor is calculated to optimize the quality assessment metrics (p0072: system 44 includes ROI weights calculator 46, ROI .rho. domain bit allocation module 4).
Regarding claim 6, Wang teaches the computer-implemented method of claim 1, wherein the regions correspond to groups of adjacent pixels (p0068 and fig. 2).
Regarding claim 7, Wang teaches the computer-implemented method of claim 1, wherein the image is a video frame of a sequence of video frames (p0006).
Regarding claim 11, Wang teaches a processor, comprising: one or more circuits to: determine, for each of a plurality of regions of an image, two or more weight-based quality metrics (46 in fig. 4: ROI weights Calculator); calculate quality assessment metrics, for individual regions of the plurality of regions, based on a weighted combination of the two or more weight-based quality metrics for the individual regions ((74 in fig. 9); and provide values for the quality assessment metrics for the individual regions to a process used to compress the image, wherein the process is allowed to be modified based in part on the quality assessment metrics (80 in fig. 9).
Regarding claim 12, recites the similar limitation as claim 2, therefore it is rejected for the same reason as claim 2.
Regarding claim 13, recites the similar limitation as claims 4 and 5, therefore it is rejected for the same reason as claim s 4 and 5.
Regarding claim 17, recites the similar limitation as claim 13, therefore it is rejected for the same reason as claim 13.
Regarding claim 20, Wang teaches the system of claim 16, wherein the system is at least one of: a system for performing simulation operations;
a system for performing simulation operations to test or validate autonomous machine applications;
a system for rendering graphical output (p0004: Video telephony (VT);
a system for performing deep learning operations;
a system implemented using an edge device;
a system for generating or presenting virtual reality (VR) content;
a system for generating or presenting augmented reality (AR) content;
a system for generating or presenting mixed reality (MR) content;
a system incorporating one or more Virtual Machines (VMs);
a system implemented at least partially in a data center;
a system for performing hardware testing using simulation;
a system for synthetic data generation;
a collaborative content creation platform for 3D assets;
or a system implemented at least partially using cloud computing resources.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Davis and Noorkami as applied to claim 1 above, and further in view of Kohli et al. (US 11170175).
Regarding claim 3, Wang teaches the computer-implemented method of claim 1, wherein the combination is a convex combination of the two or more weight-based quality metrics (p0069: using a combination of..).
Wang in view of Davis and Noorkami does not teaches combimation is a convex combination (col. 5, lines: 35-40:more weight to one metric over the other (e.g., probability of appearance accounting for 75% of the convex combination and sentiment score accounting for 25%, or vice versa).
Claims 9, 14 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Davis and Noorkami as applied to claim 1 above, and further in view of Tourapis et al. (US 20150078451).
Regarding claim 9, Wang in view of Davis and Noorkami does not teach the computer-implemented method of claim 1, wherein providing the quality assessment metrics for the individual regions is performed as part of a rate-distortion optimization (RDO) process).
Tourapis wherein providing the quality assessment metrics for the individual regions is performed as part of a rate-distortion optimization (RDO) process (abstract).
Wang in view of Davis and Tourapis are combinable because they both deal with compress image. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Wang in view of Davis with the teaching of Tourapis for purpose of compression of images for storage or transmission and for subsequent reconstruction of an approximation of the original image (p0002).
Regarding claim 14, recites the similar limitation as claim 9, therefore it is rejected for the same reason as claim 9.
Regarding claim 18, recites the similar limitation as claim 14, therefore it is rejected for the same reason as claim 14.
Claim 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Davis and Noorkami as applied to claim 7 above, and further in view of Choi et al. (US 20150271496).
Regarding claim 8, Wang in view of Davis and Noorkami does not teach the computer-implemented method of claim 7, wherein at least one of the two or more weight-based quality metrics includes a temporal quality aspect.
Chio teaches wherein at least one of the two or more weight-based quality metrics includes a temporal quality aspect (p0013: A metric of image quality of the motion video based on temporal complexity is combined with an opinion metric of image quality…).
Wang in view of Davis and Choi are combinable because they both deal with compress image. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Wang in view of Davis with the teaching of Choi for purpose of to transmit any one motion video at any given time (p0002).
Claims 10, 15 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Davis and Noorkami as applied to claim 1 above, and further in view of Bottum et al. (US 8972395).
Regarding claim 10, Wang in view of Davis and Noorkami does not teaches the computer-implemented method of claim 1, wherein the quality assessment metric is determined according to a weight value determined from within a search space relative to the two or more weight-based quality metrics in a multi-dimensional weight space
Bottum teaches wherein the quality assessment metric is determined according to a weight value determined from within a search space relative to the two or more weight-based quality metrics in a multi-dimensional weight space (claim 7) .
Wang in view of Davis and Bottum are combinable because they both deal with compress image. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine the teachings of Wang in view of Davis with the teaching of Bottum for purpose representing and manipulating the weightings of search parameters employed by database search algorithms.
Regarding claim 15, recites the similar limitation as claim 10, therefore it is rejected for the same reason as claim 10.
Regarding claim 19, recites the similar limitation as claim 10, therefore it is rejected for the same reason as claim 10.
Response to Arguments
Applicant's arguments with respect to claims have been considered but are moot in view of the new ground(s) of rejection.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HELEN Q ZONG whose telephone number is (571)270-1600. The examiner can normally be reached Mon-Fri 9-6.
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HELEN ZONG
Primary Examiner
Art Unit 2683
/HELEN ZONG/Primary Examiner, Art Unit 2683
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