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
Response to Amendment
Applicant’s amendment filed on 12/16/2025 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) 1-20 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).
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).
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 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..)
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 8, Wang teaches 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.
Regarding claim 9, Wang teaches 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 2 optimization (RDO) process (p0022 and fig. 7).
Regarding claim 10, Wang 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 (p0067: An ROI video quality metric may be applied to bias a weighted bit allocation between ROI and non-ROI areas.)
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 14, recites the similar limitation as claim 9, therefore it is rejected for the same reason as claim 9.
Regarding claim 15, recites the similar limitation as claim 10, therefore it is rejected for the same reason as claim 10.
Regarding claim 17, recites the similar limitation as claim 13, therefore it is rejected for the same reason as claim 13.
Regarding claim 18, recites the similar limitation as claim 14, therefore it is rejected for the same reason as claim 14.
Regarding claim 19, recites the similar limitation as claim 10, therefore it is rejected for the same reason as claim 10.
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.
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.
Regarding to claim rejections for 35 USC § 112
The claim rejections are removed because of the claim amendment.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 extension fee 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 date of this final action.
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 on Mon-Fri 9-6.
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HELEN ZONG
Primary Examiner
Art Unit 2683
/HELEN ZONG/Primary Examiner, Art Unit 2683