DETAILED ACTION Election/Restrictions 1. Applicant’s election with out traverse of Group I: Claims 1-8, 16-20 in the repl y filed on 09/04/2025 , is acknowledged. Claims 9-15 have been withdrawn. Currently, c laims 1-8, 16-20 have been submitted for examination and are pending. Information Disclosure Statement 2. The information disclosure statement (IDS) was submitted on 03/06/2025. The submissions are in compliance with the provisions of 37 CFR § 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Claim Objections 3 . Claim 4 is objected to because of the following informalities: In claim 4, lines 2-3, “ a switching point” and “a streamed video” seem to refer back to “ a switching point” and “a streamed video” recited at claim 1. If this is true, it is suggested to change it to ---- the switching point ------ and ----- the streamed video -------. Appropriate correction is required. Claim Rejections - 35 USC § 103 4 . The following is a quotation of 35 U.S.C. § 103(a) which forms the basis for all obviousness rejections set forth in this Office action: A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made. 5 . 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(a) 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. 6 . Claims 1-8 and 16 -20 are rejected under 35 U.S.C. § 103(a) as being unpatentable over Thomas et al. (WO 2022/144262A1) (hereinafter Thomas) (cited by IDS) in view of Reznik et al. (US2019/0081998A1) (hereinafter Reznik) (cited by IDS) . Regarding claim 1 , Thomas discloses an apparatus (e.g., see abstract; Figs. 9, 11-12) comprising: at least one processor assembly (e.g., see Fig. 12, page 33, lines 30-36: processor) configured to: for each of at least some of plural selected resolutions (e.g., see abstract, page 5 lines 9-26: first and second resolutions of video; Fig. 2, page 14 lines 15-24) , for each of at least some of plural bitrates for each resolution, compress at least some of plural selected videos to render compressed selected videos (e.g., see page 29 lines 12-25: video decoder 1100 may receive compressed video data that has been compressed for transmission via a network into so-called network abstraction layer (NAL) units ; page 25 lines 15-20: bit rate) ; decompress each compressed selected video to generate a respective output video (e.g., see page 21 lines 11-23: t ypically, an encoded representation of a sequence of pictures comprises a picture sequence structure such as a coded video sequence (CVS) structure comprising one or more groups of pictures (GOP) structure. Such coded pictures in such picture sequence structure may form a self-contained set of video data which can be decoded without requiring any further information external to the picture sequence structure ) ; determine at least one quality metric between each selected video and respective output video for respective bitrates (e.g., see p a g e 26, l i n es 29 to p a g e 27 l i n e 12 : The distortion D may be regarded a measure of the video quality. Known metrics for objectively determining the quality (objectively in the sense that the metric is content agnostic) include means squared error (MSE), peak-signal-to-noise (PSNR) and sum of absolute differences (SAD) ... the rate distortion analysis may involve computing the rate R for each encoded video block associated with of one of the prediction modes, wherein the rate R includes a number of bits used to signal an encoded video block. The thus determined RD costs, the distortion D and the rate R of the encoded blocks for each of the prediction modes, are then used to select an encoded video block that provides the best trade-off between the number of bits used for encoding the block versus the distortion that is introduced by using the number of bits for encoding ) ; for each of at least some of plural selected resolutions, for each of at least some of plural bitrates for each resolution, upscale at least some of the output videos to render upscaled videos (e.g., see p a g e 17 l i n e 15-21 : After decoding, the base picture and the one or more auxiliary pictures may be upsampled to the resolution of the high-resolution pictures based on the sampling lattices of the spatial subsampling scheme ) ; and using the switching point, change a resolution of a streamed video (e.g., see p a g e 12 l i n e 19-27 : During decoding of the coded low-resolution pictures a switch to the high resolution version may be triggered. For example, based on metrics, the client may decide that there is enough bandwidth for the high resolution version. In that case, the switch from the low resolution to the high-resolution version of the video title may be initiated either upon client requests or based on a network entity providing the video streams to the client ) ; Thomas does not explicitly disclose at least in part based on the quality metrics and bitrates, generate a first data structure; at least in part based on the quality metrics between the respective selected video and the respective upscaled video and respective bitrates, generate a second data structure; at least in part using the first and second data structures, identify a switching point for switching resolution of a streamed video. However, Reznik discloses at least in part based on the quality metrics and bitrates, generate a first data structure (e.g., see paragraphs 0091-0092: One way to add information about the quality of encoded segments may be by using tags (e.g., additional tags) in the Segment List portions of the MPD file. The Adaptation Set may comprise tags indicating the presence of quality values in the segment list. An example of such declarations is presented below ) ; for each upscaled video calculate at least one quality metric for respective bitrates between the respective selected video and the respective upscaled video (e.g., see paragraphs 0087-0088: A client adaptation model using per-segment quality and rate information may be provided. FIG. 8 is a diagram illustrating a model representation 800 of an operation of a DASH streaming client using quality information, which for example, may prevent selection of streams/segments whose quality is greater than a threshold quality cap, for example, regardless of the bitrate ... To enable quality-based decisions in a streaming client, the client may have access to information about the quality of one or more of the encoded segments ) ; at least in part based on the quality metrics between the respective selected video and the respective upscaled video and respective bitrates, generate a second data structure (e.g., see paragraphs 0091-0092) ; at least in part using the first and second data structures, identify a switching point for switching resolution of a streamed video (e.g., see paragraph 0080: A client application may be used to dynamically select between one or more encoded streams. Stream switches implemented by a client may have a certain granularity, which, for example, may be around 2-10 seconds in practice. The points at which a client may switch between encoded streams may be referred to as switch points ) . It would have been obvious to the person of ordinary skill in the art at the time of the invention to modify the system disclosed by Thomas to add the teachings of Reznik as above, in order to provide s ystems and methods to enable quality-based optimizations of the delivery process of streaming content ( s ee paragraph 000 3 : Reznik ). Regarding claim 2 , Thomas and Reznik disclose all t he limitations of claim 1 , and are analyzed as previously discussed wit h respect to that claim . Furthermore, Thomas discloses wherein the first and second data structures comprise rate distortion (RD) curves (e.g., see p a g e 26, l i n es 34 to p a g e 27, l i n e 12 : the rate-distortion Cost ) . Regarding claim 3 , Thomas and Reznik disclose all t he limitations of claim 1 , and are analyzed as previously discussed wit h respect to that claim . Furthermore, Thomas discloses wherein the first and second data structures comprise respective equations fitted to respective rate distortion (RD) curves (e.g., see p a g e 26, l i n es 21-26: the RDO problem can be expressed as a minimization of a Lagrangian cost function J with respect to a Lagrangian multiplier ) . Regarding claim 4 , Thomas and Reznik disclose all t he limitations of claim 1 , and are analyzed as previously discussed wit h respect to that claim . Furthermore, Thomas discloses wherein the processor assembly is configured to, at least in part using the first and second data structures, identify a switching point for switching resolution of a streamed video by identifying an intersection between the two data structures as the switching point (e.g., see p a g e 12 l i n e 19-27 : During decoding of the coded low-resolution pictures a switch to the high resolution version may be triggered. For example, based on metrics, the client may decide that there is enough bandwidth for the high resolution version. In that case, the switch from the low resolution to the high-resolution version of the video title may be initiated either upon client requests or based on a network entity providing the video streams to the client ) . Regarding claim 5 , Thomas and Reznik disclose all t he limitations of claim 1 , and are analyzed as previously discussed wit h respect to that claim . Furthermore, Thomas discloses wherein the quality metric comprises peak signal-to-noise ratio (PSNR) (e.g., see p a g e 26, l i n es 29-33 : Known metrics for objectively determining the quality (objectively in the sense that the metric is content agnostic) include means- squared error (MSE), peak-signal-to-noise (PSNR) and sum of absolute differences (SAD) Regarding claim 6 , Thomas and Reznik disclose all t he limitations of claim 1 , and are analyzed as previously discussed wit h respect to that claim . Thomas does not explicitly disclose wherein the quality metric comprises structural similarity index (SSIM). However, Reznik discloses wherein the quality metric comprises structural similarity index (SSIM) (e.g., see paragraph 0076: T o report rate and/or quality parameters (e.g., PSNR, SSIM, and/or MOS) for a sequence, the average values of bitrates and/or quality parameters across one or more of the frames may be used ) . It would have been obvious to the person of ordinary skill in the art at the time of the invention to modify the system disclosed by Thomas to add the teachings of Reznik as above, in order to provide s ystems and methods to enable quality-based optimizations of the delivery process of streaming content (see paragraph 0003: Reznik). Regarding claim 7 , Thomas and Reznik disclose all t he limitations of claim 1 , and are analyzed as previously discussed wit h respect to that claim . Thomas does not explicitly disclose wherein the processor assembly is configured to generate the first data structure using averages based on the bit rates. However, Reznik discloses wherein the processor assembly is configured to generate the first data structure using averages based on the bit rates (e.g., see paragraph 0076) . It would have been obvious to the person of ordinary skill in the art at the time of the invention to modify the system disclosed by Thomas to add the teachings of Reznik as above, in order to provide s ystems and methods to enable quality-based optimizations of the delivery process of streaming content (see paragraph 0003: Reznik). Regarding claim 8 , Thomas and Reznik disclose all t he limitations of claim 1 , and are analyzed as previously discussed wit h respect to that claim . Thomas does not explicitly disclose wherein the processor assembly is configured to generate the first data structure using averages based on the quality metrics. However, Reznik discloses wherein the processor assembly is configured to generate the first data structure using averages based on the quality metrics (e.g., see paragraph 0076) . It would have been obvious to the person of ordinary skill in the art at the time of the invention to modify the system disclosed by Thomas to add the teachings of Reznik as above, in order to provide s ystems and methods to enable quality-based optimizations of the delivery process of streaming content (see paragraph 0003: Reznik). Regarding claim 16 , this claim is a method claim of an apparatus version as applied to claim 1 above, wherein the method performs the same limitations cited in claim 1, the rejections of which are incorporated herein. Regarding claim 17 , Thomas and Reznik disclose all t he limitations of claim 16 , and are analyzed as previously discussed wit h respect to that claim . Furthermore, Thomas discloses comprising using the switching point to switch resolution of the first video while streaming the first video (e.g., see p a g e 12, l i n es 19-27 : The sequence of coded pictures received by the video streaming client around the resolution change as a function of time is depicted in Fig. 1 C, which depicts a sequence of low-resolution pictures ... followed after the resolution switch - by a sequence of high-resolution pictures ) . Regarding claim 18 , Thomas and Reznik disclose all t he limitations of claim 16 , and are analyzed as previously discussed wit h respect to that claim . Furthermore, Thomas discloses comprising identifying the switching point by identifying an intersection between a first rate distortion (RD) data structure derived from the first quality metrics and a second RD data structure derived from the second quality metrics (e.g., see p a g e 26, l i n es 21 to p a g e 27, l i n e 12 : the rate-distortion (RD) c ost ) . Regarding claim 19 , Thomas and Reznik disclose all t he limitations of claim 16 , and are analyzed as previously discussed wit h respect to that claim . Furthermore, Thomas discloses wherein the first and second RD data structures comprise rate distortion (RD) curves (e.g., see p a g e 26, l i n e 34 to p a g e 27, l i n e 12) . Regarding claim 20 , Thomas and Reznik disclose all t he limitations of claim 16 , and are analyzed as previously discussed wit h respect to that claim . Furthermore, Thomas discloses wherein the first and second RD data structures comprise respective equations fitted to respective rate distortion (RD) curves (e.g., see p a g e 26, l i n es 21-26 ) . Conclusion 7 . Any inquiry concerning this communication or earlier communications from the examiner should be directed to ON MUNG whose telephone number is (571) 270-7557 and whose direct fax number is (571) 270-8557. The examiner can normally be reached on Mon-Fri 9am - 6pm (ET). If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, JAMIE ATALA can be reached on (571)272-7384 . The fax phone 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). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /ON S MUNG/ Primary Examiner, Art Unit 2486