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 .
Claims 1, 3-14, 17-18, and 20 have been amended.
Response to Arguments
Applicant’s arguments, filed 05/27/2026, with respect to the rejection(s) of claim(s) 1 and 18 under 35 U.S.C. 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Kim (US 11582462 B1).
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 1 is rejected under 35 U.S.C. 103 as being unpatentable over Ramaswamy (US 20210092399 A1) in view of Kim (US 11582462 B1).
Regarding claim 1, Ramaswamy teaches a method of dynamically adjusting picture resolution in streaming video, the method comprising:
encoding, using control circuitry, first segment image frames in a first video segment of a source video at a first bitrate for streaming at a first picture resolution using a network connection to a recipient device, the network connection having a first estimated bandwidth, wherein the first bitrate and the first picture resolution are determined based on the first estimated bandwidth ([0056] At step 620, the encoder may send, at a first bitrate, via a content delivery network and to a computing device, at least one segment of the plurality of first segments.);
determining a second bitrate and a second picture resolution for streaming using the network connection in response to the detected change in the network connection, the second bitrate based on the second estimated bandwidth, and the second picture resolution based on the determined second bitrate and the selected representative encoding option (At step 530, the encoder may receive, from the computing device, a request for segments encoded at a second bitrate. The request may be based on at least one of: changing network bandwidth [0052]); and
encoding, using the control circuitry, subsequent image frames of the second video segment of the source video at the second bitrate for streaming at the second picture resolution using the network connection to the recipient device (The request may be enabled by detection by the computing device of an indication in the bitstream that a stream at another bitrate is available. At step 540, the encoder may send, at the second bitrate, to the computing device, a segment of the plurality of second segments and a subsequent segment of the plurality of first segments in the sequence [0052].).
Ramaswamy does not teach the following limitations, however, in an analogous art, Kim teaches generating, using the control circuitry, first image frame quality data by comparing an image frame from the source video with an associated encoded image frame of the first segment image frames in the first video segment (At 406, a quality metric is determined for each of the plurality of different candidate encodings. In order to generate a two-dimensional rate distortion graph in which rate is plotted against quality, a quality metric needs to be determined for each of the different candidate encodings so that quality can be plotted (e.g., on the y-axis of a graph). [col 12: lines 48-60] Compressed videos are compared with an original video to calculate quality metrics [col 11 lines 25-50]);
detecting, using the control circuitry, a change in the network connection from the first estimated bandwidth to a second estimated bandwidth (if the user device determines that network throughput (bandwidth) has deteriorated, it will request a lower bitrate stream. [Col 10 lines 21-25]);
generating, using the control circuitry, a respective second image frame quality data for each of at least two representative encoding options for a subsequent image frame by comparing the subsequent image frame from the source video with the at least two representative encoding options for the subsequent image frame of a second video segment of the source video, the second video segment being subsequent to the first video segment within the source video (At 404, the video is encoded into a plurality of different candidate encodings using different candidate encoding parameters. [Col 12: lines 21-25]);
comparing the first image frame quality data with the respective second image frame quality data for each of the at least two representative encoding options to select one of the at least two representative encoding options for the subsequent image frame (the candidate encoding parameters determine encoding outputs. In various embodiments, the encoding outputs that are determined are rate, such as a bitrate, and quality (or distortion). The encoding outputs can be plotted against each other (e.g., bitrate on an x-axis and quality on a y-axis). Examples of candidate encoding parameters include video output resolution, QP, CRF, AQ, etc. Typically, in a two-dimensional rate distortion graph, two candidate encoding parameters can be varied. [Col 12 lines 30-45]).
It would have been obvious for a person of ordinary skill in the art, before the effective filling date of the claimed invention, to take the teachings of Kim and apply them to Ramaswamy. One would be motivated as such as in order to improve the perceptual quality of the encoded video.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Ramaswamy in view of Kim further in view of Phillips (US 10523914 B1)
Regarding claim 2, Ramaswamy in view of Kim teaches the method of claim 1. Ramaswamy does not teach the following limitations, however, in an analogous art, Phillips teaches encoding, using the control circuitry, the first segment image frames using single-pass encoding; and encoding, using the control circuitry, the subsequent image frame using single-pass encoding ( a methodology combining weighted allocation techniques with bitrate caps in a single pass may be provided [Col 57: lines 50-53]).
It would have been obvious for a person of ordinary skill in the art, before the effective filling date of the claimed invention, to take the teachings of Phillips and apply them to Ramaswamy in view of Kim. One would be motivated as such as in order to improve computational complexity (Phillips [Col 57: lines 50-53]).
Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Ramaswamy in view of Kim further in view of SETHURAMAN (US 20190075299 A1)
Regarding claim 3, Ramaswamy in view of Kim teaches the method of claim 1. Ramaswamy in view of Kim does not teach the following limitations, however, in an analogous art, SETHURAMAN teaches using the control circuitry, comparing the first image frame quality data with the respective second image frame quality data for each of the at least two representative encoding options to generate a first picture quality estimate for the subsequent image frame encoded for streaming at the second picture resolution for comparison to a second picture quality estimate for the subsequent image frame encoded for streaming at the first picture resolution ([0040] Encoding parameters estimation unit 116 may determine the content adaptive bitrate and the resolution for the segment of media content by looking-up bits required to achieve the predefined maximum quality measure for each of the k-nearest neighbors using the retrieved bitrate and the quality data at the multiple resolutions. Further, encoding parameters estimation unit 116 may derive the content adaptive bitrate and the resolution for the segment of media content based on the looked-up bits for each of the k-nearest neighbors.).
It would have been obvious for a person of ordinary skill in the art, before the effective filling date of the claimed invention, to take the teachings of SETHURAMAN and apply them to Ramaswamy in view of Kim. One would be motivated as such to improve the quality of the complex frames towards a consistent quality level (SETHURAMAN [0016]).
Regarding claim 4, Ramaswamy in view of Kim and SETHURAMAN teaches the method of claim 3. SETHURAMAN teaches comparing, using the control circuitry, the first image frame quality data with the respective second image frame quality data for each of the at least two representative encoding options using machine learning ( system for determining content adaptive encoding parameters based on k-nearest neighbor (K-NN) model. Examples described herein may provide a non-iterative, codec-agnostic approach that employs machine learning techniques to perform consistent quality content-adaptive encoding within the constraints of a maximum bitrate in a manner that makes it equally suitable for live and on-demand workflows. [0018]).
It would have been obvious for a person of ordinary skill in the art, before the effective filling date of the claimed invention, to take the teachings of SETHURAMAN and apply them to Ramaswamy in view of Kim. One would be motivated as such to improve the quality of the complex frames towards a consistent quality level (SETHURAMAN [0016]).
Claims 6-17 are rejected under 35 U.S.C. 103 as being unpatentable over Ramaswamy in view of Kim further in view of CUTLER (US 20260006259 A1)
Regarding claim 6, Ramaswamy in view of Kim teaches the method of claim 1. Ramaswamy in view of Kim does not teach the following limitations, however, in an analogous art, CUTLER teaches wherein comparing the image frame from the source video with the associated encoded image frame to generate the first image frame quality data comprises using a structural similarity index measure ([0032] In some example implementations, the reward function evaluation module (190) provides feedback to the post-processing model (170) according to a reward function for actor-critic reinforcement learning… The reward function can implement an objective measure of quality degradation between sample values of the input frame and corresponding sample values of the restored frame, such as … a structural similarity index, a multi-scale structural similarity index).
It would have been obvious for a person of ordinary skill in the art, before the effective filling date of the claimed invention, to take the teachings of CUTLER and apply them to Ramaswamy in view of Kim. One would be motivated as such to perform post-processing operations for upsampling and/or quality improvement based on information in multiple frames. (CUTLER [0038]).
Regarding claim 7, Ramaswamy in view of Kim teaches the method of claim 1. Ramaswamy in view of Kim does not teach the following limitations, however, in an analogous art, CUTLER teaches wherein comparing the image frame from the source video with the associated encoded image frame to generate the first image frame quality data comprises generating a resampled version of the associated encoded image frame. (The input video can be downsampled by a factor of 2:1, 4:1, or another downsampling factor, with the post-processing model (170) compensating for the downsampling by upsampling by a corresponding upsampling factor.).
It would have been obvious for a person of ordinary skill in the art, before the effective filling date of the claimed invention, to take the teachings of CUTLER and apply them to Ramaswamy in view of Kim. One would be motivated as such to perform post-processing operations for upsampling and/or quality improvement based on information in multiple frames. (CUTLER [0038]).
Regarding claim 8, Ramaswamy in view of Kim and CUTLER teaches the method of claim 7. CUTLER teaches wherein comparing the image frame from the source video with the associated encoded frame to generate the first image frame quality data comprises using a structural similarity index measure to compare the image frame from the source video with the resampled version ([0032] In some example implementations, the reward function evaluation module (190) provides feedback to the post-processing model (170) according to a reward function for actor-critic reinforcement learning… The reward function can implement an objective measure of quality degradation between sample values of the input frame and corresponding sample values of the restored frame, such as … a structural similarity index, a multi-scale structural similarity index). The same motivation used to combine Ramaswamy in view of Kim and CUTLER in claim 7 is applicable.
Regarding claim 9, Ramaswamy in view of Kim teaches the method of claim 1. Ramaswamy in view of Kim does not teach the following limitations, however, in an analogous art, CUTLER teaches wherein comparing the image frame from the source video with the associated encoded image to generate the first image frame quality data comprises generating a resampled and encoded version of the associated encoded image frame (The input video can be downsampled by a factor of 2:1, 4:1, or another downsampling factor, with the post-processing model (170) compensating for the downsampling by upsampling by a corresponding upsampling factor.).
It would have been obvious for a person of ordinary skill in the art, before the effective filling date of the claimed invention, to take the teachings of CUTLER and apply them to Ramaswamy in view of Kim. One would be motivated as such to perform post-processing operations for upsampling and/or quality improvement based on information in multiple frames. (CUTLER [0038]).
Regarding claim 10, Ramaswamy in view of Kim and CUTLER teaches the method of claim 9. CUTLER wherein comparing the image frame from the source video with the associated encoded image frame to generate the first image frame quality data comprises using a structural similarity index measure to compare the image frame from the source video with the resampled and encoded version ([0032] In some example implementations, the reward function evaluation module (190) provides feedback to the post-processing model (170) according to a reward function for actor-critic reinforcement learning… The reward function can implement an objective measure of quality degradation between sample values of the input frame and corresponding sample values of the restored frame, such as … a structural similarity index, a multi-scale structural similarity index). The same motivation used to combine Ramaswamy in view of Kim and CUTLER in claim 9 is applicable.
Regarding claim 11, Ramaswamy in view of Kim teaches the method of claim 1. Ramaswamy in view of Kim does not teach the following limitations, however, in an analogous art, CUTLER teaches downsampling, using the control circuitry, the associated encoded image frame to generate a downsampled image frame (The input video can be downsampled by a factor of 2:1, 4:1, or another downsampling factor [0022]);
upsampling, using the control circuitry, the downsampled image frame to generate a processed image frame (the post-processing model (170) compensating for the downsampling by upsampling by a corresponding upsampling factor. [0022]); and
wherein comparing the image frame from the source video with the associated encoded image frame to generate the first image frame quality data comprises comparing the processed image frame with the image frame from source video (Fig. 1: feedback. Based on feedback from the reward function evaluation module (190), the post-processing model (170) learns to both restore the 1080p decoded video and upsample the 1080p decoded video to the spatial resolution of 2160p based on the 2160 input video. [0029]).
It would have been obvious for a person of ordinary skill in the art, before the effective filling date of the claimed invention, to take the teachings of CUTLER and apply them to Ramaswamy in view of Kim. One would be motivated as such to perform post-processing operations for upsampling and/or quality improvement based on information in multiple frames. (CUTLER [0038]).
Regarding claim 12, Ramaswamy in view of Kim teaches the method of claim 1. Ramaswamy in view of Kim does not teach the following limitations, however, in an analogous art, CUTLER teaches decoding, using the control circuitry, the associated encoded image frame to generate a decoded image frame (decode (640) the encoded data, thereby producing decoded video for the subsequent unit of the video sequence [0154]); and
wherein comparing the image frame from the source video with the associated encoded image frame to generate the first image frame quality data comprises comparing, the decoded image frame with the image frame from the source video (Fig. 1: feedback. Based on feedback from the reward function evaluation module (190), the post-processing model (170) learns to both restore the 1080p decoded video and upsample the 1080p decoded video to the spatial resolution of 2160p based on the 2160 input video. [0029]).
It would have been obvious for a person of ordinary skill in the art, before the effective filling date of the claimed invention, to take the teachings of CUTLER and apply them to Ramaswamy in view of Kim. One would be motivated as such to perform post-processing operations for upsampling and/or quality improvement based on information in multiple frames. (CUTLER [0038]).
Regarding claim 13, Ramaswamy in view of Kim teaches the method of claim 1. Ramaswamy in view of Kim does not teach the following limitations, however, in an analogous art, CUTLER teaches decoding, using the control circuitry, the associated encoded image frame to generate a decoded image frame (decode (640) the encoded data, thereby producing decoded video for the subsequent unit of the video sequence [0154]);
downsampling, using the control circuitry, the decoded image frame to generate a downsampled image frame (The input video can be downsampled by a factor of 2:1, 4:1, or another downsampling factor [0022]);
upsampling, using the control circuitry, the downsampled image frame to generate a processed image frame (the post-processing model (170) compensating for the downsampling by upsampling by a corresponding upsampling factor. [0022]); and
wherein comparing the image frame from the source video with the associated encoded image frame to generate the first image frame quality data comprises comparing the processed image frame with the image frame from the source video (Fig. 1: feedback. Based on feedback from the reward function evaluation module (190), the post-processing model (170) learns to both restore the 1080p decoded video and upsample the 1080p decoded video to the spatial resolution of 2160p based on the 2160 input video. [0029]).
It would have been obvious for a person of ordinary skill in the art, before the effective filling date of the claimed invention, to take the teachings of CUTLER and apply them to Ramaswamy in view of Kim. One would be motivated as such to perform post-processing operations for upsampling and/or quality improvement based on information in multiple frames. (CUTLER [0038]).
Regarding claim 14, Ramaswamy in view of Kim teaches the method of claim 1. Ramaswamy in view of Kim does not teach the following limitations, however, in an analogous art, CUTLER teaches downsampling, using the control circuitry, the subsequent image frame from the source video to generate a downsampled image frame (The input video can be downsampled by a factor of 2:1, 4:1, or another downsampling factor [0022]);
upsampling, using the control circuitry, the downsampled image frame to generate a processed image frame (the post-processing model (170) compensating for the downsampling by upsampling by a corresponding upsampling factor. [0022]); and
wherein comparing the subsequent image frame from the source video with the at least two representative encoding options to generate the respective second image frame quality data comprises comparing the processed image frame with the subsequent image frame from the source video (Fig. 1: feedback. Based on feedback from the reward function evaluation module (190), the post-processing model (170) learns to both restore the 1080p decoded video and upsample the 1080p decoded video to the spatial resolution of 2160p based on the 2160 input video. [0029]).
It would have been obvious for a person of ordinary skill in the art, before the effective filling date of the claimed invention, to take the teachings of CUTLER and apply them to Ramaswamy in view of Kim. One would be motivated as such to perform post-processing operations for upsampling and/or quality improvement based on information in multiple frames. (CUTLER [0038]).
Regarding claim 15, Ramaswamy in view of Kim teaches the method of claim 1. Ramaswamy in view of Kim does not teach the following limitations, however, in an analogous art, CUTLER teaches detecting a videographic change associated with the source video (with new scenes identified using an ML model configured to detect scene changes) [0077]); and
modifying the first image frame quality data in response to the detected videographic change (if quality changes during a conference, the post-processing engine (290) can react to the change. [0077]).
It would have been obvious for a person of ordinary skill in the art, before the effective filling date of the claimed invention, to take the teachings of CUTLER and apply them to Ramaswamy in view of Kim. One would be motivated as such to perform post-processing operations for upsampling and/or quality improvement based on information in multiple frames. (CUTLER [0038]).
Regarding claim 16, Ramaswamy in view of Kim teaches the method of claim 1. Ramaswamy in view of Kim does not teach the following limitations, however, in an analogous art, CUTLER teaches detecting a change indicator in video metadata associated with the source video (with new scenes identified using an ML model configured to detect scene changes) [0077]); and
modifying the first image frame quality data in response to the detected change indicator (if quality changes during a conference, the post-processing engine (290) can react to the change. [0077]).
It would have been obvious for a person of ordinary skill in the art, before the effective filling date of the claimed invention, to take the teachings of CUTLER and apply them to Ramaswamy in view of Kim. One would be motivated as such to perform post-processing operations for upsampling and/or quality improvement based on information in multiple frames. (CUTLER [0038]).
Regarding claim 17, Ramaswamy in view of Kim teaches the method of claim 1. Ramaswamy in view of Kim does not teach the following limitations, however, in an analogous art, CUTLER teaches receiving feedback data from the recipient device, the feedback relating to video quality; and modifying the respective second image frame quality data for each of the at least two representative encoding options in response to the feedback data (Fig. 1: feedback. Based on feedback from the reward function evaluation module (190), the post-processing model (170) learns to both restore the 1080p decoded video and upsample the 1080p decoded video to the spatial resolution of 2160p based on the 2160 input video. [0029]).
It would have been obvious for a person of ordinary skill in the art, before the effective filling date of the claimed invention, to take the teachings of CUTLER and apply them to Ramaswamy in view of Kim. One would be motivated as such to perform post-processing operations for upsampling and/or quality improvement based on information in multiple frames. (CUTLER [0038]).
Allowable Subject Matter
Claim 5 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.
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 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 HESHAM K ABOUZAHRA whose telephone number is (571)270-0425. The examiner can normally be reached M-F 8-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jamie Atala can be reached at 57127227384. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HESHAM K ABOUZAHRA/ Primary Examiner, Art Unit 2486
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