DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 03/13/2026 has been entered.
Following prior arts are considered pertinent to applicant's disclosure.
US 20240397067 A1 (Damghanian)
US 20210385443 A1 (Masule)
US 20040017850 A1 (Kim)
US 20150350560 A1 (Zhou)
H. Lee, T. Kim, T. -y. Chung, D. Pak, Y. Ban and S. Lee, "AdaCoF: Adaptive Collaboration of Flows for Video Frame Interpolation," 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020, pp. 5315-5324, (Lee)
US 20230095541 A1 (used in international opinion)
Response to Remarks/Arguments
Double patenting rejection has been withdrawn in view of approved terminal disclaimer.
Applicant’s arguments with respect to claim prior art rejection have been fully considered but are moot in view of the new grounds of rejection.
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.
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 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, 4-5, 7, 16-17 & 20 are rejected under 35 U.S.C. 103 as being unpatentable over Damghanian in view of Dinh.
Regarding Claim 1. Damghanian teaches an apparatus comprising: at least one processor assembly [(Figs.1-2, para 102)] configured to:
Invoke at least one machine learning (ML) model, the at least one ML model being trained using training data based at least on subjective index representing noticeable difference in variations of at least one of bitrate, frame rate or resolution for a video input at least a first frame of video to at least one neural network [(Fig.2 unit 201/VTF, Video thinning function 201 receives the pictures of the source video and analyzes the pictures to determine whether or not it should be dropped {para 43}, the analysis is done by neural network {para 84})] responsive to output from the neural network control encoding the first frame [(Fig.2, unit 201 dropped/passed frame from further processing by encoder {para 43} )] :for transmission to a receiver. [(Fig.1 )] :
Damghanian uses neural network and does not explicitly show using machine learning model, additionally it does not teach the at least one ML model being trained using training data based at least on subjective index representing noticeable difference in variations of at least one of bitrate, frame rate or resolution for a video
However, in the same/related field of endeavor, Dinh teaches ML model and the at least one ML model being trained using training data based at least on subjective index representing noticeable difference in variations of at least one of bitrate, frame rate or resolution for a video [(para 10, DNN is a ML model. “downscaling DNN is trained based on first loss information corresponding to a result of comparing a quality-enhanced image selected from a plurality of quality-enhanced images through a video quality assessment”. The video quality assessment network used MOS/subjective index {para 33-34, 30} the downscaler is trained on resolution and bitrate {para 61} )]
Therefore, in light of above discussion it would have been obvious to one of the ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teaching of the prior arts because such combination would improve coding efficiency [( Dinh para 60)]
Damghanian additionally teaches, with respect to claim 4. The apparatus of claim 1, wherein the first frame comprises a keyframe. [(para 54-55; determination key picture is equivalent to determining not to drop)]
Damghanian additionally teaches, with respect to claim 5. The apparatus of claim 1, comprising at least one decoder assembly comprising at least one decoder ML model configured to receive frames from a decoder of the decoder assembly and insert reconstructed frames between frames received from the decoder [(para 58-60; decoder reconstruct the missing frame B using interpolation)] .
Masule additionally teaches, with respect to claim 7. The apparatus of claim 1, wherein the processor assembly is configured to use compressed domain information in video to eliminate portions of frames from encoding. [(Masule para 59; motion displacement vector for skip frame)]
Regarding Claims 16, 17 & 20: See analysis of claim 1 and 7
Claims 2-3 & 19 are rejected under 35 U.S.C. 103 as being unpatentable over Damghanian in view of Dinh in view of Zhou.
Regarding Claim 3: Damghanian teaches responsive to output from the ML model indicating that the first frame should be encoded, encode the first frame at a first quality for transmission to a receiver[(Fig. 2)]
Damghanian in view of Dinh does not explicitly show responsive to output from the ML model indicating that the first frame should not be encoded, not encode the first frame and encode a second frame with a second quality higher than the first quality.
However, in the same/related field of endeavor, Zhou teaches responsive skipping a first frame and encode a second frame with a second quality higher than the first frame quality [(para 40)]
Therefore, in light of above discussion it would have been obvious to one of the ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teaching of the prior arts because such combination would improve coding quality of region of interest without increasing bandwidth.
Zhou additionally teaches, with respect to claims 2, 19. The apparatus of claim 1, wherein the video comprises at least one computer game.[[(Zhou para 29, 62)]
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Damghanian in view of Dinh in view of Lee.
Regarding Claim 6: Damghanian in view of Dinh does not explicitly show the decoder ML model is trained on a training set comprising sequences of video frames at a first frame rate along with ground truth frames missing from the sequences of video frames.
However, in the same/related field of endeavor, Lee teaches the decoder ML model is trained on a training set comprising sequences of video frames at a first frame rate along with ground truth frames missing from the sequences of video frames. [(teaches ML model is trained to interpolate a frame in between frames/missing frame. The training involved sequence of fame including frame to be interpolated/missing, and ground truth frame {Section 3.4; equation 7 & 8, ground truth Igt}; section 4.1 training dataset , three consecutive frames In, Iout {the missing frame} and In+1 are used to train {equation 9 and 10})]
Therefore, in light of above discussion it would have been obvious to one of the ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teaching of the prior arts because such combination would provide predictable result and improve coding quality.
Claims 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Damghanian in view of Dinh in view of Cohen.
Regarding Claims 21-22. Damghanian in view of Dinh does not explicitly teaches invoking ML model based on a bitrate capacity.
However, in the same/related field of endeavor, Cohen invoking ML model based on a bitrate capacity. [(para 38; based on network bandwidth constraints reducing frame rate and AI-based techniques to reduce file size)]
Therefore, in light of above discussion it would have been obvious to one of the ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teaching of the prior arts because such combination would improve reliability through intelligently coding for constraints [( Cohen para 3)]
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Shahan Rahaman whose telephone number is (571)270-1438. The examiner can normally be reached on 7am - 3:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nasser Goodarzi can be reached at telephone number (571) 272-4195. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/SHAHAN UR RAHAMAN/Primary Examiner, Art Unit 2426
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