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 .
Status of the Claims
Currently, claims 1-20 are pending in the application. Claims 1, 3, 6-8, 10, 11, 13, 15, 17, and 20 are currently amended.
Continued Examination Under 37 CFR 1.114 1.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 06/08/2026 has been entered.
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 of this title, 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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gomila et al. (US 20060140278, hereinafter Gomila) in view of Abdelhamed et al. (US 20220222871, hereinafter Abdelhamed) and Norkin et al. (US 20190066272, hereinafter Norkin).
Regarding Claim 1, Gomila discloses a method comprising: determining, ([0020], A film grain modeler 16 accepts the input video stream, as well as the output signal of the film grain remover 14 and establishes the film grain in the incoming video signal, comprises a look up table containing film grain models for different film stocks);
determining, based on the plurality of film grain parameters, ([0020], specify the particular film stock originally used to record the image prior to conversion into a video signal to select the appropriate film grain model for such film stock and executes one or more algorithms to sample the incoming video and determine the film grain pattern; [0010], pre-computed block of transformed coefficients then undergoes filtering responsive to a frequency range that characterize a desired pattern of the film grain); and
sending, to a client device, an encoding message ([0019] FIG. 1, the transmitter 10 includes a video encoder 12 which encodes the incoming video stream using video compression techniques such as the H.264 video compression standard; [0220], FIG. 1, film grain modeling occurs at the transmitter 10 and film grain simulation occurs at the receiver 11 along with the decoding the incoming video stream from the transmitter 10 upstream of the output of the decoded video stream The film grain simulation requires information concerning the grain pattern in the incoming video signal, which information typically undergoes transmission in a film grain characteristics Supplemental Enhancement Information (SEI).
Gomila does not explicitly disclose determining the plurality of film grain parameters by a computing device via a convolutional neural network (CNN) wherein the CNN is trained based on one or more training content items comprising one or more labeled film grain parameters.
Abdelhamed teaches determining the plurality of film grain parameters by a computing device via a convolutional neural network (CNN) wherein the CNN is trained based on one or more training content items comprising one or more labeled film grain parameters ([0028], FIG. 1; [0068], FIG. 6A, obtain an input image 610 that includes a grain effect into a machine learning model 620, and obtain a grain parameter 630 based on an output of the machine learning model 620; [0075] FIG. 8, machine learning model for obtaining and applying a grain layer)
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Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of plurality of film grain parameters based on a trained neural network as taught by Abdelhamed ([0028]) into the encoding & decoding system of GOMILA in order to provide systems for image stylizing filters modify the colors, tone, or contrast of the images and the machine learning model is trained based on a training images including different grain strengths (Abdelhamed, [0004]).
Gomila & Abdelhamed do not explicitly disclose a de-noised version of the content item, wherein the de-noised version lacks the film grain noise.
Norkin teaches a de-noised version of the content item, wherein the de-noised version lacks the film grain noise ([0005], computes different film grain parameters that characterize the film grain associated with the color component based on the auto-regressive model, the linear scaling assumption, the de-noised video content and the source video content. In a complementary fashion, the reconstruction application generates synthesized film grain associated with each color component based on the auto-regressive model, the linear scaling assumption, the decoded video content, and the film grain parameters; [0021], generates a piecewise linear scaling function that accurately models the correlation between the de-noised video content and the film grain).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of de-noised version of the content item as taught by Norkin ([0005]) into the encoding & decoding system of GOMILA & Abdelhamed in order to enable executing a film grain modeling application so as to select relatively smooth spatial areas within a Y-component of a frame for avoiding the formation of edges and textures, so that a film grain parameter set associated with a particular portion of the source video content can be computed allowing a video streaming service provider to increase the accuracy of the synthesized film grain while avoiding inefficient translations (Norkin, [0006])
Regarding Claim 2, Gomila in view of Abdelhamed and Norkin discloses the method of claim 1, Gomila discloses wherein the plurality of film grain parameters comprises one or more of a film grain pattern, a film grain size, a film grain density, a film grain color, or a film grain intensity ([0023], FIG. 2, film grain size serves as a good characteristic for controlling edge scaling because the blockiness resulting from mosaicking blocks of film grain to create a seamless image become less visible for smaller grain sizes as fewer shapes become affected by the edge; [0027] FIG. 4, film grain pattern simulation using pre-computed DCT coefficients of multiple images of Gaussian random noise initiating entry into a loop that repeats for all possible film grain size and shape)
Regarding Claim 3, Gomila in view of Abdelhamed and Norkin discloses the method of claim 1, Gomila discloses further comprising synthesizing, by the client device, and based on the encoding message, the film grain noise into the de-noised version of the content ([00220], FIG. 1, film grain modeling occurs at the transmitter 10 and film grain simulation occurs at the receiver 11 along with the decoding the incoming video stream from the transmitter 10 upstream of the output of the decoded video stream The film grain simulation requires information concerning the grain pattern in the incoming video signal, which information typically undergoes transmission in a film grain characteristics Supplemental Enhancement Information (SEI)).
Regarding Claim 4, Gomila in view of Abdelhamed and Norkin discloses the method of claim 1, Gomila discloses further comprising: determining, for at least a portion of the content item, based on the plurality of film grain parameters, a component of an encoding cost function ([0022], film grain simulation presents an interesting tradeoff between complexity and memory requirements: reduces the complexity of the transform-based approaches and reduces the memory requirements of database-based approaches by storing transformed coefficients instead of film grain patterns) and encoding, based on the component of the encoding cost function, at least a portion of the version of the content item lacking the film grain noise ([0019], FIG. 1, video encoder 12 encodes the incoming video stream and a film grain remover 14 exist upstream of the encoder 12 to remove any film grain in the incoming video stream prior to encoding).
Regarding Claim 5, Gomila in view of Abdelhamed and Norkin discloses the method of claim 4, Gomila discloses the method of claim 4, wherein the component of the encoding cost function comprises at least one of: a Lagrangian multiplier, and wherein the method further comprises determining, based on a quantization parameter, the Lagrangian multiplier ([0019], FIG. 1, encodes the video stream using video compression techniques such as the ITU-T Rec. H.264|ISO/IEC 14496-10 video compression standard (based on a quantization parameter); or a quality factor, and wherein the method further comprises: determining, based on the plurality of film grain parameters, the quality factor ([0018], the encoder provides information with respect to the film grain in the incoming video: transmitter 10 “models” the film grain and the decoder simulates the film grain according to the film grain information received).
Regarding Claim 6, Gomila in view of Abdelhamed and Norkin discloses the method of claim 1, Gomila discloses wherein determining the de-noised version of the content comprises: determining, based on the plurality of film grain parameters, an amount of the film grain noise ([0020], A film grain modeler 16 accepts the input video stream, as well as the output signal of the film grain remover 14 and establishes the film grain in the incoming video signal, comprises a look up table containing film grain models for different film stocks); determining, based on the amount of the film grain noise, a quantization parameter (QP) for encoding at least one portion of the content item, and encoding, based on the QP, at least one portion of the version of the content item lacking the film grain noise ([0019], FIG. 1, video encoder 12 encodes the incoming video stream [inherently using the quantization process with QP)] and a film grain remover 14 exist upstream of the encoder 12 to remove any film grain in the incoming video stream prior to encoding).
Regarding Claim 7, Gomila in view of Abdelhamed and Norkin discloses the method of claim 1, Gomila discloses further comprising generating, based on the plurality of film grain parameters, the de-noised version of the content item lacking the film grain noise ([00220], FIG. 1, film grain modeling occurs at the transmitter 10 and film grain simulation occurs at the receiver 11 along with the decoding the incoming video stream from the transmitter 10 upstream of the output of the decoded video stream The film grain simulation requires information concerning the grain pattern in the incoming video signal, which information typically undergoes transmission in a film grain characteristics Supplemental Enhancement Information (SEI)).
Regarding Claims 8-14, Method claims 8-14 of using the corresponding method claimed in claims 1-7, and the rejections of which are incorporated herein for the same reasons of obviousness as used above.
Regarding Claims 15-20, Method claims 15-20 of using the corresponding method claimed in claim 1-7, and the rejections of which are incorporated herein for the same reasons of obviousness as used above.
Conclusion
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/SAMUEL D FEREJA/ Primary Examiner, Art Unit 2487
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