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 .
Response to Arguments
Applicant's arguments filed 01/26/2026 have been fully considered but they are not persuasive.
Wang discloses ([0062]) allowing the grain reduction 206 to control the level/type of grain to be reduced based on bandwidth. Bandwidth corresponds to network condition data. Those parameters related to the level and type of grain to be reduced correspond to data indicative of one or more film grain parameters. Therefore, Wang discloses based on network condition data, determining data indicative of one or more film grain parameters.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 8-13, 15-17, 19-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by WANG et al. (WO 2024214077 A1).
Regarding 1. WANG discloses A method (abstract, The video asset is streamed and decoded. After the decode, grain synthesis is performed to re-grain the video asset using grain synthesis parameters indicative of properties of the grain to be added to the video asset) comprising:
receiving a frame of a video content item (figure 1A, [0030] A pre-delivery processor 104 can be a software module or a unit that is a device or an assembly that receives a video asset 102);
based on removing at least a portion of high-frequency spatial information from the frame (figure 2, [0047] The grain reduction 206 may be configured to reduce the grain that is present in the video asset 102. The filter may then apply a smoothing algorithm that averages the pixel values in the area, effectively reducing the noise or grain), encoding the frame ([0032] compressing the video asset 102 with the grain removed);
based on network condition data ([0062] The video encoding /rate control parameters 402 and grain quality / fidelity assessment parameters 312 can be used to allow the grain reduction 206 to control the level/type of grain to be reduced to achieve the highest QoE to the end viewer, while consuming the minimum bandwidth. Those parameters can be jointly optimized given the final viewer’s QoE and overall bandwidth required as the overall target function), determining data indicative of one or more film grain parameters ([0050] the grain as assessed by the grain assessment / modeling 204 and/or as reduced by the grain reduction 206); and
sending, to a device, the encoded frame and the data indicative of the one or more film grain parameters, wherein the device is configured to decode the encoded frame, generate a film grain based on the one or more film grain parameters, and modify the decoded frame based on the film grain (abstract, The video asset is streamed and decoded. After the decode, grain synthesis is performed to re-grain the video asset using grain synthesis parameters indicative of properties of the grain to be added to the video asset).
Regarding claim 8. WANG discloses The method of claim 1, wherein determining the data indicative of the one or more film grain parameters is further based on data associated with the video content item ([0047] The grain reduction 206 may be configured to reduce the grain that is present in the video asset 102. The grain reduction 206 may be performed in various ways by the predelivery processor 104. In another example, a machine learning model may be trained on a large dataset of grainy and non-grainy images, to learn to identify and remove film grain; figure 4, the video encoding / rate control parameters 402; [0050] the grain as assessed by the grain assessment / modeling 204 and/or as reduced by the grain reduction 206), wherein the data associated with the video content item comprises data indicating at least one of a content type associated with the video content item, a quality associated with the video content item, a resolution associated with the video content item, or a frame rate associated with the video content item ([0048] Examples of the video encoding / rate control parameters 402 (as more specifically shown in FIG. 4) may include the video bite rate, spatial resolution, frame rate or temporal resolution, encoding mode selections at video, frame, and local block levels, and the quantization step parameters at video, frame, and local block levels).
Regarding claim 9. WANG discloses The method of claim 1, wherein the network condition data comprises data indicating one or more of available bandwidth, quality of service, latency, packet loss ratio, rebuffering state, or quality of experience ([0025] when the total bandwidth is limited, much fewer bits are available to encode other important visual information in the videos; [0062] The video encoding /rate control parameters 402 and grain quality / fidelity assessment parameters 312 can be used to allow the grain reduction 206 to control the level/type of grain to be reduced to achieve the highest QoE to the end viewer, while consuming the minimum bandwidth. Those parameters can be jointly optimized given the final viewer’s QoE and overall bandwidth required as the overall target function).
Regarding claim 10. WANG discloses The method of claim 1, wherein the film grain comprises a digital representation of optical texture ([0045] the film grain may be modeled using root-mean-square (RMS) granularity, which is a numerical quantification of density non-uniformity, equal to the RMS fluctuations in optical density).
Regarding claim 11. WANG discloses The method of claim 1, wherein the one or more film grain parameters comprise one or more of film grain intensity, film grain density, film grain size, or film grain color ([0030] The video aspects may include, for example, to detect grain in the video asset 102, assess or model the grain in the video asset 102, reduce the grain in the video asset 102, and provide grain parameters with respect to the film grain in the video asset 102. Film grain can refer to randomly distributed noise across frames of the video asset 102 where this noise may be characterized by various parameters, such as grain size, grain density, and/or grain contrast).
Regarding claim 12, the same analysis has been stated in claims 1 and 4.
Regarding claim 13, the same analysis has been stated in claim 1.
Regarding claim 15, the same analysis has been stated in claim 10.
Regarding claim 16, the same analysis has been stated in claim 11.
Regarding claim 17, the same analysis has been stated in claim 1.
Regarding claim 19, the same analysis has been stated in claim 10.
Regarding claim 20, the same analysis has been stated in claim 11.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over WANG et al. (WO 2024214077 A1) in view of TERTEROV et al. (US 20190246138 A1).
Regarding claim 2. TERTEROV discloses removing the at least the portion of high-frequency spatial information from the frame comprises applying a Gaussian filter to the frame ([0116] Filtering a frame with a filter strength parameter can be realized by using any Low Pass, smoothing or blurring filter such as a Gaussian smoothing filter).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the inventions of WANG and TERTEROV, to apply a Gaussian filter to remove high-frequency spatial information.
Claims 3, 14, 18 are rejected under 35 U.S.C. 103 as being unpatentable over WANG et al. (WO 2024214077 A1) in view of Mironica (US 20220270209 A1).
Regarding claim 3. WANG discloses The method of claim 1, wherein removing the at least the portion of high-frequency spatial information from the frame comprises smoothing the frame (figure 2, [0047] The grain reduction 206 may be configured to reduce the grain that is present in the video asset 102. The filter may then apply a smoothing algorithm that averages the pixel values in the area, effectively reducing the noise or grain).
However, WANG doesn’t explicitly disclose modifying the decoded frame based on the film grain comprises masking at least a portion of artifacts caused by the smoothing.
Mironica discloses masking at least a portion of artifacts caused by smoothing ([0028] these conventional systems are often unable to reproduce sharp edges in the image and commonly over-smooth texture regions. As a result, deblocking algorithms often remove one type of complex compression artifact at the cost of introducing other types of complex compression artifacts; [0033] accurately remove complex compression artifacts from a compressed digital image in a manner that does not introduce additional distortion artifacts).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of WANG according to the invention of Mironica, to remove artifacts caused by the smoothing, in order to improve the quality of the video content (Mironica abstract).
Regarding claim 14, the same analysis has been stated in claim 3.
Regarding claim 18, the same analysis has been stated in claim 3.
Claims 4-6 are rejected under 35 U.S.C. 103 as being unpatentable over WANG et al. (WO 2024214077 A1).
Regarding claim 4. WANG discloses The method of claim 1, wherein determining the data indicative of the one or more film grain parameters comprises:
determining film grain data using one or more neural networks, wherein the one or more neural networks are pre-trained ([0047] The grain reduction 206 may be configured to reduce the grain that is present in the video asset 102. The grain reduction 206 may be performed in various ways by the predelivery processor 104. In another example, a machine learning model may be trained on a large dataset of grainy and non-grainy images, to learn to identify and remove film grain; [0063] the tuning of the noise reduction filters or machine learning model for a first frame of the video asset 102 may be provided as an input to the grain assessment / modeling 204 for a second frame of the video asset 102 (e.g., the next frame); [0065] These grain reduction parameters 502 may include, as some examples, parameters of the noise reduction filters such as area size and sensitivity, average color or brightness from which to identify outlier pixels, smoothing filter parameters such as area size and smoothing amount, machine learning model configurations for removing film grain, etc.).
WANG also discloses
based on network condition data, determining data indicative of one or more film grain parameters ([0062] The video encoding /rate control parameters 402 and grain quality / fidelity assessment parameters 312 can be used to allow the grain reduction 206 to control the level/type of grain to be reduced to achieve the highest QoE to the end viewer, while consuming the minimum bandwidth. Those parameters can be jointly optimized given the final viewer’s QoE and overall bandwidth required as the overall target function).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the inventions of WANG, to train the machine learning model also based on previous network condition data, in order to achieve the highest QoE to the end viewer, while consuming the minimum bandwidth (WANG [0062]).
Regarding claim 5. WANG discloses The method of claim 4, wherein the film grain data comprises the data indicative of the one or more film grain parameters, or wherein determining the data indicative of the one or more film grain parameters comprises generating the data indicative of the one or more film grain parameters based on the film grain data ([0030] The video aspects may include, for example, to detect grain in the video asset 102, assess or model the grain in the video asset 102, reduce the grain in the video asset 102, and provide grain parameters with respect to the film grain in the video asset 102. Film grain can refer to randomly distributed noise across frames of the video asset 102 where this noise may be characterized by various parameters, such as grain size, grain density, and/or grain contrast).
Regarding claim 6. WANG discloses The method of claim 1, wherein determining the data indicative of the one or more film grain parameters comprises:
selecting, based on the network condition data, one or more neural networks from a plurality of neural networks pre-trained to output film grain data ([0062] The video encoding /rate control parameters 402 and grain quality / fidelity assessment parameters 312 can be used to allow the grain reduction 206 to control the level/type of grain to be reduced to achieve the highest QoE to the end viewer, while consuming the minimum bandwidth. Those parameters can be jointly optimized given the final viewer’s QoE and overall bandwidth required as the overall target function; [0075] An optimization method is to search all combinations of grain reduction parameters 502, video encoding parameters of the video encoding / rate control parameters 402 (e.g., including quantization steps, and encoding mode selections), and rate control parameters of the video encoding / rate control parameters 402 (including target bit rates for a video segment, a video frame and encoding blocks in each video frame), and find the selection that produces the highest value of the optimization objective function; [0047] The grain reduction 206 may be configured to reduce the grain that is present in the video asset 102. The grain reduction 206 may be performed in various ways by the predelivery processor 104. In another example, a machine learning model may be trained on a large dataset of grainy and non-grainy images, to learn to identify and remove film grain; [0063] the tuning of the noise reduction filters or machine learning model for a first frame of the video asset 102 may be provided as an input to the grain assessment / modeling 204 for a second frame of the video asset 102 (e.g., the next frame); [0065] These grain reduction parameters 502 may include, as some examples, parameters of the noise reduction filters such as area size and sensitivity, average color or brightness from which to identify outlier pixels, smoothing filter parameters such as area size and smoothing amount, machine learning model configurations for removing film grain, etc.).
The same motivation has been stated in claim 4.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over WANG et al. (WO 2024214077 A1) in view of KESKI-VALKAMA et al. (US 20210279585 A1).
Regarding claim 7. WANG discloses The method of claim 6, the method further comprising pre-training the plurality of neural networks to output the film grain data ([0047] a machine learning model may be trained on a large dataset of grainy and non-grainy images, to learn to identify and remove film grain).
KESKI-VALKAMA discloses pre-training a plurality of neural networks comprises at least one of:
removing one or more layers of at least one of the plurality of neural networks;
adding one or more layers to at least one of the plurality of neural networks;
adjusting one or more weights associated with one or more neurons of at least one of the plurality of neural networks;
removing one or more neurons from one or more layers of at least one of the plurality of neural networks;
adding one or more neurons to one or more layers of at least one of the plurality of neural networks;
adjusting an activation function associated with at least one of the plurality of neural networks; or
adjusting a loss function associated with at least one of the plurality of neural networks ([0040] the progressive path migrates an old architecture of a machine learning model 109 into a new architecture by incrementally adding and removing single neurons or neuronal layers, or smoothly changing activation functions; [0046] the one or more migration steps architectural options or changes to the machine learning model including, but not limited to, adding or removing a neuron, adding or removing a neuronal layer, changing an activation function of the neuron, changing a loss function, and/or the like of the machine learning model 109; [0067] the weights of the original input neurons 609a and 609b adjusted for the presence of the new input neuron 601).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the inventions of WANG and KESKI-VALKAMA, to use progressive training of long-lived, evolving machine learning architectures to pre-train the plurality of neural networks to output the film grain data, in order to advantageously preserve the training already performed and maintain model performance at a target level (KESKI-VALKAMA abstract, [0040]).
Conclusion
THIS ACTION IS MADE FINAL. 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 XIAOLAN XU whose telephone number is (571)270-7580. The examiner can normally be reached Mon. to Fri. 9am-5pm.
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/XIAOLAN XU/ Primary Examiner, Art Unit 2488
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