Ction of claims 4 and 5 need more detail 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 .
Claim Rejections - 35 USC § 102
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 11 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ameur et al. (“Deep-based Film Grain Removal and Synthesis”).
In regards to claim 1, Ameur teaches an apparatus comprising:
a processor to generate grainy texture parameters based on grainy texture depicted in images of a training image dataset (e.g. Section IV-A,pp.5052-5053: to train our proposed models for film grain removal and synthesis, a large dataset of images was collected; pairs of clean (grain-free) and grainy images to train our models; a wide range of grain types and intensities can be generated by varying the parameters of this model; the two main parameters are the average grain radius
μ
r
and its standard deviation
σ
r
; see also Section III-A,pp.5050-5051: film grain synthesis can be viewed as the translation of a given grain-free input image into a corresponding grainy output image while preserving the content; the goal is then to learn a mapping function from one input domain (grain-free images)
x
to another output domain (grainy images)
y
; Examiner’s note: this shows parameters are generated based on training data for use in film grain synthesis; as directed to machine learning/training as well as image processing, this shows a processor would be used); and
memory to store a grainy texture image generated by modifying image data using the grainy texture parameters (e.g. Section IV-B,pp.5053-5054: film grain synthesis is either performed on original clean images (artistic content creation) or on filtered/decoded images (video compression); therefore, we evaluate the results of our synthesis model on both clean and filtered images (as output by proposed grain removal models for both blind and non-blind configurations); Examiner’s notes: as directed to machine learning/training as well as image processing, where a resulting image is outputted/displayed, this shows that a memory to store the resulting image, at the very least temporarily, would be used (see example results in Fig.3)).
In regards to claim 11, Ameur teaches an apparatus, further comprising a display configured to output a visual representation of the grainy texture image (e.g. as above, Section III-A,pp.5050-5051: film grain synthesis can be viewed as the translation of a given grain-free input image into a corresponding grainy output image while preserving the content; Section IV-B,pp.5053-5054: we evaluate the results of our synthesis model on both clean and filtered images (as output by proposed grain removal models for both blind and non-blind configurations; Examiner’s notes: as directed to machine learning/training as well as image processing, where a resulting image is outputted, this shows that a display would be used to visualize resulting images (see example results in Fig.3)).
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(s) 2-4, 10, 17, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ameur et al. (“Deep-based Film Grain Removal and Synthesis”) in view of Meng et al. (US 2025/0117909 A1).
In regards to claim 2, Ameur teaches the apparatus of claim 1, wherein the processor is configured to generate the grainy texture parameters by analyzing the grainy texture depicted in the images of the training image dataset (e.g. as above, Section IV-A,pp.5052-5053: to train our proposed models for film grain removal and synthesis, a large dataset of images was collected; pairs of clean (grain-free) and grainy images to train our models; a wide range of grain types and intensities can be generated by varying the parameters of this model; the two main parameters are the average grain radius
μ
r
and its standard deviation
σ
r
), but does not explicitly teach the apparatus, wherein the analyzing is done in the frequency domain.
However, Meng teaches an apparatus, wherein the analyzing of the grainy texture depicted in the images are done in the frequency domain (e.g. [0019]: in the process, a first image (e.g. a reference image) and a second image (e.g. a test image) may be input into a film grain assessment system; film grain of the test image may be analyzed to determine the similarity of its film grain to the film grain of the reference image; film grain assessment system transforms the reference image and the test image from the spatial domain to the frequency domain; this results in a frequency domain representation for each of the reference image and the test image; film grain assessment system compares a distribution of frequencies (e.g. subband noise power spectra) between the reference image and the test image; the film grain assessment system may analyze a difference between the distributions to determine an assessment score; [0024]: processing system 106 may receive the assessment score and use the assessment score to perform an action; an action may be to change parameters for synthesizing the film grain based on the assessment score, such as parameters may be automatically changed to make the synthesized film grain more similar to the original film grain).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings/combination of Ameur to analyze in the frequency domain, in the same conventional manner as taught by Meng as both deal with film grain synthesis. The motivation to combine the two would be that frequency domain analysis can better extract the feature of noise and describe the look of the noise compared to the spatial domain (see [0020]).
In regards to claim 3, the combination of Ameur and Meng teaches an apparatus, wherein the processor is configured to generate the grainy texture parameters by extracting at least one flat region from each of the images included in the training image dataset (e.g. Meng as above, [0024]: processing system 106 may receive the assessment score and use the assessment score to perform an action; an action may be to change parameters for synthesizing the film grain based on the assessment score, such as parameters may be automatically changed to make the synthesized film grain more similar to the original film grain; see also [0061]: may use an adaptive region detection to adaptively select regions to analyze for the comparison of film grain between the reference image and the test image; for example, there may be regions in which the film grain may be more noticeable to human viewers; in some embodiments, non-texture regions, such as flat regions, may be regions in which film grain may be more noticeable).
In addition, the same rationale/motivation of claim 2 is used for claim 3.
In regards to claim 4, the combination of Ameur and Meng teaches an apparatus, wherein the processor is configured to generate the grainy texture parameters by estimating a target spectrum, based on the at least one flat region (e.g. Meng as above, [0024]: action may be to change parameters for synthesizing the film grain based on the assessment score; [0061]: may use an adaptive region detection to adaptively select regions to analyze; non-texture regions, such as flat regions, may be regions in which film grain may be more noticeable; see also [0067]: in some embodiments adaptive region detection system 902 generates a frequency domain film grain objective metric with adaptive region selection; if a region is flat, it mainly has low-frequency bands and it does not have impact on the high frequency film grain in texture regions).
In addition, the same rationale/motivation of claim 3 is used for claim 4.
In regards to claim 10, the combination of Ameur and Meng teaches an apparatus, wherein the processor is further configured to generate the grainy texture image by modifying at least one non-texture parameter of the image data (e.g. Meng as above, [0019]: film grain assessment system transforms the reference image and the test image from the spatial domain to the frequency domain; Examiner’s note: this shows that the images are transformed (parameters are modified)).
In addition, the same rationale/motivation of claim 2 is used for claim 10.
In regards to claim 17, Ameur teaches a method implemented by at least one processing device, the method comprising:
receiving a training image dataset that includes a plurality of training images (e.g. Section IV-A,pp.5052-5053: to train our proposed models for film grain removal and synthesis, a large dataset of images was collected; pairs of clean (grain-free) and grainy images to train our models; a wide range of grain types and intensities can be generated by varying the parameters of this model; the two main parameters are the average grain radius
μ
r
and its standard deviation
σ
r
; see also Section III-A,pp.5050-5051: film grain synthesis can be viewed as the translation of a given grain-free input image into a corresponding grainy output image while preserving the content; the goal is then to learn a mapping function from one input domain (grain-free images)
x
to another output domain (grainy images)
y
; Examiner’s note: as directed to machine learning/training as well as image processing, this shows a processor would be used);
generating grainy texture parameters that represent grainy texture as depicted in the plurality of training images of the training image dataset (e.g. as above, Section III-A,pp.5050-5051: film grain synthesis can be viewed as the translation of a given grain-free input image into a corresponding grainy output image while preserving the content; the goal is then to learn a mapping function from one input domain (grain-free images)
x
to another output domain (grainy images)
y
; Section IV-A,pp.5052-5053: to train our proposed models for film grain removal and synthesis, a large dataset of images was collected; a wide range of grain types and intensities can be generated by varying the parameters of this model; the two main parameters are the average grain radius
μ
r
and its standard deviation
σ
r
; Examiner’s note: this shows parameters are generated based on training data for use in film grain synthesis;);
generating a grainy texture image by modifying image data using the grainy texture parameters (e.g. as above, Section III-A,pp.5050-5051: film grain synthesis can be viewed as the translation of a given grain-free input image into a corresponding grainy output image while preserving the content; Examiner’s note: this shows output of grainy image); and
displaying the grainy texture image (e.g. as above, Section III-A,pp.5050-5051: film grain synthesis can be viewed as the translation of a given grain-free input image into a corresponding grainy output image while preserving the content; see also Section IV-B,pp.5053-5054: film grain synthesis is either performed on original clean images (artistic content creation) or on filtered/decoded images (video compression); therefore, we evaluate the results of our synthesis model on both clean and filtered images (as output by proposed grain removal models for both blind and non-blind configurations); Examiner’s notes: as directed to machine learning/training as well as image processing, where a resulting image is outputted, this shows that a display would be used to visualize resulting images (see example results in Fig.3)),
but does not explicitly teach the method,
wherein generating grainy texture parameters is done the frequency domain.
However, Meng teaches a method,
wherein generating grainy texture parameters is done the frequency domain (e.g. [0019]: in the process, a first image (e.g. a reference image) and a second image (e.g. a test image) may be input into a film grain assessment system; film grain of the test image may be analyzed to determine the similarity of its film grain to the film grain of the reference image; film grain assessment system transforms the reference image and the test image from the spatial domain to the frequency domain; this results in a frequency domain representation for each of the reference image and the test image; film grain assessment system compares a distribution of frequencies (e.g. subband noise power spectra) between the reference image and the test image; the film grain assessment system may analyze a difference between the distributions to determine an assessment score; [0024]: processing system 106 may receive the assessment score and use the assessment score to perform an action; an action may be to change parameters for synthesizing the film grain based on the assessment score, such as parameters may be automatically changed to make the synthesized film grain more similar to the original film grain).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings/combination of Ameur to analyze in the frequency domain, in the same conventional manner as taught by Meng as both deal with film grain synthesis. The motivation to combine the two would be that frequency domain analysis can better extract the feature of noise and describe the look of the noise compared to the spatial domain (see [0020]).
In regards to claim 20, the combination of Ameur and Meng teaches a method, further comprising generating the grainy texture image by modifying at least one non-texture parameter of the image data independent of the grainy texture parameters (e.g. Meng as above, [0019]: film grain assessment system transforms the reference image and the test image from the spatial domain to the frequency domain; Examiner’s note: this shows that the images are transformed (parameters are modified)).
In addition, the same rationale/motivation of claim 17 is used for claim 20.
Allowable Subject Matter
Claim(s) 12-16 is/are allowed.
The following is an examiner’s statement of reasons for allowance:
Claim(s) 12-16 was/were carefully reviewed and a search has been made. Accordingly, those claim(s) are believed to be distinct from the prior art searched.
Regarding claim(s) 12-16 and specifically independent claim(s) 12, the prior art search was found to neither anticipate nor suggest a system comprising: connection circuitry configured to receive a training image dataset comprising a plurality of images; and grainy texture circuitry configured to: extract one or more flat regions from each of the plurality of images included in the training image dataset; estimate a target spectrum based on the one or more flat regions; generate gain values for at least two grain bases based on the target spectrum; generate at least one band power value based on the gain values for the at least two grain bases; generate at least one brightness power value based on the gain values for the at least two grain bases; and output grainy texture parameters that include the at least one band power value and the at least one brightness power value - in combination with other claimed limitations (emphasis added).
It is viewed that any of the previously cited references or any of the prior art searched, in part or in whole, cannot be combined in such a way to render the claimed invention obvious.
Claim(s) 5-9, 18-19 is/are 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. To note, claim 6-9 are included as they depend on claim 5, and claim 19 is included as it depends on claim 18.
The following is a statement of reasons for the indication of allowable subject matter:
Claim(s) 5-9, 18-19 was/were carefully reviewed and a search with regards to independent claim(s) 1, 17 respectively and intervening claim(s) 3-4 (for claims 5-9) has been made. Accordingly, those claim(s) are believed to be distinct from the prior art searched.
Regarding claim(s) 5-9 (and specifically independent claim(s) 1), the prior art search was found to neither anticipate nor suggest the apparatus of claim 4, wherein the processor is configured to generate the grainy texture parameters by generating grain bases gain values based on the target spectrum (emphasis added).
Regarding claim(s) 18-19 (and specifically independent claim(s) 17), the prior art search was found to neither anticipate nor suggest the method of claim 17, wherein generating the grainy texture parameters comprises: extracting at least one flat region from each of the plurality of training images of the training image dataset; estimating a target spectrum based on the at least one flat region; generating grain bases gain values based on the target spectrum; deriving at least one band power value from the grain bases gain values; and deriving at least one brightness power value from the grain bases gain values (emphasis added).
It is viewed that any of the previously cited references or any of the prior art searched, in part or in whole, cannot be combined in such a way to render the claimed invention obvious.
Conclusion
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/JED-JUSTIN IMPERIAL/ Examiner, Art Unit 2616
/KEE M TUNG/ Supervisory Patent Examiner, Art Unit 2611
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