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 .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11 August 2026 has been entered.
Response to Arguments
Applicant's arguments filed 11 August 2026 have been fully considered but they are not persuasive.
On pages 13 – 16, applicant argues that Misra does not teach determining a classifier for a respective sample of the second component and determining a sample offset for the respective sample of the second component as claimed because the filtering in Misra is used to directly generate filtered sample values and does not require first determining the sample offset based on the classifier and then using the sample offset to correct the sample values as recited in the claim limitations. While applicant’s arguments are understood, examiner respectfully disagrees. Examiner relies on Misra in maintaining the rejection.
Misra first teaches that application of cross component filtering is based on characteristics of samples in the filter support region, the filter support region including both luma and chroma components. See, e.g. par. 161: describing that the system determines how to apply cross component filtering based on properties of samples included in the support region, the properties including an evaluation of sample variance and luma classification, the samples in the support region including luma components and one or more chroma components [see, e.g. pars. 150 - 159: describing that the cross component filter takes luma components and one or more chroma components as input, the filtering determined based on the input luma and one or more chroma components]. Misra next teaches these characteristics include a classification of samples in the sample support region. See, e.g. par. 161: describing that the system determines filtering coefficients for the cross component filter based on thresholding of variance of samples in the sample support region, wherein thresholding of variance of samples in the sample support region is the equivalent of classifying samples in the sample support region. This is the equivalent of determining a classifier based on a set of samples in the support region as claimed. While Misra exemplifies this classification using the luma component, this exemplification does not take away from its broader teaching of classifying based on characteristics of samples in the filter support region. See MPEP 2123. Misra then teaches generating a sample offset based on the determined classification as claimed. Misra teaches determining filter coefficients based on the classification of samples in the sample support region. See, e.g. par. 161: describing that the system determines filter coefficients for the cross component filter based on a characterization of samples in the sample support region, the system determining a different coefficient based on the band of variance values. These determined filter coefficients are then used to generate an offset to be added to a to-be-filtered component value. See, e.g. pars. 151 – 159: describing a filtering equation where an offset value is generated based on the selected filter coefficients [three summation [Symbol font/0x53] portions of input component values from the filter support region (I0, I1, and I2) multiplied by filter coefficients (c0, c1, and c2) which are selected based on sample classification [see discussion above]. The result of this calculation is then added to the value of the to-be-filtered component value. See, e.g. pars. 151 – 159: describing that the offset generated by the summation portions of the filtering equation are then added to the sample value of a component to be filtered prior to filtering [Ii(x,y)]. This is the functional equivalent of determining an offset based on a classification and modifying the sample based on the determined offset as claimed. The rejection, therefore, is maintained.
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.
Claim(s) 1, 2, 12, 13, 20, and 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Misra et al. (US 2022/0248007) (hereinafter Misra) in view of Li et al. (US 2021/0044834) (hereinafter Li) in view of Li et al. (US 2022/0182635) (hereinafter Li 2).
Regarding claims 1, 12, and 20, Misra teaches a method of decoding a video signal, an electronic apparatus comprising one or more processing units, a memory coupled to the one or more processing units, and a plurality of programs stored in the memory that, when executed by the one or more processing units, cause the electronic apparatus to perform the method (e.g. par. 445: describing that the system includes hardware processors, a computer readable storage media, and computer programs store on the computer readable storage media that when executed cause the system to perform a method), and a computer readable storage medium storing a bitstream to be decoded by the decoding method, the method comprising:
receiving, from the video signal, a picture frame that includes a first component, and a second component (e.g. Fig. 17 and pars. 438 – 439: depicting and describing that the system receives video data, the video data including a picture that includes a first component and a second component [see, e.g. par. 63: describing that video data includes picture frames, the picture frames including luma, chroma Cb and chroma Cr components], wherein luma is the equivalent of the first component and wherein chroma Cb is the equivalent of the second component);
determining a classifier for a respective sample of the second component using a set of sample values, the set of sample values being derived from a first set of samples of the first component associated with the respective sample of the second component, and a second set of samples of the second component associated with the respective sample of the second component, wherein the first set of samples of the first component comprises a collocated sample of the first component relative to the respective sample of the second component and neighbouring samples of the collocated sample of the first component, and the second set of samples of the second component comprise a current sample of the second component relative to the respective sample of the second component and neighbouring samples of the current sample of the second component (e.g. Figs. 7, 18, 19A – 19C and pars. 149 – 161, and 443 – 444: depicting and describing that the system determines filtering for a chroma component (Cr) based on a set of luma samples values and a set of chroma (Cr) sample values, the set of luma sample values being a collocated luma sample of the chroma (Cr) sample and neighboring luma samples of the collocated luma sample and the set of chroma (Cr) sample values being the current chroma (Cr) sample and neighboring chroma (Cr) samples [see, e.g. Figs. 9A – 9F and par. 160: depicting and describing that the component sample values used to determine filtering of the chroma (Cr) component are located in a support area surrounding the sample to be filtered, the support area including a collocated component samples to the chroma sample to be filtered and neighboring component samples surrounding the sample to be filtered, the support area being determined by chroma type and sample format], wherein determining filtering for the chroma component using luma samples and chroma samples is the equivalent of determining a classifier for the chroma component [see, e.g. par. 161: describing that filtering is determined based on classification of samples in the filter support region], and wherein the luma component is the equivalent of the first component and the chroma component [Cr] is the equivalent of the second component);
determining a sample offset for the respective sample of the second component according to the classifier (e.g. pars. 149 – 161: describing that coefficients for filtering are determined according to the classification of samples in the filter support region, the determined coefficients used to generate an offset to be added to the to-be-filtered second component, wherein generating an offset to be added to the to-be-filtered second component using coefficients selected based on the classification of samples in the sample support region is the equivalent of determining a sample offset for the second component according to the classifier); and
modifying the respective sample of the second component based on the determined sample offset (e.g. Figs. 7 and 19A – 19C and pars. 149 – 161 and 443 – 444: depicting and describing that the chroma sample (Cr) is modified by adding the generated offset to the unfiltered value of the chroma sample).
Misra does not explicitly teach:
wherein the classifier is further determined from a second set of samples of the second component, wherein the second set of samples of the second component is associated with the respective sample of the second component, and
Wherein the sample values from the first set of samples of the first component are weighted sample values.
Li, however, teaches an image decoding method, an image decoding apparatus, and a non-transitory computer readable storage medium storing a bitstream:
Wherein the sample values from the first set of samples of the first component are weighted sample values (e.g. pars. 828 – 835: describing that the set of luma sample values are weighted sample values).
Li 2, however, teaches an image decoding method, an image decoding apparatus, and a non-transitory computer readable storage medium storing a bitstream:
wherein the classifier is further determined from a second set of samples of the second component, wherein the second set of samples of the second component is associated with the respective sample of the second component (e.g. Fig. 11 and pars. 112 – 121: depicting and describing that the system classifies a chroma sample using both co-located luma samples and chroma samples, wherein the chroma samples are the equivalent of the second set of samples of the second component).
It therefore would have been obvious to one of ordinary skill in the art to modify the teachings of Misra by adding the teachings of Li in order for the sample values from the first set of samples of the first component to be weighted sample values, and by adding the teachings of Li 2 in order for the classifier to further be determined from a second set of samples of the second component, wherein the second set of samples of the second component is associated with the respective sample of the second component. One of ordinary skill in the art would have been motivated to make such a modification because the modification improves coding efficiency while reducing an increase in the use of processing resources (Li, e.g. par. 36: describing a desire to improve coding efficiency while reducing an increase in the use of processing), and because the modification improves classification accuracy (Li 2, e.g. par. 121: describing a desire to improve classification accuracy in CCSAO classification by using both luma and chroma information).
Turning to claims 2 and 13, Misra, Li, and Li 2 teach all of the limitations of claims 1 and 12, respectively, as discussed above. Misra further teaches:
wherein determining the classifier for the respective sample of the second component comprises: determining the classifier for the respective sample of the second component using a summation of the set of weighted sample values. (e.g. pars. 149 – 161: describing that the system determines filtering for a chroma component using a summation of luma samples in the filter support region, wherein the luma samples are weighted luma samples [see discussion above], and wherein the chroma component is the equivalent of the second component), or
wherein the first component is one selected from the group consisting of a luma component, a first chroma component and a second chroma component, and the second component is one selected from the group consisting of the luma component, the first chroma component and the second chroma component, wherein the first component is different from the second component (e.g. pars. 149 – 161: describing that the first component is a luma component, and the second component is one of the first chroma component or the second chroma component, the first component and the second component being different from each other).
Turning to claim 23, Misra teaches a method for storing a bitstream, the method comprising:
performing the following steps of an encoding method to generate a bitstream:
obtaining a picture frame that includes a first component, and a second component (e.g. Fig. 17 and pars. 438 – 439: depicting and describing that the system obtains video data, the video data including a picture that includes a first component and a second component [see, e.g. par. 63: describing that video data includes picture frames, the picture frames including luma, chroma Cb and chroma Cr components], wherein luma is the equivalent of the first component and wherein chroma Cb is the equivalent of the second component);
determining a classifier for a respective sample of the second component using a set of sample values from a first set of samples of the first component associated with the respective sample of the second component, and a second set of samples of the second component associated with the respective sample of the second component, wherein the first set of samples of the first component comprises a collocated sample of the first component relative to the respective sample of the second component and neighbouring samples of the collocated sample of the first component, and the second set of samples of the second component comprise a current sample of the second component relative to the respective sample of the second component and neighbouring samples of the current sample of the second component (e.g. Figs. 7, 18, 19A – 19C and pars. 149 – 161, and 443 – 444: depicting and describing that the system determines filtering for a chroma component (Cr) based on a set of luma samples values and a set of chroma (Cr) sample values, the set of luma sample values being a collocated luma sample of the chroma (Cr) sample and neighboring luma samples of the collocated luma sample and the set of chroma (Cr) sample values being the current chroma (Cr) sample and neighboring chroma (Cr) samples [see, e.g. Figs. 9A – 9F and par. 160: depicting and describing that the component sample values used to determine filtering of the chroma (Cr) component are located in a support area surrounding the sample to be filtered, the support area including a collocated component samples to the chroma sample to be filtered and neighboring component samples surrounding the sample to be filtered, the support area being determined by chroma type and sample format], wherein determining filtering for the chroma component using luma samples and chroma samples is the equivalent of determining a classifier for the chroma component [see, e.g. par. 161: describing that filtering is determined based on classification of samples in the filter support region], and wherein the luma component is the equivalent of the first component and the chroma component [Cr] is the equivalent of the second component);
determining a sample offset for the respective sample of the second component according to the classifier (e.g. pars. 149 – 161: describing that filtering is determined for the chroma sample [Cr] according to the classification of samples in the filter support region, wherein determining filtering according to the classification of the samples in the filtering support region is the equivalent of determining a sample offset for the second component according to the classifier);
modifying the respective sample of the second component based on the determined sample offset (e.g. Figs. 7 and 19A – 19C and pars. 149 – 161 and 443 – 444: depicting and describing that the chroma sample (Cr) is modified based on the determined filter, wherein the determined filter is the equivalent of the determined sample offset), and
storing the bitstream (e.g. Fig. 5 and pars. 138 – 141: depicting and describing that the system stores a generated bitstream on a storage device).
Misra does not explicitly teach:
wherein the classifier is further determined from a second set of samples of the second component, wherein the second set of samples of the second component is associated with the respective sample of the second component, and
Wherein the sample values from the first set of samples of the first component are weighted sample values.
Li, however, teaches a method of storing a bitstream:
Wherein the sample values from the first set of samples of the first component are weighted sample values (e.g. pars. 828 – 835: describing that the set of luma sample values are weighted sample values).
Li 2, however, teaches a method of storing a bitstream:
wherein the classifier is further determined from a second set of samples of the second component, wherein the second set of samples of the second component is associated with the respective sample of the second component (e.g. Fig. 11 and pars. 112 – 121: depicting and describing that the system classifies a chroma sample using both co-located luma samples and chroma samples, wherein the chroma samples are the equivalent of the second set of samples of the second component).
It therefore would have been obvious to one of ordinary skill in the art to modify the teachings of Misra by adding the teachings of Li in order for the sample values from the first set of samples of the first component to be weighted sample values, and by adding the teachings of Li 2 in order for the classifier to further be determined from a second set of samples of the second component, wherein the second set of samples of the second component is associated with the respective sample of the second component. One of ordinary skill in the art would have been motivated to make such a modification because the modification improves coding efficiency while reducing an increase in the use of processing resources (Li, e.g. par. 36: describing a desire to improve coding efficiency while reducing an increase in the use of processing), and because the modification improves classification accuracy (Li 2, e.g. par. 121: describing a desire to improve classification accuracy in CCSAO classification by using both luma and chroma information).
Claim(s) 4, 5, 15, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Misra et al. (US 2022/024807) (hereinafter Misra) in view of Li et al. (US 2021/0044834) (hereinafter Li) in view of Li et al. (US 2022/0182635) (hereinafter Li 2) as applied to claims 1 and 12, respectively, above, and further in view of Yang et. al (WO2021/061814) (hereinafter Yang).
Regarding claims 4 and 15, Misra, Li, and Li 2 teach all of the limitations of claims 1 and 12, respectively, as discussed above. Misra further teaches:
wherein the picture frame further comprises a third component (e.g. par. 63: describing that the picture frame includes a second chroma component [Cb] in addition to the luma component and the first chroma component [Cr], wherein the second chroma component [Cb] is the equivalent of the third component), and
determining the classifier for the respective sample of the second component comprises: determining the classifier for the respective sample of the second component using the set of weighted sample values and an additional set of sample values from a third set of samples of the third component associated with the respective sample of the second component, wherein the third set of samples of the third component comprises a collocated sample of the third component relative to the respective sample of the second component and neighbouring samples of the collocated sample of the third component (e.g. Figs. 19A- 19C, and pars. 149 – 161 and 444: depicting and describing that system determines filtering for a first chroma component [Cr] using a set of sample values of a luma component and a set of sample values of a second chroma component [Cb], the luma sample values and the second chroma component sample values being collocated and neighboring the first chroma component [Cr], [see, e.g. Figs. 9A – 9E, and par. 160: depicting and describing that sample values used to determine filtering are collocated and surround the component sample to be filtered], the luma sample values being weighted luma sample values [see discussion above], wherein the second chroma component values [Cb] are the equivalent of the third component).
Misra does not explicitly teach:
Wherein the additional set of sample values of the third component are weighted.
Yang, however, teaches a decoding method and device:
Wherein the additional set of sample values of the third component are weighted (e.g. Figs. 17A and 17B, and par. 259: describing that sample values of a second chroma component [Cb] are weighted to obtain refined values of a first chroma component [Cr], wherein the second chroma component [Cb] is the equivalent of the third component).
It therefore would have been obvious to one of ordinary skill in the art to modify the teachings of Misra by adding the teachings of Yang in order for the additional set of sample values of the third component to be weighted. One of ordinary skill in the art would have been motivated to make such a modification because the modification improves coding efficiency.
Turning to claims 5 and 16, Misra, Li, Li 2, and Yang teach all of the limitations of claims 1 and 4, and claims 12 and 15, respectively, as discussed above. Misra further teaches:
wherein determining the classifier for the respective sample of the second component comprises: determining the classifier for the respective sample of the second component using a summation of the set of weighted sample values and the additional set of weighted sample values (e.g. pars. 149 – 161: describing that the system determines a filtering for a first chroma component [Cr] using a summation of the set of luma component sample values and a summation of the set of second chroma [Cb] sample values, wherein determining a filtering is the equivalent of determining a classification, wherein the set of luma component sample values are the equivalent of weighted sample values of the first component [see discussion above], and wherein the set of second chroma component [Cb] sample values are the equivalent of weighted sample values of the third component [see discussion above]).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Misra et al. (US 2022/024807) (hereinafter Misra) in view of Li et al. (US 2021/0044834) (hereinafter Li) in view of Li et al. (US 2022/0182635) (hereinafter Li 2) as applied to claim 2 above, and further in view of He et al. (WO 2016/168529) (hereinafter He).
Regarding claim 9, Misra, Li, and Li 2 teach all of the limitations of claims 1 and 2, as discussed above. Misra further teaches:
Wherein filtering is determined based on properties of samples included in the filter support region (e.g. par. 161: describing that the system analyzes samples in the filter support region surround the sample to be filtered and determines a filtering for the sample to be filtered based on properties of the samples in the filter support region).
Misra does not explicitly teach:
wherein the classifier for the respective sample of the second component includes a first classifier using the summation of the set of weighted sample values jointly combined with a second classifier determined from an edge direction and strength of the collocated sample of the first component relative to the respective sample of the second component, wherein the first classifier is different from the second classifier.
He, however, teaches a method for video decoding:
wherein the classifier for the respective sample of the second component includes a first classifier using the summation of the set of weighted sample values jointly combined with a second classifier determined from an edge direction and strength of the collocated sample of the first component relative to the respective sample of the second component, wherein the first classifier is different from the second classifier (e.g. par. 60: describing that system classifies and determines filtering for the chroma component based on sample values of the luma component and an edge strength and direction of the luma component, wherein the luma component is the equivalent of the first component, the chroma component is the equivalent of the second component, and wherein sample values of the luma component are the equivalent of the summation of the set of weighted sample values [see discussion above]).
It therefore would have been obvious to one of ordinary skill in the art to modify the teachings of Misra by adding the teachings of Yang in order for the classifier for the respective sample of the second component to include a first classifier using the summation of the set of weighted sample values jointly combined with a second classifier determined from an edge direction and strength of the collocated sample of the first component relative to the respective sample of the second component, wherein the first classifier is different from the second classifier. One of ordinary skill in the art would have been motivated to make such a modification because the modification improves coding efficiency.
Claim(s) 21 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Misra et al. (US 2022/0248007) (hereinafter Misra) in view of Li et al. (US 2021/0044834) (hereinafter Li) in view of Li et al. (US 2022/0182635) (hereinafter Li 2) as applied to claims 1 and 12, respectively, above, and further in view of Kim et al. (US 2014/0301438) (hereinafter Kim).
Regarding claims 21 and 22, Misra, Li, and Li 2 teach all of the limitations of claims 1 and 12, respectively, as discussed above. Misra does not explicitly teach:
Wherein a bitdepth of the video signal is equal to 12.
Kim, however, teaches a method of decoding a video signal and an electronic apparatus:
Wherein a bitdepth of the video signal is equal to 12 (e.g. par. 24: describing that the bitdepth of the video is 12).
It therefore would have been obvious to one of ordinary skill in the art to modify the teachings of Misra by adding the teachings of Kim in order for the bitdepth of the video signal to be 12. One of ordinary skill in the art would have been motivated to make such a modification because the modification improves coding performance of SAO filtering when the video bitdepth is greater than 10 (Kim, e.g. par. 25: describing a desire to improve coding performance of SAO filtering when the video bitdepth is greater than 10).
Allowable Subject Matter
Claims 6 – 8, 10, and 17 – 19 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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHANIKA M BRUMFIELD whose telephone number is (571)270-3700. The examiner can normally be reached M-F 8:30 - 5 PM AWS.
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SHANIKA M. BRUMFIELD
Examiner
Art Unit 2487
/SHANIKA M BRUMFIELD/Examiner, Art Unit 2487
/Dave Czekaj/Supervisory Patent Examiner, Art Unit 2487
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