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 .
DETAILED ACTION
This office action is in response to the application filed on July 2, 2025. Claims 1 – 20 are pending.
Priority
Acknowledgment is made of applicant's claim for priority based on U.S. provisional applications 63/462,235 filed on April 26,2023.
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on July 2, 2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the Examiner.
Specification
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words. The form and legal phraseology often used in patent claims, such as "means" and "said," should be avoided. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, "The disclosure concerns," "The disclosure defined by this invention," "The disclosure describes," etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The abstract of the disclosure is objected to because it uses the phrase "Aspects of the disclosure includes", which can be implied. Correction is required. See MPEP § 608.01(b).
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 13 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Regarding Claim 13, it recites the limitation “when the at least one gradient filter includes a horizontal gradient filter and a vertical gradient filter”. This limitation is not clear. The claim recites at least one gradient filter but also recites two gradient filters (horizontal and vertical). It is not understood how one gradient filter can include both a vertical gradient filter and horizontal gradient filter, as it only needs one gradient filter to meet the claim requirement. Therefore, the meets and bounds of the claims requirements regarding at least one gradient filter is unclear. For examination purposes the Examiner has broadly interpreted the limitation.
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 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)(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 20 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by ZHANG et al., (US 2025/0317552 A1) referred to as ZHANG hereinafter.
Regarding Claim 20, ZHANG discloses a non-transitory computer readable medium (Fig. 1, The encoded video data may also be stored onto a storage medium/server 130B for access by destination device 120) storing a video media bitstream encoded by an encoding method (Par. [0446] The non-transitory computer-readable recording medium stores a bitstream of a video which is generated by a method performed by an apparatus for video processing), the encoding method comprising: determining a linear predicted value of a current sample in a current block by applying a linear filter to samples that are predicted using one of an intra block copy (IBC) mode and an intra template matching (IntraTMP) mode, the current block being predicted using a filtered IBC (FIBC) mode; determining a gradient value associated with the current sample in the current block using at least one gradient filter; determining a predicted value of the current sample based on a sum of the linear predicted value and at least one modification value that includes the gradient value, an FIBC filter in the FIBC mode including the linear filter, and the at least one gradient filter; and encoding the current sample from the predicted value of the current sample (See MPEP §2113 recites: "Product-by-Process claims are not limited to the manipulations of the recited steps, only the structure implied by the steps").
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 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 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 - 19 are rejected under 35 U.S.C. 103 as being unpatentable over GHAZNAVI YOUVALARI et al. (US 2026/0214243 A1) in view in view of MA et al., (US 2025/0343900 A1) referred to as MA hereinafter.
Regarding Claim 1, GHAZNAVI teaches an apparatus for video decoding (Fig.1), comprising:
processing circuitry (Fig. 2) configured to:
receive coded information indicating that a current block in a current picture is predicted (Par. [0211] the filter parameters could be entirely predefined and located at the encoder side and signaled to the decoder side (i.e. receive coded information) in various granularities such as per block, picture, or sequence for example) using a filtered intra block copy (FIBC) mode (Par. [0211] one or more filters may be applied (i.e. filtered intra block copy mode) to one or more samples of the first prediction from the intra block copy process);
determine a linear predicted value of a current sample in the current block by applying a linear filter to prediction samples (Par. [0225] the M-tap filter (i.e. linear filter) is illustrated with pred= P0*C0+P1*C1+P2*C2+…+PM-2*CM-2+....+PM-1*β. In this formula:, pred is the output of the filtering process, Par. [0227] (P.sub.0, . . . , P.sub.M-1) (i.e. linear predicted value) are the filter parameters calculated based on the one or more available information described above) that are predicted using one of an IBC mode (Par. [0213] With the IBC example, the filtering process of the copied samples (i.e., the first prediction) from the reference area in IBC may be used in enhancing the compression efficiency of the final prediction by adapting the samples from the reference region (i.e., the region the samples are copied from) to the texture of the local region (i.e., the region in the neighborhood of current block) so that the prediction error is reduced) and an intra template matching (IntraTMP) mode (Par. [0213] As noted above, features as described herein are not limited to an IBC process);
determine a gradient value associated with the current sample in the current block using at least one gradient filter (Par. [0214] The filter(s) may be M-tap filter(s) trained using certain information available at the time of coding the current block, such as for example: Par. [0221] One or more of gradient information of spatial reconstructed samples in the neighborhood of the reference block);
determine a predicted value of the current sample based on a sum of the linear predicted value and at least one modification value that includes the gradient value, an FIBC filter in the FIBC mode including the linear filter (Par. [0225] - [0229] an example of the M-tap filter (i.e. linear filter) is illustrated with: pred= P0*C0+P1*C1+P2*C2+…+PM-2*CM-2+....+PM-1*β. In this formula:, pred (i.e. prediction value) is the output of the filtering process, (P.sub.0, . . . , P.sub.M-1) (i.e. linear predicted value) are the filter parameters calculated based on the one or more available information described above. (C.sub.0, . . . , C.sub.M-2) are the input information to the filter as described above, and the β is the bias or offset value (i.e. plus or adding at least one modification value)), and the at least one gradient filter; and
reconstruct the current sample (Par. [0063] The decoder 40 analyzes the coded video sequence 15 and produces a reconstructed video sequence 35).
GHAZNAVI does not specifically teach reconstruct the current sample from the predicted value of the current sample.
However, MA teaches reconstruct the current sample from the predicted value of the current sample (Fig. 4B, Par. [0143] The decoded video block is formed by summing the residual block from the inverse transform and inverse quantization unit 202 and the corresponding prediction block generated by the intra prediction unit 203 or the motion compensation unit 204 Fig. 6, Par. [0286] –[0288] In S605, a reconstructed value of the current block is determined according to the prediction value of the current block, where determining the reconstructed value of the current block according to the prediction value of the current block and the prediction residual value of the current block may include: performing an add operation on the prediction value of the current block and the prediction residual value of the current block, to determine the reconstructed value of the current block).
References GHAZNAVI and MA are considered to be analogous art because they relate to video coding. Therefore, it would be obvious to one possessing ordinary skill in the art before the effective filing date of the claimed invention to specify reconstructing the current sample from the predicted value of the current sample as suggest by MA in the invention of GHAZNAVI used in an intra prediction method based on an intra block copy based on filter-based linear model (IBC-FLM) (See MA, Par. [0149]).
Regarding Claim 2, GHAZNAVI in view of MA teaches claim 1. GHAZNAVI further teaches wherein the processing circuitry is configured to: determine a location value using a location of a center sample that is at a center of the linear filter (Fig. 6, Par. [0230] The C shows the center spatial sample, N, S, W, E show the north, south, west and east spatial samples, and so on); and
determine the predicted value of the current sample based on a sum of the linear predicted value and the at least one modification value that includes the gradient value and the location value, the FIBC filter in the FIBC mode including the linear filter (Par. [0225] - [0229] an example of the M-tap filter (i.e. linear filter) is illustrated with: pred=P0*C0+P1*C1+P2*C2+…+PM-2*CM-2+....+PM-1*β. In this formula:, pred (i.e. prediction value) is the output of the filtering process, (P.sub.0, . . . , P.sub.M-1) (i.e. linear predicted value) are the filter parameters calculated based on the one or more available information described above. (C.sub.0, . . . , C.sub.M-2) are the input information to the filter as described above, and the β is the bias or offset value), the at least one gradient filter, and coefficients for the location.
Regarding Claim 3, GHAZNAVI in view of MA teaches claim 1. GHAZNAVI further teaches wherein the processing circuitry is configured to: determine a nonlinear value associated with the current sample from at least one of the current sample and neighboring samples of the current sample using a nonlinear relationship between the nonlinear value and values of the at least one of the current sample and the neighboring samples (Par. [0237] the modification of the filter inputs may be done adaptively. This could be for example done by analyzing the intensity range or one or more of the other inputs and then determine a modification parameter to be applied to one or more of the parameters. This could be helpful for example in balancing the intensity magnitudes of the input filters in order to have a better parameter calculation for the filter. Par. [0240] the filter may include nonlinear variants (e.g., power of two, square root, etc.) (i.e. nonlinear value) of the described information above as inputs); and
determine the predicted value of the current sample based on a sum of the linear predicted value and the at least one modification value that includes the gradient value and the nonlinear value, the FIBC filter in the FIBC mode including the linear filter (Par. [0225] - [0229] an example of the M-tap filter (i.e. linear filter) is illustrated with: pred=P0*C0+P1*C1+P2*C2+…+PM-2*CM-2+....+PM-1*β. In this formula:, pred (i.e. prediction value) is the output of the filtering process, (P.sub.0, . . . , P.sub.M-1) (i.e. linear predicted value) are the filter parameters calculated based on the one or more available information described above. (C.sub.0, . . . , C.sub.M-2) are the input information to the filter as described above, and the β is the bias or offset value), the at least one gradient filter, and a coefficient for the nonlinear value.
Regarding Claim 4, GHAZNAVI in view of MA teaches claim 1. GHAZNAVI further teaches wherein the linear filter includes a bias term (Par. [0225] - [0229] an example of the M-tap filter (i.e. linear filter) is illustrated with: pred=P0*C0+P1*C1+P2*C2+…+PM-2*CM-2+....+PM-1*β. In this formula:, pred (i.e. prediction value) is the output of the filtering process, (P.sub.0, . . . , P.sub.M-1) (i.e. linear predicted value) are the filter parameters calculated based on the one or more available information described above. (C.sub.0, . . . , C.sub.M-2) are the input information to the filter as described above, and the β is the bias or offset value).
Regarding Claim 5, GHAZNAVI in view of MA teaches claim 1. GHAZNAVI further teaches wherein the linear filter adds a mean value of the current block and removes the mean value of the current block from each of the samples that are predicted using the one of the IBC mode and the IntraTMP mode (Par. [0264] an outlier removal process may be conducted to the training samples in order to exclude the ones which do not correlate well with the rest of the samples in the training area and/or in the reference block. The correlation of samples may be identified via various means, for example, if the texture characteristics do not align well with other regions or intensity range is highly different compared to other regions. This may be done in various ways such as, for example, using the mean value of the samples in the reference block as a parameter to exclude the samples which are in certain threshold of the said mean value).
Regarding Claim 6, GHAZNAVI in view of MA teaches claim 1. GHAZNAVI further teaches wherein the processing circuitry is configured to limit the predicted value of the current sample (Par. [0198] When CTU size is 256, the reference area is limited to one CTU row above. This setting ensures that for CTU size being 128 or 256, IBC does not require extra memory in the current ETM platform. The per-sample block vector search (or called local search) range is limited to [−(C<<1), C>>2] horizontally and [−C, C>>2] vertically to adapt to the reference area extension, where C denotes the CTU size).
GHAZNAVI does not explicitly teach clip the predicted value of the current sample.
However, MA teaches clip the predicted value of the current sample (Par. [0270] – [0271] for the first process, the first process may be to set the prediction value of the current block to be equal to the filtered reference block; alternatively, the first process may be a clip operation that limits the filtered reference block to a preset value range, where for the clip operation, the preset value range may be between 0 and (1<<BitDepth)-1, where BitDepth represents bit depth).
References GHAZNAVI and MA are considered to be analogous art because they relate to video coding. Therefore, it would be obvious to one possessing ordinary skill in the art before the effective filing date of the claimed invention to specify clipping the predicted value as suggest by MA in the invention of GHAZNAVI so that a corresponding correction operation is performed on the filtered reference block (See MA, Par. [0271]).
Regarding Claim 7, GHAZNAVI in view of MA teaches claim 1. GHAZNAVI further teaches wherein the processing circuitry is configured to determine coefficients of the FIBC filter in the FIBC mode (Par. [0144] the above template and left template can be used to calculate the linear model coefficients) from a current template of the current block and a reference template of a reference block indicated by a block vector of the current block (Par. [0230] FIG. 15 shows an example reference block and the current block together with associated templates and a block vector in IBC).
Regarding Claim 8, GHAZNAVI in view of MA teaches claim 7. GHAZNAVI further teaches the processing circuitry is configured to determine the coefficients of the FIBC filter in the FIBC mode (Par. [0144] the above template and left template can be used to calculate the linear model coefficients). GHAZNAVI does not specifically teach LDL decomposition. Therefore, GHAZNAVI fails to explicitly teach the processing circuitry is configured to determine the coefficients of the FIBC filter in the FIBC mode using LDL decomposition.
However, MA teaches the processing circuitry is configured to determine the coefficients of the FIBC filter in the FIBC mode (Par. [0405] FIG. 15, a schematic flowchart of calculating filter coefficients. Par. [0149] an intra prediction method based on an intra block copy based on filter-based linear model (IBC-FLM)) using LDL decomposition (Par. [0419] the autocorrelation matrix of the reconstructed sample refTemp within the reconstructed region R is first calculated, and then the cross-correlation vector between refTemp and the reconstructed sample recTemp of the current template is calculated. Then, the autocorrelation matrix is subjected to Cholesky decomposition or LDL decomposition, and finally, each filter coefficient c.sub.n is calculated in sequence by reverse recursion).
References GHAZNAVI and MA are considered to be analogous art because they relate to video coding. Therefore, it would be obvious to one possessing ordinary skill in the art before the effective filing date of the claimed invention to specify using LDL decomposition as suggest by MA in the invention of GHAZNAVI. This modification would minimize the MSE (mean square error) to derive the filter coefficients (See MA, Par. [0413]).
Regarding Claim 9, GHAZNAVI in view of MA teaches claim 1. GHAZNAVI further teaches wherein the linear filter has a cross-shape (Par. [0159] The dimensions of the filter kernel can be for example 1×3 (1D vertical), 3×1 (1D horizontal), 3×3, 7×7 or any dimensions, and can be shaped (by selecting only a subset of all possible kernel locations) as a cross or a diamond (as shown in FIG. 6) or as any given shape) that includes:
(i) 5 samples that include a center sample of the linear filter with an offset of (0, 0), a North sample N with an offset of (0, -1), a South sample S with an offset of (0, 1), an East sample E with an offset of (1, 0), and a West sample W with an offset of (-1, 0), the offsets of the 5 samples in the linear filter are with respect to the center sample (Par. [0159] 5-tap cross 310-3. When referring to the samples within the filter kernel the following notation is used: north (above), east (right), south (below), west (left) and center, as illustrated in FIG. 6 using the letters N, E, S, W, C); or
(ii) 9 samples that include a center sample of the linear filter with an offset of (0, 0), two North samples with respective offsets of (0, -1) and (0, -2), two South samples with respective offsets of (0, 1) and (0, 2), two East samples with respective offsets of (1, 0) and (2, 0), and two West samples with respective offsets of (-1, 0) and (-2, 0), the offsets of the 9 samples in the linear filter are with respect to the center sample.
Regarding Claim 10, GHAZNAVI in view of MA teaches claim 9. GHAZNAVI further teaches wherein when the linear filter has the 5 samples, the current sample is located at one of 5 positions of the respective 5 samples (Par. [0159] When referring to the samples within the filter kernel the following notation is used: north (above), east (right), south (below), west (left) and center, as illustrated in FIG. 6 using the letters N, E, S, W, C.); and when the linear filter has the 9 samples, the current sample is located at one of 9 positions of the respective 9 samples.
Regarding Claim 11, GHAZNAVI in view of MA teaches claim 1. GHAZNAVI further teaches wherein a shape of the linear filter is predefined (Par. [0211] The filter parameters could be predefined, and/or they may be derived in the encoder side and/or the decoder side), and one or more shapes of the at least one gradient filter are predefined (Par. [0238] the vertical gradient may be calculated by subtracting north sample from south sample (N-S), horizontal gradient may be calculated by subtracting west sample from east sample (W-E)), Par. [0244] The refinement process may include adding an offset value to one or both of the horizontal and vertical coordinates of the block vectors of the candidates. The offset values may be predefined).
Regarding Claim 12, GHAZNAVI in view of MA teaches claim 1. GHAZNAVI further teaches wherein a sample in the linear filter is spatially separated from all remaining samples in the linear filter (Par. [0230] FIG. 6. The C shows the center spatial sample, N, S, W, E show the north, south, west and east spatial samples, and so on).
Regarding Claim 13, GHAZNAVI in view of MA teaches claim 1. GHAZNAVI further teaches wherein
when the at least one gradient filter consists of a horizontal gradient filter, the gradient value is a horizontal gradient value, and a number of first input samples and positions of the first input samples in the horizontal gradient filter are set independently from the linear filter (Par. [0238] the gradient inputs may be calculated using one or more of the spatial samples by calculating by difference or weighted difference of one or more of the spatial samples, where the vertical gradient may be calculated by subtracting north sample from south sample (N-S));
when the at least one gradient filter consists of a vertical gradient filter, the gradient value is a vertical gradient value, and a number of second input samples and positions of the second input samples in the vertical gradient filter are set independently from the linear filter (Par. [0238] the gradient inputs may be calculated using one or more of the spatial samples by calculating by difference or weighted difference of one or more of the spatial samples, where horizontal gradient may be calculated by subtracting west sample from east sample (W-E)); and
when the at least one gradient filter includes a horizontal gradient filter and a vertical gradient filter, the gradient value is a sum of a horizontal gradient value and a vertical gradient value, and the number of the first input samples and the positions of the first input samples in the horizontal gradient filter and the number of the second input samples and the positions of the second input samples in the vertical gradient filter are set independently from each other and from the linear filter (Par. [0238] the gradient inputs may be calculated using one or more of the spatial samples by calculating by difference or weighted difference of one or more of the spatial samples, where the vertical gradient may be calculated by subtracting north sample from south sample (N-S) and horizontal gradient may be calculated by subtracting west sample from east sample (W-E). Par. [0267] a 5-tap cross shaped filter, the process of filtering may make use of the reconstructed area one line above, to the right, to the left and to the bottom of the reference block).
Regarding Claim 14, GHAZNAVI in view of MA teaches claim 1. GHAZNAVI further teaches wherein the at least one gradient filter includes a horizontal gradient filter; the gradient value includes a horizontal gradient value that is a sum of horizontal gradients of respective first input samples in the horizontal gradient filter (Par. [0238] the gradient inputs may be calculated using one or more of the spatial samples by calculating by difference or weighted difference of one or more of the spatial samples, where horizontal gradient may be calculated by subtracting west sample from east sample (W-E)); and
the processing circuitry is configured to determine each horizontal gradient of the respective first input samples based on one of:
a difference between the respective first input sample and a left neighbor of the respective first input sample;
a difference between the left neighbor of the first input sample and a right neighbor of the respective first input sample (Par. [0238] horizontal gradient may be calculated by subtracting west sample (i.e. left neighbor) from east sample (i.e. right neighbor) (W-E)); and
a difference between a first value and a second value, the first value being a sum based on a top-left neighbor, the left neighbor, and a bottom-left neighbor of the first input sample, the second value being a sum based on a top-right neighbor, the right neighbor, and a bottom-right neighbor of the first input sample.
Regarding Claim 15, GHAZNAVI in view of MA teaches claim 1. GHAZNAVI further teaches wherein the at least one gradient fitter includes a vertical gradient filter; the gradient value includes a vertical gradient value that is a sum of vertical gradients of respective second input sample in the vertical gradient filter (Par. [0238] the gradient inputs may be calculated using one or more of the spatial samples by calculating by difference or weighted difference of one or more of the spatial samples, where the vertical gradient may be calculated by subtracting north sample from south sample (N-S)); and
the processing circuitry is configured to determine each vertical gradient of the respective second input sample based on one of:
a difference between the respective second input sample and a top neighbor of the respective second input sample;
a difference between the top neighbor of the respective second input sample and a bottom neighbor of the respective second input sample (Par. [0238] the vertical gradient may be calculated by subtracting north sample (i.e. top neighbor) from south sample (i.e. bottom neighbor) (N-S)); and
a difference between a first value and a second value, the first value being a sum based on a top-left neighbor, the top neighbor, and a top-right neighbor of the second input sample, the second value being a sum based on bottom-left neighbor, the bottom neighbor, and a bottom-right neighbor of the respective second input sample.
Regarding Claims 16 - 19, the limitations are similar to those treated in the above rejection(s) of claim 1 - 5, and are met by the reference as discussed above. Claims 16 – 19 however recite an encoding method, rather than a decoding method, which is similar in structure expect in reverse operation. Therefore, Claims 16 - 19 are rejected for the same reasons of obviousness as used above.
Computer-readable medium Claim 20 is drawn to the corresponding method claimed in Claim 1. Therefore computer-readable medium Claim 20 corresponds to method Claim 1 and is rejected for the same reasons of obviousness as used above.
Conclusion
The prior art references made of record are not relied upon but are considered pertinent to applicant's disclosure. Chang et al. (US 2024/0015280 A1) teaches template selection for intra prediction in video coding. HUO et al. (US 2026/0039793 A1) teaches a prediction process based on an Intra Block Copy based on Filter-based Linear Model (IBC-FLM) technology.
Any inquiry concerning this communication should be directed to SUSAN E HODGES whose telephone number is (571)270-0498. The Examiner can normally be reached on Monday - Friday from 8:00 am (EST) to 4:00 pm (EST).
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner's supervisor, Brian T. Pendleton, can be reached on (571) . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Susan E. Hodges/Primary Examiner, Art Unit 2425
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