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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 6/12/26 has been entered.
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) 1-4, 7, 9, 11 and 13 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by WO 2020/234512 A2 (“Ghaznavi Youvalari”) (Note: Ghaznavi Youvalari was previously attached in the PTO-892).
Regarding claim 1, Ghaznavi Youvalari discloses an image decoding method comprising: constructing a reference sample for linear model intra prediction based on neighboring reconstructed samples of a current block (e.g. see at least prediction model, e.g. linear, can be derived that relates sample or pixel value P of a block 300 to its (x, y) location, neighboring sample values and neighboring samples’ locations as illustrated in Fig. 3, page 11, l. 24 – page 12, l. 32); deriving linear model parameters based on the reference sample (e.g. see at least parameters (e.g. a, b, c) derived using neighboring information, page 12, l. 34 – page 13, l. 27); and generating a prediction sample of the current block based on the linear model parameters (e.g. see at least deriving prediction model, e.g. linear, such as equations (2)-(7) and generally by (8) and/or using (9)-(10), to perform intra prediction as illustrated in Fig. 3, page 11, l. 24 – page 12, l. 32), wherein the linear model parameters include at least one of a horizontal component parameter or a vertical component parameter (e.g. see at least a or b, page 11, l. 24 – page 12, l. 32), and wherein the deriving the linear model parameters includes setting the horizontal component parameter or the vertical component parameter to 0 among the linear model parameters derived based on the reference sample (e.g. see at least equations (6) or (7) or (9), page 11, l. 24 – page 12, l. 32).
Regarding claim 2, Ghaznavi Youvalari further discloses wherein the reference sample includes at least one of reference samples included in a left sample line adjacent to the current block, reference samples included in an upper sample line adjacent to the current block, or an upper-left reference sample adjacent to the current block (e.g. see at least neighboring samples 310 shown in Fig. 3, page 11, l. 24 – page 12, l. 32; page 13, ll. 29-36).
Regarding claim 3, Ghaznavi Youvalari further teaches wherein, based on the current block being NxN, the reference sample includes (N+ 1) reference samples that are consecutively adjacent to the bottom of the upper-left reference sample within the left sample line and (N+ 1) reference samples that are consecutively adjacent to a right of the upper-left reference sample within the upper sample line (e.g. see at least neighboring samples 310 shown in Fig. 3, page 11, l. 24 – page 12, l. 32; page 13, ll. 29-36).
Regarding claim 4, Ghaznavi Youvalari further discloses wherein the constructing of the reference sample includes checking whether the neighboring reconstructed samples of the current block are available (e.g. see at least available and non-available samples, page 15, l. 30 – page 16, l. 24).
Regarding claim 7, Ghaznavi Youvalari further discloses wherein the generating of the prediction sample includes: generating a first prediction sample using the linear model parameters derived based on the reference sample (e.g. see at least deriving prediction model, e.g. linear, such as equations (2)-(7) and generally by (8) and/or using (9)-(10), to perform intra prediction as illustrated in Fig. 3, page 11, l. 24 – page 12, l. 32; also see page 16, ll. 4-12, and page 17, ll. 14-30); generating a second prediction sample using the linear model parameters in which the horizontal component parameter or the vertical component parameter is set 0 (e.g. see at least equations (6) or (7) or (9), page 11, l. 24 – page 12, l. 32); and generating a final prediction sample by weighted-summing the first prediction sample and the second prediction sample (e.g. see at least final predicted sample using weights, page 11, l. 24 – page 12, l. 32; also see final prediction by weighting, page 16, ll. 4-12, and page 17, ll. 14-30).
Regarding claim 9, Ghaznavi Youvalari further discloses wherein the prediction sample of the current block is generated by weighted-summing a third prediction sample generated using the linear model parameters of the first linear model and a fourth prediction sample generated using the linear model parameters of the second linear model (e.g. see at least final predicted sample using weights, page 11, l. 24 – page 12, l. 32; also see final prediction by weighting, page 16, ll. 4-12, and page 17, ll. 14-30).
Regarding claim 13, Ghaznavi Youvalari further discloses a method of transmitting data for image information, comprising: constructing a reference sample for linear model intra prediction based on neighboring reconstructed samples of a current block (e.g. see at least prediction model, e.g. linear, can be derived that relates sample or pixel value P of a block 300 to its (x, y) location, neighboring sample values and neighboring samples’ locations as illustrated in Fig. 3, page 11, l. 24 – page 12, l. 32); deriving linear model parameters based on the reference sample (e.g. see at least parameters (e.g. a, b, c) derived using neighboring information, page 12, l. 34 – page 13, l. 27); generating a prediction sample of the current block based on the linear model parameters (e.g. see at least deriving prediction model, e.g. linear, such as equations (2)-(7) and generally by (8) and/or using (9)-(10), to perform intra prediction as illustrated in Fig. 3, page 11, l. 24 – page 12, l. 32); generating a bitstream by encoding the current block based on the prediction sample (e.g. see output bitstream of encoder as illustrated in Fig. 1); and transmitting data including the bitstream (e.g. see data received by decoder as shown in Fig. 2 from the output bitstream of encoder as illustrated in Fig. 1), wherein the linear model parameters include at least one of a horizontal component parameter or a vertical component parameter (e.g. see at least a or b, page 11, l. 24 – page 12, l. 32), and wherein the deriving of the linear model parameters includes setting the horizontal component parameter or the vertical component parameter to 0 among the linear model parameters derived based on the reference sample (e.g. see at least equations (6) or (7), page 11, l. 24 – page 12, l. 32).
Regarding claim 11, the claim recites analogous limitations to the claims above and is therefore rejected on the same premise.
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) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ghaznavi Youvalari in view of US 2018/0332284 A1 (“Liu”).
Regarding claim 10, although Ghaznavi Youvalari discloses the reference sample, it is noted Ghaznavi Youvalari differs from the present invention in that it fails to particularly disclose further comprising obtaining a reference line index indicating one of multi-reference sample lines of the current block, wherein the reference sample is constructed using reference samples included in a reference sample line indicated by the reference line index. Liu however, teaches further comprising obtaining a reference line index indicating one of multi-reference sample lines of the current block, wherein the reference sample is constructed using reference samples included in a reference sample line indicated by the reference line index (e.g. see at least index of selected reference line, paragraphs [0096]-[0097] and Fig. 8).
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the references of Ghaznavi Youvalari and Liu before him/her, to modify the Method, an apparatus and a computer program product for video encoding and video decoding of Ghaznavi Youvalari with the teachings of Liu in order to improve coding efficiency, e.g. by at least improving the matching between prediction and actual values.
Allowable Subject Matter
Claim 8 is 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.
Response to Arguments
Applicant's arguments filed 6/12/26 have been fully considered but they are not persuasive.
Applicant asserts on pages 8-10 of the Remarks that Ghaznavi Youvalari does not disclose “wherein the linear model parameters include at least one of a horizontal component parameter or a vertical component parameter, and wherein the deriving of the linear model parameters includes setting the horizontal component parameter or the vertical component parameter to 0 among the linear model parameters derived based on the reference sample” because “the various prediction models listed on page 12, including Equations (1) through (8), are independently presented as separate candidate prediction model forms… there is no disclosure anywhere in Youvalari that any parameter of any equation is set to 0 to arrive at Equation (6) or Equation (7)”.
However, the examiner respectfully disagrees. Ghaznavi Youvalari, in at least page 12, defines in equation (8) a general prediction model P(x, y) = a*f(x) + b*f(y) + c from which linear prediction model examples such as equations (2) and (6)-(7) are derived from. Thus, equation (6), which is P(x, y) = a*x + c, or equation (7), which is P(x, y) = b*y = c, are derived by definition from equation (8) by setting either parameter a to 0 or parameter b to 0, respectively, and using a linear equation for the functions f(x) or f(y), i.e. f(x) = x and f(y) = y; on the other hand, equation (2), which is P(x, y) = a*x + b*y + c, is derived by not setting any parameters a and b to 0. Therefore, the use of the general prediction model in equation (8) to generate a prediction sample in equation (6) or equation (7) meet the limitations in the broadest reasonable sense.
Applicant also asserts that Ghaznavi Youvalari does not disclose “wherein the linear model parameters include at least one of a horizontal component parameter or a vertical component parameter, and wherein the deriving of the linear model parameters includes setting the horizontal component parameter or the vertical component parameter to 0 among the linear model parameters derived based on the reference sample” because equation (9) “is not a model derived by setting a parameter to 0”.
However, the examiner respectfully disagrees. Equation (9) defines an equation that has two parts, namely Px and Py, to calculate a weighted average prediction in equation (10). As can be seen, Px is basically equation (6), which only depends in x or the horizontal direction and Py is basically equation (7), which only depends in y or the vertical direction, that can be both derived from the general prediction model equation (8) by setting one of the parameters a or b to 0 as explained above. That is, the horizontal direction part of (9), i.e. Px = ax + c0, can be derived from the general prediction model (8) by setting b to 0 and similarly the vertical direction part of (9), i.e. Py = by + c1, can be derived from (8) by setting a to 0 with f(x) = x and f(y) = y. The first part, Px, and the second part, Py, are then weighted averaged to determine P(x, y). So similar to the previous argument above, the use of the general prediction model in equation (8) to generate a prediction sample in equations (9)-(10) meet the limitations in the broadest reasonable sense. The argument “[t]his is structurally distinct from the present application, in which a full two-dimensional model with parameters a, b, and c is first derived, and one parameter is subsequently set to 0” is not persuasive because these features are not in the claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Linear Model-Based Intra Prediction in VVC Test Model, Ghaznavi-Youvalari (previously attached in the PTO-892)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FRANCIS G GEROLEO whose telephone number is (571)270-7206. The examiner can normally be reached M-F 7:00 am - 3:30 pm.
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/Francis Geroleo/Primary Examiner, Art Unit 3619
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